https://github.com/aman-095/stock-price-predictiion

Using the google stock prices dataset and build a model to predict the future prices using previous instances.
https://github.com/aman-095/stock-price-predictiion

forecasting keras lstm rnn time-series

Last synced: 26 days ago
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Using the google stock prices dataset and build a model to predict the future prices using previous instances.

• Host: GitHub
• URL: https://github.com/aman-095/stock-price-predictiion
• Owner: aman-095
• License: mit
• Created: 2022-05-25T06:16:40.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-10-26T14:49:41.000Z (about 2 years ago)
• Last Synced: 2023-08-09T15:28:51.072Z (about 1 year ago)
• Topics: forecasting, keras, lstm, rnn, time-series
• Language: Jupyter Notebook
• Homepage:
• Size: 1.71 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

# Stock Price Prediction

Using the google stock prices dataset and build a model to predict the future prices using previous instances. A pipeline is also developed through which allows continuous predictions taking the present data itself and generating the future estimated stock prices.

## Tools Required

**Scikit Learn:** ML Library used

**Matplotlib:** Data Visualization

**Tensorflow:** Deep Learning Models

**Pandas:** Python data manipulation libraries

**Numpy:** Working with data in form of arrays

## Roadmap

1. [Stock Price Prediction.ipynb](https://github.com/aman-095/Stock-Price-Predictiion/blob/main/Stock_Price_Prediction%20(1).ipynb)

This is the main file with all the preprocessing, various Machine learning, Deep Learning Models and a real-time Pipeline.

- Installing libraries and dependency

- Importing the dataset - [Google Stock Price Prediction Dataset](https://www.kaggle.com/code/bhupendersharma999/google-stock-price-prediction-rnn/data)

- Exploratory Data Analysis and Visualisation

- Data Preprocessing - Basic preprocessing and structuring the dataset

- Dividing the dataset into train and test

- Applying Machine Learning models

- Linear Regression

- Random Forest Regressor

- Light GBM Regressor

- XG Boost Regressor

- Applying Deep Learning models

- Pipeline developed with the best model for future predictions with the real time data

2. [Report Stock Price Prediction](https://github.com/aman-095/Stock-Price-Predictiion/blob/main/Report_Stock%20Price%20Prediction.pdf)

This contains all the qualitative analysis of the results and detailed data visualization.

## Real-time stock prediction with the pipeline developed

![Prediction](https://i.postimg.cc/nrBQf5rB/Pipeline.png)

## Documentation

[Documentation](https://github.com/aman-095/Stock-Price-Predictiion/blob/main/Report_Stock%20Price%20Prediction.pdf)

## Feedback

If you have any feedback, please reach out to us at [email protected]

## 🔗 Links

[![portfolio](https://img.shields.io/badge/my_portfolio-000?style=for-the-badge&logo=ko-fi&logoColor=white)](https://aman-095.github.io/)

[![linkedin](https://img.shields.io/badge/linkedin-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/aman-bhansali-b4aa26228/)