diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml
new file mode 100644
index 0000000..1d06de3
--- /dev/null
+++ b/.github/workflows/main.yaml
@@ -0,0 +1,98 @@
+name: workflow
+
+on:
+  push:
+    branches:
+      - main
+    paths-ignore:
+      - 'README.md'
+
+permissions:
+  id-token: write
+  contents: read
+
+jobs:
+  integration:
+    name: Continuous Integration
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+
+      - name: Lint code
+        run: echo "Linting repository"
+
+      - name: Run unit tests
+        run: echo "Running unit tests"
+
+  build-and-push-ecr-image:
+    name: Continuous Delivery
+    needs: integration
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+
+      - name: Install Utilities
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y jq unzip
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v1
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ secrets.AWS_REGION }}
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        uses: aws-actions/amazon-ecr-login@v1
+
+      - name: Build, tag, and push image to Amazon ECR
+        id: build-image
+        env:
+          ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
+          ECR_REPOSITORY: ${{ secrets.ECR_REPOSITORY_NAME }}
+          IMAGE_TAG: latest
+        run: |
+          # Build a docker container and
+          # push it to ECR so that it can
+          # be deployed to ECS.
+          docker build -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG .
+          docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG
+          echo "::set-output name=image::$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG"
+          
+          
+  Continuous-Deployment:
+    needs: build-and-push-ecr-image
+    runs-on: self-hosted
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v1
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ secrets.AWS_REGION }}
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        uses: aws-actions/amazon-ecr-login@v1
+      
+      
+      - name: Pull latest images
+        run: |
+         docker pull ${{secrets.AWS_ECR_LOGIN_URI}}/${{ secrets.ECR_REPOSITORY_NAME }}:latest
+         
+      # - name: Stop and remove  container if running
+      #   run: |
+      #    docker ps -q --filter "name=mltest" | grep -q . && docker stop mltest && docker rm -fv mltest
+       
+      - name: Run Docker Image to serve users
+        run: |
+         docker run -d -p 8080:8080 --ipc="host" --name=mltest -e 'AWS_ACCESS_KEY_ID=${{ secrets.AWS_ACCESS_KEY_ID }}' -e 'AWS_SECRET_ACCESS_KEY=${{ secrets.AWS_SECRET_ACCESS_KEY }}' -e 'AWS_REGION=${{ secrets.AWS_REGION }}'  ${{secrets.AWS_ECR_LOGIN_URI}}/${{ secrets.ECR_REPOSITORY_NAME }}:latest
+      - name: Clean previous images and containers
+        run: |
+         docker system prune -f
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..c8404b5
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,131 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+mlproject.egg-info
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..a25afb1
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,8 @@
+FROM python:3.8-slim-buster
+WORKDIR /app
+COPY . /app
+
+RUN apt update -y && apt install awscli -y
+
+RUN apt-get update && apt-get install ffmpeg libsm6 libxext6 unzip -y && pip install -r requirements.txt
+CMD ["python3", "app.py"]
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..0d8e620
--- /dev/null
+++ b/README.md
@@ -0,0 +1,37 @@
+## End to End MAchine Learning Project
+
+1. Docker Build checked
+2. Github Workflow
+3. Iam User In AWS
+
+## Docker Setup In EC2 commands to be Executed
+
+#optinal
+
+sudo apt-get update -y
+
+sudo apt-get upgrade
+
+#required
+
+curl -fsSL https://get.docker.com -o get-docker.sh
+
+sudo sh get-docker.sh
+
+sudo usermod -aG docker ubuntu
+
+newgrp docker
+
+## Configure EC2 as self-hosted runner:
+
+## Setup github secrets:
+
+AWS_ACCESS_KEY_ID=
+
+AWS_SECRET_ACCESS_KEY=
+
+AWS_REGION = us-east-1
+
+AWS_ECR_LOGIN_URI = demo>>  566373416292.dkr.ecr.ap-south-1.amazonaws.com
+
+ECR_REPOSITORY_NAME = simple-app
diff --git a/app.py b/app.py
new file mode 100644
index 0000000..2721575
--- /dev/null
+++ b/app.py
@@ -0,0 +1,46 @@
+from flask import Flask,request,render_template
+import numpy as np
+import pandas as pd
+
+
+from sklearn.preprocessing import StandardScaler
+from src.pipeline.predict_pipeline import CustomData,PredictPipeline
+
+application=Flask(__name__)
+
+app=application
+
+## Route for a home page
+
+@app.route('/')
+def index():
+    return render_template('index.html') 
+
+@app.route('/predictdata',methods=['GET','POST'])
+def predict_datapoint():
+    if request.method=='GET':
+        return render_template('home.html')
+    else:
+        data=CustomData(
+            gender=request.form.get('gender'),
+            race_ethnicity=request.form.get('ethnicity'),
+            parental_level_of_education=request.form.get('parental_level_of_education'),
+            lunch=request.form.get('lunch'),
+            test_preparation_course=request.form.get('test_preparation_course'),
+            reading_score=float(request.form.get('writing_score')),
+            writing_score=float(request.form.get('reading_score')))
+        
+        pred_df=data.get_data_as_data_frame()
+        print(pred_df)
+
+        predict_pipeline=PredictPipeline()
+        results=predict_pipeline.predict(pred_df)
+        return render_template('home.html',results=results[0])
+    
+    
+
+if __name__=="__main__":
+    # app.run(host="0.0.0.0",port=8080)        
+    app.run(host='0.0.0.0', port=8080)        
+
+
diff --git a/artifacts/data.csv b/artifacts/data.csv
new file mode 100644
index 0000000..72bd8af
--- /dev/null
+++ b/artifacts/data.csv
@@ -0,0 +1,1001 @@
+gender,race_ethnicity,parental_level_of_education,lunch,test_preparation_course,math_score,reading_score,writing_score
+female,group B,bachelor's degree,standard,none,72,72,74
+female,group C,some college,standard,completed,69,90,88
+female,group B,master's degree,standard,none,90,95,93
+male,group A,associate's degree,free/reduced,none,47,57,44
+male,group C,some college,standard,none,76,78,75
+female,group B,associate's degree,standard,none,71,83,78
+female,group B,some college,standard,completed,88,95,92
+male,group B,some college,free/reduced,none,40,43,39
+male,group D,high school,free/reduced,completed,64,64,67
+female,group B,high school,free/reduced,none,38,60,50
+male,group C,associate's degree,standard,none,58,54,52
+male,group D,associate's degree,standard,none,40,52,43
+female,group B,high school,standard,none,65,81,73
+male,group A,some college,standard,completed,78,72,70
+female,group A,master's degree,standard,none,50,53,58
+female,group C,some high school,standard,none,69,75,78
+male,group C,high school,standard,none,88,89,86
+female,group B,some high school,free/reduced,none,18,32,28
+male,group C,master's degree,free/reduced,completed,46,42,46
+female,group C,associate's degree,free/reduced,none,54,58,61
+male,group D,high school,standard,none,66,69,63
+female,group B,some college,free/reduced,completed,65,75,70
+male,group D,some college,standard,none,44,54,53
+female,group C,some high school,standard,none,69,73,73
+male,group D,bachelor's degree,free/reduced,completed,74,71,80
+male,group A,master's degree,free/reduced,none,73,74,72
+male,group B,some college,standard,none,69,54,55
+female,group C,bachelor's degree,standard,none,67,69,75
+male,group C,high school,standard,none,70,70,65
+female,group D,master's degree,standard,none,62,70,75
+female,group D,some college,standard,none,69,74,74
+female,group B,some college,standard,none,63,65,61
+female,group E,master's degree,free/reduced,none,56,72,65
+male,group D,some college,standard,none,40,42,38
+male,group E,some college,standard,none,97,87,82
+male,group E,associate's degree,standard,completed,81,81,79
+female,group D,associate's degree,standard,none,74,81,83
+female,group D,some high school,free/reduced,none,50,64,59
+female,group D,associate's degree,free/reduced,completed,75,90,88
+male,group B,associate's degree,free/reduced,none,57,56,57
+male,group C,associate's degree,free/reduced,none,55,61,54
+female,group C,associate's degree,standard,none,58,73,68
+female,group B,associate's degree,standard,none,53,58,65
+male,group B,some college,free/reduced,completed,59,65,66
+female,group E,associate's degree,free/reduced,none,50,56,54
+male,group B,associate's degree,standard,none,65,54,57
+female,group A,associate's degree,standard,completed,55,65,62
+female,group C,high school,standard,none,66,71,76
+female,group D,associate's degree,free/reduced,completed,57,74,76
+male,group C,high school,standard,completed,82,84,82
+male,group E,some college,standard,none,53,55,48
+male,group E,associate's degree,free/reduced,completed,77,69,68
+male,group C,some college,standard,none,53,44,42
+male,group D,high school,standard,none,88,78,75
+female,group C,some high school,free/reduced,completed,71,84,87
+female,group C,high school,free/reduced,none,33,41,43
+female,group E,associate's degree,standard,completed,82,85,86
+male,group D,associate's degree,standard,none,52,55,49
+male,group D,some college,standard,completed,58,59,58
+female,group C,some high school,free/reduced,none,0,17,10
+male,group E,bachelor's degree,free/reduced,completed,79,74,72
+male,group A,some high school,free/reduced,none,39,39,34
+male,group A,associate's degree,free/reduced,none,62,61,55
+female,group C,associate's degree,standard,none,69,80,71
+female,group D,some high school,standard,none,59,58,59
+male,group B,some high school,standard,none,67,64,61
+male,group D,some high school,free/reduced,none,45,37,37
+female,group C,some college,standard,none,60,72,74
+male,group B,associate's degree,free/reduced,none,61,58,56
+female,group C,associate's degree,standard,none,39,64,57
+female,group D,some college,free/reduced,completed,58,63,73
+male,group D,some college,standard,completed,63,55,63
+female,group A,associate's degree,free/reduced,none,41,51,48
+male,group C,some high school,free/reduced,none,61,57,56
+male,group C,some high school,standard,none,49,49,41
+male,group B,associate's degree,free/reduced,none,44,41,38
+male,group E,some high school,standard,none,30,26,22
+male,group A,bachelor's degree,standard,completed,80,78,81
+female,group D,some high school,standard,completed,61,74,72
+female,group E,master's degree,standard,none,62,68,68
+female,group B,associate's degree,standard,none,47,49,50
+male,group B,high school,free/reduced,none,49,45,45
+male,group A,some college,free/reduced,completed,50,47,54
+male,group E,associate's degree,standard,none,72,64,63
+male,group D,high school,free/reduced,none,42,39,34
+female,group C,some college,standard,none,73,80,82
+female,group C,some college,free/reduced,none,76,83,88
+female,group D,associate's degree,standard,none,71,71,74
+female,group A,some college,standard,none,58,70,67
+female,group D,some high school,standard,none,73,86,82
+female,group C,bachelor's degree,standard,none,65,72,74
+male,group C,high school,free/reduced,none,27,34,36
+male,group C,high school,standard,none,71,79,71
+male,group C,associate's degree,free/reduced,completed,43,45,50
+female,group B,some college,standard,none,79,86,92
+male,group C,associate's degree,free/reduced,completed,78,81,82
+male,group B,some high school,standard,completed,65,66,62
+female,group E,some college,standard,completed,63,72,70
+female,group D,some college,free/reduced,none,58,67,62
+female,group D,bachelor's degree,standard,none,65,67,62
+male,group B,some college,standard,none,79,67,67
+male,group D,bachelor's degree,standard,completed,68,74,74
+female,group D,associate's degree,standard,none,85,91,89
+male,group B,high school,standard,completed,60,44,47
+male,group C,some college,standard,completed,98,86,90
+female,group C,some college,standard,none,58,67,72
+female,group D,master's degree,standard,none,87,100,100
+male,group E,associate's degree,standard,completed,66,63,64
+female,group B,associate's degree,free/reduced,none,52,76,70
+female,group B,some high school,standard,none,70,64,72
+female,group D,associate's degree,free/reduced,completed,77,89,98
+male,group C,high school,standard,none,62,55,49
+male,group A,associate's degree,standard,none,54,53,47
+female,group D,some college,standard,none,51,58,54
+female,group E,bachelor's degree,standard,completed,99,100,100
+male,group C,high school,standard,none,84,77,74
+female,group B,bachelor's degree,free/reduced,none,75,85,82
+female,group D,bachelor's degree,standard,none,78,82,79
+female,group D,some high school,standard,none,51,63,61
+female,group C,some college,standard,none,55,69,65
+female,group C,bachelor's degree,standard,completed,79,92,89
+male,group B,associate's degree,standard,completed,91,89,92
+female,group C,some college,standard,completed,88,93,93
+male,group D,high school,free/reduced,none,63,57,56
+male,group E,some college,standard,none,83,80,73
+female,group B,high school,standard,none,87,95,86
+male,group B,some high school,standard,none,72,68,67
+male,group D,some college,standard,completed,65,77,74
+male,group D,master's degree,standard,none,82,82,74
+female,group A,bachelor's degree,standard,none,51,49,51
+male,group D,master's degree,standard,none,89,84,82
+male,group C,some high school,free/reduced,completed,53,37,40
+male,group E,some college,free/reduced,completed,87,74,70
+female,group C,some college,standard,completed,75,81,84
+male,group D,bachelor's degree,free/reduced,completed,74,79,75
+male,group C,bachelor's degree,standard,none,58,55,48
+male,group B,some high school,standard,completed,51,54,41
+male,group E,high school,standard,none,70,55,56
+female,group C,associate's degree,standard,none,59,66,67
+male,group D,some college,standard,completed,71,61,69
+female,group D,some high school,standard,none,76,72,71
+female,group C,some college,free/reduced,none,59,62,64
+female,group E,some college,free/reduced,completed,42,55,54
+male,group A,high school,standard,none,57,43,47
+male,group D,some college,standard,none,88,73,78
+female,group C,some college,free/reduced,none,22,39,33
+male,group B,some high school,standard,none,88,84,75
+male,group C,associate's degree,free/reduced,none,73,68,66
+female,group D,bachelor's degree,standard,completed,68,75,81
+male,group E,associate's degree,free/reduced,completed,100,100,93
+male,group A,some high school,standard,completed,62,67,69
+male,group A,bachelor's degree,standard,none,77,67,68
+female,group B,associate's degree,standard,completed,59,70,66
+male,group D,bachelor's degree,standard,none,54,49,47
+male,group D,some high school,standard,none,62,67,61
+female,group C,some college,standard,completed,70,89,88
+female,group E,high school,free/reduced,completed,66,74,78
+male,group B,some college,free/reduced,none,60,60,60
+female,group B,associate's degree,standard,completed,61,86,87
+male,group D,associate's degree,free/reduced,none,66,62,64
+male,group B,associate's degree,free/reduced,completed,82,78,74
+female,group E,some college,free/reduced,completed,75,88,85
+male,group B,master's degree,free/reduced,none,49,53,52
+male,group C,high school,standard,none,52,53,49
+female,group E,master's degree,standard,none,81,92,91
+female,group C,bachelor's degree,standard,completed,96,100,100
+male,group C,high school,free/reduced,completed,53,51,51
+female,group B,master's degree,free/reduced,completed,58,76,78
+female,group B,high school,standard,completed,68,83,78
+female,group C,some college,free/reduced,completed,67,75,70
+male,group A,high school,standard,completed,72,73,74
+male,group E,some high school,standard,none,94,88,78
+female,group D,some college,standard,none,79,86,81
+female,group C,associate's degree,standard,none,63,67,70
+female,group C,bachelor's degree,free/reduced,completed,43,51,54
+female,group C,master's degree,standard,completed,81,91,87
+female,group B,high school,free/reduced,completed,46,54,58
+female,group C,associate's degree,standard,completed,71,77,77
+female,group B,master's degree,free/reduced,completed,52,70,62
+female,group D,some high school,standard,completed,97,100,100
+male,group C,master's degree,free/reduced,completed,62,68,75
+female,group C,some college,free/reduced,none,46,64,66
+female,group E,high school,standard,none,50,50,47
+female,group D,associate's degree,standard,none,65,69,70
+male,group C,some high school,free/reduced,completed,45,52,49
+male,group C,associate's degree,free/reduced,completed,65,67,65
+male,group E,high school,standard,none,80,76,65
+male,group D,some high school,standard,completed,62,66,68
+male,group B,some high school,free/reduced,none,48,52,45
+female,group C,bachelor's degree,standard,none,77,88,87
+female,group E,associate's degree,standard,none,66,65,69
+male,group D,some college,standard,completed,76,83,79
+female,group B,some high school,standard,none,62,64,66
+male,group D,some college,standard,completed,77,62,62
+female,group C,master's degree,standard,completed,69,84,85
+male,group D,associate's degree,standard,none,61,55,52
+male,group C,some high school,free/reduced,completed,59,69,65
+male,group E,high school,free/reduced,none,55,56,51
+female,group B,some college,free/reduced,none,45,53,55
+female,group B,bachelor's degree,free/reduced,none,78,79,76
+female,group C,associate's degree,standard,completed,67,84,86
+female,group D,some college,free/reduced,none,65,81,77
+male,group C,associate's degree,standard,none,69,77,69
+female,group B,associate's degree,standard,none,57,69,68
+male,group C,some college,standard,none,59,41,42
+male,group D,some high school,standard,completed,74,71,78
+male,group E,bachelor's degree,standard,none,82,62,62
+male,group E,high school,standard,completed,81,80,76
+female,group B,some college,free/reduced,none,74,81,76
+female,group B,some college,free/reduced,none,58,61,66
+male,group D,some high school,free/reduced,completed,80,79,79
+male,group C,some college,free/reduced,none,35,28,27
+female,group C,high school,free/reduced,none,42,62,60
+male,group C,associate's degree,free/reduced,completed,60,51,56
+male,group E,high school,standard,completed,87,91,81
+male,group B,some high school,standard,completed,84,83,75
+female,group E,associate's degree,free/reduced,completed,83,86,88
+female,group C,high school,free/reduced,none,34,42,39
+male,group B,high school,free/reduced,none,66,77,70
+male,group B,some high school,standard,completed,61,56,56
+female,group D,high school,standard,completed,56,68,74
+male,group B,associate's degree,standard,none,87,85,73
+female,group C,some high school,free/reduced,none,55,65,62
+male,group D,some high school,standard,none,86,80,75
+female,group B,associate's degree,standard,completed,52,66,73
+female,group E,master's degree,free/reduced,none,45,56,54
+female,group C,some college,standard,none,72,72,71
+male,group D,high school,standard,none,57,50,54
+male,group A,some high school,free/reduced,none,68,72,64
+female,group C,some college,standard,completed,88,95,94
+male,group D,some college,standard,none,76,64,66
+male,group C,associate's degree,standard,none,46,43,42
+female,group B,bachelor's degree,standard,none,67,86,83
+male,group E,some high school,standard,none,92,87,78
+male,group C,bachelor's degree,standard,completed,83,82,84
+male,group D,associate's degree,standard,none,80,75,77
+male,group D,bachelor's degree,free/reduced,none,63,66,67
+female,group D,some high school,standard,completed,64,60,74
+male,group B,some college,standard,none,54,52,51
+male,group C,associate's degree,standard,none,84,80,80
+male,group D,high school,free/reduced,completed,73,68,66
+female,group E,bachelor's degree,standard,none,80,83,83
+female,group D,high school,standard,none,56,52,55
+male,group E,some college,standard,none,59,51,43
+male,group D,some high school,standard,none,75,74,69
+male,group C,associate's degree,standard,none,85,76,71
+male,group E,associate's degree,standard,none,89,76,74
+female,group B,high school,standard,completed,58,70,68
+female,group B,high school,standard,none,65,64,62
+male,group C,high school,standard,none,68,60,53
+male,group A,some high school,standard,completed,47,49,49
+female,group D,some college,free/reduced,none,71,83,83
+female,group B,some high school,standard,completed,60,70,70
+male,group D,master's degree,standard,none,80,80,72
+male,group D,high school,standard,none,54,52,52
+female,group E,some college,standard,none,62,73,70
+female,group C,associate's degree,free/reduced,none,64,73,68
+male,group C,associate's degree,standard,completed,78,77,77
+female,group B,some college,standard,none,70,75,78
+female,group C,master's degree,free/reduced,completed,65,81,81
+female,group C,some high school,free/reduced,completed,64,79,77
+male,group C,some college,standard,completed,79,79,78
+female,group C,some high school,free/reduced,none,44,50,51
+female,group E,high school,standard,none,99,93,90
+male,group D,high school,standard,none,76,73,68
+male,group D,some high school,free/reduced,none,59,42,41
+female,group C,bachelor's degree,standard,none,63,75,81
+female,group D,high school,standard,none,69,72,77
+female,group D,associate's degree,standard,completed,88,92,95
+female,group E,some college,free/reduced,none,71,76,70
+male,group C,bachelor's degree,standard,none,69,63,61
+male,group C,some college,standard,none,58,49,42
+female,group D,associate's degree,free/reduced,none,47,53,58
+female,group D,some college,standard,none,65,70,71
+male,group B,some college,standard,completed,88,85,76
+male,group C,bachelor's degree,standard,none,83,78,73
+female,group C,some high school,standard,completed,85,92,93
+female,group E,high school,standard,completed,59,63,75
+female,group C,some high school,free/reduced,none,65,86,80
+male,group B,bachelor's degree,free/reduced,none,73,56,57
+male,group D,high school,standard,none,53,52,42
+male,group D,high school,standard,none,45,48,46
+female,group D,bachelor's degree,free/reduced,none,73,79,84
+female,group D,some college,free/reduced,completed,70,78,78
+female,group B,some high school,standard,none,37,46,46
+male,group B,associate's degree,standard,completed,81,82,82
+male,group E,associate's degree,standard,completed,97,82,88
+female,group B,some high school,standard,none,67,89,82
+male,group B,bachelor's degree,free/reduced,none,88,75,76
+male,group E,some high school,standard,completed,77,76,77
+male,group C,associate's degree,standard,none,76,70,68
+male,group D,some high school,standard,none,86,73,70
+male,group C,some high school,standard,completed,63,60,57
+female,group E,bachelor's degree,standard,none,65,73,75
+male,group D,high school,free/reduced,completed,78,77,80
+male,group B,associate's degree,free/reduced,none,67,62,60
+male,group A,some high school,standard,completed,46,41,43
+male,group E,associate's degree,standard,completed,71,74,68
+male,group C,high school,free/reduced,completed,40,46,50
+male,group D,associate's degree,free/reduced,none,90,87,75
+male,group A,some college,free/reduced,completed,81,78,81
+male,group D,some high school,free/reduced,none,56,54,52
+female,group C,associate's degree,standard,completed,67,84,81
+male,group B,associate's degree,standard,none,80,76,64
+female,group C,associate's degree,standard,completed,74,75,83
+male,group A,some college,standard,none,69,67,69
+male,group E,some college,standard,completed,99,87,81
+male,group C,some high school,standard,none,51,52,44
+female,group B,associate's degree,free/reduced,none,53,71,67
+female,group D,high school,free/reduced,none,49,57,52
+female,group B,associate's degree,standard,none,73,76,80
+male,group B,bachelor's degree,standard,none,66,60,57
+male,group D,bachelor's degree,standard,completed,67,61,68
+female,group C,associate's degree,free/reduced,completed,68,67,69
+female,group C,bachelor's degree,standard,completed,59,64,75
+male,group C,high school,standard,none,71,66,65
+female,group D,master's degree,standard,completed,77,82,91
+male,group C,associate's degree,standard,none,83,72,78
+male,group B,bachelor's degree,standard,none,63,71,69
+female,group D,associate's degree,free/reduced,none,56,65,63
+female,group C,high school,free/reduced,completed,67,79,84
+female,group E,high school,standard,none,75,86,79
+female,group C,some college,standard,none,71,81,80
+female,group C,some high school,free/reduced,none,43,53,53
+female,group C,high school,free/reduced,none,41,46,43
+female,group C,some college,standard,none,82,90,94
+male,group C,some college,standard,none,61,61,62
+male,group A,some college,free/reduced,none,28,23,19
+male,group C,associate's degree,standard,completed,82,75,77
+female,group B,some high school,standard,none,41,55,51
+male,group C,high school,standard,none,71,60,61
+male,group C,associate's degree,standard,none,47,37,35
+male,group E,associate's degree,standard,completed,62,56,53
+male,group B,associate's degree,standard,none,90,78,81
+female,group C,bachelor's degree,standard,none,83,93,95
+female,group B,some college,free/reduced,none,61,68,66
+male,group D,some high school,standard,completed,76,70,69
+male,group C,associate's degree,standard,none,49,51,43
+female,group B,some high school,free/reduced,none,24,38,27
+female,group D,some high school,free/reduced,completed,35,55,60
+male,group C,high school,free/reduced,none,58,61,52
+female,group C,high school,standard,none,61,73,63
+female,group B,high school,standard,completed,69,76,74
+male,group D,associate's degree,standard,completed,67,72,67
+male,group D,some college,standard,none,79,73,67
+female,group C,high school,standard,none,72,80,75
+male,group B,some college,standard,none,62,61,57
+female,group C,bachelor's degree,standard,completed,77,94,95
+male,group D,high school,free/reduced,none,75,74,66
+male,group E,associate's degree,standard,none,87,74,76
+female,group B,bachelor's degree,standard,none,52,65,69
+male,group E,some college,standard,none,66,57,52
+female,group C,some college,standard,completed,63,78,80
+female,group C,associate's degree,standard,none,46,58,57
+female,group C,some college,standard,none,59,71,70
+female,group B,bachelor's degree,standard,none,61,72,70
+male,group A,associate's degree,standard,none,63,61,61
+female,group C,some college,free/reduced,completed,42,66,69
