StockCast

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📈 GitHub Repository Stats

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This project is now OFFICIALLY accepted for

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✨ Project Structure

Check the project structure here Project Structure

📚 Table of Contents

• 📈 GitHub Repository Stats
• ✨ Project Structure
• 📚 Table of Contents
• 🌞 Overview
• 🛠️ Features
• 🔍 Algorithms Used
• 📊 Dataset
• 📁 Project Structure
• 🚀 How to Run main.py
• 📈 Results
• 📊 Performance Metrics
• 🔧 Optimization Techniques
• 🔮 Future Work
• 🏆 Conclusion
• 🤝 Contributing
• ✍️ Author
• 🌍 Our Valuable Contributors
• 📝 License
• 📱 Connect with Us

---

🌞 Overview

This project aims to predict the stock prices of The State Bank of India (SBI) using various machine learning regression algorithms. By leveraging historical stock data sourced from Yahoo Finance, this project provides insights into the performance of different regression models in stock price prediction for SBI.

The primary objective is to compare model accuracy and performance metrics, such as RMSE, MAE, and MAPE, across multiple algorithms, ultimately identifying the most suitable regression approach for stock price forecasting.

🛠️ Features

• Utilizes various regression algorithms for stock price prediction.
• Dataset collected from Yahoo Finance for The State Bank Of India.

🔍 Algorithms Used

We implemented the following regression algorithms for stock price prediction:

| 🤖 Algorithm | 📜 Description | |-------------------------------------------|----------------------------------------------------| | Linear Regression | A basic regression algorithm. | | Support Vector Regression (SVR) | Effective for non-linear relationships. | | Random Forest | Ensemble learning method using decision trees. | | Gradient Boosting Models (GBM) | Sequentially builds models to improve predictions. | | Extreme Gradient Boosting (XGBoost) | Advanced boosting technique with regularization. | | AdaBoostRegressor | Combines multiple weak learners. | | Decision Tree | Simple yet effective model based on tree structure. | | KNeighborsRegressor (KNN) | Predicts based on nearest neighbors' average. | | Artificial Neural Networks (ANN) | Mimics human brain for complex data patterns. | | Long Short Term Memory (LSTM) | Suitable for time-series prediction. |

📊 Dataset

The dataset used in this project is sourced from Yahoo Finance and includes historical stock data for The State Bank Of India. It comprises relevant features such as:

• 📈 Open prices
• 📉 High prices
• 📉 Low prices
• 💵 Close prices
• 📦 Volume

📁 Project Structure

Directory structure:
└── rohitinu6-stockcast.git/
  ├── readme.md
  ├── License
  ├── repo_structure.txt
  ├── requirements.txt
  ├── ARIMA/
  │   ├── README.md
  │   ├── ARIMA_V2.ipynb
  │   ├── hybrid.ipynb
  │   ├── saved_model/
  │   │   ├── arima_model.pkl
  │   │   ├── lstm_model.h5
  │   │   └── scaler.pkl
  │   └── .ipynb_checkpoints/
  │       ├── ARIMA_V2-checkpoint.ipynb
  │       └── hybrid-checkpoint.ipynb
  ├── Data/
  │   ├── SBI Test data.csv
  │   ├── SBI Train data.csv
  │   └── SBIN.csv
  ├── Data Analysis/
  │   ├── SBI Stock Analysis Updated.pptx
  │   └── SBI Stock Analysis Updated.twbx
  ├── Images/
  │   └── Image
  ├── Intel_Optimized/
  │   ├── readme.md
  │   ├── ARIMA_V2.ipynb
  │   ├── Intel_Optimization.md
  │   ├── Stock_Price_Prediction_1.ipynb
  │   ├── Stock_prediction_Data_Analysis.ipynb
  │   ├── buy_sell_recommendation_system.ipynb
  │   ├── hybrid.ipynb
  │   ├── reduced_redundancy_stock_price_prediction.ipynb
  │   ├── requirements.txt
  │   └── images/
  ├── Jupyter Source Files/
  │   ├── Buy Sell Recommendation.ipynb
  │   ├── EDA + Models.ipynb
  │   ├── Reduced Redundancy.ipynb
  │   ├── Stock Price Prediction.ipynb
  │   └── VWAP based Models.ipynb
  ├── Markdown Source Files/
  │   ├── Code of conduct.md
  │   ├── Contributing.md
  │   ├── Leaderboard.md
  │   ├── Project structure.md
  │   └── Security.md
  ├── Python Files/
  │   ├── Stock_Price_Prediction.ipynb
  │   ├── Stock_Price_Prediction_BACKUP_19716.ipynb
  │   ├── Stock_Price_Prediction_BACKUP_20502.ipynb
  │   ├── Stock_Price_Prediction_BASE_19716.ipynb
  │   ├── Stock_Price_Prediction_BASE_20502.ipynb
  │   ├── Stock_Price_Prediction_LOCAL_19716.ipynb
  │   ├── Stock_Price_Prediction_LOCAL_20502.ipynb
  │   ├── Stock_Price_Prediction_REMOTE_19716.ipynb
  │   └── Stock_Price_Prediction_REMOTE_20502.ipynb
  ├── catboost_info/
  │   ├── catboost_training.json
  │   ├── learn_error.tsv
  │   ├── time_left.tsv
  │   └── learn/
  │       └── events.out.tfevents
  ├── images/
  └── .ipynb_checkpoints/
      ├── More_Charts.ipynb
      ├── Stock_Price_Prediction(Updated) MultiLayer LSTM-checkpoint.ipynb
      ├── Stock_Price_Prediction-checkpoint.ipynb
      ├── Stock_prediction_Data_Analysis-checkpoint.ipynb
      ├── candlestick_chart.html
      ├── stock_market(complete)-checkpoint.ipynb
      └── stock_sentimental-checkpoint.ipynb

🚀 How to Run main.py

Steps: 1.If Flask is not installed, install it:

pip install flask

2.Install dependencies using:

pip install -r requirements.txt

3.Run the Flask app:

python main.py

📈 Results

The sequence of all the algorithms used is as follows:

1. Linear Regression
2. SVR
3. Random Forest
4. Gradient Boosting Models (GBM)
5. Extreme Gradient Boosting (XGBoost)
6. AdaBoostRegressor
7. Decision Tree
8. KNeighborsRegressor (KNN)
9. Artificial Neural Networks (ANN)
10. Long Short Term Memory (LSTM)

📊 Performance Metrics

The Root Mean Square Error (RMSE) of all the following 10 Regression Algorithms is provided below: <img src="images/f23e9194-72de-438d-bd69-744667680d3e.jpeg" alt="Performance-Metrices" width="400" height="300">

The Mean Absolute Error (MAE) of all the following 10 Regression Algorithms is provided below:

<img src="images/085ee2d1-3544-4bed-a558-5b0b801e806b.jpeg" alt="Performance-Metrices" width="400" height="300">

The Mean Absolute Percentage Error (MAPE) of all the f