Overview This repository contains an implementation of a Linear Regression model, a fundamental supervised learning algorithm used for predicting continuous numerical outcomes based on one or more predictor variables. The model is trained to establish a linear relationship between the input features and the target variable, optimizing the coefficients to minimize prediction error.

Key Features Data Preprocessing: Includes handling missing values, feature scaling, and categorical variable encoding.

Model Training: Implements the Ordinary Least Squares (OLS) method to estimate model parameters.

Evaluation Metrics: Assesses performance using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²).

Visualization: Provides plots for residual analysis, prediction vs. actual values, and feature importance.

Cross-Validation: Uses k-fold cross-validation to ensure model robustness.

Technologies Used Python (NumPy, Pandas, Scikit-learn, Matplotlib/Seaborn)

Jupyter Notebook (for interactive analysis)

Applications Predictive analysis in finance, economics, healthcare, and marketing.

Trend forecasting and risk assessment.