This project integrates Machine Learning (ML) and Sentiment Analysis to predict stock prices. It leverages Python for analyzing stock data and sentiment from various sources to forecast market trends.

• ML-Based Prediction: Utilizes machine learning models for accurate stock price forecasting.
• Sentiment Analysis: Incorporates sentiment analysis to understand market opinions, using textblob_analysis.py and sentiment.py.
• Comprehensive Data Analysis: Employs main.py for orchestrating data collection, analysis, and prediction processes.

• Python
• Libraries: pandas, NumPy, scikit-learn, TextBlob

1. Clone the repository:

   git clone https://github.com/adityap02/Stock-Price-Prediction-Using-ML-and-Sentimental-Analysis.git
   

2. Install required Python packages:

   pip install pandas numpy scikit-learn textblob
   

• Run main.py to start the analysis.
• The scripts will process stock data and apply sentiment analysis for predictions.

• sentiment.py: Handles sentiment analysis of market news.
• textblob_analysis.py: Utilizes TextBlob for processing and analyzing textual data.
• main.py: Main script coordinating data processing and predictions.

• Data Collection: Gathers stock data and relevant news articles.
• Sentiment Analysis: Analyzes news sentiment to understand market mood.
• Stock Price Prediction: ML models predict future stock prices based on historical data and sentiment scores.

Contributions to improve this project are highly appreciated. Please refer to CONTRIBUTING.md for contribution guidelines.

This project is licensed under the MIT License - see LICENSE.md for details.

• Financial data sources
• Open-source Python libraries
• Community contributors