Skip to content

Series of notebooks, which are my submissions to a online Hackathon : Code-2-ML

Notifications You must be signed in to change notification settings

gauravreddy08/Stock-Price-Prediction-Model

Repository files navigation

Predicting Stock Price using Machine Learning

This repo contains series of notebooks, which are my submissions to a online Hack-a-thon : Code-2-ML hosted by CodeChef Chapter VIT AP and ML Club VIT AP.

Problem Overview

Initially I was given two datasets namely Train_dataset_ - Train_Aug10.csv and Test_dataset - Put-Call_TS.csv.

Train_dataset_ - Train_Aug10.csv

This dataset contains various stock's fractors (features) and the respective stock price. The Problem is to make an Machine Learning Regression Model that first learns on the training dataset and then predicts the value for the test dataset. The predicted .csv file is saved as file_01.csv

Algorithm used : Random Forest Regressor

Test_dataset - Put-Call_TS.csv

This dataset contains a single stock's PutCall Ratio parameter, but on 5 different consecutive days. The Problem is to make an efficient Machine Learning Time Series Model that first learns on the training dataset and then predicts the value on test dataset. The predicted .csv file is saved as file_02.csv

Algorithm used : VAR : Vector Auto Regressor


How to use our Notebooks

You can also avoid all the below steps and instead load the notebooks on Google Colab appropriately with the datasets.

1. Install the requirements.txt

pip install -r requirements.txt #Preferable to install on a Virtual Environment 

If OSx / Linux use pip3 instead of pip

2. Running the Notebooks

Problem 01 :

There are 2 Notebooks names problem_01.ipynb and test_dataprep.ipynb.

problem_01.ipynb is the actual problem notebook.

test_dataprep.ipynb is to clean the test Dataset. Which is then exported to be used by the main Notebook.

This all could have avoided and made into a single Notebook but it was clumsy. So for better readability, we had to make two different Notebook.

Problem 02 :

Run the problem_02.ipynb and the solution will be outputted. And respective csv file is save in Answer_files directory as file_02.csv.

3. Answer Folder

After the execution of both the notebooks the .csv file solutions wil be exported.

These .csv files (solutions) are present into the Answer_files.

Update

This Project has secured 1st Place 🥇 in the Code-2-ML Hack-a-thon.

This repo contains other participant's submissions for the Hack-a-thon.

drawing

About

Series of notebooks, which are my submissions to a online Hackathon : Code-2-ML

Topics

Resources

Stars

Watchers

Forks