+male,group D,some college,free/reduced,none,59,62,61
+female,group D,some college,standard,none,80,90,89
+female,group B,high school,standard,none,58,62,59
+male,group B,some high school,standard,completed,85,84,78
+female,group C,some college,standard,none,52,58,58
+female,group D,some high school,free/reduced,none,27,34,32
+male,group C,some college,standard,none,59,60,58
+male,group A,bachelor's degree,free/reduced,completed,49,58,60
+male,group C,high school,standard,completed,69,58,53
+male,group C,bachelor's degree,free/reduced,none,61,66,61
+female,group A,some high school,free/reduced,none,44,64,58
+female,group D,some high school,standard,none,73,84,85
+male,group E,some college,standard,none,84,77,71
+female,group C,some college,free/reduced,completed,45,73,70
+male,group D,some high school,standard,none,74,74,72
+female,group D,some college,standard,completed,82,97,96
+female,group D,bachelor's degree,standard,none,59,70,73
+male,group E,associate's degree,free/reduced,none,46,43,41
+female,group D,some high school,standard,none,80,90,82
+female,group D,master's degree,free/reduced,completed,85,95,100
+female,group A,some high school,standard,none,71,83,77
+male,group A,bachelor's degree,standard,none,66,64,62
+female,group B,associate's degree,standard,none,80,86,83
+male,group C,associate's degree,standard,completed,87,100,95
+male,group C,master's degree,free/reduced,none,79,81,71
+female,group E,some high school,free/reduced,none,38,49,45
+female,group A,some high school,free/reduced,none,38,43,43
+female,group E,some college,standard,none,67,76,75
+female,group E,bachelor's degree,standard,none,64,73,70
+female,group C,associate's degree,free/reduced,none,57,78,67
+female,group D,high school,standard,none,62,64,64
+male,group D,master's degree,standard,none,73,70,75
+male,group E,some high school,free/reduced,completed,73,67,59
+female,group D,some college,standard,none,77,68,77
+male,group E,some college,standard,none,76,67,67
+male,group C,associate's degree,standard,completed,57,54,56
+female,group C,some high school,standard,completed,65,74,77
+male,group A,high school,free/reduced,none,48,45,41
+female,group B,high school,free/reduced,none,50,67,63
+female,group C,associate's degree,standard,none,85,89,95
+male,group B,some high school,standard,none,74,63,57
+male,group D,some high school,standard,none,60,59,54
+female,group C,some high school,standard,completed,59,54,67
+male,group A,some college,standard,none,53,43,43
+female,group A,some college,free/reduced,none,49,65,55
+female,group D,high school,standard,completed,88,99,100
+female,group C,high school,standard,none,54,59,62
+female,group C,some high school,standard,none,63,73,68
+male,group B,associate's degree,standard,completed,65,65,63
+female,group B,associate's degree,standard,none,82,80,77
+female,group D,high school,free/reduced,completed,52,57,56
+male,group D,associate's degree,standard,completed,87,84,85
+female,group D,master's degree,standard,completed,70,71,74
+male,group E,some college,standard,completed,84,83,78
+male,group D,associate's degree,standard,none,71,66,60
+male,group B,some high school,standard,completed,63,67,67
+female,group C,bachelor's degree,free/reduced,completed,51,72,79
+male,group E,high school,standard,none,84,73,69
+male,group C,bachelor's degree,standard,completed,71,74,68
+male,group C,associate's degree,standard,none,74,73,67
+male,group D,some college,standard,none,68,59,62
+male,group E,high school,free/reduced,completed,57,56,54
+female,group C,associate's degree,free/reduced,completed,82,93,93
+female,group D,high school,standard,completed,57,58,64
+female,group D,master's degree,free/reduced,completed,47,58,67
+female,group A,some high school,standard,completed,59,85,80
+male,group B,some college,free/reduced,none,41,39,34
+female,group C,some college,free/reduced,none,62,67,62
+male,group C,bachelor's degree,standard,none,86,83,86
+male,group C,some high school,free/reduced,none,69,71,65
+male,group A,some high school,free/reduced,none,65,59,53
+male,group C,some high school,free/reduced,none,68,63,54
+male,group C,associate's degree,free/reduced,none,64,66,59
+female,group C,high school,standard,none,61,72,70
+male,group C,high school,standard,none,61,56,55
+female,group A,some high school,free/reduced,none,47,59,50
+male,group C,some high school,standard,none,73,66,66
+male,group C,some college,free/reduced,completed,50,48,53
+male,group D,associate's degree,standard,none,75,68,64
+male,group D,associate's degree,free/reduced,none,75,66,73
+male,group C,high school,standard,none,70,56,51
+male,group D,some high school,standard,completed,89,88,82
+female,group C,some college,standard,completed,67,81,79
+female,group D,high school,standard,none,78,81,80
+female,group A,some high school,free/reduced,none,59,73,69
+female,group B,associate's degree,standard,none,73,83,76
+male,group A,some high school,free/reduced,none,79,82,73
+female,group C,some high school,standard,completed,67,74,77
+male,group D,some college,free/reduced,none,69,66,60
+male,group C,high school,standard,completed,86,81,80
+male,group B,high school,standard,none,47,46,42
+male,group B,associate's degree,standard,none,81,73,72
+female,group C,some college,free/reduced,completed,64,85,85
+female,group E,some college,standard,none,100,92,97
+female,group C,associate's degree,free/reduced,none,65,77,74
+male,group C,some college,free/reduced,none,65,58,49
+female,group C,associate's degree,free/reduced,none,53,61,62
+male,group C,bachelor's degree,free/reduced,none,37,56,47
+female,group D,bachelor's degree,standard,none,79,89,89
+male,group D,associate's degree,free/reduced,none,53,54,48
+female,group E,bachelor's degree,standard,none,100,100,100
+male,group B,high school,standard,completed,72,65,68
+male,group C,bachelor's degree,free/reduced,none,53,58,55
+male,group B,some college,free/reduced,none,54,54,45
+female,group E,some college,standard,none,71,70,76
+female,group C,some college,free/reduced,none,77,90,91
+male,group A,bachelor's degree,standard,completed,75,58,62
+female,group C,some college,standard,none,84,87,91
+female,group D,associate's degree,free/reduced,none,26,31,38
+male,group A,high school,free/reduced,completed,72,67,65
+female,group A,high school,free/reduced,completed,77,88,85
+male,group C,some college,standard,none,91,74,76
+female,group C,associate's degree,standard,completed,83,85,90
+female,group C,high school,standard,none,63,69,74
+female,group C,associate's degree,standard,completed,68,86,84
+female,group D,some high school,standard,none,59,67,61
+female,group B,associate's degree,standard,completed,90,90,91
+female,group D,bachelor's degree,standard,completed,71,76,83
+male,group E,bachelor's degree,standard,completed,76,62,66
+male,group D,associate's degree,standard,none,80,68,72
+female,group D,master's degree,standard,none,55,64,70
+male,group E,associate's degree,standard,none,76,71,67
+male,group B,high school,standard,completed,73,71,68
+female,group D,associate's degree,free/reduced,none,52,59,56
+male,group C,some college,free/reduced,none,68,68,61
+male,group A,high school,standard,none,59,52,46
+female,group B,associate's degree,standard,none,49,52,54
+male,group C,high school,standard,none,70,74,71
+male,group D,some college,free/reduced,none,61,47,56
+female,group C,associate's degree,free/reduced,none,60,75,74
+male,group B,some high school,standard,completed,64,53,57
+male,group A,associate's degree,free/reduced,completed,79,82,82
+female,group A,associate's degree,free/reduced,none,65,85,76
+female,group C,associate's degree,standard,none,64,64,70
+female,group C,some college,standard,none,83,83,90
+female,group C,bachelor's degree,standard,none,81,88,90
+female,group B,high school,standard,none,54,64,68
+male,group D,high school,standard,completed,68,64,66
+female,group C,some college,standard,none,54,48,52
+female,group D,some college,free/reduced,completed,59,78,76
+female,group B,some high school,standard,none,66,69,68
+male,group E,some college,standard,none,76,71,72
+female,group D,master's degree,standard,none,74,79,82
+female,group B,associate's degree,standard,completed,94,87,92
+male,group C,some college,free/reduced,none,63,61,54
+female,group E,associate's degree,standard,completed,95,89,92
+female,group D,master's degree,free/reduced,none,40,59,54
+female,group B,some high school,standard,none,82,82,80
+male,group A,high school,standard,none,68,70,66
+male,group B,bachelor's degree,free/reduced,none,55,59,54
+male,group C,master's degree,standard,none,79,78,77
+female,group C,bachelor's degree,standard,none,86,92,87
+male,group D,some college,standard,none,76,71,73
+male,group A,some high school,standard,none,64,50,43
+male,group D,some high school,free/reduced,none,62,49,52
+female,group B,some high school,standard,completed,54,61,62
+female,group B,master's degree,free/reduced,completed,77,97,94
+female,group C,some high school,standard,completed,76,87,85
+female,group D,some college,standard,none,74,89,84
+female,group E,some college,standard,completed,66,74,73
+female,group D,some high school,standard,completed,66,78,78
+female,group B,high school,free/reduced,completed,67,78,79
+male,group D,some college,standard,none,71,49,52
+female,group C,associate's degree,standard,none,91,86,84
+male,group D,bachelor's degree,standard,none,69,58,57
+male,group C,master's degree,free/reduced,none,54,59,50
+male,group C,high school,standard,completed,53,52,49
+male,group E,some college,standard,none,68,60,59
+male,group C,some high school,free/reduced,completed,56,61,60
+female,group C,high school,free/reduced,none,36,53,43
+female,group D,bachelor's degree,free/reduced,none,29,41,47
+female,group C,associate's degree,standard,none,62,74,70
+female,group C,associate's degree,standard,completed,68,67,73
+female,group C,some high school,standard,none,47,54,53
+male,group E,associate's degree,standard,completed,62,61,58
+female,group E,associate's degree,standard,completed,79,88,94
+male,group B,high school,standard,completed,73,69,68
+female,group C,bachelor's degree,free/reduced,completed,66,83,83
+male,group C,associate's degree,standard,completed,51,60,58
+female,group D,high school,standard,none,51,66,62
+male,group E,bachelor's degree,standard,completed,85,66,71
+male,group A,associate's degree,standard,completed,97,92,86
+male,group C,high school,standard,completed,75,69,68
+male,group D,associate's degree,free/reduced,completed,79,82,80
+female,group C,associate's degree,standard,none,81,77,79
+female,group D,associate's degree,standard,none,82,95,89
+female,group D,master's degree,standard,none,64,63,66
+male,group E,some high school,free/reduced,completed,78,83,80
+female,group A,some high school,standard,completed,92,100,97
+male,group C,high school,standard,completed,72,67,64
+female,group C,high school,free/reduced,none,62,67,64
+male,group C,master's degree,standard,none,79,72,69
+male,group C,some high school,free/reduced,none,79,76,65
+male,group B,bachelor's degree,free/reduced,completed,87,90,88
+female,group B,associate's degree,standard,none,40,48,50
+male,group D,some college,free/reduced,none,77,62,64
+male,group E,associate's degree,standard,none,53,45,40
+female,group C,some college,free/reduced,none,32,39,33
+female,group C,associate's degree,standard,completed,55,72,79
+male,group C,master's degree,free/reduced,none,61,67,66
+female,group B,associate's degree,free/reduced,none,53,70,70
+male,group D,some high school,standard,none,73,66,62
+female,group D,some college,standard,completed,74,75,79
+female,group C,some college,standard,none,63,74,74
+male,group C,bachelor's degree,standard,completed,96,90,92
+female,group D,some college,free/reduced,completed,63,80,80
+male,group B,bachelor's degree,free/reduced,none,48,51,46
+male,group B,associate's degree,standard,none,48,43,45
+female,group E,bachelor's degree,free/reduced,completed,92,100,100
+female,group D,master's degree,free/reduced,completed,61,71,78
+male,group B,high school,free/reduced,none,63,48,47
+male,group D,bachelor's degree,free/reduced,none,68,68,67
+male,group B,some college,standard,completed,71,75,70
+male,group A,bachelor's degree,standard,none,91,96,92
+female,group C,some college,standard,none,53,62,56
+female,group C,high school,free/reduced,completed,50,66,64
+female,group E,high school,standard,none,74,81,71
+male,group A,associate's degree,free/reduced,completed,40,55,53
+male,group A,some college,standard,completed,61,51,52
+female,group B,high school,standard,none,81,91,89
+female,group B,some college,free/reduced,completed,48,56,58
+female,group D,master's degree,standard,none,53,61,68
+female,group D,some high school,standard,none,81,97,96
+female,group E,some high school,standard,none,77,79,80
+female,group D,bachelor's degree,free/reduced,none,63,73,78
+female,group D,associate's degree,standard,completed,73,75,80
+female,group D,some college,standard,none,69,77,77
+female,group C,associate's degree,standard,none,65,76,76
+female,group A,high school,standard,none,55,73,73
+female,group C,bachelor's degree,free/reduced,none,44,63,62
+female,group C,some college,standard,none,54,64,65
+female,group A,some high school,standard,none,48,66,65
+male,group C,some college,free/reduced,none,58,57,54
+male,group A,some high school,standard,none,71,62,50
+male,group E,bachelor's degree,standard,none,68,68,64
+female,group E,high school,standard,none,74,76,73
+female,group C,bachelor's degree,standard,completed,92,100,99
+female,group C,bachelor's degree,standard,completed,56,79,72
+male,group B,high school,free/reduced,none,30,24,15
+male,group A,some high school,standard,none,53,54,48
+female,group D,high school,standard,none,69,77,73
+female,group D,some high school,standard,none,65,82,81
+female,group D,master's degree,standard,none,54,60,63
+female,group C,high school,standard,none,29,29,30
+female,group E,some college,standard,none,76,78,80
+male,group D,high school,free/reduced,none,60,57,51
+male,group D,master's degree,free/reduced,completed,84,89,90
+male,group C,some high school,standard,none,75,72,62
+female,group C,associate's degree,standard,none,85,84,82
+female,group C,master's degree,free/reduced,none,40,58,54
+female,group E,some college,standard,none,61,64,62
+female,group B,associate's degree,standard,none,58,63,65
+male,group D,some college,free/reduced,completed,69,60,63
+female,group C,some college,standard,none,58,59,66
+male,group C,bachelor's degree,standard,completed,94,90,91
+female,group C,associate's degree,standard,none,65,77,74
+female,group A,associate's degree,standard,none,82,93,93
+female,group C,high school,standard,none,60,68,72
+female,group E,bachelor's degree,standard,none,37,45,38
+male,group D,bachelor's degree,standard,none,88,78,83
+male,group D,master's degree,standard,none,95,81,84
+male,group C,associate's degree,free/reduced,completed,65,73,68
+female,group C,high school,free/reduced,none,35,61,54
+male,group B,bachelor's degree,free/reduced,none,62,63,56
+male,group C,high school,free/reduced,completed,58,51,52
+male,group A,some college,standard,completed,100,96,86
+female,group E,bachelor's degree,free/reduced,none,61,58,62
+male,group D,some college,standard,completed,100,97,99
+male,group B,associate's degree,free/reduced,completed,69,70,63
+male,group D,associate's degree,standard,none,61,48,46
+male,group D,some college,free/reduced,none,49,57,46
+female,group C,some high school,standard,completed,44,51,55
+male,group D,some college,standard,none,67,64,70
+male,group B,high school,standard,none,79,60,65
+female,group B,bachelor's degree,standard,completed,66,74,81
+female,group C,high school,standard,none,75,88,85
+male,group D,some high school,standard,none,84,84,80
+male,group A,high school,standard,none,71,74,64
+female,group B,high school,free/reduced,completed,67,80,81
+female,group D,some high school,standard,completed,80,92,88
+male,group E,some college,standard,none,86,76,74
+female,group D,associate's degree,standard,none,76,74,73
+male,group D,high school,standard,none,41,52,51
+female,group D,associate's degree,free/reduced,completed,74,88,90
+female,group B,some high school,free/reduced,none,72,81,79
+female,group E,high school,standard,completed,74,79,80
+male,group B,high school,standard,none,70,65,60
+female,group B,bachelor's degree,standard,completed,65,81,81
+female,group D,associate's degree,standard,none,59,70,65
+female,group E,high school,free/reduced,none,64,62,68
+female,group B,high school,standard,none,50,53,55
+female,group D,some college,standard,completed,69,79,81
+male,group C,some high school,free/reduced,completed,51,56,53
+female,group A,high school,standard,completed,68,80,76
+female,group D,some college,standard,completed,85,86,98
+female,group A,associate's degree,standard,completed,65,70,74
+female,group B,some high school,standard,none,73,79,79
+female,group B,some college,standard,none,62,67,67
+male,group C,associate's degree,free/reduced,none,77,67,64
+male,group D,some high school,standard,none,69,66,61
+female,group D,associate's degree,free/reduced,none,43,60,58
+male,group D,associate's degree,standard,none,90,87,85
+male,group C,some college,free/reduced,none,74,77,73
+male,group C,some high school,standard,none,73,66,63
+female,group D,some college,free/reduced,none,55,71,69
+female,group C,high school,standard,none,65,69,67
+male,group D,associate's degree,standard,none,80,63,63
+female,group C,some high school,free/reduced,completed,50,60,60
+female,group C,some college,free/reduced,completed,63,73,71
+female,group B,bachelor's degree,free/reduced,none,77,85,87
+male,group C,some college,standard,none,73,74,61
+male,group D,associate's degree,standard,completed,81,72,77
+female,group C,high school,free/reduced,none,66,76,68
+male,group D,associate's degree,free/reduced,none,52,57,50
+female,group C,some college,standard,none,69,78,76
+female,group C,associate's degree,standard,completed,65,84,84
+female,group D,high school,standard,completed,69,77,78
+female,group B,some college,standard,completed,50,64,66
+female,group E,some college,standard,completed,73,78,76
+female,group C,some high school,standard,completed,70,82,76
+male,group D,associate's degree,free/reduced,none,81,75,78
+male,group D,some college,free/reduced,none,63,61,60
+female,group D,high school,standard,none,67,72,74
+male,group B,high school,standard,none,60,68,60
+male,group B,high school,standard,none,62,55,54
+female,group C,some high school,free/reduced,completed,29,40,44
+male,group B,some college,standard,completed,62,66,68
+female,group E,master's degree,standard,completed,94,99,100
+male,group E,some college,standard,completed,85,75,68
+male,group D,associate's degree,free/reduced,none,77,78,73
+male,group A,high school,free/reduced,none,53,58,44
+male,group E,some college,free/reduced,none,93,90,83
+female,group C,associate's degree,standard,none,49,53,53
+female,group E,associate's degree,free/reduced,none,73,76,78
+female,group C,bachelor's degree,free/reduced,completed,66,74,81
+female,group D,associate's degree,standard,none,77,77,73
+female,group C,some high school,standard,none,49,63,56
+female,group D,some college,free/reduced,none,79,89,86
+female,group C,associate's degree,standard,completed,75,82,90
+female,group A,bachelor's degree,standard,none,59,72,70
+female,group D,associate's degree,standard,completed,57,78,79
+male,group C,high school,free/reduced,none,66,66,59
+female,group E,bachelor's degree,standard,completed,79,81,82
+female,group B,some high school,standard,none,57,67,72
+male,group A,bachelor's degree,standard,completed,87,84,87
+female,group D,some college,standard,none,63,64,67
+female,group B,some high school,free/reduced,completed,59,63,64
+male,group A,bachelor's degree,free/reduced,none,62,72,65
+male,group D,high school,standard,none,46,34,36
+male,group C,some college,standard,none,66,59,52
+male,group D,high school,standard,none,89,87,79
+female,group D,associate's degree,free/reduced,completed,42,61,58
+male,group C,some college,standard,completed,93,84,90
+female,group E,some high school,standard,completed,80,85,85
+female,group D,some college,standard,none,98,100,99
+male,group D,master's degree,standard,none,81,81,84
+female,group B,some high school,standard,completed,60,70,74
+female,group B,associate's degree,free/reduced,completed,76,94,87
+male,group C,associate's degree,standard,completed,73,78,72
+female,group C,associate's degree,standard,completed,96,96,99
+female,group C,high school,standard,none,76,76,74
+male,group E,associate's degree,free/reduced,completed,91,73,80
+female,group C,some college,free/reduced,none,62,72,70
+male,group D,some high school,free/reduced,completed,55,59,59
+female,group B,some high school,free/reduced,completed,74,90,88
+male,group C,high school,standard,none,50,48,42
+male,group B,some college,standard,none,47,43,41
+male,group E,some college,standard,completed,81,74,71
+female,group E,associate's degree,standard,completed,65,75,77
+male,group E,some high school,standard,completed,68,51,57
+female,group D,high school,free/reduced,none,73,92,84
+male,group C,some college,standard,none,53,39,37
+female,group B,associate's degree,free/reduced,completed,68,77,80
+male,group A,some high school,free/reduced,none,55,46,43
+female,group C,some college,standard,completed,87,89,94
+male,group D,some high school,standard,none,55,47,44
+female,group E,some college,free/reduced,none,53,58,57
+male,group C,master's degree,standard,none,67,57,59
+male,group C,associate's degree,standard,none,92,79,84
+female,group B,some college,free/reduced,completed,53,66,73
+male,group D,associate's degree,standard,none,81,71,73
+male,group C,high school,free/reduced,none,61,60,55
+male,group D,bachelor's degree,standard,none,80,73,72
+female,group A,associate's degree,free/reduced,none,37,57,56
+female,group C,high school,standard,none,81,84,82
+female,group C,associate's degree,standard,completed,59,73,72
+male,group B,some college,free/reduced,none,55,55,47
+male,group D,associate's degree,standard,none,72,79,74
+male,group D,high school,standard,none,69,75,71
+male,group C,some college,standard,none,69,64,68
+female,group C,bachelor's degree,free/reduced,none,50,60,59
+male,group B,some college,standard,completed,87,84,86
+male,group D,some high school,standard,completed,71,69,68
+male,group E,some college,standard,none,68,72,65
+male,group C,master's degree,free/reduced,completed,79,77,75
+female,group C,some high school,standard,completed,77,90,85
+male,group C,associate's degree,free/reduced,none,58,55,53
+female,group E,associate's degree,standard,none,84,95,92
+male,group D,some college,standard,none,55,58,52
+male,group E,bachelor's degree,free/reduced,completed,70,68,72
+female,group D,some college,free/reduced,completed,52,59,65
+male,group B,some college,standard,completed,69,77,77
+female,group C,high school,free/reduced,none,53,72,64
+female,group D,some high school,standard,none,48,58,54
+male,group D,some high school,standard,completed,78,81,86
+female,group B,high school,standard,none,62,62,63
+male,group D,some college,standard,none,60,63,59
+female,group B,high school,standard,none,74,72,72
+female,group C,high school,standard,completed,58,75,77
+male,group B,high school,standard,completed,76,62,60
+female,group D,some high school,standard,none,68,71,75
+male,group A,some college,free/reduced,none,58,60,57
+male,group B,high school,standard,none,52,48,49
+male,group D,bachelor's degree,standard,none,75,73,74
+female,group B,some high school,free/reduced,completed,52,67,72
+female,group C,bachelor's degree,free/reduced,none,62,78,79
+male,group B,some college,standard,none,66,65,60
+female,group B,some high school,free/reduced,none,49,58,55
+female,group B,high school,standard,none,66,72,70
+female,group C,some college,free/reduced,none,35,44,43
+female,group A,some college,standard,completed,72,79,82
+male,group E,associate's degree,standard,completed,94,85,82
+female,group D,associate's degree,free/reduced,none,46,56,57
+female,group B,master's degree,standard,none,77,90,84
+female,group B,high school,free/reduced,completed,76,85,82
+female,group C,associate's degree,standard,completed,52,59,62
+male,group C,bachelor's degree,standard,completed,91,81,79
+female,group B,some high school,standard,completed,32,51,44
+female,group E,some high school,free/reduced,none,72,79,77
+female,group B,some college,standard,none,19,38,32
+male,group C,associate's degree,free/reduced,none,68,65,61
+female,group C,master's degree,free/reduced,none,52,65,61
+female,group B,high school,standard,none,48,62,60
+female,group D,some college,free/reduced,none,60,66,70
+male,group D,high school,free/reduced,none,66,74,69
+male,group E,some high school,standard,completed,89,84,77
+female,group B,high school,standard,none,42,52,51
+female,group E,associate's degree,free/reduced,completed,57,68,73
+male,group D,high school,standard,none,70,70,70
+female,group E,associate's degree,free/reduced,none,70,84,81
+male,group E,some college,standard,none,69,60,54
+female,group C,associate's degree,standard,none,52,55,57
+male,group C,some high school,standard,completed,67,73,68
+male,group C,some high school,standard,completed,76,80,73
+female,group E,associate's degree,standard,none,87,94,95
+female,group B,some college,standard,none,82,85,87
+female,group C,some college,standard,none,73,76,78
+male,group A,some college,free/reduced,none,75,81,74
+female,group D,some college,free/reduced,none,64,74,75
+female,group E,high school,free/reduced,none,41,45,40
+male,group C,high school,standard,none,90,75,69
+male,group B,bachelor's degree,standard,none,59,54,51
+male,group A,some high school,standard,none,51,31,36
+male,group A,high school,free/reduced,none,45,47,49
+female,group C,master's degree,standard,completed,54,64,67
+male,group E,some high school,standard,completed,87,84,76
+female,group C,high school,standard,none,72,80,83
+male,group B,some high school,standard,completed,94,86,87
+female,group A,bachelor's degree,standard,none,45,59,64
+male,group D,bachelor's degree,free/reduced,completed,61,70,76
+female,group B,high school,free/reduced,none,60,72,68
+female,group C,some high school,standard,none,77,91,88
