Merge branch 'master' of https://github.com/AmzadHossainrafis/Stock_p…

…rediction

.github/workflows/pylint.yml

@@ -0,0 +1,23 @@
+name: Pylint
+
+on: [push]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.8", "3.9", "3.10"]
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install pylint
+    - name: Analysing the code with pylint
+      run: |
+        pylint $(git ls-files '*.py')

.gitignore

@@ -130,4 +130,8 @@ dmypy.json
 data/ 
 fig/ 
 artifacts/ 
-project/
+project/
+wandb/
+tasks.docx
+figs
+

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 amzad rafi
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,6 +1,10 @@
-# Project Title
+# PREDICTOO (Predict the future of your time series data)
+( under development , not ready for production , looking for contributors  )
+
+## Description 
+
+Predictoo is a python package which allows to predict the future of a time series.Predictoo contain 10 deep learning model .It is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with time series that have strong seasonal effects and several seasons of historical data.
 
-A short description about the project and/or client.
 
 ## Getting Started
 
@@ -10,50 +14,50 @@ These instructions will get you a copy of the project up and running on your loc
 
 The things you need before installing the software.
 
-* You need this
-* And you need this
-* Oh, and don't forget this
+* python 3.6 or higher 
+* tensorflow 2.0 or higher 
+
+
 
 ### Installation
 
 A step by step guide that will tell you how to get the development environment up and running.
 
+bash  
 ```
-$ First step
-$ Another step
-$ Final step
+$conda create -n predictoo python=3.6
+$pip install -r requirements.txt 
+
 ```
+ollah your good to go 
 
-## Usage
 
-A few examples of useful commands and/or tasks.
 
-```
-$ First example
-$ Second example
-$ And keep this in mind
-```
+## how to train 
+config the cofig.py accordingly to your training enviroment . Predictoo requre dataset ['Date','Open', 'High', 'Low', 'Close', 'Volume',] following formate .
 
-## Deployment
+for every time serice data there is a simple preprocessing pipeline which will formate your date and split the data into test , train inside the aftifact folder 
 
-Additional notes on how to deploy this on a live or release system. Explaining the most important branches, what pipelines they trigger and how to update the database (if anything special).
 
-### Server
+### must change in config.py
+dataset_path: must reset according to your dataset dir 
+
+model_path: must reset with a dir where you want to store your train weights 
 
-* Live:
-* Release:
-* Development:
 
-### Branches
+```
+$ python data_injections.py
+```
+
+bash  
+```
+$ cd src/components
+$ python model_train.py -p 14 -f 1 
 
-* Master:
-* Feature:
-* Bugfix:
-* etc...
+```
+model evaluation 
+```
+$ python model_evaluation.py 
+```
 
-## Additional Documentation and Acknowledgments
 
-* Project folder on server:
-* Confluence link:
-* Asana board:
-* etc...

logs/2023-04-22._log/2023-04-22._log

@@ -0,0 +1,6 @@
+[2023-04-22 02:27:00 ] 28 root INFO : Initiating data injection
+[2023-04-22 02:27:00 ] 32 root INFO : Data loaded successfully
+[2023-04-22 02:27:00 ] 35 root INFO : Artifats created successfully
+[2023-04-22 02:27:00 ] 39 root INFO : Train test split initiated
+[2023-04-22 02:27:00 ] 47 root INFO : Train data saved successfully
+[2023-04-22 02:27:00 ] 50 root INFO : data injection completed successfully

logs/2023-04-24._log/2023-04-24._log

@@ -0,0 +1,20 @@
+[2023-04-24 00:09:26 ] 164 root ERROR : Data transformation failed
+[2023-04-24 00:10:02 ] 164 root ERROR : Data transformation failed
+[2023-04-24 00:11:06 ] 109 root INFO : train test data loaded successfully
+[2023-04-24 00:11:06 ] 111 root INFO : Data transformation started
+[2023-04-24 00:11:06 ] 101 root ERROR : Data transformation failed
+[2023-04-24 00:11:06 ] 164 root ERROR : Data transformation failed
+[2023-04-24 00:12:16 ] 92 root INFO : train test data loaded successfully
+[2023-04-24 00:12:16 ] 94 root INFO : Data transformation started
+[2023-04-24 00:12:16 ] 78 root INFO : Data transformation completed successfully
+[2023-04-24 00:12:16 ] 147 root ERROR : Data transformation failed
+[2023-04-24 00:29:54 ] 92 root INFO : train test data loaded successfully
+[2023-04-24 00:29:54 ] 94 root INFO : Data transformation started
+[2023-04-24 00:29:54 ] 78 root INFO : Data transformation completed successfully
+[2023-04-24 00:29:54 ] 147 root ERROR : Data transformation failed
+[2023-04-24 19:50:32 ] 28 root INFO : Initiating data injection
+[2023-04-24 19:50:32 ] 32 root INFO : Data loaded successfully
+[2023-04-24 19:50:32 ] 35 root INFO : Artifats created successfully
+[2023-04-24 19:50:32 ] 39 root INFO : Train test split initiated
+[2023-04-24 19:50:32 ] 47 root INFO : Train data saved successfully
+[2023-04-24 19:50:32 ] 50 root INFO : data injection completed successfully

logs/2023-04-25._log/2023-04-25._log

@@ -0,0 +1,12 @@
+[2023-04-25 02:03:19 ] 29 root INFO : Initiating data injection
+[2023-04-25 02:03:19 ] 33 root INFO : Data loaded successfully
+[2023-04-25 02:03:19 ] 36 root INFO : Artifats created successfully
+[2023-04-25 02:03:19 ] 40 root INFO : Train test split initiated
+[2023-04-25 02:03:19 ] 48 root INFO : Train data saved successfully
+[2023-04-25 02:03:19 ] 51 root INFO : data injection completed successfully
+[2023-04-25 02:04:01 ] 29 root INFO : Initiating data injection
+[2023-04-25 02:04:01 ] 33 root INFO : Data loaded successfully
+[2023-04-25 02:04:01 ] 36 root INFO : Artifats created successfully
+[2023-04-25 02:04:01 ] 40 root INFO : Train test split initiated
+[2023-04-25 02:04:01 ] 48 root INFO : Train data saved successfully
+[2023-04-25 02:04:01 ] 51 root INFO : data injection completed successfully