The Machine Learning Project deployment using Flask and Docker

Name: Student Performance Prediction

This project aims to find the best model for the dataset base on r2_score among Linear Regression, Random Forest Regressor, Decision Tree Regressor, Gradient Bosting regressor, K-Neighbor Regressor, XGBRegressor, Cat Boost regressor, AdaBoost regressor using Hyperparameter tunning and GridSearchCV.

Life cycle of Machine learning Project

• Problem statement: This project investigates how additional variables such as gender, ethnicity, parental level of education, lunch, and test preparation course affect student performance (test scores).

• Data collection: Dataset source https://www.kaggle.com/datasets/spscientist/students-performance-in-exams?datasetId=74977 This data set consists of the marks secured by the students in various subjects. Inspiration is understanding the influence of the parent's background, test preparation, etc on students' performance.

• Dataset Information:

  • gender: sex of students -> (Male/female)
  • race/ethnicity: ethnicity of students -> (Group A, B, C, D, E)
  • parental level of education : parents' final education ->(bachelor's degree,some college,master's degree,associate's degree,high school)
  • lunch: having lunch before the test (standard or free/reduced)
  • test preparation course: complete or not complete before the test
  • math score
  • reading score
  • writing score

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• Repository Structure:

src/logger.py: scripts for logging Information

src/exception.py: for handline exception

src/utils.py: To load and save the .pkl file

src/components/data_ingesion.py: For loading the dataset and converting train and test dataset

src/components/data_transformation.py: To transform and preprocess data using ML Pipeline and ColumnTransformer

src/components/model_trainer.py: To train and find the best model suited for the dataset

app.py: File contains Flask web app code

Docker: Docker file to build docker image name as shreyaskorde16/student_performance