+female,group A,some high school,standard,completed,85,90,92
+female,group D,bachelor's degree,free/reduced,none,78,90,93
+male,group E,some college,free/reduced,completed,49,52,51
+female,group B,high school,free/reduced,none,71,87,82
+female,group C,some high school,free/reduced,none,48,58,52
+male,group C,high school,standard,none,62,67,58
+female,group C,associate's degree,free/reduced,completed,56,68,70
+female,group C,some high school,standard,none,65,69,76
+female,group D,some high school,free/reduced,completed,69,86,81
+male,group B,some high school,standard,none,68,54,53
+female,group A,some college,free/reduced,none,61,60,57
+female,group C,bachelor's degree,free/reduced,completed,74,86,89
+male,group A,bachelor's degree,standard,none,64,60,58
+female,group B,high school,standard,completed,77,82,89
+male,group B,some college,standard,none,58,50,45
+female,group C,high school,standard,completed,60,64,74
+male,group E,high school,standard,none,73,64,57
+female,group A,high school,standard,completed,75,82,79
+male,group B,associate's degree,free/reduced,completed,58,57,53
+female,group C,associate's degree,standard,none,66,77,73
+female,group D,high school,free/reduced,none,39,52,46
+male,group C,some high school,standard,none,64,58,51
+female,group B,high school,free/reduced,completed,23,44,36
+male,group B,some college,free/reduced,completed,74,77,76
+female,group D,some high school,free/reduced,completed,40,65,64
+male,group E,master's degree,standard,none,90,85,84
+male,group C,master's degree,standard,completed,91,85,85
+male,group D,high school,standard,none,64,54,50
+female,group C,high school,standard,none,59,72,68
+male,group D,associate's degree,standard,none,80,75,69
+male,group C,master's degree,standard,none,71,67,67
+female,group A,high school,standard,none,61,68,63
+female,group E,some college,standard,none,87,85,93
+male,group E,some high school,standard,none,82,67,61
+male,group C,some high school,standard,none,62,64,55
+female,group B,bachelor's degree,standard,none,97,97,96
+male,group B,some college,free/reduced,none,75,68,65
+female,group C,bachelor's degree,standard,none,65,79,81
+male,group B,high school,standard,completed,52,49,46
+male,group C,associate's degree,free/reduced,none,87,73,72
+female,group C,associate's degree,standard,none,53,62,53
+female,group E,master's degree,free/reduced,none,81,86,87
+male,group D,bachelor's degree,free/reduced,completed,39,42,38
+female,group C,some college,standard,completed,71,71,80
+male,group C,associate's degree,standard,none,97,93,91
+male,group D,some college,standard,completed,82,82,88
+male,group C,high school,free/reduced,none,59,53,52
+male,group B,associate's degree,standard,none,61,42,41
+male,group E,associate's degree,free/reduced,completed,78,74,72
+male,group C,associate's degree,free/reduced,none,49,51,51
+male,group B,high school,standard,none,59,58,47
+female,group C,some college,standard,completed,70,72,76
+male,group B,associate's degree,standard,completed,82,84,78
+male,group E,associate's degree,free/reduced,none,90,90,82
+female,group C,bachelor's degree,free/reduced,none,43,62,61
+male,group C,some college,free/reduced,none,80,64,66
+male,group D,some college,standard,none,81,82,84
+male,group C,some high school,standard,none,57,61,54
+female,group D,some high school,standard,none,59,72,80
+female,group D,associate's degree,standard,none,64,76,74
+male,group C,bachelor's degree,standard,completed,63,64,66
+female,group E,bachelor's degree,standard,completed,71,70,70
+female,group B,high school,free/reduced,none,64,73,71
+male,group D,bachelor's degree,free/reduced,none,55,46,44
+female,group E,associate's degree,standard,none,51,51,54
+female,group C,associate's degree,standard,completed,62,76,80
+female,group E,associate's degree,standard,completed,93,100,95
+male,group C,high school,free/reduced,none,54,72,59
+female,group D,some college,free/reduced,none,69,65,74
+male,group D,high school,free/reduced,none,44,51,48
+female,group E,some college,standard,completed,86,85,91
+female,group E,associate's degree,standard,none,85,92,85
+female,group A,master's degree,free/reduced,none,50,67,73
+male,group D,some high school,standard,completed,88,74,75
+female,group E,associate's degree,standard,none,59,62,69
+female,group E,some high school,free/reduced,none,32,34,38
+male,group B,high school,free/reduced,none,36,29,27
+female,group B,some high school,free/reduced,completed,63,78,79
+male,group D,associate's degree,standard,completed,67,54,63
+female,group D,some high school,standard,completed,65,78,82
+male,group D,master's degree,standard,none,85,84,89
+female,group C,master's degree,standard,none,73,78,74
+female,group A,high school,free/reduced,completed,34,48,41
+female,group D,bachelor's degree,free/reduced,completed,93,100,100
+female,group D,some high school,free/reduced,none,67,84,84
+male,group D,some college,standard,none,88,77,77
+male,group B,high school,standard,none,57,48,51
+female,group D,some college,standard,completed,79,84,91
+female,group C,bachelor's degree,free/reduced,none,67,75,72
+male,group E,bachelor's degree,standard,completed,70,64,70
+male,group D,bachelor's degree,free/reduced,none,50,42,48
+female,group A,some college,standard,none,69,84,82
+female,group C,bachelor's degree,standard,completed,52,61,66
+female,group C,bachelor's degree,free/reduced,completed,47,62,66
+female,group B,associate's degree,free/reduced,none,46,61,55
+female,group E,some college,standard,none,68,70,66
+male,group E,bachelor's degree,standard,completed,100,100,100
+female,group C,high school,standard,none,44,61,52
+female,group C,associate's degree,standard,completed,57,77,80
+male,group B,some college,standard,completed,91,96,91
+male,group D,high school,free/reduced,none,69,70,67
+female,group C,high school,free/reduced,none,35,53,46
+male,group D,high school,standard,none,72,66,66
+female,group B,associate's degree,free/reduced,none,54,65,65
+male,group D,high school,free/reduced,none,74,70,69
+male,group E,some high school,standard,completed,74,64,60
+male,group E,associate's degree,free/reduced,none,64,56,52
+female,group D,high school,free/reduced,completed,65,61,71
+male,group E,associate's degree,free/reduced,completed,46,43,44
+female,group C,some high school,free/reduced,none,48,56,51
+male,group C,some college,free/reduced,completed,67,74,70
+male,group D,some college,free/reduced,none,62,57,62
+male,group D,associate's degree,free/reduced,completed,61,71,73
+male,group C,bachelor's degree,free/reduced,completed,70,75,74
+male,group C,associate's degree,standard,completed,98,87,90
+male,group D,some college,free/reduced,none,70,63,58
+male,group A,associate's degree,standard,none,67,57,53
+female,group E,high school,free/reduced,none,57,58,57
+male,group D,some college,standard,completed,85,81,85
+male,group D,some high school,standard,completed,77,68,69
+male,group C,master's degree,free/reduced,completed,72,66,72
+female,group D,master's degree,standard,none,78,91,96
+male,group C,high school,standard,none,81,66,64
+male,group A,some high school,free/reduced,completed,61,62,61
+female,group B,high school,standard,none,58,68,61
+female,group C,associate's degree,standard,none,54,61,58
+male,group B,high school,standard,none,82,82,80
+female,group D,some college,free/reduced,none,49,58,60
+male,group B,some high school,free/reduced,completed,49,50,52
+female,group E,high school,free/reduced,completed,57,75,73
+male,group E,high school,standard,none,94,73,71
+female,group D,some college,standard,completed,75,77,83
+female,group E,some high school,free/reduced,none,74,74,72
+male,group C,high school,standard,completed,58,52,54
+female,group C,some college,standard,none,62,69,69
+male,group E,associate's degree,standard,none,72,57,62
+male,group C,some college,standard,none,84,87,81
+female,group D,master's degree,standard,none,92,100,100
+female,group D,high school,standard,none,45,63,59
+male,group C,high school,standard,none,75,81,71
+female,group A,some college,standard,none,56,58,64
+female,group D,some high school,free/reduced,none,48,54,53
+female,group E,associate's degree,standard,none,100,100,100
+female,group C,some high school,free/reduced,completed,65,76,75
+male,group D,some college,standard,none,72,57,58
+female,group D,some college,standard,none,62,70,72
+male,group A,some high school,standard,completed,66,68,64
+male,group C,some college,standard,none,63,63,60
+female,group E,associate's degree,standard,none,68,76,67
+female,group B,bachelor's degree,standard,none,75,84,80
+female,group D,bachelor's degree,standard,none,89,100,100
+male,group C,some high school,standard,completed,78,72,69
+female,group A,high school,free/reduced,completed,53,50,60
+female,group D,some college,free/reduced,none,49,65,61
+female,group A,some college,standard,none,54,63,67
+female,group C,some college,standard,completed,64,82,77
+male,group B,some college,free/reduced,completed,60,62,60
+male,group C,associate's degree,standard,none,62,65,58
+male,group D,high school,standard,completed,55,41,48
+female,group C,associate's degree,standard,none,91,95,94
+female,group B,high school,free/reduced,none,8,24,23
+male,group D,some high school,standard,none,81,78,78
+male,group B,some high school,standard,completed,79,85,86
+female,group A,some college,standard,completed,78,87,91
+female,group C,some high school,standard,none,74,75,82
+male,group A,high school,standard,none,57,51,54
+female,group C,associate's degree,standard,none,40,59,51
+male,group E,some high school,standard,completed,81,75,76
+female,group A,some high school,free/reduced,none,44,45,45
+female,group D,some college,free/reduced,completed,67,86,83
+male,group E,high school,free/reduced,completed,86,81,75
+female,group B,some high school,standard,completed,65,82,78
+female,group D,associate's degree,free/reduced,none,55,76,76
+female,group D,bachelor's degree,free/reduced,none,62,72,74
+male,group A,high school,standard,none,63,63,62
+female,group E,master's degree,standard,completed,88,99,95
+male,group C,high school,free/reduced,none,62,55,55
+female,group C,high school,free/reduced,completed,59,71,65
+female,group D,some college,standard,completed,68,78,77
+female,group D,some college,free/reduced,none,77,86,86
diff --git a/artifacts/model.pkl b/artifacts/model.pkl
new file mode 100644
index 0000000..a14e7c0
Binary files /dev/null and b/artifacts/model.pkl differ
diff --git a/artifacts/preprocessor.pkl b/artifacts/preprocessor.pkl
new file mode 100644
index 0000000..3fbeb94
Binary files /dev/null and b/artifacts/preprocessor.pkl differ
diff --git a/artifacts/test.csv b/artifacts/test.csv
new file mode 100644
index 0000000..c993112
--- /dev/null
+++ b/artifacts/test.csv
@@ -0,0 +1,201 @@
+gender,race_ethnicity,parental_level_of_education,lunch,test_preparation_course,math_score,reading_score,writing_score
+female,group C,associate's degree,standard,none,91,86,84
+female,group B,some college,free/reduced,completed,53,66,73
+male,group D,bachelor's degree,standard,none,80,73,72
+male,group C,some college,free/reduced,none,74,77,73
+male,group E,some college,standard,completed,84,83,78
+male,group D,associate's degree,free/reduced,none,81,75,78
+male,group B,associate's degree,free/reduced,completed,69,70,63
+female,group B,some high school,standard,completed,54,61,62
+male,group C,associate's degree,free/reduced,none,87,73,72
+male,group B,some high school,standard,completed,51,54,41
+male,group A,high school,free/reduced,none,45,47,49
+male,group E,some high school,standard,none,30,26,22
+female,group B,high school,free/reduced,completed,67,80,81
+female,group D,some college,free/reduced,none,49,65,61
+male,group D,some college,standard,completed,85,81,85
+female,group D,some high school,standard,completed,65,78,82
+male,group D,high school,standard,none,53,52,42
+male,group D,bachelor's degree,free/reduced,none,55,46,44
+female,group D,some high school,standard,none,48,58,54
+female,group D,associate's degree,free/reduced,none,56,65,63
+male,group C,master's degree,standard,none,79,72,69
+female,group C,bachelor's degree,free/reduced,completed,43,51,54
+female,group C,some college,free/reduced,completed,45,73,70
+female,group C,high school,free/reduced,none,36,53,43
+male,group D,some high school,free/reduced,completed,80,79,79
+male,group D,associate's degree,standard,none,80,75,77
+male,group D,bachelor's degree,standard,completed,68,74,74
+female,group C,associate's degree,standard,none,40,59,51
+female,group A,high school,free/reduced,completed,34,48,41
+female,group D,some college,free/reduced,none,49,58,60
+male,group B,some college,standard,none,62,61,57
+male,group D,some college,standard,completed,71,61,69
+male,group B,bachelor's degree,free/reduced,none,62,63,56
+male,group E,some college,standard,none,76,71,72
+male,group E,some college,standard,none,84,77,71
+female,group B,some college,free/reduced,none,45,53,55
+male,group D,associate's degree,free/reduced,none,77,78,73
+female,group D,some college,standard,none,69,77,77
+female,group C,master's degree,standard,none,73,78,74
+female,group C,some high school,free/reduced,none,0,17,10
+male,group C,associate's degree,standard,completed,82,75,77
+male,group B,some high school,standard,completed,65,66,62
+male,group D,bachelor's degree,standard,completed,67,61,68
+female,group A,some college,standard,none,54,63,67
+male,group D,associate's degree,free/reduced,none,90,87,75
+female,group E,high school,standard,completed,59,63,75
+male,group D,high school,free/reduced,none,74,70,69
+female,group C,high school,standard,none,29,29,30
+male,group D,some high school,standard,completed,89,88,82
+female,group A,high school,standard,completed,75,82,79
+male,group B,some college,standard,completed,71,75,70
+female,group D,associate's degree,standard,none,64,76,74
+male,group C,some college,standard,completed,79,79,78
+female,group B,some college,free/reduced,completed,48,56,58
+female,group C,some high school,standard,none,69,73,73
+female,group D,some college,standard,none,69,74,74
+male,group D,bachelor's degree,standard,none,88,78,83
+male,group C,associate's degree,standard,none,58,54,52
+male,group B,associate's degree,standard,none,87,85,73
+female,group A,some high school,standard,completed,85,90,92
+male,group A,some high school,standard,completed,46,41,43
+female,group C,some high school,free/reduced,completed,71,84,87
+female,group C,associate's degree,standard,none,81,77,79
+female,group B,some college,free/reduced,none,58,61,66
+male,group D,master's degree,free/reduced,completed,84,89,90
+female,group C,bachelor's degree,free/reduced,completed,66,74,81
+female,group D,some college,free/reduced,none,55,71,69
+male,group C,high school,free/reduced,none,59,53,52
+female,group D,some college,free/reduced,completed,58,63,73
+female,group D,associate's degree,standard,none,82,95,89
+male,group E,associate's degree,standard,completed,66,63,64
+female,group C,bachelor's degree,standard,none,81,88,90
+male,group C,some college,free/reduced,none,58,57,54
+female,group A,associate's degree,free/reduced,none,37,57,56
+male,group C,some high school,standard,completed,63,60,57
+male,group E,some high school,standard,completed,77,76,77
+female,group D,some college,standard,completed,85,86,98
+male,group B,associate's degree,free/reduced,none,57,56,57
+female,group A,some high school,standard,none,48,66,65
+male,group C,some high school,standard,none,51,52,44
+male,group D,some college,free/reduced,none,63,61,60
+male,group D,some high school,free/reduced,none,45,37,37
+male,group C,bachelor's degree,standard,none,83,78,73
+female,group C,some college,standard,none,60,72,74
+male,group B,bachelor's degree,standard,none,63,71,69
+female,group C,high school,free/reduced,none,62,67,64
+female,group D,some college,standard,completed,68,78,77
+female,group B,some high school,standard,completed,60,70,74
+female,group C,some high school,standard,completed,77,90,85
+male,group A,some college,free/reduced,none,28,23,19
+male,group C,master's degree,free/reduced,none,79,81,71
+female,group E,some college,standard,none,100,92,97
+male,group D,bachelor's degree,standard,none,69,58,57
+male,group B,high school,free/reduced,none,66,77,70
+female,group B,some college,standard,none,19,38,32
+male,group D,associate's degree,standard,none,75,68,64
+male,group D,some college,standard,none,60,63,59
+female,group A,some college,standard,none,58,70,67
+female,group C,associate's degree,standard,none,69,80,71
+female,group C,associate's degree,free/reduced,completed,56,68,70
+male,group C,associate's degree,standard,completed,73,78,72
+male,group E,some college,standard,none,66,57,52
+male,group A,associate's degree,standard,none,67,57,53
+female,group C,associate's degree,free/reduced,none,64,73,68
+male,group A,high school,standard,none,71,74,64
+male,group B,high school,standard,none,70,65,60
+male,group E,associate's degree,standard,none,53,45,40
+male,group C,high school,standard,none,75,81,71
+female,group B,high school,standard,completed,68,83,78
+female,group C,high school,standard,none,44,61,52
+female,group D,bachelor's degree,free/reduced,none,29,41,47
+female,group B,high school,free/reduced,none,71,87,82
+male,group A,high school,standard,none,57,51,54
+female,group A,bachelor's degree,standard,none,45,59,64
+female,group C,some college,free/reduced,none,76,83,88
+male,group C,high school,standard,none,61,56,55
+male,group C,some high school,free/reduced,completed,45,52,49
+male,group D,high school,standard,completed,55,41,48
+male,group B,high school,standard,completed,73,69,68
+male,group D,high school,free/reduced,completed,78,77,80
+female,group A,master's degree,free/reduced,none,50,67,73
+female,group C,some college,free/reduced,none,62,67,62
+male,group D,master's degree,standard,none,81,81,84
+female,group C,some high school,free/reduced,completed,64,79,77
+female,group D,some high school,standard,completed,64,60,74
+male,group D,some high school,standard,none,73,66,62
+female,group D,associate's degree,standard,completed,73,75,80
+female,group C,some high school,standard,completed,67,74,77
+male,group B,associate's degree,standard,none,61,42,41
+male,group C,some high school,standard,completed,67,73,68
+female,group D,some high school,standard,none,65,82,81
+male,group D,associate's degree,standard,none,80,75,69
+male,group D,some high school,free/reduced,none,59,42,41
+female,group E,master's degree,standard,completed,88,99,95
+female,group C,associate's degree,standard,none,62,74,70
+female,group C,high school,free/reduced,none,33,41,43
+female,group C,bachelor's degree,standard,completed,79,92,89
+male,group B,some high school,standard,completed,84,83,75
+male,group A,master's degree,free/reduced,none,73,74,72
+female,group A,associate's degree,free/reduced,none,41,51,48
+female,group E,associate's degree,free/reduced,none,50,56,54
+female,group B,high school,standard,completed,58,70,68
+male,group D,some high school,free/reduced,completed,55,59,59
+male,group D,high school,standard,none,45,48,46
+male,group D,some high school,standard,completed,88,74,75
+female,group B,associate's degree,free/reduced,none,46,61,55
+male,group A,some high school,standard,none,51,31,36
+male,group D,some high school,standard,none,75,74,69
+male,group E,some college,free/reduced,completed,49,52,51
+female,group E,high school,standard,none,75,86,79
+female,group E,high school,standard,completed,74,79,80
+female,group B,associate's degree,standard,completed,61,86,87
+male,group C,associate's degree,standard,none,62,65,58
+male,group C,some high school,free/reduced,none,68,63,54
+female,group D,master's degree,standard,none,78,91,96
+female,group E,some college,standard,none,71,70,76
+female,group D,high school,free/reduced,none,49,57,52
+female,group A,bachelor's degree,standard,none,59,72,70
+male,group E,bachelor's degree,free/reduced,completed,79,74,72
+female,group E,associate's degree,standard,none,51,51,54
+female,group C,bachelor's degree,standard,completed,56,79,72
+male,group B,high school,standard,completed,76,62,60
+female,group D,some college,standard,completed,69,79,81
+male,group C,some high school,free/reduced,completed,51,56,53
+male,group D,some college,standard,completed,82,82,88
+male,group C,some college,standard,none,73,74,61
+male,group C,high school,free/reduced,completed,40,46,50
+male,group E,some college,free/reduced,none,93,90,83
+female,group C,bachelor's degree,standard,completed,59,64,75
+female,group B,associate's degree,standard,none,73,76,80
+male,group B,some high school,standard,completed,85,84,78
+male,group E,associate's degree,standard,none,76,71,67
+female,group D,associate's degree,free/reduced,completed,77,89,98
+female,group D,some college,free/reduced,completed,67,86,83
+male,group D,some college,free/reduced,none,61,47,56
+female,group D,some high school,free/reduced,none,27,34,32
+male,group D,high school,standard,none,54,52,52
+female,group C,master's degree,free/reduced,completed,65,81,81
+female,group E,associate's degree,standard,none,87,94,95
+female,group C,some high school,standard,completed,70,82,76
+female,group B,high school,standard,none,54,64,68
+female,group C,high school,free/reduced,none,66,76,68
+female,group D,master's degree,free/reduced,completed,85,95,100
+male,group C,some high school,free/reduced,completed,56,61,60
+male,group E,master's degree,standard,none,90,85,84
+male,group E,high school,standard,none,70,55,56
+female,group B,bachelor's degree,standard,none,61,72,70
+male,group A,bachelor's degree,free/reduced,completed,49,58,60
+male,group C,high school,standard,none,81,66,64
+male,group B,some college,standard,completed,87,84,86
+male,group B,some high school,free/reduced,completed,49,50,52
+male,group B,some high school,standard,none,68,54,53
+male,group C,associate's degree,free/reduced,none,77,67,64
+female,group B,bachelor's degree,free/reduced,none,78,79,76
+male,group C,associate's degree,free/reduced,completed,60,51,56
+female,group D,high school,free/reduced,completed,52,57,56
+male,group E,associate's degree,standard,completed,62,56,53
+female,group B,some college,free/reduced,none,74,81,76
+female,group C,associate's degree,standard,none,65,77,74
+female,group D,some high school,standard,completed,61,74,72
diff --git a/artifacts/train.csv b/artifacts/train.csv
new file mode 100644
index 0000000..376a5ef
--- /dev/null
+++ b/artifacts/train.csv
@@ -0,0 +1,801 @@
+gender,race_ethnicity,parental_level_of_education,lunch,test_preparation_course,math_score,reading_score,writing_score
+female,group D,master's degree,standard,none,62,70,75
+female,group C,bachelor's degree,free/reduced,completed,66,83,83
+female,group D,some college,free/reduced,none,79,89,86
+male,group C,master's degree,free/reduced,none,61,67,66
+male,group E,high school,standard,none,73,64,57
+male,group B,high school,free/reduced,none,30,24,15
+female,group C,bachelor's degree,standard,completed,96,100,100
+female,group C,associate's degree,standard,completed,57,77,80
+male,group D,high school,standard,completed,68,64,66
+female,group C,some high school,free/reduced,none,48,58,52
+male,group B,some high school,standard,none,67,64,61
+female,group C,some college,free/reduced,none,59,62,64
+male,group E,some high school,standard,completed,74,64,60
+female,group C,some high school,standard,none,65,69,76
+female,group B,some college,standard,none,62,67,67
+male,group C,associate's degree,standard,none,47,37,35
+male,group D,associate's degree,standard,none,80,63,63
+male,group C,high school,standard,none,68,60,53
+female,group D,some college,standard,completed,79,84,91
+male,group D,high school,standard,none,89,87,79
+male,group A,some college,standard,none,69,67,69
+female,group E,some college,free/reduced,none,53,58,57
+female,group C,some college,standard,completed,64,82,77
+male,group C,high school,standard,completed,82,84,82
+male,group B,high school,free/reduced,none,36,29,27
+female,group B,master's degree,standard,none,90,95,93
+female,group D,master's degree,standard,none,64,63,66
+female,group B,bachelor's degree,standard,none,52,65,69
+female,group D,some high school,free/reduced,none,67,84,84
+male,group C,associate's degree,standard,completed,51,60,58
+male,group D,some college,standard,none,79,73,67
+male,group A,high school,standard,none,63,63,62
+female,group D,associate's degree,free/reduced,none,52,59,56
+male,group A,associate's degree,free/reduced,completed,40,55,53
+male,group D,some college,standard,none,40,42,38
+female,group B,some college,standard,none,63,65,61
+male,group C,associate's degree,standard,none,46,43,42
+female,group C,some high school,free/reduced,completed,65,76,75
+female,group B,some high school,standard,none,62,64,66
+male,group B,associate's degree,standard,none,90,78,81
+male,group A,associate's degree,free/reduced,none,47,57,44
+male,group C,some college,standard,none,59,41,42
+female,group B,master's degree,free/reduced,completed,77,97,94
+female,group C,associate's degree,standard,none,52,55,57
+male,group E,some college,standard,completed,99,87,81
+female,group B,some high school,standard,none,70,64,72
+male,group C,associate's degree,free/reduced,none,64,66,59
+male,group A,bachelor's degree,standard,completed,80,78,81
+male,group D,high school,free/reduced,none,42,39,34
+male,group E,associate's degree,standard,completed,97,82,88
+male,group A,some college,free/reduced,completed,50,47,54
+female,group B,some high school,standard,completed,65,82,78
+female,group C,master's degree,free/reduced,none,52,65,61
+female,group E,associate's degree,standard,none,59,62,69
+male,group B,some high school,standard,none,74,63,57
+female,group C,some high school,free/reduced,none,43,53,53
+female,group B,high school,free/reduced,completed,67,78,79
+male,group E,bachelor's degree,standard,completed,100,100,100
+male,group D,high school,standard,none,72,66,66
+female,group B,associate's degree,standard,none,71,83,78
+male,group A,some high school,free/reduced,none,55,46,43
+female,group C,some college,standard,none,84,87,91
+female,group E,some college,standard,completed,63,72,70
+female,group C,bachelor's degree,standard,none,63,75,81
+female,group C,some college,free/reduced,completed,42,66,69
