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ft_linear_regression

Introduction to machine learning through linear regression implementation.

Overview

This project implements a linear regression algorithm from scratch to predict car prices based on mileage. It demonstrates fundamental machine learning concepts including gradient descent and data normalization.

Features

  • Training program (train.py): Learns the relationship between mileage and price
  • Prediction program (predict.py): Predicts car prices based on trained model
  • Data visualization with regression line
  • Gradient descent optimization
  • Model persistence (saves theta values)

Files

  • train.py: Training algorithm implementation
  • predict.py: Price prediction using trained model
  • data.csv: Training dataset (mileage, price)
  • theta.json: Saved model parameters
  • regression.png: Visualization of the regression line
  • requirements.txt: Python dependencies

Technologies

  • Python: Programming language
  • NumPy: Numerical computations
  • Matplotlib: Data visualization
  • Pandas: Data manipulation (if used)

Usage

Training the Model

python train.py

This reads data.csv, trains the model using gradient descent, and saves parameters to theta.json.

Making Predictions

python predict.py

Enter a mileage value to get a price prediction based on the trained model.

Mathematical Background

The project implements the linear regression formula:

price = θ₀ + θ₁ × mileage

Using gradient descent to minimize the cost function and find optimal θ values.

Learning Objectives

  • Understanding linear regression fundamentals
  • Implementing gradient descent from scratch
  • Data normalization techniques
  • Model evaluation and visualization
  • Machine learning workflow (train/predict)