LinearRegPy

LinearRegPy is a Python project that implements linear regression for predicting data trends using popular libraries such as pandas, numpy, and matplotlib.

Overview

Linear regression is a fundamental technique in machine learning and statistics used to model the relationship between a dependent variable and one or more independent variables. This project focuses on implementing linear regression in Python, specifically for predicting trends in datasets.

Features

• Data Preprocessing: The project includes functionality for reading CSV data files into pandas DataFrames, shuffling the data, and splitting it into training and test sets.
• Model Training: Linear regression is performed using the least squares method to find the coefficients of the fitted line.
• Visualization: The project visualizes the original data points, the fitted line, and provides insights into the model's performance using matplotlib.
• Error Calculation: The error between the estimated values and the actual test data values is calculated and displayed.

Usage

1. Clone the repository:

   git clone https://github.com/alireza-nasirian/LinearRegPy.git

2. Replace the data.csv file with your own dataset if necessary.

3. Run the linear_regression.py script:

   python linear_regression.py

Contributing

Contributions are welcome! If you find any bugs or have suggestions for improvement, feel free to open an issue or submit a pull request.