+male,group E,associate's degree,free/reduced,completed,78,74,72
+male,group E,some college,standard,none,69,60,54
+female,group B,associate's degree,standard,none,80,86,83
+male,group B,high school,standard,none,79,60,65
+male,group C,associate's degree,standard,completed,87,100,95
+female,group A,associate's degree,free/reduced,none,65,85,76
+female,group D,some high school,standard,none,51,63,61
+male,group D,master's degree,standard,none,85,84,89
+male,group A,some high school,standard,completed,47,49,49
+male,group C,master's degree,free/reduced,none,54,59,50
+female,group B,high school,free/reduced,none,38,60,50
+male,group C,some high school,free/reduced,completed,59,69,65
+male,group D,high school,free/reduced,none,60,57,51
+male,group B,high school,free/reduced,none,49,45,45
+female,group C,associate's degree,standard,completed,52,59,62
+female,group C,bachelor's degree,free/reduced,none,44,63,62
+female,group E,associate's degree,free/reduced,none,70,84,81
+male,group C,associate's degree,standard,none,84,80,80
+male,group C,associate's degree,standard,none,76,70,68
+male,group C,some college,free/reduced,none,35,28,27
+female,group C,associate's degree,standard,completed,96,96,99
+female,group D,some college,standard,none,80,90,89
+male,group B,associate's degree,standard,none,81,73,72
+male,group D,high school,standard,none,57,50,54
+male,group E,high school,standard,none,94,73,71
+male,group B,high school,standard,none,82,82,80
+male,group D,high school,standard,none,70,70,70
+female,group B,associate's degree,standard,completed,94,87,92
+male,group A,bachelor's degree,standard,completed,75,58,62
+female,group C,some college,standard,none,52,58,58
+female,group A,high school,free/reduced,completed,77,88,85
+male,group D,some college,free/reduced,none,70,63,58
+male,group A,some high school,free/reduced,none,65,59,53
+male,group B,some college,free/reduced,none,40,43,39
+female,group C,some college,standard,completed,70,89,88
+male,group D,associate's degree,free/reduced,completed,79,82,80
+female,group C,some college,standard,completed,67,81,79
+male,group C,some college,free/reduced,none,68,68,61
+female,group D,master's degree,free/reduced,completed,47,58,67
+female,group A,some college,standard,completed,72,79,82
+female,group E,high school,free/reduced,completed,57,75,73
+female,group C,bachelor's degree,standard,none,83,93,95
+male,group A,some college,standard,completed,61,51,52
+male,group C,associate's degree,standard,completed,98,87,90
+female,group D,master's degree,free/reduced,completed,61,71,78
+female,group C,bachelor's degree,standard,completed,92,100,99
+female,group C,associate's degree,standard,completed,68,67,73
+female,group E,some high school,standard,none,77,79,80
+male,group C,some college,standard,none,66,59,52
+male,group B,high school,standard,none,47,46,42
+male,group C,some college,free/reduced,none,65,58,49
+male,group A,some high school,free/reduced,none,68,72,64
+female,group C,some college,standard,completed,63,78,80
+female,group D,high school,free/reduced,none,73,92,84
+female,group C,high school,free/reduced,none,42,62,60
+female,group E,master's degree,standard,none,62,68,68
+female,group D,bachelor's degree,standard,completed,68,75,81
+female,group C,associate's degree,standard,completed,67,84,81
+male,group D,some college,free/reduced,none,49,57,46
+female,group C,some college,free/reduced,none,35,44,43
+male,group A,high school,standard,none,68,70,66
+female,group B,high school,standard,completed,69,76,74
+male,group C,high school,standard,none,70,74,71
+female,group E,some high school,standard,completed,80,85,85
+female,group C,some college,standard,completed,75,81,84
+female,group D,some college,standard,none,63,64,67
+male,group B,bachelor's degree,standard,none,66,60,57
+female,group B,some high school,free/reduced,completed,74,90,88
+female,group C,some high school,standard,completed,44,51,55
+female,group B,bachelor's degree,standard,none,72,72,74
+female,group D,master's degree,standard,completed,77,82,91
+male,group D,high school,standard,none,46,34,36
+male,group C,high school,standard,completed,72,67,64
+male,group B,associate's degree,standard,completed,82,84,78
+male,group E,associate's degree,standard,completed,62,61,58
+male,group D,associate's degree,standard,none,80,68,72
+female,group C,high school,standard,none,54,59,62
+female,group D,some college,standard,none,79,86,81
+female,group B,high school,standard,none,87,95,86
+female,group C,some high school,standard,completed,65,74,77
+female,group C,associate's degree,free/reduced,completed,82,93,93
+female,group B,associate's degree,standard,none,40,48,50
+female,group D,bachelor's degree,free/reduced,completed,93,100,100
+female,group C,bachelor's degree,standard,none,65,72,74
+male,group D,some high school,standard,completed,77,68,69
+female,group C,some college,free/reduced,none,46,64,66
+male,group B,some college,standard,completed,88,85,76
+female,group E,some high school,free/reduced,none,32,34,38
+female,group C,associate's degree,standard,none,39,64,57
+male,group D,some high school,standard,none,86,73,70
+male,group C,some high school,free/reduced,completed,53,37,40
+male,group A,some college,free/reduced,completed,81,78,81
+male,group B,some college,free/reduced,none,41,39,34
+male,group C,some college,standard,none,61,61,62
+male,group D,some college,standard,none,88,73,78
+female,group A,some high school,standard,completed,59,85,80
+female,group D,some high school,standard,none,81,97,96
+male,group C,bachelor's degree,standard,none,58,55,48
+female,group C,some college,free/reduced,completed,64,85,85
+female,group E,master's degree,standard,none,81,92,91
+male,group C,high school,standard,none,70,70,65
+female,group C,bachelor's degree,free/reduced,none,62,78,79
+male,group D,some college,standard,completed,77,62,62
+female,group D,high school,standard,none,62,64,64
+female,group E,some college,standard,none,87,85,93
+female,group C,some college,free/reduced,completed,67,75,70
+male,group A,bachelor's degree,free/reduced,none,62,72,65
+female,group D,some high school,standard,none,76,72,71
+female,group C,associate's degree,standard,none,63,67,70
+female,group B,some college,standard,completed,88,95,92
+male,group D,associate's degree,standard,none,72,79,74
+female,group D,master's degree,standard,none,55,64,70
+male,group C,some high school,free/reduced,none,61,57,56
+male,group D,bachelor's degree,free/reduced,none,50,42,48
+male,group E,some high school,standard,completed,87,84,76
+male,group B,some college,standard,none,54,52,51
+female,group C,some college,free/reduced,none,22,39,33
+male,group D,high school,free/reduced,none,66,74,69
+male,group C,bachelor's degree,standard,completed,83,82,84
+female,group D,high school,standard,completed,56,68,74
+female,group B,associate's degree,free/reduced,none,54,65,65
+female,group D,master's degree,standard,none,74,79,82
+male,group E,some college,free/reduced,completed,87,74,70
+male,group E,high school,free/reduced,completed,86,81,75
+male,group B,some college,standard,none,66,65,60
+male,group C,associate's degree,free/reduced,completed,65,67,65
+female,group C,associate's degree,standard,none,58,73,68
+female,group C,associate's degree,standard,completed,75,82,90
+female,group B,associate's degree,free/reduced,none,52,76,70
+female,group C,some college,standard,none,54,64,65
+female,group E,associate's degree,standard,completed,82,85,86
+female,group C,some high school,standard,none,63,73,68
+female,group A,some high school,free/reduced,none,59,73,69
+male,group E,bachelor's degree,free/reduced,completed,70,68,72
+female,group C,high school,free/reduced,completed,59,71,65
+male,group D,bachelor's degree,free/reduced,completed,74,71,80
+male,group A,high school,free/reduced,completed,72,67,65
+male,group A,associate's degree,standard,completed,97,92,86
+female,group C,some high school,standard,none,47,54,53
+male,group D,master's degree,standard,none,95,81,84
+female,group C,some high school,standard,none,49,63,56
+male,group E,associate's degree,free/reduced,none,64,56,52
+female,group B,some high school,free/reduced,none,24,38,27
+male,group E,associate's degree,free/reduced,completed,77,69,68
+male,group B,bachelor's degree,free/reduced,none,55,59,54
+female,group D,some college,standard,none,74,89,84
+male,group D,high school,free/reduced,none,69,70,67
+female,group B,master's degree,standard,none,77,90,84
+male,group D,high school,standard,none,76,73,68
+male,group D,bachelor's degree,free/reduced,completed,61,70,76
+male,group C,some college,standard,completed,93,84,90
+male,group B,high school,standard,none,62,55,54
+male,group A,bachelor's degree,standard,none,64,60,58
+female,group D,some high school,standard,completed,66,78,78
+male,group C,high school,standard,completed,86,81,80
+male,group C,master's degree,free/reduced,completed,46,42,46
+female,group B,associate's degree,free/reduced,completed,76,94,87
+male,group A,high school,standard,none,59,52,46
+male,group B,high school,free/reduced,none,63,48,47
+female,group A,some high school,free/reduced,none,47,59,50
+male,group C,bachelor's degree,free/reduced,none,61,66,61
+male,group E,associate's degree,standard,none,72,64,63
+male,group A,some high school,free/reduced,none,39,39,34
+male,group E,some college,standard,none,86,76,74
+female,group D,associate's degree,free/reduced,none,47,53,58
+male,group B,associate's degree,standard,completed,81,82,82
+female,group B,high school,standard,none,58,62,59
+female,group C,some college,standard,none,59,71,70
+female,group D,bachelor's degree,standard,none,79,89,89
+female,group C,some high school,free/reduced,none,65,86,80
+female,group B,high school,standard,none,65,81,73
+female,group E,high school,standard,none,50,50,47
+female,group A,some high school,free/reduced,none,44,64,58
+female,group E,bachelor's degree,standard,completed,71,70,70
+female,group C,high school,standard,none,60,68,72
+male,group D,some high school,standard,none,86,80,75
+female,group C,some college,standard,none,53,62,56
+female,group D,bachelor's degree,standard,none,89,100,100
+female,group A,associate's degree,standard,completed,65,70,74
+male,group E,some high school,free/reduced,completed,78,83,80
+female,group D,bachelor's degree,free/reduced,none,63,73,78
+female,group C,high school,standard,none,75,88,85
+female,group B,high school,free/reduced,completed,46,54,58
+female,group C,some high school,free/reduced,completed,50,60,60
+female,group C,associate's degree,standard,completed,65,84,84
+female,group C,associate's degree,standard,none,65,76,76
+male,group E,associate's degree,free/reduced,none,90,90,82
+male,group C,associate's degree,standard,completed,57,54,56
+male,group C,high school,standard,none,52,53,49
+female,group B,high school,standard,none,65,64,62
+male,group D,some high school,standard,none,84,84,80
+female,group C,associate's degree,standard,completed,62,76,80
+male,group D,associate's degree,standard,completed,81,72,77
+male,group E,associate's degree,free/reduced,none,46,43,41
+male,group D,associate's degree,standard,none,71,66,60
+male,group C,some high school,standard,none,49,49,41
+female,group D,some college,standard,none,51,58,54
+female,group D,high school,standard,none,69,77,73
+female,group D,some high school,free/reduced,none,48,54,53
+male,group E,some high school,free/reduced,completed,73,67,59
+male,group C,some college,standard,completed,98,86,90
+female,group E,bachelor's degree,standard,completed,99,100,100
+male,group C,associate's degree,standard,none,74,73,67
+male,group E,some college,standard,none,68,60,59
+male,group D,associate's degree,free/reduced,none,53,54,48
+male,group D,associate's degree,standard,completed,87,84,85
+male,group C,high school,standard,none,71,79,71
+male,group C,some college,free/reduced,completed,67,74,70
+female,group D,some high school,standard,none,73,86,82
+male,group D,some high school,standard,completed,76,70,69
+female,group A,some high school,free/reduced,none,44,45,45
+female,group C,high school,free/reduced,none,35,53,46
+male,group C,bachelor's degree,free/reduced,completed,70,75,74
+male,group C,some high school,standard,none,75,72,62
+female,group E,high school,standard,none,74,76,73
+female,group C,some college,standard,none,58,59,66
+female,group B,some college,standard,none,79,86,92
+male,group D,associate's degree,standard,none,40,52,43
+female,group B,high school,free/reduced,none,50,67,63
+female,group E,associate's degree,standard,completed,79,88,94
+male,group B,some college,free/reduced,completed,59,65,66
+female,group B,associate's degree,standard,none,53,58,65
+female,group B,some high school,standard,none,41,55,51
+female,group B,master's degree,free/reduced,completed,58,76,78
+female,group D,some college,free/reduced,completed,59,78,76
+male,group D,some college,standard,none,81,82,84
+male,group A,some high school,standard,none,53,54,48
+male,group D,some college,standard,none,55,58,52
+male,group B,some college,standard,none,79,67,67
+male,group C,bachelor's degree,standard,none,86,83,86
+female,group B,master's degree,free/reduced,completed,52,70,62
+male,group A,some high school,free/reduced,none,79,82,73
+male,group C,bachelor's degree,standard,completed,71,74,68
+male,group B,high school,standard,none,59,58,47
+female,group B,high school,free/reduced,none,64,73,71
+female,group D,high school,standard,none,67,72,74
+female,group C,associate's degree,standard,completed,71,77,77
+male,group A,high school,free/reduced,none,48,45,41
+female,group A,some college,standard,none,69,84,82
+male,group E,some high school,standard,completed,89,84,77
+female,group A,some college,standard,none,56,58,64
+male,group B,some college,standard,completed,62,66,68
+female,group E,some high school,free/reduced,none,38,49,45
+male,group C,some college,standard,none,84,87,81
+male,group E,some college,standard,none,68,72,65
+male,group C,associate's degree,standard,completed,78,77,77
+male,group E,bachelor's degree,standard,completed,85,66,71
+female,group B,some college,free/reduced,none,61,68,66
+female,group C,some high school,standard,none,69,75,78
+female,group C,high school,free/reduced,none,41,46,43
+female,group D,some college,free/reduced,completed,52,59,65
+female,group C,some high school,free/reduced,none,55,65,62
+female,group D,some high school,standard,completed,97,100,100
+female,group A,some college,standard,completed,78,87,91
+male,group D,some college,standard,none,44,54,53
+male,group A,associate's degree,standard,none,63,61,61
+female,group C,some college,free/reduced,completed,63,73,71
+female,group E,master's degree,free/reduced,none,81,86,87
+male,group C,high school,free/reduced,none,58,61,52
+female,group C,high school,standard,none,61,72,70
+male,group B,bachelor's degree,free/reduced,completed,87,90,88
+female,group B,high school,standard,completed,77,82,89
+female,group B,associate's degree,standard,none,57,69,68
+male,group D,some college,standard,none,67,64,70
+male,group C,associate's degree,free/reduced,completed,43,45,50
+female,group B,associate's degree,free/reduced,none,53,70,70
+male,group B,associate's degree,free/reduced,none,61,58,56
+male,group C,high school,free/reduced,completed,58,51,52
+female,group B,some high school,standard,none,37,46,46
+female,group D,some high school,free/reduced,completed,40,65,64
+male,group C,some high school,standard,none,73,66,66
+male,group D,bachelor's degree,standard,none,54,49,47
+male,group B,associate's degree,free/reduced,none,44,41,38
+female,group B,associate's degree,free/reduced,completed,68,77,80
+male,group D,some college,free/reduced,none,69,66,60
+male,group B,some high school,free/reduced,none,48,52,45
+male,group C,some college,standard,none,58,49,42
+male,group D,bachelor's degree,free/reduced,none,63,66,67
+female,group C,associate's degree,free/reduced,none,60,75,74
+female,group D,bachelor's degree,standard,none,78,82,79
+male,group A,some high school,free/reduced,completed,61,62,61
+male,group D,some high school,free/reduced,none,62,49,52
+male,group C,high school,standard,none,62,67,58
+male,group A,some high school,standard,none,71,62,50
+male,group B,some high school,standard,none,72,68,67
+female,group B,bachelor's degree,free/reduced,none,75,85,82
+female,group D,some high school,standard,none,59,67,61
+female,group D,associate's degree,standard,none,77,77,73
+male,group D,associate's degree,standard,none,52,55,49
+female,group C,some college,standard,completed,71,71,80
+female,group C,bachelor's degree,standard,completed,52,61,66
+female,group D,some high school,standard,none,73,84,85
+female,group D,associate's degree,standard,completed,88,92,95
+female,group A,associate's degree,standard,completed,55,65,62
+male,group E,associate's degree,standard,none,87,74,76
+male,group D,associate's degree,standard,none,61,55,52
+female,group D,some college,free/reduced,none,77,86,86
+male,group B,some college,standard,none,58,50,45
+male,group C,associate's degree,standard,none,92,79,84
+female,group E,high school,standard,none,99,93,90
+female,group B,associate's degree,standard,none,73,83,76
+female,group B,some college,standard,completed,50,64,66
+female,group C,associate's degree,standard,completed,74,75,83
+female,group C,high school,standard,none,61,73,63
+male,group A,some high school,standard,completed,66,68,64
+male,group E,associate's degree,free/reduced,completed,100,100,93
+male,group E,some college,standard,none,83,80,73
+female,group E,some high school,free/reduced,none,72,79,77
+male,group E,some college,standard,none,53,55,48
+female,group C,associate's degree,standard,none,46,58,57
+female,group D,high school,free/reduced,completed,65,61,71
+female,group E,some college,free/reduced,completed,42,55,54
+female,group C,associate's degree,standard,completed,83,85,90
+male,group D,some high school,standard,none,60,59,54
+male,group D,some college,standard,completed,100,97,99
+female,group C,high school,free/reduced,completed,67,79,84
+female,group C,associate's degree,free/reduced,none,54,58,61
+male,group B,bachelor's degree,standard,none,59,54,51
+female,group B,high school,standard,none,48,62,60
+male,group C,high school,standard,none,90,75,69
+female,group B,associate's degree,standard,none,82,80,77
+female,group D,high school,standard,none,51,66,62
+female,group C,high school,free/reduced,none,35,61,54
+female,group D,associate's degree,free/reduced,completed,75,90,88
+female,group C,master's degree,standard,completed,81,91,87
+male,group C,associate's degree,standard,none,85,76,71
+female,group D,some high school,free/reduced,completed,69,86,81
+female,group B,bachelor's degree,free/reduced,none,77,85,87
+male,group C,associate's degree,free/reduced,none,58,55,53
+male,group B,high school,standard,completed,52,49,46
+male,group D,some high school,standard,none,62,67,61
+female,group B,some high school,standard,none,67,89,82
+female,group E,some college,standard,none,76,78,80
+male,group D,bachelor's degree,free/reduced,none,68,68,67
+female,group C,associate's degree,standard,completed,59,73,72
+female,group B,some high school,free/reduced,none,18,32,28
+male,group D,some college,standard,completed,65,77,74
+female,group C,some college,standard,none,71,81,80
+female,group E,some college,standard,none,62,73,70
+male,group D,some high school,standard,none,69,66,61
+male,group D,some college,standard,none,72,57,58
+female,group E,associate's degree,standard,none,66,65,69
+male,group C,high school,standard,none,84,77,74
+female,group E,bachelor's degree,standard,none,37,45,38
+female,group C,associate's degree,standard,none,85,84,82
+male,group C,master's degree,free/reduced,completed,62,68,75
+male,group D,some high school,free/reduced,none,56,54,52
+male,group B,some college,standard,completed,69,77,77
+female,group D,some college,standard,none,98,100,99
+male,group C,high school,standard,none,50,48,42
+female,group E,master's degree,standard,completed,94,99,100
+female,group C,associate's degree,standard,none,91,95,94
+female,group E,some college,standard,completed,66,74,73
+female,group C,some high school,standard,none,74,75,82
+male,group B,associate's degree,standard,none,65,54,57
+male,group E,bachelor's degree,standard,completed,70,64,70
+male,group B,some college,free/reduced,none,60,60,60
+female,group A,high school,standard,none,61,68,63
+male,group E,some high school,standard,none,94,88,78
+male,group C,high school,standard,none,88,89,86
+male,group A,some high school,standard,none,64,50,43
+female,group D,associate's degree,free/reduced,completed,57,74,76
+male,group C,some high school,standard,completed,78,72,69
+male,group C,master's degree,free/reduced,completed,72,66,72
+female,group C,some high school,standard,completed,76,87,85
+female,group E,some high school,free/reduced,none,74,74,72
+male,group B,high school,standard,completed,73,71,68
+female,group D,some college,free/reduced,completed,70,78,78
+female,group C,high school,standard,none,76,76,74
+male,group C,some high school,standard,none,57,61,54
+female,group E,master's degree,free/reduced,none,45,56,54
+male,group B,some college,standard,none,69,54,55
+female,group C,some college,standard,none,62,69,69
+male,group D,associate's degree,free/reduced,none,75,66,73
+female,group D,some college,standard,completed,75,77,83
+male,group C,some college,standard,none,59,60,58
+female,group C,some college,standard,completed,88,95,94
+female,group D,some high school,free/reduced,none,50,64,59
+female,group D,some college,standard,none,62,70,72
+female,group D,bachelor's degree,standard,none,59,70,73
+male,group C,some college,standard,none,91,74,76
+male,group C,some college,standard,none,63,63,60
+male,group C,master's degree,standard,none,71,67,67
+female,group B,some high school,free/reduced,completed,59,63,64
+male,group C,some high school,standard,none,64,58,51
+female,group C,master's degree,standard,completed,69,84,85
+male,group C,some high school,standard,none,62,64,55
+male,group C,associate's degree,standard,none,97,93,91
+female,group E,associate's degree,standard,completed,95,89,92
+female,group B,bachelor's degree,standard,none,75,84,80
+female,group A,some college,free/reduced,none,61,60,57
+female,group D,master's degree,standard,none,53,61,68
+female,group E,associate's degree,standard,none,68,76,67
+male,group B,master's degree,free/reduced,none,49,53,52
+female,group C,bachelor's degree,free/reduced,none,67,75,72
+female,group B,associate's degree,standard,completed,59,70,66
+male,group C,some high school,standard,completed,76,80,73
+female,group D,bachelor's degree,free/reduced,none,62,72,74
+female,group E,associate's degree,standard,none,84,95,92
+male,group C,high school,standard,none,62,55,49
+female,group C,some college,standard,none,72,72,71
+male,group A,high school,free/reduced,none,53,58,44
+male,group B,high school,standard,completed,60,44,47
+female,group D,high school,standard,completed,57,58,64
+male,group E,high school,free/reduced,completed,57,56,54
+male,group D,some high school,standard,completed,71,69,68
+female,group A,high school,standard,none,55,73,73
+female,group D,associate's degree,free/reduced,none,46,56,57
+female,group C,some college,standard,none,69,78,76
+female,group C,some college,standard,none,55,69,65
+male,group D,high school,standard,none,88,78,75
+male,group A,bachelor's degree,standard,none,77,67,68
+female,group D,high school,standard,completed,88,99,100
+female,group C,associate's degree,standard,none,54,61,58
+male,group E,high school,standard,completed,81,80,76
+female,group D,associate's degree,free/reduced,none,43,60,58
+male,group B,some college,free/reduced,completed,74,77,76
+male,group D,some high school,standard,completed,78,81,86
+male,group D,high school,free/reduced,completed,64,64,67
+female,group E,high school,free/reduced,none,41,45,40
+female,group D,associate's degree,standard,none,74,81,83
+female,group C,associate's degree,free/reduced,none,65,77,74
+female,group A,high school,standard,completed,68,80,76
+male,group D,master's degree,standard,none,80,80,72
+male,group B,associate's degree,standard,none,80,76,64
+male,group D,high school,standard,none,69,75,71
+male,group A,bachelor's degree,standard,none,91,96,92
+male,group A,some college,standard,completed,100,96,86
+female,group C,some college,standard,completed,87,89,94
+female,group E,associate's degree,standard,none,85,92,85
+female,group C,some high school,free/reduced,none,44,50,51
+male,group D,some college,free/reduced,completed,69,60,63
+male,group E,associate's degree,standard,completed,71,74,68
+female,group C,bachelor's degree,free/reduced,completed,51,72,79
+male,group A,some high school,standard,completed,62,67,69
+male,group C,associate's degree,free/reduced,none,68,65,61
+male,group D,high school,standard,none,41,52,51
+male,group D,high school,free/reduced,none,44,51,48
+male,group C,some high school,free/reduced,none,79,76,65
+female,group E,associate's degree,standard,completed,93,100,95
+male,group B,some high school,standard,completed,64,53,57
+male,group C,associate's degree,free/reduced,none,73,68,66
+male,group B,some high school,standard,none,88,84,75
+female,group C,some college,free/reduced,none,62,72,70
+male,group D,some college,free/reduced,none,62,57,62
+male,group C,high school,free/reduced,none,61,60,55
+male,group D,associate's degree,standard,none,90,87,85
+male,group D,high school,free/reduced,none,75,74,66
+female,group C,some college,free/reduced,none,77,90,91
+female,group C,some college,standard,none,82,90,94
+male,group E,high school,standard,none,80,76,65
+male,group D,high school,free/reduced,none,63,57,56
+male,group E,some high school,standard,none,82,67,61
+female,group E,some college,standard,none,61,64,62
+male,group A,high school,standard,none,57,43,47
+female,group D,high school,standard,none,45,63,59
+male,group E,high school,free/reduced,none,55,56,51
+female,group B,associate's degree,standard,none,58,63,65
+male,group B,bachelor's degree,free/reduced,none,73,56,57
+female,group E,bachelor's degree,standard,none,65,73,75
+female,group C,some high school,standard,completed,59,54,67
+female,group C,some college,standard,completed,88,93,93
+female,group D,associate's degree,standard,none,65,69,70
