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+# ISBM Stock Price Prediction
+
+## Project Overview
+
+This project aims to predict stock prices using RandomForest Regression based on historical stock data of ISBM. The dataset spans from **January 1996 to 2024**, including key stock price attributes such as Open, High, Low, Close, Adjusted Close, and Volume. The model evaluates the performance using various metrics and aims to assist in making data-driven predictions for future stock prices.
+
+### Datasets
+
+The following CSV files are included in the project:
+
+1. **isbm.csv**: This file contains historical stock data for ISBM from 1996 to 2024, including:
+   - `Date`: The date of the stock price
+   - `Open`: The opening price of the stock
+   - `High`: The highest price of the stock during the day
+   - `Low`: The lowest price of the stock during the day
+   - `Close`: The closing price of the stock at the end of the day
+   - `Adj Close`: The adjusted closing price, accounting for dividends and stock splits
+   - `Volume`: The total number of shares traded during the day
+
+2. **SBI Train data.csv**: This file contains the training data used to build the stock price prediction model.
+3. **SBI Test data.csv**: This file contains the testing data used to evaluate the model's performance.
+
+## Requirements
+
+To run the project, you need the following Python libraries:
+- `pandas`
+- `numpy`
+- `matplotlib`
+- `seaborn`
+- `scikit-learn`
+
+You can install these libraries by running:
+```bash
+pip install -r requirements.txt