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https://github.com/shivamgupta92/analysis-of-market-trends-using-deep-learning
Analysis of market trend using Deep Learning is project that forecasts stock prices using historical data and ML models. Leveraging data collection, feature engineering, and model training. Primarily designed for the Indian stock market, it is adaptable for international markets, providing valuable insights for investors and analysts.
https://github.com/shivamgupta92/analysis-of-market-trends-using-deep-learning
deep-neural-networks keras mysql numpy pandas python3 scikit-learn-api tensorflow time-series-analysis yfinance-api
Last synced: 1 day ago
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Analysis of market trend using Deep Learning is project that forecasts stock prices using historical data and ML models. Leveraging data collection, feature engineering, and model training. Primarily designed for the Indian stock market, it is adaptable for international markets, providing valuable insights for investors and analysts.
- Host: GitHub
- URL: https://github.com/shivamgupta92/analysis-of-market-trends-using-deep-learning
- Owner: ShivamGupta92
- License: mit
- Created: 2024-07-20T06:00:56.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-08-04T18:46:02.000Z (3 months ago)
- Last Synced: 2024-08-04T20:43:30.512Z (3 months ago)
- Topics: deep-neural-networks, keras, mysql, numpy, pandas, python3, scikit-learn-api, tensorflow, time-series-analysis, yfinance-api
- Language: Jupyter Notebook
- Homepage:
- Size: 1.34 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Analysis of Market Trends Using Deep Learning
## Overview
This project aims to predict stock prices and market trend using advanced machine learning techniques. Although primarily designed for the Indian stock market, the code can also be applied to international markets. The project involves collecting historical stock data, preprocessing it, training various machine learning models, and evaluating their performance to make accurate predictions.
## Importance
Stock price prediction and financial trend analysis is a crucial aspect of financial markets, allowing traders, investors, and financial analysts to make informed decisions. Accurate predictions can lead to profitable trades and investments, minimizing risks and maximizing returns.
## Features
1. **Data Collection**: Automatically fetch historical stock data using Yahoo Finance.
2. **Feature Engineering**: Preprocess data, extract relevant features, and engineer new features that might be predictive of stock prices, such as technical indicators and sentiment analysis.
3. **Model Selection**: Implement and compare various machine learning models, including linear regression, decision trees, random forests, support vector machines, and advanced techniques like LSTMs.
4. **Training and Evaluation**: Train models on historical data, optimize hyperparameters, prevent overfitting, and evaluate performance using metrics like MSE, RMSE, and MAE.
5. **Regular Updates**: The model can be regularly updated with new data to adapt to changing market conditions.
## Technologies Used
- **Python**: Programming language
- **Pandas**: Data manipulation and analysis
- **NumPy**: Numerical computing
- **Matplotlib** and **Seaborn**: Data visualization
- **yfinance**: Fetching stock data from Yahoo Finance
- **scikit-learn**: Machine learning library
- **TensorFlow/Keras**: Deep learning framework
## How to Use This Code
### Prerequisites
- Python 3.x
- Git
### Installation
1. **Fork the Repository**
Fork the repository to your own GitHub account by clicking the "Fork" button on the top right of the repository page.
2. **Clone the Repository**
Clone the forked repository to your local machine:
```sh
git clone https://github.com/ShivamGupta92/StockPrice_Prediction-using-deep-learning.git
```
3. Changeing working directory
```sh
cd StockPrice_Prediction-using-deep-learning
```
4. Install Requirements
Install the necessary packages by running:
```sh
pip install -r requirements.txt
```
### Running the Code
- Prepare Data
- Train the Model
Train the model using the provided script:
```sh
python train_model.py
```
- Make Predictions
Use the trained model to make predictions:
```sh
python predict.py
```
### Future Work
- Incorporate sentiment analysis of news articles to enhance predictive accuracy.
- Explore additional technical indicators and economic indicators for feature engineering.
- Implement an automated pipeline for regular model updates and re-training.
## Author
Shivam Gupta
## LICENSE
This project is licensed under the [MIT License](LICENSE).