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Stock-prediction-using-sentiment-analysis

Overview

The Stock-prediction-using-sentiment-analysis is a machine learning application designed to predict stock prices using historical data. This project leverages XGBoost, a powerful gradient boosting framework, to analyze trends in stock prices and make predictions based on datetime features.

Features

• Data Loading: Read and preprocess historical stock data from CSV files.
• Data Visualization: Visualize stock trends using Streamlit for a user-friendly experience.
• Model Training: Train an XGBoost model using extracted features from datetime (year, month, day) and the closing price of stocks.
• Price Prediction: Predict the end-of-day stock price based on the trained model.

Technologies Used

• Python
• Pandas
• NumPy
• XGBoost
• Streamlit

Installation

1. Clone the repository:

   git clone https://github.com/your-username/Stock-prediction-using-sentiment-analysis.git
   cd integrative-stock-trend-predictor

2. Set up a virtual environment:

   python -m venv myenv
   source myenv/bin/activate  # On Windows use `myenv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Run the Streamlit application:

   streamlit run app.py

2. Interact with the application:

   • Select a stock from the dropdown menu.
   • Visualize the historical stock trends.
   • Click on the "Predict Price" button to see the predicted end-of-day price for the selected stock.