+male,group C,associate's degree,standard,none,69,77,69
+male,group C,high school,standard,none,70,56,51
+male,group E,associate's degree,standard,none,89,76,74
+male,group E,high school,standard,none,84,73,69
+male,group D,associate's degree,free/reduced,completed,61,71,73
+female,group B,high school,standard,none,74,72,72
+male,group B,high school,standard,none,57,48,51
+female,group C,high school,standard,completed,60,64,74
+male,group C,high school,free/reduced,none,54,72,59
+female,group A,bachelor's degree,standard,none,51,49,51
+female,group D,some high school,standard,completed,80,92,88
+female,group A,some college,free/reduced,none,49,65,55
+male,group C,bachelor's degree,standard,completed,91,81,79
+male,group B,high school,standard,none,52,48,49
+male,group C,master's degree,standard,none,67,57,59
+female,group C,bachelor's degree,free/reduced,completed,47,62,66
+male,group B,some high school,standard,completed,61,56,56
+female,group D,associate's degree,free/reduced,completed,74,88,90
+female,group E,some college,standard,none,68,70,66
+male,group C,master's degree,free/reduced,completed,79,77,75
+female,group D,some college,free/reduced,none,64,74,75
+male,group B,some college,standard,completed,91,96,91
+female,group E,bachelor's degree,free/reduced,none,61,58,62
+female,group C,bachelor's degree,free/reduced,none,43,62,61
+female,group C,high school,free/reduced,none,53,72,64
+female,group E,bachelor's degree,standard,none,64,73,70
+female,group A,high school,free/reduced,completed,53,50,60
+female,group C,bachelor's degree,standard,none,86,92,87
+female,group D,high school,standard,none,69,72,77
+female,group C,some high school,standard,none,77,91,88
+female,group D,high school,standard,none,78,81,80
+female,group C,some college,standard,none,54,48,52
+male,group A,associate's degree,standard,none,54,53,47
+female,group E,associate's degree,free/reduced,none,73,76,78
+female,group B,bachelor's degree,standard,none,67,86,83
+male,group C,associate's degree,free/reduced,none,49,51,51
+female,group C,master's degree,free/reduced,none,40,58,54
+male,group D,associate's degree,free/reduced,none,52,57,50
+female,group D,some college,standard,completed,82,97,96
+male,group D,some high school,standard,none,81,78,78
+female,group B,high school,free/reduced,completed,23,44,36
+male,group E,some high school,standard,none,92,87,78
+female,group B,some high school,standard,completed,32,51,44
+female,group E,some college,standard,completed,73,78,76
+male,group C,associate's degree,standard,none,83,72,78
+female,group B,high school,standard,none,50,53,55
+female,group D,master's degree,standard,completed,70,71,74
+male,group D,associate's degree,standard,completed,67,54,63
+female,group D,associate's degree,free/reduced,completed,42,61,58
+male,group D,some college,free/reduced,none,59,62,61
+female,group B,some college,standard,none,70,75,78
+male,group B,some college,free/reduced,none,55,55,47
+male,group D,associate's degree,standard,none,61,48,46
+female,group B,bachelor's degree,standard,completed,66,74,81
+female,group D,bachelor's degree,free/reduced,none,73,79,84
+female,group D,some high school,standard,none,80,90,82
+female,group A,some high school,free/reduced,none,38,43,43
+female,group B,associate's degree,standard,completed,52,66,73
+male,group C,high school,standard,completed,58,52,54
+female,group C,high school,standard,none,72,80,83
+female,group C,associate's degree,standard,completed,68,86,84
+female,group C,bachelor's degree,standard,completed,77,94,95
+female,group B,some high school,standard,none,82,82,80
+female,group D,associate's degree,standard,none,76,74,73
+male,group E,some high school,standard,completed,81,75,76
+male,group E,associate's degree,free/reduced,completed,46,43,44
+male,group D,some college,standard,none,88,77,77
+male,group C,associate's degree,free/reduced,completed,65,73,68
+female,group B,bachelor's degree,standard,none,97,97,96
+female,group B,some college,standard,none,82,85,87
+female,group B,bachelor's degree,standard,completed,65,81,81
+male,group C,master's degree,standard,completed,91,85,85
+female,group C,associate's degree,standard,completed,55,72,79
+female,group D,master's degree,standard,none,92,100,100
+female,group B,high school,standard,none,81,91,89
+female,group E,associate's degree,free/reduced,completed,57,68,73
+female,group C,some college,standard,none,73,80,82
+female,group D,high school,standard,none,56,52,55
+female,group D,associate's degree,standard,completed,57,78,79
+male,group D,associate's degree,free/reduced,none,66,62,64
+male,group C,high school,standard,completed,53,52,49
+male,group E,associate's degree,standard,completed,81,81,79
+male,group C,high school,standard,completed,75,69,68
+male,group A,high school,standard,completed,72,73,74
+female,group B,some high school,standard,none,73,79,79
+female,group E,some college,standard,completed,86,85,91
+female,group C,bachelor's degree,standard,none,65,79,81
+male,group D,high school,standard,none,64,54,50
+female,group B,high school,standard,none,58,68,61
+male,group D,some high school,standard,none,55,47,44
+male,group C,associate's degree,free/reduced,completed,78,81,82
+female,group D,some college,free/reduced,completed,63,80,80
+male,group D,high school,free/reduced,completed,73,68,66
+female,group C,high school,standard,none,81,84,82
+female,group E,high school,standard,none,74,81,71
+female,group C,associate's degree,standard,none,49,53,53
+male,group C,bachelor's degree,free/reduced,none,53,58,55
+male,group D,some college,free/reduced,none,77,62,64
+female,group D,some college,free/reduced,none,69,65,74
+male,group E,bachelor's degree,standard,none,82,62,62
+male,group E,some college,standard,none,76,67,67
+female,group B,high school,standard,none,42,52,51
+female,group C,associate's degree,standard,none,85,89,95
+female,group C,high school,standard,completed,58,75,77
+female,group C,high school,standard,none,59,72,68
+female,group C,high school,free/reduced,none,34,42,39
+male,group C,some college,standard,none,76,78,75
+female,group D,some high school,standard,none,68,71,75
+female,group B,some high school,free/reduced,none,72,81,79
+female,group C,some high school,free/reduced,none,48,56,51
+male,group C,bachelor's degree,standard,completed,94,90,91
+male,group D,associate's degree,standard,none,81,71,73
+female,group A,some high school,standard,completed,92,100,97
+male,group E,some college,standard,completed,81,74,71
+female,group C,some high school,free/reduced,completed,29,40,44
+female,group D,some college,free/reduced,none,58,67,62
+female,group C,some college,standard,none,73,76,78
+male,group E,some high school,standard,completed,68,51,57
+female,group C,high school,free/reduced,completed,50,66,64
+male,group B,associate's degree,standard,completed,65,65,63
+male,group C,some college,free/reduced,none,63,61,54
+female,group C,high school,standard,none,66,71,76
+female,group E,master's degree,free/reduced,none,56,72,65
+male,group E,associate's degree,standard,completed,94,85,82
+female,group C,associate's degree,standard,none,66,77,73
+female,group C,associate's degree,standard,completed,67,84,86
+male,group D,bachelor's degree,free/reduced,completed,74,79,75
+female,group C,bachelor's degree,standard,none,67,69,75
+male,group C,bachelor's degree,standard,completed,63,64,66
+male,group D,some college,standard,none,76,64,66
+male,group A,associate's degree,free/reduced,completed,79,82,82
+female,group B,some high school,free/reduced,completed,52,67,72
+female,group A,some high school,standard,none,71,83,77
+male,group B,bachelor's degree,free/reduced,none,88,75,76
+male,group D,some college,standard,none,68,59,62
+female,group D,high school,standard,completed,69,77,78
+female,group D,some college,standard,none,77,68,77
+male,group E,bachelor's degree,standard,none,68,68,64
+female,group B,some high school,standard,none,66,69,68
+female,group C,associate's degree,standard,none,59,66,67
+male,group A,associate's degree,free/reduced,none,62,61,55
+female,group C,high school,standard,none,63,69,74
+female,group E,high school,free/reduced,none,64,62,68
+male,group D,master's degree,standard,none,82,82,74
+male,group B,some college,free/reduced,completed,60,62,60
+male,group D,some college,standard,none,71,49,52
+male,group B,associate's degree,free/reduced,completed,58,57,53
+female,group E,associate's degree,standard,none,100,100,100
+female,group D,some high school,standard,none,59,58,59
+female,group C,master's degree,standard,completed,54,64,67
+female,group A,master's degree,standard,none,50,53,58
+female,group E,high school,free/reduced,completed,66,74,78
+male,group C,associate's degree,free/reduced,none,55,61,54
+female,group C,some college,standard,none,83,83,90
+male,group A,bachelor's degree,standard,none,66,64,62
+male,group D,some high school,standard,completed,62,66,68
+female,group B,high school,standard,none,62,62,63
+female,group E,associate's degree,free/reduced,completed,83,86,88
+female,group D,some college,free/reduced,none,60,66,70
+male,group C,some college,standard,none,53,44,42
+female,group D,some high school,standard,none,59,72,80
+male,group C,associate's degree,standard,none,49,51,43
+female,group C,bachelor's degree,free/reduced,none,50,60,59
+male,group E,associate's degree,free/reduced,completed,91,73,80
+male,group B,some college,standard,none,47,43,41
+male,group B,associate's degree,free/reduced,none,67,62,60
+female,group B,some high school,standard,none,57,67,72
+female,group D,some college,free/reduced,none,71,83,83
+female,group E,associate's degree,standard,completed,65,75,77
+male,group B,some college,free/reduced,none,54,54,45
+male,group C,bachelor's degree,free/reduced,none,37,56,47
+male,group C,high school,free/reduced,none,62,55,55
+male,group B,some high school,standard,completed,94,86,87
+male,group D,bachelor's degree,free/reduced,completed,39,42,38
+female,group E,some college,free/reduced,none,71,76,70
+female,group D,some college,free/reduced,none,65,81,77
+female,group E,some college,free/reduced,completed,75,88,85
+female,group C,some college,free/reduced,none,32,39,33
+male,group C,some college,standard,none,53,39,37
+male,group A,some college,standard,none,53,43,43
+male,group A,bachelor's degree,standard,completed,87,84,87
+male,group E,bachelor's degree,standard,completed,76,62,66
+female,group D,bachelor's degree,free/reduced,none,78,90,93
+male,group D,bachelor's degree,standard,none,75,73,74
+female,group C,some college,standard,none,58,67,72
+male,group B,associate's degree,standard,none,48,43,45
+male,group D,master's degree,standard,none,73,70,75
+female,group C,some college,standard,completed,69,90,88
+female,group E,high school,free/reduced,none,57,58,57
+male,group B,some high school,standard,completed,79,85,86
+female,group C,some college,standard,none,63,74,74
+female,group B,associate's degree,standard,none,47,49,50
+male,group D,some high school,standard,completed,74,71,78
+male,group E,some college,standard,none,97,87,82
+female,group B,some high school,free/reduced,none,49,58,55
+male,group C,master's degree,standard,none,79,78,77
+male,group C,some high school,free/reduced,none,69,71,65
+female,group C,associate's degree,free/reduced,none,53,61,62
+male,group C,high school,standard,completed,69,58,53
+male,group C,high school,free/reduced,none,27,34,36
+female,group D,some high school,free/reduced,completed,35,55,60
+female,group B,some high school,free/reduced,completed,63,78,79
+male,group B,bachelor's degree,free/reduced,none,48,51,46
+female,group C,high school,standard,none,72,80,75
+female,group B,high school,standard,none,66,72,70
+female,group E,bachelor's degree,standard,none,80,83,83
+male,group A,some college,standard,completed,78,72,70
+male,group C,high school,standard,none,71,66,65
+female,group D,master's degree,standard,none,54,60,63
+female,group C,associate's degree,free/reduced,none,57,78,67
+female,group D,some college,standard,none,65,70,71
+male,group C,high school,free/reduced,completed,53,51,51
+female,group D,high school,free/reduced,none,39,52,46
+female,group D,associate's degree,free/reduced,none,55,76,76
+female,group D,associate's degree,standard,none,59,70,65
+female,group B,high school,free/reduced,none,60,72,68
+female,group B,associate's degree,standard,none,49,52,54
+female,group B,high school,free/reduced,none,8,24,23
+female,group D,master's degree,free/reduced,none,40,59,54
+female,group C,bachelor's degree,free/reduced,completed,74,86,89
+male,group E,some college,standard,none,59,51,43
+female,group E,bachelor's degree,free/reduced,completed,92,100,100
+male,group C,some college,free/reduced,none,80,64,66
+male,group C,bachelor's degree,standard,completed,96,90,92
+male,group E,some college,standard,completed,85,75,68
+female,group C,bachelor's degree,standard,none,77,88,87
+female,group B,high school,free/reduced,completed,76,85,82
+male,group C,high school,free/reduced,none,66,66,59
+female,group D,bachelor's degree,standard,completed,71,76,83
+male,group B,high school,standard,none,60,68,60
+male,group D,some college,standard,none,76,71,73
+male,group D,some college,standard,completed,58,59,58
+female,group B,associate's degree,standard,completed,90,90,91
+female,group D,some college,standard,completed,74,75,79
+male,group B,some college,free/reduced,none,75,68,65
+male,group C,some college,standard,none,69,64,68
+female,group B,some high school,standard,completed,60,70,70
+female,group B,some college,free/reduced,completed,65,75,70
+female,group C,associate's degree,free/reduced,completed,68,67,69
+male,group B,high school,standard,completed,72,65,68
+male,group B,associate's degree,free/reduced,completed,82,78,74
+female,group C,some high school,standard,completed,85,92,93
+male,group E,associate's degree,standard,none,72,57,62
+male,group D,some college,standard,completed,76,83,79
+female,group E,some college,standard,none,67,76,75
+male,group A,some college,free/reduced,none,75,81,74
+male,group B,some high school,standard,completed,63,67,67
+female,group C,associate's degree,standard,none,64,64,70
+male,group D,associate's degree,standard,completed,67,72,67
+male,group A,some college,free/reduced,none,58,60,57
+female,group B,associate's degree,free/reduced,none,53,71,67
+male,group C,some high school,standard,none,73,66,63
+male,group D,master's degree,standard,none,89,84,82
+female,group C,high school,standard,none,65,69,67
+female,group C,some college,standard,completed,70,72,76
+female,group D,bachelor's degree,standard,none,65,67,62
+male,group D,some high school,standard,none,74,74,72
+female,group D,associate's degree,standard,none,71,71,74
+female,group E,bachelor's degree,standard,none,100,100,100
+male,group C,high school,standard,none,71,60,61
+male,group E,high school,standard,completed,87,91,81
+female,group D,associate's degree,free/reduced,none,26,31,38
+male,group B,associate's degree,standard,completed,91,89,92
+female,group A,associate's degree,standard,none,82,93,93
+male,group D,high school,standard,none,66,69,63
+female,group E,bachelor's degree,standard,completed,79,81,82
+male,group D,some college,standard,completed,63,55,63
+female,group D,master's degree,standard,none,87,100,100
+male,group C,bachelor's degree,standard,none,69,63,61
+female,group C,associate's degree,standard,none,53,62,53
+male,group C,some college,free/reduced,completed,50,48,53
+female,group D,associate's degree,standard,none,85,91,89
diff --git a/notebook/1 . EDA STUDENT PERFORMANCE .ipynb b/notebook/1 . EDA STUDENT PERFORMANCE .ipynb
new file mode 100644
index 0000000..d2df5aa
--- /dev/null
+++ b/notebook/1 . EDA STUDENT PERFORMANCE .ipynb	
@@ -0,0 +1,1841 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "1e233234",
+   "metadata": {},
+   "source": [
+    "## Student Performance Indicator\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62e05101",
+   "metadata": {},
+   "source": [
+    "#### Life cycle of Machine learning Project\n",
+    "\n",
+    "- Understanding the Problem Statement\n",
+    "- Data Collection\n",
+    "- Data Checks to perform\n",
+    "- Exploratory data analysis\n",
+    "- Data Pre-Processing\n",
+    "- Model Training\n",
+    "- Choose best model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfcea981",
+   "metadata": {},
+   "source": [
+    "### 1) Problem statement\n",
+    "- This project understands how the student's performance (test scores) is affected by other variables such as Gender, Ethnicity, Parental level of education, Lunch and Test preparation course.\n",
+    "\n",
+    "\n",
+    "### 2) Data Collection\n",
+    "- Dataset Source - https://www.kaggle.com/datasets/spscientist/students-performance-in-exams?datasetId=74977\n",
+    "- The data consists of 8 column and 1000 rows."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15b1355f",
+   "metadata": {},
+   "source": [
+    "### 2.1 Import Data and Required Packages\n",
+    "####  Importing Pandas, Numpy, Matplotlib, Seaborn and Warings Library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7eaae1d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3caeb0bb",
+   "metadata": {},
+   "source": [
+    "#### Import the CSV Data as Pandas DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "57907087",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('data/stud.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92c8fd8a",
+   "metadata": {},
+   "source": [
+    "#### Show Top 5 Records"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7d1a2a0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race_ethnicity</th>\n",
+       "      <th>parental_level_of_education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test_preparation_course</th>\n",
+       "      <th>math_score</th>\n",
+       "      <th>reading_score</th>\n",
+       "      <th>writing_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>bachelor's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>69</td>\n",
+       "      <td>90</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>90</td>\n",
+       "      <td>95</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group A</td>\n",
+       "      <td>associate's degree</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "      <td>57</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>76</td>\n",
+       "      <td>78</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   gender race_ethnicity parental_level_of_education         lunch  \\\n",
+       "0  female        group B           bachelor's degree      standard   \n",
+       "1  female        group C                some college      standard   \n",
+       "2  female        group B             master's degree      standard   \n",
+       "3    male        group A          associate's degree  free/reduced   \n",
+       "4    male        group C                some college      standard   \n",
+       "\n",
+       "  test_preparation_course  math_score  reading_score  writing_score  \n",
+       "0                    none          72             72             74  \n",
+       "1               completed          69             90             88  \n",
+       "2                    none          90             95             93  \n",
+       "3                    none          47             57             44  \n",
+       "4                    none          76             78             75  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56a49220",
+   "metadata": {},
+   "source": [
+    "#### Shape of the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "417e5820",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 8)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "041aa39d",
+   "metadata": {},
+   "source": [
+    "### 2.2 Dataset information"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e3cff9d",
+   "metadata": {},
+   "source": [
+    "- gender : sex of students  -> (Male/female)\n",
+    "- race/ethnicity : ethnicity of students -> (Group A, B,C, D,E)\n",
+    "- parental level of education : parents' final education ->(bachelor's degree,some college,master's degree,associate's degree,high school)\n",
+    "- lunch : having lunch before test (standard or free/reduced) \n",
+    "- test preparation course : complete or not complete before test\n",
+    "- math score\n",
+    "- reading score\n",
+    "- writing score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27c4b61b",
+   "metadata": {},
+   "source": [
+    "### 3. Data Checks to perform\n",
+    "\n",
+    "- Check Missing values\n",
+    "- Check Duplicates\n",
+    "- Check data type\n",
+    "- Check the number of unique values of each column\n",
+    "- Check statistics of data set\n",
+    "- Check various categories present in the different categorical column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c31d4123",
+   "metadata": {},
+   "source": [
+    "### 3.1 Check Missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "707d6a7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gender                         0\n",
+       "race_ethnicity                 0\n",
+       "parental_level_of_education    0\n",
+       "lunch                          0\n",
+       "test_preparation_course        0\n",
+       "math_score                     0\n",
+       "reading_score                  0\n",
+       "writing_score                  0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce8f7b83",
+   "metadata": {},
+   "source": [
+    "#### There are no missing values in the data set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5840ff7f",
+   "metadata": {},
+   "source": [
+    "### 3.2 Check Duplicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ae16686e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.duplicated().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5d7ae8e",
+   "metadata": {},
+   "source": [
+    "#### There are no duplicates  values in the data set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30dfacc8",
+   "metadata": {},
+   "source": [
+    "### 3.3 Check data types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "25f95bc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   gender                       1000 non-null   object\n",
+      " 1   race_ethnicity               1000 non-null   object\n",
+      " 2   parental_level_of_education  1000 non-null   object\n",
+      " 3   lunch                        1000 non-null   object\n",
+      " 4   test_preparation_course      1000 non-null   object\n",
+      " 5   math_score                   1000 non-null   int64 \n",
+      " 6   reading_score                1000 non-null   int64 \n",
+      " 7   writing_score                1000 non-null   int64 \n",
+      "dtypes: int64(3), object(5)\n",
+      "memory usage: 62.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check Null and Dtypes\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dbbf5b4",
+   "metadata": {},
+   "source": [
+    "### 3.4 Checking the number of unique values of each column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2c2b61b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gender                          2\n",
+       "race_ethnicity                  5\n",
+       "parental_level_of_education     6\n",
+       "lunch                           2\n",
+       "test_preparation_course         2\n",
+       "math_score                     81\n",
+       "reading_score                  72\n",
+       "writing_score                  77\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4f6b022",
+   "metadata": {},
+   "source": [
+    "### 3.5 Check statistics of data set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "76c608dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>math_score</th>\n",
+       "      <th>reading_score</th>\n",
+       "      <th>writing_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1000.00000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>66.08900</td>\n",
+       "      <td>69.169000</td>\n",
+       "      <td>68.054000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>15.16308</td>\n",
+       "      <td>14.600192</td>\n",
+       "      <td>15.195657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>57.00000</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>57.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>66.00000</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>69.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>77.00000</td>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>79.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>100.00000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       math_score  reading_score  writing_score\n",
+       "count  1000.00000    1000.000000    1000.000000\n",
+       "mean     66.08900      69.169000      68.054000\n",
+       "std      15.16308      14.600192      15.195657\n",
+       "min       0.00000      17.000000      10.000000\n",
+       "25%      57.00000      59.000000      57.750000\n",
+       "50%      66.00000      70.000000      69.000000\n",
+       "75%      77.00000      79.000000      79.000000\n",
+       "max     100.00000     100.000000     100.000000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dc41207",
+   "metadata": {},
+   "source": [
+    "#### Insight\n",
+    "- From above description of numerical data, all means are very close to each other - between 66 and 68.05;\n",
+    "- All standard deviations are also close - between 14.6 and 15.19;\n",
+    "- While there is a minimum score  0 for math, for writing minimum is much higher = 10 and for reading myet higher = 17"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac52d9cb",
+   "metadata": {},
+   "source": [
+    "### 3.7 Exploring Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1afd3c09",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race_ethnicity</th>\n",
+       "      <th>parental_level_of_education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test_preparation_course</th>\n",
+       "      <th>math_score</th>\n",
+       "      <th>reading_score</th>\n",
+       "      <th>writing_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>bachelor's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>69</td>\n",
