From fbc38db8f4926ae4b8943467971907505dce8c23 Mon Sep 17 00:00:00 2001 From: Muhammad Ahsan Atiq Date: Tue, 15 Oct 2024 10:04:29 -0700 Subject: [PATCH] Create README.md --- Data/README.md | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 Data/README.md diff --git a/Data/README.md b/Data/README.md new file mode 100644 index 0000000..00f4b05 --- /dev/null +++ b/Data/README.md @@ -0,0 +1,34 @@ +# 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