ML Regression Model

A machine learning project demonstrating the implementation, training, and evaluation of multiple regression models using a real-world dataset.

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Overview

This repository showcases a complete regression workflow including data preprocessing, model training, performance evaluation, and comparison across different regression algorithms.

The project is intended for learning, experimentation, and as a reference for regression-based ML pipelines.

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Project Structure

├── ML_Project_Done.ipynb # Jupyter notebook with full workflow
├── ML_Project_Done.py # Python script for model training and evaluation
├── auto-mpg.csv # Dataset
├── model_results_kml.csv # Model evaluation results
└── README.md

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Models Implemented

• Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
• Support Vector Regression (SVR)
• Decision Tree Regression
• Random Forest Regression
• MLP Regressor (Neural Network)

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Workflow

1. Load and preprocess the dataset
2. Perform feature scaling and train-test split
3. Train multiple regression models
4. Evaluate models using standard regression metrics
5. Store and compare results

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Evaluation Metrics

• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R² Score

Evaluation results are saved in model_results_kml.csv.

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Requirements

1. Python 3.x with the following libraries:
2. numpy
3. pandas
4. scikit-learn
5. matplotlib
6. seaborn

Install dependencies:

pip install numpy pandas scikit-learn matplotlib seaborn