+       "      <td>90</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>90</td>\n",
+       "      <td>95</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group A</td>\n",
+       "      <td>associate's degree</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "      <td>57</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>76</td>\n",
+       "      <td>78</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   gender race_ethnicity parental_level_of_education         lunch  \\\n",
+       "0  female        group B           bachelor's degree      standard   \n",
+       "1  female        group C                some college      standard   \n",
+       "2  female        group B             master's degree      standard   \n",
+       "3    male        group A          associate's degree  free/reduced   \n",
+       "4    male        group C                some college      standard   \n",
+       "\n",
+       "  test_preparation_course  math_score  reading_score  writing_score  \n",
+       "0                    none          72             72             74  \n",
+       "1               completed          69             90             88  \n",
+       "2                    none          90             95             93  \n",
+       "3                    none          47             57             44  \n",
+       "4                    none          76             78             75  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b9081742",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Categories in 'gender' variable:      ['female' 'male']\n",
+      "Categories in 'race_ethnicity' variable:   ['group B' 'group C' 'group A' 'group D' 'group E']\n",
+      "Categories in'parental level of education' variable: [\"bachelor's degree\" 'some college' \"master's degree\" \"associate's degree\"\n",
+      " 'high school' 'some high school']\n",
+      "Categories in 'lunch' variable:      ['standard' 'free/reduced']\n",
+      "Categories in 'test preparation course' variable:      ['none' 'completed']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Categories in 'gender' variable:     \",end=\" \" )\n",
+    "print(df['gender'].unique())\n",
+    "\n",
+    "print(\"Categories in 'race_ethnicity' variable:  \",end=\" \")\n",
+    "print(df['race_ethnicity'].unique())\n",
+    "\n",
+    "print(\"Categories in'parental level of education' variable:\",end=\" \" )\n",
+    "print(df['parental_level_of_education'].unique())\n",
+    "\n",
+    "print(\"Categories in 'lunch' variable:     \",end=\" \" )\n",
+    "print(df['lunch'].unique())\n",
+    "\n",
+    "print(\"Categories in 'test preparation course' variable:     \",end=\" \" )\n",
+    "print(df['test_preparation_course'].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2dd97e26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We have 3 numerical features : ['math_score', 'reading_score', 'writing_score']\n",
+      "\n",
+      "We have 5 categorical features : ['gender', 'race_ethnicity', 'parental_level_of_education', 'lunch', 'test_preparation_course']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# define numerical & categorical columns\n",
+    "numeric_features = [feature for feature in df.columns if df[feature].dtype != 'O']\n",
+    "categorical_features = [feature for feature in df.columns if df[feature].dtype == 'O']\n",
+    "\n",
+    "# print columns\n",
+    "print('We have {} numerical features : {}'.format(len(numeric_features), numeric_features))\n",
+    "print('\\nWe have {} categorical features : {}'.format(len(categorical_features), categorical_features))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ae2822d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race_ethnicity</th>\n",
+       "      <th>parental_level_of_education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test_preparation_course</th>\n",
+       "      <th>math_score</th>\n",
+       "      <th>reading_score</th>\n",
+       "      <th>writing_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>bachelor's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>69</td>\n",
+       "      <td>90</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   gender race_ethnicity parental_level_of_education     lunch  \\\n",
+       "0  female        group B           bachelor's degree  standard   \n",
+       "1  female        group C                some college  standard   \n",
+       "\n",
+       "  test_preparation_course  math_score  reading_score  writing_score  \n",
+       "0                    none          72             72             74  \n",
+       "1               completed          69             90             88  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "204aa708",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd42eda6",
+   "metadata": {},
+   "source": [
+    "### 3.8 Adding columns for \"Total Score\" and \"Average\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3ffbfdf7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race_ethnicity</th>\n",
+       "      <th>parental_level_of_education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test_preparation_course</th>\n",
+       "      <th>math_score</th>\n",
+       "      <th>reading_score</th>\n",
+       "      <th>writing_score</th>\n",
+       "      <th>total score</th>\n",
+       "      <th>average</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>bachelor's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>74</td>\n",
+       "      <td>218</td>\n",
+       "      <td>72.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>69</td>\n",
+       "      <td>90</td>\n",
+       "      <td>88</td>\n",
+       "      <td>247</td>\n",
+       "      <td>82.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>90</td>\n",
+       "      <td>95</td>\n",
+       "      <td>93</td>\n",
+       "      <td>278</td>\n",
+       "      <td>92.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group A</td>\n",
+       "      <td>associate's degree</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "      <td>57</td>\n",
+       "      <td>44</td>\n",
+       "      <td>148</td>\n",
+       "      <td>49.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>76</td>\n",
+       "      <td>78</td>\n",
+       "      <td>75</td>\n",
+       "      <td>229</td>\n",
+       "      <td>76.333333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   gender race_ethnicity parental_level_of_education         lunch  \\\n",
+       "0  female        group B           bachelor's degree      standard   \n",
+       "1  female        group C                some college      standard   \n",
+       "2  female        group B             master's degree      standard   \n",
+       "3    male        group A          associate's degree  free/reduced   \n",
+       "4    male        group C                some college      standard   \n",
+       "\n",
+       "  test_preparation_course  math_score  reading_score  writing_score  \\\n",
+       "0                    none          72             72             74   \n",
+       "1               completed          69             90             88   \n",
+       "2                    none          90             95             93   \n",
+       "3                    none          47             57             44   \n",
+       "4                    none          76             78             75   \n",
+       "\n",
+       "   total score    average  \n",
+       "0          218  72.666667  \n",
+       "1          247  82.333333  \n",
+       "2          278  92.666667  \n",
+       "3          148  49.333333  \n",
+       "4          229  76.333333  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['total score'] = df['math_score'] + df['reading_score'] + df['writing_score']\n",
+    "df['average'] = df['total score']/3\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "26dc3844",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of students with full marks in Maths: 7\n",
+      "Number of students with full marks in Writing: 14\n",
+      "Number of students with full marks in Reading: 17\n"
+     ]
+    }
+   ],
+   "source": [
+    "reading_full = df[df['reading_score'] == 100]['average'].count()\n",
+    "writing_full = df[df['writing_score'] == 100]['average'].count()\n",
+    "math_full = df[df['math_score'] == 100]['average'].count()\n",
+    "\n",
+    "print(f'Number of students with full marks in Maths: {math_full}')\n",
+    "print(f'Number of students with full marks in Writing: {writing_full}')\n",
+    "print(f'Number of students with full marks in Reading: {reading_full}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "572c8a75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of students with less than 20 marks in Maths: 4\n",
+      "Number of students with less than 20 marks in Writing: 3\n",
+      "Number of students with less than 20 marks in Reading: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "reading_less_20 = df[df['reading_score'] <= 20]['average'].count()\n",
+    "writing_less_20 = df[df['writing_score'] <= 20]['average'].count()\n",
+    "math_less_20 = df[df['math_score'] <= 20]['average'].count()\n",
+    "\n",
+    "print(f'Number of students with less than 20 marks in Maths: {math_less_20}')\n",
+    "print(f'Number of students with less than 20 marks in Writing: {writing_less_20}')\n",
+    "print(f'Number of students with less than 20 marks in Reading: {reading_less_20}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "190e078c",
+   "metadata": {},
+   "source": [
+    "#####  Insights\n",
+    " - From above values we get students have performed the worst in Maths \n",
+    " - Best performance is in reading section"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e598bc93",
+   "metadata": {},
+   "source": [
+    "### 4. Exploring Data ( Visualization )\n",
+    "#### 4.1 Visualize average score distribution to make some conclusion. \n",
+    "- Histogram\n",
+    "- Kernel Distribution Function (KDE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4726058",
+   "metadata": {},
+   "source": [
+    "#### 4.1.1 Histogram & KDE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c2510266",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 2, figsize=(15, 7))\n",
+    "plt.subplot(121)\n",
+    "sns.histplot(data=df,x='average',bins=30,kde=True,color='g')\n",
+    "plt.subplot(122)\n",
+    "sns.histplot(data=df,x='average',kde=True,hue='gender')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e7967c7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 2, figsize=(15, 7))\n",
+    "plt.subplot(121)\n",
+    "sns.histplot(data=df,x='total score',bins=30,kde=True,color='g')\n",
+    "plt.subplot(122)\n",
+    "sns.histplot(data=df,x='total score',kde=True,hue='gender')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d200b76",
+   "metadata": {},
+   "source": [
+    "#####  Insights\n",
+    "- Female students tend to perform well then male students."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "15522737",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2500x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplots(1,3,figsize=(25,6))\n",
+    "plt.subplot(141)\n",
+    "sns.histplot(data=df,x='average',kde=True,hue='lunch')\n",
+    "plt.subplot(142)\n",
+    "sns.histplot(data=df[df.gender=='female'],x='average',kde=True,hue='lunch')\n",
+    "plt.subplot(143)\n",
+    "sns.histplot(data=df[df.gender=='male'],x='average',kde=True,hue='lunch')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "569113e7",
+   "metadata": {},
+   "source": [
+    "#####  Insights\n",
+    "- Standard lunch helps perform well in exams.\n",
+    "- Standard lunch helps perform well in exams be it a male or a female."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b6c697a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.subplots(1,3,figsize=(25,6))\n",
+    "plt.subplot(141)\n",
+    "ax =sns.histplot(data=df,x='average',kde=True,hue='parental level of education')\n",
+    "plt.subplot(142)\n",
+    "ax =sns.histplot(data=df[df.gender=='male'],x='average',kde=True,hue='parental level of education')\n",
+    "plt.subplot(143)\n",
+    "ax =sns.histplot(data=df[df.gender=='female'],x='average',kde=True,hue='parental level of education')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e7fd489",
+   "metadata": {},
+   "source": [
+    "#####  Insights\n",
+    "- In general parent's education don't help student perform well in exam.\n",
+    "- 2nd plot shows that parent's whose education is of associate's degree or master's degree their male child tend to perform well in exam\n",
+    "- 3rd plot we can see there is no effect of parent's education on female students."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b30cbd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.subplots(1,3,figsize=(25,6))\n",
+    "plt.subplot(141)\n",
+    "ax =sns.histplot(data=df,x='average',kde=True,hue='race/ethnicity')\n",
+    "plt.subplot(142)\n",
+    "ax =sns.histplot(data=df[df.gender=='female'],x='average',kde=True,hue='race/ethnicity')\n",
+    "plt.subplot(143)\n",
+    "ax =sns.histplot(data=df[df.gender=='male'],x='average',kde=True,hue='race/ethnicity')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6180a334",
+   "metadata": {},
+   "source": [
+    "#####  Insights\n",
+    "- Students of group A and group B tends to perform poorly in exam.\n",
+    "- Students of group A and group B tends to perform poorly in exam irrespective of whether they are male or female"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1f7eef3",
+   "metadata": {},
+   "source": [
+    "#### 4.2 Maximumum score of students in all three subjects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14db115f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(18,8))\n",
+    "plt.subplot(1, 4, 1)\n",
+    "plt.title('MATH SCORES')\n",
+    "sns.violinplot(y='math score',data=df,color='red',linewidth=3)\n",
+    "plt.subplot(1, 4, 2)\n",
+    "plt.title('READING SCORES')\n",
+    "sns.violinplot(y='reading score',data=df,color='green',linewidth=3)\n",
+    "plt.subplot(1, 4, 3)\n",
+    "plt.title('WRITING SCORES')\n",
+    "sns.violinplot(y='writing score',data=df,color='blue',linewidth=3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66d1041f",
+   "metadata": {},
+   "source": [
+    "#### Insights\n",
+    "- From the above three plots its clearly visible that most of the students score in between 60-80 in Maths whereas in reading and writing most of them score from 50-80"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae77a33d",
+   "metadata": {},
+   "source": [
+    "#### 4.3 Multivariate analysis using pieplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ddf9ce3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams['figure.figsize'] = (30, 12)\n",
+    "\n",
+    "plt.subplot(1, 5, 1)\n",
+    "size = df['gender'].value_counts()\n",
+    "labels = 'Female', 'Male'\n",
+    "color = ['red','green']\n",
+    "\n",
+    "\n",
+    "plt.pie(size, colors = color, labels = labels,autopct = '.%2f%%')\n",
+    "plt.title('Gender', fontsize = 20)\n",
+    "plt.axis('off')\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.subplot(1, 5, 2)\n",
+    "size = df['race/ethnicity'].value_counts()\n",
+    "labels = 'Group C', 'Group D','Group B','Group E','Group A'\n",
+    "color = ['red', 'green', 'blue', 'cyan','orange']\n",
+    "\n",
+    "plt.pie(size, colors = color,labels = labels,autopct = '.%2f%%')\n",
+    "plt.title('Race/Ethnicity', fontsize = 20)\n",
+    "plt.axis('off')\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.subplot(1, 5, 3)\n",
+    "size = df['lunch'].value_counts()\n",
+    "labels = 'Standard', 'Free'\n",
+    "color = ['red','green']\n",
+    "\n",
+    "plt.pie(size, colors = color,labels = labels,autopct = '.%2f%%')\n",
+    "plt.title('Lunch', fontsize = 20)\n",
+    "plt.axis('off')\n",
+    "\n",
+    "\n",
+    "plt.subplot(1, 5, 4)\n",
+    "size = df['test preparation course'].value_counts()\n",
+    "labels = 'None', 'Completed'\n",
+    "color = ['red','green']\n",
+    "\n",
+    "plt.pie(size, colors = color,labels = labels,autopct = '.%2f%%')\n",
+    "plt.title('Test Course', fontsize = 20)\n",
+    "plt.axis('off')\n",
+    "\n",
+    "\n",
+    "plt.subplot(1, 5, 5)\n",
+    "size = df['parental level of education'].value_counts()\n",
+    "labels = 'Some College', \"Associate's Degree\",'High School','Some High School',\"Bachelor's Degree\",\"Master's Degree\"\n",
+    "color = ['red', 'green', 'blue', 'cyan','orange','grey']\n",
+    "\n",
+    "plt.pie(size, colors = color,labels = labels,autopct = '.%2f%%')\n",
+    "plt.title('Parental Education', fontsize = 20)\n",
+    "plt.axis('off')\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.grid()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e2d686a",
+   "metadata": {},
+   "source": [
+    "#####  Insights\n",
+    "- Number of Male and Female students is almost equal\n",
+    "- Number students are greatest in Group C\n",
+    "- Number of students who have standard lunch are greater\n",
+    "- Number of students who have not enrolled in any test preparation course is greater\n",
+    "- Number of students whose parental education is \"Some College\" is greater followed closely by \"Associate's Degree\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab008237",
+   "metadata": {},
+   "source": [
+    "#### 4.4 Feature Wise Visualization\n",
+    "#### 4.4.1 GENDER COLUMN\n",
+    "- How is distribution of Gender ?\n",
+    "- Is gender has any impact on student's performance ?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1a2c8f5",
+   "metadata": {},
+   "source": [
+    "#### UNIVARIATE ANALYSIS ( How is distribution of Gender ? )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c435f53b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f,ax=plt.subplots(1,2,figsize=(20,10))\n",
+    "sns.countplot(x=df['gender'],data=df,palette ='bright',ax=ax[0],saturation=0.95)\n",
+    "for container in ax[0].containers:\n",
+    "    ax[0].bar_label(container,color='black',size=20)\n",
+    "    \n",
+    "plt.pie(x=df['gender'].value_counts(),labels=['Male','Female'],explode=[0,0.1],autopct='%1.1f%%',shadow=True,colors=['#ff4d4d','#ff8000'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfb8c9b2",
+   "metadata": {},
+   "source": [
+    "#### Insights \n",
+    "- Gender has balanced data with female students are 518 (48%) and male students are 482 (52%) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e440a3a5",
+   "metadata": {},
+   "source": [
+    "#### BIVARIATE ANALYSIS ( Is gender has any impact on student's performance ? ) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "526d49f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gender_group = df.groupby('gender').mean()\n",
+    "gender_group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b704f144",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10, 8))\n",
+    "\n",
+    "X = ['Total Average','Math Average']\n",
+    "\n",
+    "\n",
+    "female_scores = [gender_group['average'][0], gender_group['math score'][0]]\n",
+    "male_scores = [gender_group['average'][1], gender_group['math score'][1]]\n",
+    "\n",
+    "X_axis = np.arange(len(X))\n",
+    "  \n",
+    "plt.bar(X_axis - 0.2, male_scores, 0.4, label = 'Male')\n",
+    "plt.bar(X_axis + 0.2, female_scores, 0.4, label = 'Female')\n",
+    "  \n",
+    "plt.xticks(X_axis, X)\n",
+    "plt.ylabel(\"Marks\")\n",
+    "plt.title(\"Total average v/s Math average marks of both the genders\", fontweight='bold')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72fbab62",
+   "metadata": {},
+   "source": [
+    "#### Insights \n",
+    "- On an average females have a better overall score than men.\n",
+    "- whereas males have scored higher in Maths."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a903c5c",
+   "metadata": {},
+   "source": [
+    "#### 4.4.2 RACE/EHNICITY COLUMN\n",
+    "- How is Group wise distribution ?\n",
+    "- Is Race/Ehnicity has any impact on student's performance ?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69fe557f",
+   "metadata": {},
+   "source": [
+    "#### UNIVARIATE ANALYSIS ( How is Group wise distribution ?)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acbc5c8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f,ax=plt.subplots(1,2,figsize=(20,10))\n",
+    "sns.countplot(x=df['race/ethnicity'],data=df,palette = 'bright',ax=ax[0],saturation=0.95)\n",
+    "for container in ax[0].containers:\n",
+    "    ax[0].bar_label(container,color='black',size=20)\n",
+    "    \n",
+    "plt.pie(x = df['race/ethnicity'].value_counts(),labels=df['race/ethnicity'].value_counts().index,explode=[0.1,0,0,0,0],autopct='%1.1f%%',shadow=True)\n",
+    "plt.show()   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1762646a",
+   "metadata": {},
+   "source": [
+    "#### Insights \n",
+    "- Most of the student belonging from group C /group D.\n",
+    "- Lowest number of students belong to groupA."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d3a3719",
+   "metadata": {},
+   "source": [
+    "#### BIVARIATE ANALYSIS ( Is Race/Ehnicity has any impact on student's performance ? )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db261c61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Group_data2=df.groupby('race/ethnicity')\n",
+    "f,ax=plt.subplots(1,3,figsize=(20,8))\n",
+    "sns.barplot(x=Group_data2['math score'].mean().index,y=Group_data2['math score'].mean().values,palette = 'mako',ax=ax[0])\n",
+    "ax[0].set_title('Math score',color='#005ce6',size=20)\n",
+    "\n",
+    "for container in ax[0].containers:\n",
+    "    ax[0].bar_label(container,color='black',size=15)\n",
+    "\n",
+    "sns.barplot(x=Group_data2['reading score'].mean().index,y=Group_data2['reading score'].mean().values,palette = 'flare',ax=ax[1])\n",
+    "ax[1].set_title('Reading score',color='#005ce6',size=20)\n",
+    "\n",
+    "for container in ax[1].containers:\n",
+    "    ax[1].bar_label(container,color='black',size=15)\n",
+    "\n",
+    "sns.barplot(x=Group_data2['writing score'].mean().index,y=Group_data2['writing score'].mean().values,palette = 'coolwarm',ax=ax[2])\n",
+    "ax[2].set_title('Writing score',color='#005ce6',size=20)\n",
+    "\n",
+    "for container in ax[2].containers:\n",
+    "    ax[2].bar_label(container,color='black',size=15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e292ddd",
+   "metadata": {},
+   "source": [
+    "#### Insights \n",
+    "- Group E students have scored the highest marks. \n",
+    "- Group A students have scored the lowest marks. \n",
+    "- Students from a lower Socioeconomic status have a lower avg in all course subjects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1409042e",
+   "metadata": {},
+   "source": [
+    "#### 4.4.3 PARENTAL LEVEL OF EDUCATION COLUMN\n",
+    "- What is educational background of student's parent ?\n",
+    "- Is parental education has any impact on student's performance ?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38aca4fc",
+   "metadata": {},
+   "source": [
+    "#### UNIVARIATE ANALYSIS ( What is educational background of student's parent ? )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c05ab987",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams['figure.figsize'] = (15, 9)\n",
+    "plt.style.use('fivethirtyeight')\n",
+    "sns.countplot(df['parental level of education'], palette = 'Blues')\n",
+    "plt.title('Comparison of Parental Education', fontweight = 30, fontsize = 20)\n",
+    "plt.xlabel('Degree')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d351e1d",
+   "metadata": {},
+   "source": [
+    "#### Insights \n",
+    "- Largest number of parents are from some college."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f38ab41",
+   "metadata": {},
+   "source": [
+    "#### BIVARIATE ANALYSIS ( Is parental education has any impact on student's performance ? )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "122b2581",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.groupby('parental level of education').agg('mean').plot(kind='barh',figsize=(10,10))\n",
+    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58b3999d",
+   "metadata": {},
+   "source": [
+    "#### Insights \n",
+    "- The score of student whose parents possess master and bachelor level education are higher than others."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "079f4f29",
+   "metadata": {},
+   "source": [
+    "#### 4.4.4 LUNCH COLUMN \n",
+    "- Which type of lunch is most common amoung students ?\n",
+    "- What is the effect of lunch type on test results?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8584c755",
+   "metadata": {},
+   "source": [
+    "#### UNIVARIATE ANALYSIS ( Which type of lunch is most common amoung students ? )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3a277e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams['figure.figsize'] = (15, 9)\n",
+    "plt.style.use('seaborn-talk')\n",
+    "sns.countplot(df['lunch'], palette = 'PuBu')\n",
+    "plt.title('Comparison of different types of lunch', fontweight = 30, fontsize = 20)\n",
+    "plt.xlabel('types of lunch')\n",
+    "plt.ylabel('count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "332b0c50",
+   "metadata": {},
+   "source": [
+    "#### Insights \n",
+    "- Students being served Standard lunch was more than free lunch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d75db26f",
+   "metadata": {},
+   "source": [
+    "#### BIVARIATE ANALYSIS (  Is lunch type intake has any impact on student's performance ? )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "666babd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f,ax=plt.subplots(1,2,figsize=(20,8))\n",
+    "sns.countplot(x=df['parental level of education'],data=df,palette = 'bright',hue='test preparation course',saturation=0.95,ax=ax[0])\n",
+    "ax[0].set_title('Students vs test preparation course ',color='black',size=25)\n",
