Linear Regression

Overview

This repository implements a Linear Regression model to analyze and predict numerical data. It includes steps for data preprocessing, visualization, model training, and evaluation.

Features

• Loads and explores a dataset
• Handles missing values and duplicates
• Visualizes data distributions and correlations
• Implements a Linear Regression model
• Evaluates model performance using common metrics

Requirements

Ensure you have the following Python libraries installed:

pip install pandas numpy matplotlib seaborn scipy scikit-learn

Usage

1. Place the dataset file (dataset.csv) in the same directory as the notebook.
2. Open and run the LinearRegression.ipynb notebook in Jupyter Notebook or JupyterLab.
3. Follow the structured steps in the notebook to understand and analyze the dataset.

Data Preprocessing

• Reads the dataset from a CSV file
• Checks for missing values and removes them
• Selects numerical columns for analysis
• Generates summary statistics and visualizations

Model Training

• Splits data into training and testing sets
• Trains a Linear Regression model
• Plots regression results and residuals

Evaluation Metrics

• Mean Squared Error (MSE)
• R-squared Score (R²)

Visualization

• Histograms and distribution plots
• Scatter plots with regression lines
• Residual plots