+    "for container in ax[0].containers:\n",
+    "    ax[0].bar_label(container,color='black',size=20)\n",
+    "    \n",
+    "sns.countplot(x=df['parental level of education'],data=df,palette = 'bright',hue='lunch',saturation=0.95,ax=ax[1])\n",
+    "for container in ax[1].containers:\n",
+    "    ax[1].bar_label(container,color='black',size=20)   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0677b04c",
+   "metadata": {},
+   "source": [
+    "#### Insights \n",
+    "- Students who get Standard Lunch tend to perform better than students who got free/reduced lunch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edd0ec29",
+   "metadata": {},
+   "source": [
+    "#### 4.4.5 TEST PREPARATION COURSE COLUMN \n",
+    "- Which type of lunch is most common amoung students ?\n",
+    "- Is Test prepration course has any impact on student's performance ?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf8f65bd",
+   "metadata": {},
+   "source": [
+    "#### BIVARIATE ANALYSIS ( Is Test prepration course has any impact on student's performance ? )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b08ed26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(12,6))\n",
+    "plt.subplot(2,2,1)\n",
+    "sns.barplot (x=df['lunch'], y=df['math score'], hue=df['test preparation course'])\n",
+    "plt.subplot(2,2,2)\n",
+    "sns.barplot (x=df['lunch'], y=df['reading score'], hue=df['test preparation course'])\n",
+    "plt.subplot(2,2,3)\n",
+    "sns.barplot (x=df['lunch'], y=df['writing score'], hue=df['test preparation course'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bab116e",
+   "metadata": {},
+   "source": [
+    "#### Insights  \n",
+    "- Students who have completed the Test Prepration Course have scores higher in all three categories than those who haven't taken the course"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4069d6e6",
+   "metadata": {},
+   "source": [
+    "#### 4.4.6 CHECKING OUTLIERS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62a813a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.subplots(1,4,figsize=(16,5))\n",
+    "plt.subplot(141)\n",
+    "sns.boxplot(df['math score'],color='skyblue')\n",
+    "plt.subplot(142)\n",
+    "sns.boxplot(df['reading score'],color='hotpink')\n",
+    "plt.subplot(143)\n",
+    "sns.boxplot(df['writing score'],color='yellow')\n",
+    "plt.subplot(144)\n",
+    "sns.boxplot(df['average'],color='lightgreen')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "957e8bef",
+   "metadata": {},
+   "source": [
+    "#### 4.4.7 MUTIVARIATE ANALYSIS USING PAIRPLOT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f09f746c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.pairplot(df,hue = 'gender')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7991322",
+   "metadata": {},
+   "source": [
+    "#### Insights\n",
+    "- From the above plot it is clear that all the scores increase linearly with each other."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7e20716",
+   "metadata": {},
+   "source": [
+    "### 5. Conclusions\n",
+    "- Student's Performance is related with lunch, race, parental level education\n",
+    "- Females lead in pass percentage and also are top-scorers\n",
+    "- Student's Performance is not much related with test preparation course\n",
+    "- Finishing preparation course is benefitial."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "7f9dc718a552478e14f37d36e0ca2ac7de1ac15c04a40a415ea3abc3fe8a0a39"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebook/2. MODEL TRAINING.ipynb b/notebook/2. MODEL TRAINING.ipynb
new file mode 100644
index 0000000..c51ba3a
--- /dev/null
+++ b/notebook/2. MODEL TRAINING.ipynb	
@@ -0,0 +1,1053 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "92e48866",
+   "metadata": {},
+   "source": [
+    "## Model Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25791a74",
+   "metadata": {},
+   "source": [
+    "#### 1.1 Import Data and Required Packages\n",
+    "##### Importing Pandas, Numpy, Matplotlib, Seaborn and Warings Library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b080dfb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Basic Import\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt \n",
+    "import seaborn as sns\n",
+    "# Modelling\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor,AdaBoostRegressor\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.linear_model import LinearRegression, Ridge,Lasso\n",
+    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
+    "from sklearn.model_selection import RandomizedSearchCV\n",
+    "from catboost import CatBoostRegressor\n",
+    "from xgboost import XGBRegressor\n",
+    "import warnings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e45079ad",
+   "metadata": {},
+   "source": [
+    "#### Import the CSV Data as Pandas DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e11c6255",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('data/stud.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20634923",
+   "metadata": {},
+   "source": [
+    "#### Show Top 5 Records"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e7e412a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race_ethnicity</th>\n",
+       "      <th>parental_level_of_education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test_preparation_course</th>\n",
+       "      <th>math_score</th>\n",
+       "      <th>reading_score</th>\n",
+       "      <th>writing_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>bachelor's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>69</td>\n",
+       "      <td>90</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>90</td>\n",
+       "      <td>95</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group A</td>\n",
+       "      <td>associate's degree</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "      <td>57</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>76</td>\n",
+       "      <td>78</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   gender race_ethnicity parental_level_of_education         lunch  \\\n",
+       "0  female        group B           bachelor's degree      standard   \n",
+       "1  female        group C                some college      standard   \n",
+       "2  female        group B             master's degree      standard   \n",
+       "3    male        group A          associate's degree  free/reduced   \n",
+       "4    male        group C                some college      standard   \n",
+       "\n",
+       "  test_preparation_course  math_score  reading_score  writing_score  \n",
+       "0                    none          72             72             74  \n",
+       "1               completed          69             90             88  \n",
+       "2                    none          90             95             93  \n",
+       "3                    none          47             57             44  \n",
+       "4                    none          76             78             75  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbd32281",
+   "metadata": {},
+   "source": [
+    "#### Preparing X and Y variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "56d72fde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df.drop(columns=['math_score'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd613177",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race_ethnicity</th>\n",
+       "      <th>parental_level_of_education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test_preparation_course</th>\n",
+       "      <th>reading_score</th>\n",
+       "      <th>writing_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>bachelor's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>90</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>95</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group A</td>\n",
+       "      <td>associate's degree</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>57</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>78</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   gender race_ethnicity parental_level_of_education         lunch  \\\n",
+       "0  female        group B           bachelor's degree      standard   \n",
+       "1  female        group C                some college      standard   \n",
+       "2  female        group B             master's degree      standard   \n",
+       "3    male        group A          associate's degree  free/reduced   \n",
+       "4    male        group C                some college      standard   \n",
+       "\n",
+       "  test_preparation_course  reading_score  writing_score  \n",
+       "0                    none             72             74  \n",
+       "1               completed             90             88  \n",
+       "2                    none             95             93  \n",
+       "3                    none             57             44  \n",
+       "4                    none             78             75  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f237ea14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Categories in 'gender' variable:      ['female' 'male']\n",
+      "Categories in 'race_ethnicity' variable:   ['group B' 'group C' 'group A' 'group D' 'group E']\n",
+      "Categories in'parental level of education' variable: [\"bachelor's degree\" 'some college' \"master's degree\" \"associate's degree\"\n",
+      " 'high school' 'some high school']\n",
+      "Categories in 'lunch' variable:      ['standard' 'free/reduced']\n",
+      "Categories in 'test preparation course' variable:      ['none' 'completed']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Categories in 'gender' variable:     \",end=\" \" )\n",
+    "print(df['gender'].unique())\n",
+    "\n",
+    "print(\"Categories in 'race_ethnicity' variable:  \",end=\" \")\n",
+    "print(df['race_ethnicity'].unique())\n",
+    "\n",
+    "print(\"Categories in'parental level of education' variable:\",end=\" \" )\n",
+    "print(df['parental_level_of_education'].unique())\n",
+    "\n",
+    "print(\"Categories in 'lunch' variable:     \",end=\" \" )\n",
+    "print(df['lunch'].unique())\n",
+    "\n",
+    "print(\"Categories in 'test preparation course' variable:     \",end=\" \" )\n",
+    "print(df['test_preparation_course'].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "924b7f9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = df['math_score']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ffc69816",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      72\n",
+       "1      69\n",
+       "2      90\n",
+       "3      47\n",
+       "4      76\n",
+       "       ..\n",
+       "995    88\n",
+       "996    62\n",
+       "997    59\n",
+       "998    68\n",
+       "999    77\n",
+       "Name: math_score, Length: 1000, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "1e290fe3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create Column Transformer with 3 types of transformers\n",
+    "num_features = X.select_dtypes(exclude=\"object\").columns\n",
+    "cat_features = X.select_dtypes(include=\"object\").columns\n",
+    "\n",
+    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "\n",
+    "numeric_transformer = StandardScaler()\n",
+    "oh_transformer = OneHotEncoder()\n",
+    "\n",
+    "preprocessor = ColumnTransformer(\n",
+    "    [\n",
+    "        (\"OneHotEncoder\", oh_transformer, cat_features),\n",
+    "         (\"StandardScaler\", numeric_transformer, num_features),        \n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9c68f99a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = preprocessor.fit_transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "72459f1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 19)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ed5c4e99",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((800, 19), (200, 19))"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# separate dataset into train and test\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42)\n",
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cd80317",
+   "metadata": {},
+   "source": [
+    "#### Create an Evaluate Function to give all metrics after model Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "8c247bd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate_model(true, predicted):\n",
+    "    mae = mean_absolute_error(true, predicted)\n",
+    "    mse = mean_squared_error(true, predicted)\n",
+    "    rmse = np.sqrt(mean_squared_error(true, predicted))\n",
+    "    r2_square = r2_score(true, predicted)\n",
+    "    return mae, rmse, r2_square"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "79ccb8e7",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear Regression\n",
+      "Model performance for Training set\n",
+      "- Root Mean Squared Error: 5.3243\n",
+      "- Mean Absolute Error: 4.2671\n",
+      "- R2 Score: 0.8743\n",
+      "----------------------------------\n",
+      "Model performance for Test set\n",
+      "- Root Mean Squared Error: 5.3960\n",
+      "- Mean Absolute Error: 4.2158\n",
+      "- R2 Score: 0.8803\n",
+      "===================================\n",
+      "\n",
+      "\n",
+      "Lasso\n",
+      "Model performance for Training set\n",
+      "- Root Mean Squared Error: 6.5938\n",
+      "- Mean Absolute Error: 5.2063\n",
+      "- R2 Score: 0.8071\n",
+      "----------------------------------\n",
+      "Model performance for Test set\n",
+      "- Root Mean Squared Error: 6.5197\n",
+      "- Mean Absolute Error: 5.1579\n",
+      "- R2 Score: 0.8253\n",
+      "===================================\n",
+      "\n",
+      "\n",
+      "Ridge\n",
+      "Model performance for Training set\n",
+      "- Root Mean Squared Error: 5.3233\n",
+      "- Mean Absolute Error: 4.2650\n",
+      "- R2 Score: 0.8743\n",
+      "----------------------------------\n",
+      "Model performance for Test set\n",
+      "- Root Mean Squared Error: 5.3904\n",
+      "- Mean Absolute Error: 4.2111\n",
+      "- R2 Score: 0.8806\n",
+      "===================================\n",
+      "\n",
+      "\n",
+      "K-Neighbors Regressor\n",
+      "Model performance for Training set\n",
+      "- Root Mean Squared Error: 5.7077\n",
+      "- Mean Absolute Error: 4.5167\n",
+      "- R2 Score: 0.8555\n",
+      "----------------------------------\n",
+      "Model performance for Test set\n",
+      "- Root Mean Squared Error: 7.2530\n",
+      "- Mean Absolute Error: 5.6210\n",
+      "- R2 Score: 0.7838\n",
+      "===================================\n",
+      "\n",
+      "\n",
+      "Decision Tree\n",
+      "Model performance for Training set\n",
+      "- Root Mean Squared Error: 0.2795\n",
+      "- Mean Absolute Error: 0.0187\n",
+      "- R2 Score: 0.9997\n",
+      "----------------------------------\n",
+      "Model performance for Test set\n",
+      "- Root Mean Squared Error: 7.6371\n",
+      "- Mean Absolute Error: 6.0250\n",
+      "- R2 Score: 0.7603\n",
+      "===================================\n",
+      "\n",
+      "\n",
+      "Random Forest Regressor\n",
+      "Model performance for Training set\n",
+      "- Root Mean Squared Error: 2.2851\n",
+      "- Mean Absolute Error: 1.8253\n",
+      "- R2 Score: 0.9768\n",
+      "----------------------------------\n",
+      "Model performance for Test set\n",
+      "- Root Mean Squared Error: 6.0959\n",
+      "- Mean Absolute Error: 4.7194\n",
+      "- R2 Score: 0.8473\n",
+      "===================================\n",
+      "\n",
+      "\n",
+      "XGBRegressor\n",
+      "Model performance for Training set\n",
+      "- Root Mean Squared Error: 0.9087\n",
+      "- Mean Absolute Error: 0.6148\n",
+      "- R2 Score: 0.9963\n",
+      "----------------------------------\n",
+      "Model performance for Test set\n",
+      "- Root Mean Squared Error: 6.5889\n",
+      "- Mean Absolute Error: 5.0844\n",
+      "- R2 Score: 0.8216\n",
+      "===================================\n",
+      "\n",
+      "\n",
+      "CatBoosting Regressor\n",
+      "Model performance for Training set\n",
+      "- Root Mean Squared Error: 3.0427\n",
+      "- Mean Absolute Error: 2.4054\n",
+      "- R2 Score: 0.9589\n",
+      "----------------------------------\n",
+      "Model performance for Test set\n",
+      "- Root Mean Squared Error: 6.0086\n",
+      "- Mean Absolute Error: 4.6125\n",
+      "- R2 Score: 0.8516\n",
+      "===================================\n",
+      "\n",
+      "\n",
+      "AdaBoost Regressor\n",
+      "Model performance for Training set\n",
+      "- Root Mean Squared Error: 5.7843\n",
+      "- Mean Absolute Error: 4.7564\n",
+      "- R2 Score: 0.8516\n",
+      "----------------------------------\n",
+      "Model performance for Test set\n",
+      "- Root Mean Squared Error: 6.0447\n",
+      "- Mean Absolute Error: 4.6813\n",
+      "- R2 Score: 0.8498\n",
+      "===================================\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "models = {\n",
+    "    \"Linear Regression\": LinearRegression(),\n",
+    "    \"Lasso\": Lasso(),\n",
+    "    \"Ridge\": Ridge(),\n",
+    "    \"K-Neighbors Regressor\": KNeighborsRegressor(),\n",
+    "    \"Decision Tree\": DecisionTreeRegressor(),\n",
+    "    \"Random Forest Regressor\": RandomForestRegressor(),\n",
+    "    \"XGBRegressor\": XGBRegressor(), \n",
+    "    \"CatBoosting Regressor\": CatBoostRegressor(verbose=False),\n",
+    "    \"AdaBoost Regressor\": AdaBoostRegressor()\n",
+    "}\n",
+    "model_list = []\n",
+    "r2_list =[]\n",
+    "\n",
+    "for i in range(len(list(models))):\n",
+    "    model = list(models.values())[i]\n",
+    "    model.fit(X_train, y_train) # Train model\n",
+    "\n",
+    "    # Make predictions\n",
+    "    y_train_pred = model.predict(X_train)\n",
+    "    y_test_pred = model.predict(X_test)\n",
+    "    \n",
+    "    # Evaluate Train and Test dataset\n",
+    "    model_train_mae , model_train_rmse, model_train_r2 = evaluate_model(y_train, y_train_pred)\n",
+    "\n",
+    "    model_test_mae , model_test_rmse, model_test_r2 = evaluate_model(y_test, y_test_pred)\n",
+    "\n",
+    "    \n",
+    "    print(list(models.keys())[i])\n",
+    "    model_list.append(list(models.keys())[i])\n",
+    "    \n",
+    "    print('Model performance for Training set')\n",
+    "    print(\"- Root Mean Squared Error: {:.4f}\".format(model_train_rmse))\n",
+    "    print(\"- Mean Absolute Error: {:.4f}\".format(model_train_mae))\n",
+    "    print(\"- R2 Score: {:.4f}\".format(model_train_r2))\n",
+    "\n",
+    "    print('----------------------------------')\n",
+    "    \n",
+    "    print('Model performance for Test set')\n",
+    "    print(\"- Root Mean Squared Error: {:.4f}\".format(model_test_rmse))\n",
+    "    print(\"- Mean Absolute Error: {:.4f}\".format(model_test_mae))\n",
+    "    print(\"- R2 Score: {:.4f}\".format(model_test_r2))\n",
+    "    r2_list.append(model_test_r2)\n",
+    "    \n",
+    "    print('='*35)\n",
+    "    print('\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06480b5a",
+   "metadata": {},
+   "source": [
+    "### Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e0159e5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model Name</th>\n",
+       "      <th>R2_Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Ridge</td>\n",
+       "      <td>0.880593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Linear Regression</td>\n",
+       "      <td>0.880345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>CatBoosting Regressor</td>\n",
+       "      <td>0.851632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>AdaBoost Regressor</td>\n",
+       "      <td>0.849847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Random Forest Regressor</td>\n",
+       "      <td>0.847291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Lasso</td>\n",
+       "      <td>0.825320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>XGBRegressor</td>\n",
+       "      <td>0.821589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>K-Neighbors Regressor</td>\n",
+       "      <td>0.783813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Decision Tree</td>\n",
+       "      <td>0.760313</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Model Name  R2_Score\n",
+       "2                    Ridge  0.880593\n",
+       "0        Linear Regression  0.880345\n",
+       "7    CatBoosting Regressor  0.851632\n",
+       "8       AdaBoost Regressor  0.849847\n",
+       "5  Random Forest Regressor  0.847291\n",
+       "1                    Lasso  0.825320\n",
+       "6             XGBRegressor  0.821589\n",
+       "3    K-Neighbors Regressor  0.783813\n",
+       "4            Decision Tree  0.760313"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(list(zip(model_list, r2_list)), columns=['Model Name', 'R2_Score']).sort_values(by=[\"R2_Score\"],ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "357a7c1c",
+   "metadata": {},
+   "source": [
+    "## Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9a6ad559",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Accuracy of the model is 88.03\n"
+     ]
+    }
+   ],
+   "source": [
+    "lin_model = LinearRegression(fit_intercept=True)\n",
+    "lin_model = lin_model.fit(X_train, y_train)\n",
+    "y_pred = lin_model.predict(X_test)\n",
+    "score = r2_score(y_test, y_pred)*100\n",
+    "print(\" Accuracy of the model is %.2f\" %score)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d31453e",
+   "metadata": {},
+   "source": [
+    "## Plot y_pred and y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "eb557b0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(y_test,y_pred);\n",
+    "plt.xlabel('Actual');\n",
+    "plt.ylabel('Predicted');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "1e707ec3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.regplot(x=y_test,y=y_pred,ci=None,color ='red');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79c2fe28",
+   "metadata": {},
+   "source": [
+    "#### Difference between Actual and Predicted Values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "7c9a8b48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Actual Value</th>\n",
+       "      <th>Predicted Value</th>\n",
+       "      <th>Difference</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>521</th>\n",
+       "      <td>91</td>\n",
+       "      <td>76.507812</td>\n",
+       "      <td>14.492188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>737</th>\n",
+       "      <td>53</td>\n",
+       "      <td>58.953125</td>\n",
+       "      <td>-5.953125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>740</th>\n",
+       "      <td>80</td>\n",
+       "      <td>76.960938</td>\n",
+       "      <td>3.039062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>660</th>\n",
+       "      <td>74</td>\n",
+       "      <td>76.757812</td>\n",
+       "      <td>-2.757812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>84</td>\n",
+       "      <td>87.539062</td>\n",
+       "      <td>-3.539062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>52</td>\n",
+       "      <td>43.546875</td>\n",
+       "      <td>8.453125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>62</td>\n",
+       "      <td>62.031250</td>\n",
+       "      <td>-0.031250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>74</td>\n",
+       "      <td>67.976562</td>\n",
+       "      <td>6.023438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>613</th>\n",
+       "      <td>65</td>\n",
+       "      <td>67.132812</td>\n",
+       "      <td>-2.132812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>61</td>\n",
+       "      <td>62.492188</td>\n",
+       "      <td>-1.492188</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>200 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Actual Value  Predicted Value  Difference\n",
+       "521            91        76.507812   14.492188\n",
+       "737            53        58.953125   -5.953125\n",
+       "740            80        76.960938    3.039062\n",
+       "660            74        76.757812   -2.757812\n",
+       "411            84        87.539062   -3.539062\n",
+       "..            ...              ...         ...\n",
+       "408            52        43.546875    8.453125\n",
+       "332            62        62.031250   -0.031250\n",
+       "208            74        67.976562    6.023438\n",
+       "613            65        67.132812   -2.132812\n",
+       "78             61        62.492188   -1.492188\n",
+       "\n",
+       "[200 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pred_df=pd.DataFrame({'Actual Value':y_test,'Predicted Value':y_pred,'Difference':y_test-y_pred})\n",
+    "pred_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3acf1fbc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebook/catboost_info/catboost_training.json b/notebook/catboost_info/catboost_training.json
new file mode 100644
index 0000000..ba7c000
--- /dev/null
+++ b/notebook/catboost_info/catboost_training.json
@@ -0,0 +1,1004 @@
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diff --git a/notebook/catboost_info/time_left.tsv b/notebook/catboost_info/time_left.tsv
new file mode 100644
index 0000000..7971d2a
--- /dev/null
+++ b/notebook/catboost_info/time_left.tsv
@@ -0,0 +1,1001 @@
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diff --git a/notebook/data/stud.csv b/notebook/data/stud.csv
new file mode 100644
index 0000000..28c71b4
--- /dev/null
+++ b/notebook/data/stud.csv
@@ -0,0 +1,1001 @@
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..aefcc57
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,10 @@
+pandas
+numpy
+seaborn
+matplotlib
+scikit-learn
+catboost
+xgboost
+dill
+Flask
+
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..5801cc0
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,27 @@
+from setuptools import find_packages,setup
+from typing import List
+
+HYPEN_E_DOT='-e .'
+def get_requirements(file_path:str)->List[str]:
+    '''
+    this function will return the list of requirements
+    '''
+    requirements=[]
+    with open(file_path) as file_obj:
+        requirements=file_obj.readlines()
+        requirements=[req.replace("\n","") for req in requirements]
+
+        if HYPEN_E_DOT in requirements:
+            requirements.remove(HYPEN_E_DOT)
+    
+    return requirements
+
+setup(
+name='mlproject',
+version='0.0.1',
+author='Krish',
+author_email='krishnaik06@gmail.com',
+packages=find_packages(),
+install_requires=get_requirements('requirements.txt')
+
+)
\ No newline at end of file
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/components/__init__.py b/src/components/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/components/data_ingestion.py b/src/components/data_ingestion.py
new file mode 100644
index 0000000..d2628a6
--- /dev/null
+++ b/src/components/data_ingestion.py
@@ -0,0 +1,63 @@
+import os
+import sys
+from src.exception import CustomException
+from src.logger import logging
+import pandas as pd
+
+from sklearn.model_selection import train_test_split
+from dataclasses import dataclass
+
+from src.components.data_transformation import DataTransformation
+from src.components.data_transformation import DataTransformationConfig
+
+from src.components.model_trainer import ModelTrainerConfig
+from src.components.model_trainer import ModelTrainer
+@dataclass
+class DataIngestionConfig:
+    train_data_path: str=os.path.join('artifacts',"train.csv")
+    test_data_path: str=os.path.join('artifacts',"test.csv")
+    raw_data_path: str=os.path.join('artifacts',"data.csv")
+
+class DataIngestion:
+    def __init__(self):
+        self.ingestion_config=DataIngestionConfig()
+
+    def initiate_data_ingestion(self):
+        logging.info("Entered the data ingestion method or component")
+        try:
+            df=pd.read_csv('notebook\data\stud.csv')
+            logging.info('Read the dataset as dataframe')
+
+            os.makedirs(os.path.dirname(self.ingestion_config.train_data_path),exist_ok=True)
+
+            df.to_csv(self.ingestion_config.raw_data_path,index=False,header=True)
+
+            logging.info("Train test split initiated")
+            train_set,test_set=train_test_split(df,test_size=0.2,random_state=42)
+
+            train_set.to_csv(self.ingestion_config.train_data_path,index=False,header=True)
+
+            test_set.to_csv(self.ingestion_config.test_data_path,index=False,header=True)
+
+            logging.info("Inmgestion of the data iss completed")
+
+            return(
+                self.ingestion_config.train_data_path,
+                self.ingestion_config.test_data_path
+
+            )
+        except Exception as e:
+            raise CustomException(e,sys)
+        
+if __name__=="__main__":
+    obj=DataIngestion()
+    train_data,test_data=obj.initiate_data_ingestion()
+
+    data_transformation=DataTransformation()
+    train_arr,test_arr,_=data_transformation.initiate_data_transformation(train_data,test_data)
+
+    modeltrainer=ModelTrainer()
+    print(modeltrainer.initiate_model_trainer(train_arr,test_arr))
+
+
+
diff --git a/src/components/data_transformation.py b/src/components/data_transformation.py
new file mode 100644
index 0000000..e3a098f
--- /dev/null
+++ b/src/components/data_transformation.py
@@ -0,0 +1,124 @@
+import sys
+from dataclasses import dataclass
+
+import numpy as np 
+import pandas as pd
+from sklearn.compose import ColumnTransformer
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder,StandardScaler
+
+from src.exception import CustomException
+from src.logger import logging
+import os
+
+from src.utils import save_object
+
+@dataclass
+class DataTransformationConfig:
+    preprocessor_obj_file_path=os.path.join('artifacts',"proprocessor.pkl")
+
+class DataTransformation:
+    def __init__(self):
+        self.data_transformation_config=DataTransformationConfig()
+
+    def get_data_transformer_object(self):
+        '''
+        This function si responsible for data trnasformation
+        
+        '''
+        try:
+            numerical_columns = ["writing_score", "reading_score"]
+            categorical_columns = [
+                "gender",
+                "race_ethnicity",
+                "parental_level_of_education",
+                "lunch",
+                "test_preparation_course",
+            ]
+
+            num_pipeline= Pipeline(
+                steps=[
+                ("imputer",SimpleImputer(strategy="median")),
+                ("scaler",StandardScaler())
+
+                ]
+            )
+
+            cat_pipeline=Pipeline(
+
+                steps=[
+                ("imputer",SimpleImputer(strategy="most_frequent")),
+                ("one_hot_encoder",OneHotEncoder()),
+                ("scaler",StandardScaler(with_mean=False))
+                ]
+
+            )
+
+            logging.info(f"Categorical columns: {categorical_columns}")
+            logging.info(f"Numerical columns: {numerical_columns}")
+
+            preprocessor=ColumnTransformer(
+                [
+                ("num_pipeline",num_pipeline,numerical_columns),
+                ("cat_pipelines",cat_pipeline,categorical_columns)
+
+                ]
+
+
+            )
+
+            return preprocessor
+        
+        except Exception as e:
+            raise CustomException(e,sys)
+        
+    def initiate_data_transformation(self,train_path,test_path):
+
+        try:
+            train_df=pd.read_csv(train_path)
+            test_df=pd.read_csv(test_path)
+
+            logging.info("Read train and test data completed")
+
+            logging.info("Obtaining preprocessing object")
+
+            preprocessing_obj=self.get_data_transformer_object()
+
+            target_column_name="math_score"
+            numerical_columns = ["writing_score", "reading_score"]
+
+            input_feature_train_df=train_df.drop(columns=[target_column_name],axis=1)
+            target_feature_train_df=train_df[target_column_name]
+
+            input_feature_test_df=test_df.drop(columns=[target_column_name],axis=1)
+            target_feature_test_df=test_df[target_column_name]
+
+            logging.info(
+                f"Applying preprocessing object on training dataframe and testing dataframe."
+            )
+
+            input_feature_train_arr=preprocessing_obj.fit_transform(input_feature_train_df)
+            input_feature_test_arr=preprocessing_obj.transform(input_feature_test_df)
+
+            train_arr = np.c_[
+                input_feature_train_arr, np.array(target_feature_train_df)
+            ]
+            test_arr = np.c_[input_feature_test_arr, np.array(target_feature_test_df)]
+
+            logging.info(f"Saved preprocessing object.")
+
+            save_object(
+
+                file_path=self.data_transformation_config.preprocessor_obj_file_path,
+                obj=preprocessing_obj
+
+            )
+
+            return (
+                train_arr,
+                test_arr,
+                self.data_transformation_config.preprocessor_obj_file_path,
+            )
+        except Exception as e:
+            raise CustomException(e,sys)
diff --git a/src/components/model_trainer.py b/src/components/model_trainer.py
new file mode 100644
index 0000000..72f8c5f
--- /dev/null
+++ b/src/components/model_trainer.py
@@ -0,0 +1,119 @@
+import os
+import sys
+from dataclasses import dataclass
+
+from catboost import CatBoostRegressor
+from sklearn.ensemble import (
+    AdaBoostRegressor,
+    GradientBoostingRegressor,
+    RandomForestRegressor,
+)
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import r2_score
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.tree import DecisionTreeRegressor
+from xgboost import XGBRegressor
+
+from src.exception import CustomException
+from src.logger import logging
+
+from src.utils import save_object,evaluate_models
+
+@dataclass
+class ModelTrainerConfig:
+    trained_model_file_path=os.path.join("artifacts","model.pkl")
+
+class ModelTrainer:
+    def __init__(self):
+        self.model_trainer_config=ModelTrainerConfig()
+
+
+    def initiate_model_trainer(self,train_array,test_array):
+        try:
+            logging.info("Split training and test input data")
+            X_train,y_train,X_test,y_test=(
+                train_array[:,:-1],
+                train_array[:,-1],
+                test_array[:,:-1],
+                test_array[:,-1]
+            )
+            models = {
+                "Random Forest": RandomForestRegressor(),
+                "Decision Tree": DecisionTreeRegressor(),
+                "Gradient Boosting": GradientBoostingRegressor(),
+                "Linear Regression": LinearRegression(),
+                "XGBRegressor": XGBRegressor(),
+                "CatBoosting Regressor": CatBoostRegressor(verbose=False),
+                "AdaBoost Regressor": AdaBoostRegressor(),
+            }
+            params={
+                "Decision Tree": {
+                    'criterion':['squared_error', 'friedman_mse', 'absolute_error', 'poisson'],
+                    # 'splitter':['best','random'],
+                    # 'max_features':['sqrt','log2'],
+                },
+                "Random Forest":{
+                    # 'criterion':['squared_error', 'friedman_mse', 'absolute_error', 'poisson'],
+                 
+                    # 'max_features':['sqrt','log2',None],
+                    'n_estimators': [8,16,32,64,128,256]
+                },
+                "Gradient Boosting":{
+                    # 'loss':['squared_error', 'huber', 'absolute_error', 'quantile'],
+                    'learning_rate':[.1,.01,.05,.001],
+                    'subsample':[0.6,0.7,0.75,0.8,0.85,0.9],
+                    # 'criterion':['squared_error', 'friedman_mse'],
+                    # 'max_features':['auto','sqrt','log2'],
+                    'n_estimators': [8,16,32,64,128,256]
+                },
+                "Linear Regression":{},
+                "XGBRegressor":{
+                    'learning_rate':[.1,.01,.05,.001],
+                    'n_estimators': [8,16,32,64,128,256]
+                },
+                "CatBoosting Regressor":{
+                    'depth': [6,8,10],
+                    'learning_rate': [0.01, 0.05, 0.1],
+                    'iterations': [30, 50, 100]
+                },
+                "AdaBoost Regressor":{
+                    'learning_rate':[.1,.01,0.5,.001],
+                    # 'loss':['linear','square','exponential'],
+                    'n_estimators': [8,16,32,64,128,256]
+                }
+                
+            }
+
+            model_report:dict=evaluate_models(X_train=X_train,y_train=y_train,X_test=X_test,y_test=y_test,
+                                             models=models,param=params)
+            
+            ## To get best model score from dict
+            best_model_score = max(sorted(model_report.values()))
+
+            ## To get best model name from dict
+
+            best_model_name = list(model_report.keys())[
+                list(model_report.values()).index(best_model_score)
+            ]
+            best_model = models[best_model_name]
+
+            if best_model_score<0.6:
+                raise CustomException("No best model found")
+            logging.info(f"Best found model on both training and testing dataset")
+
+            save_object(
+                file_path=self.model_trainer_config.trained_model_file_path,
+                obj=best_model
+            )
+
+            predicted=best_model.predict(X_test)
+
+            r2_square = r2_score(y_test, predicted)
+            return r2_square
+            
+
+
+
+            
+        except Exception as e:
+            raise CustomException(e,sys)
\ No newline at end of file
diff --git a/src/exception.py b/src/exception.py
new file mode 100644
index 0000000..da2f04a
--- /dev/null
+++ b/src/exception.py
@@ -0,0 +1,24 @@
+import sys
+from src.logger import logging
+
+def error_message_detail(error,error_detail:sys):
+    _,_,exc_tb=error_detail.exc_info()
+    file_name=exc_tb.tb_frame.f_code.co_filename
+    error_message="Error occured in python script name [{0}] line number [{1}] error message[{2}]".format(
+     file_name,exc_tb.tb_lineno,str(error))
+
+    return error_message
+
+    
+
+class CustomException(Exception):
+    def __init__(self,error_message,error_detail:sys):
+        super().__init__(error_message)
+        self.error_message=error_message_detail(error_message,error_detail=error_detail)
+    
+    def __str__(self):
+        return self.error_message
+    
+
+
+        
\ No newline at end of file
diff --git a/src/logger.py b/src/logger.py
new file mode 100644
index 0000000..26aafc1
--- /dev/null
+++ b/src/logger.py
@@ -0,0 +1,17 @@
+import logging
+import os
+from datetime import datetime
+
+LOG_FILE=f"{datetime.now().strftime('%m_%d_%Y_%H_%M_%S')}.log"
+logs_path=os.path.join(os.getcwd(),"logs",LOG_FILE)
+os.makedirs(logs_path,exist_ok=True)
+
+LOG_FILE_PATH=os.path.join(logs_path,LOG_FILE)
+
+logging.basicConfig(
+    filename=LOG_FILE_PATH,
+    format="[ %(asctime)s ] %(lineno)d %(name)s - %(levelname)s - %(message)s",
+    level=logging.INFO,
+
+
+)
diff --git a/src/pipeline/__init__.py b/src/pipeline/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/pipeline/predict_pipeline.py b/src/pipeline/predict_pipeline.py
new file mode 100644
index 0000000..be2a0ee
--- /dev/null
+++ b/src/pipeline/predict_pipeline.py
@@ -0,0 +1,67 @@
+import sys
+import pandas as pd
+from src.exception import CustomException
+from src.utils import load_object
+import os
+
+
+class PredictPipeline:
+    def __init__(self):
+        pass
+
+    def predict(self,features):
+        try:
+            model_path=os.path.join("artifacts","model.pkl")
+            preprocessor_path=os.path.join('artifacts','preprocessor.pkl')
+            model=load_object(file_path=model_path)
+            preprocessor=load_object(file_path=preprocessor_path)
+            data_scaled=preprocessor.transform(features)
+            preds=model.predict(data_scaled)
+            return preds
+        
+        except Exception as e:
+            raise CustomException(e,sys)
+
+
+
+class CustomData:
+    def __init__(  self,
+        gender: str,
+        race_ethnicity: str,
+        parental_level_of_education,
+        lunch: str,
+        test_preparation_course: str,
+        reading_score: int,
+        writing_score: int):
+
+        self.gender = gender
+
+        self.race_ethnicity = race_ethnicity
+
+        self.parental_level_of_education = parental_level_of_education
+
+        self.lunch = lunch
+
+        self.test_preparation_course = test_preparation_course
+
+        self.reading_score = reading_score
+
+        self.writing_score = writing_score
+
+    def get_data_as_data_frame(self):
+        try:
+            custom_data_input_dict = {
+                "gender": [self.gender],
+                "race_ethnicity": [self.race_ethnicity],
+                "parental_level_of_education": [self.parental_level_of_education],
+                "lunch": [self.lunch],
+                "test_preparation_course": [self.test_preparation_course],
+                "reading_score": [self.reading_score],
+                "writing_score": [self.writing_score],
+            }
+
+            return pd.DataFrame(custom_data_input_dict)
+
+        except Exception as e:
+            raise CustomException(e, sys)
+
diff --git a/src/utils.py b/src/utils.py
new file mode 100644
index 0000000..84bc0be
--- /dev/null
+++ b/src/utils.py
@@ -0,0 +1,61 @@
+import os
+import sys
+
+import numpy as np 
+import pandas as pd
+import dill
+from sklearn.metrics import r2_score
+from sklearn.model_selection import GridSearchCV
+
+from src.exception import CustomException
+
+def save_object(file_path, obj):
+    try:
+        dir_path = os.path.dirname(file_path)
+
+        os.makedirs(dir_path, exist_ok=True)
+
+        with open(file_path, "wb") as file_obj:
+            dill.dump(obj, file_obj)
+
+    except Exception as e:
+        raise CustomException(e, sys)
+    
+def evaluate_models(X_train, y_train,X_test,y_test,models,param):
+    try:
+        report = {}
+
+        for i in range(len(list(models))):
+            model = list(models.values())[i]
+            para=param[list(models.keys())[i]]
+
+            gs = GridSearchCV(model,para,cv=3)
+            gs.fit(X_train,y_train)
+
+            model.set_params(**gs.best_params_)
+            model.fit(X_train,y_train)
+
+            #model.fit(X_train, y_train)  # Train model
+
+            y_train_pred = model.predict(X_train)
+
+            y_test_pred = model.predict(X_test)
+
+            train_model_score = r2_score(y_train, y_train_pred)
+
+            test_model_score = r2_score(y_test, y_test_pred)
+
+            report[list(models.keys())[i]] = test_model_score
+
+        return report
+
+    except Exception as e:
+        raise CustomException(e, sys)
+    
+def load_object(file_path):
+    try:
+        with open(file_path, "rb") as file_obj:
+            return dill.load(file_obj)
+
+    except Exception as e:
+        raise CustomException(e, sys)
\ No newline at end of file
diff --git a/templates/home.html b/templates/home.html
new file mode 100644
index 0000000..ef031c2
--- /dev/null
+++ b/templates/home.html
@@ -0,0 +1,111 @@
+<html>
+<body>
+    <div class="login">
+       <h1>Student Exam Performance Indicator</h1>
+   
+       <form action="{{ url_for('predict_datapoint')}}" method="post">
+        <h1>
+            <legend>Student Exam Performance Prediction</legend>
+        </h1>
+        <div class="mb-3">
+            <label class="form-label">Gender</label>
+            <select class="form-control" name="gender" placeholder="Enter you Gender" required>
+                <option class="placeholder" selected disabled value="">Select your Gender</option>
+                <option value="male">
+                    Male
+                </option>
+                <option value="female">
+                    Female
+                </option>
+            </select>
+        </div>
+        <div class="mb-3">
+            <label class="form-label">Race or Ethnicity</label>
+            <select class="form-control" name="ethnicity" placeholder="Enter you ethnicity" required>
+                <option class="placeholder" selected disabled value="">Select Ethnicity</option>
+                <option value="group A">
+                    Group A
+                </option>
+                <option value="group B">
+                    Group B
+                </option>
+                <option value="group C">
+                    Group C
+                </option>
+                <option value="group D">
+                    Group D
+                </option>
+                <option value="group E">
+                    Group E
+                </option>
+            </select>
+        </div>
+        <div class="mb-3">
+            <label class="form-label">Parental Level of Education</label>
+            <select class="form-control" name="parental_level_of_education"
+                placeholder="Enter you Parent Education" required>
+                <option class="placeholder" selected disabled value="">Select Parent Education</option>
+                <option value="associate's degree">
+                    associate's degree
+                </option>
+                <option value="bachelor's degree">
+                    bachelor's degree
+                </option>
+                <option value="high school">
+                    high school
+                </option>
+                <option value="master's degree">
+                    master's degree
+                </option>
+                <option value="some college">
+                    some college
+                </option>
+                <option value="some high school">
+                    some high school
+                </option>
+            </select>
+        </div>
+        <div class="mb-3">
+            <label class="form-label">Lunch Type</label>
+            <select class="form-control" name="lunch" placeholder="Enter you Lunch" required>
+                <option class="placeholder" selected disabled value="">Select Lunch Type</option>
+                <option value="free/reduced">
+                    free/reduced
+                </option>
+                <option value="standard">
+                    standard
+                </option>
+            </select>
+        </div>
+        <div class="mb-3">
+            <label class="form-label">Test preparation Course</label>
+            <select class="form-control" name="test_preparation_course" placeholder="Enter you Course"
+                required>
+                <option class="placeholder" selected disabled value="">Select Test_course</option>
+                <option value="none">
+                    None
+                </option>
+                <option value="completed">
+                    Completed
+                </option>
+            </select>
+        </div>
+        <div class="mb-3">
+            <label class="form-label">Writing Score out of 100</label>
+            <input class="form-control" type="number" name="reading_score"
+                placeholder="Enter your Reading score" min='0' max='100' />
+        </div>
+        <div class="mb-3">
+            <label class="form-label">Reading Score out of 100</label>
+            <input class="form-control" type="number" name="writing_score"
+                placeholder="Enter your Reading Score" min='0' max='100' />
+        </div>
+        <div class="mb-3">
+            <input class="btn btn-primary" type="submit" value="Predict your Maths Score" required />
+        </div>
+    </form>
+    <h2>
+       THE  prediction is {{results}}
+    </h2>
+   <body>
+</html>
\ No newline at end of file
diff --git a/templates/index.html b/templates/index.html
new file mode 100644
index 0000000..6afd819
--- /dev/null
+++ b/templates/index.html
@@ -0,0 +1 @@
+<h1>Welcome to the home page</h1>
\ No newline at end of file