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+# ft_linear_regression
+
+A simple linear regression project in Python for educational purposes. The goal is to predict the price of a car based on its mileage using linear regression, and to visualize the results and the model's confidence.
+
+## Features
+
+- Reads car mileage and price data from CSV datasets.
+- Learns optimal parameters (`theta0`, `theta1`) using gradient descent.
+- Normalizes input data for effective training.
+- Exports learned parameters to a CSV file.
+- Predicts car prices for any given mileage.
+- Calculates R² (coefficient of determination) to indicate model confidence.
+- Visualizes the regression line, cost function evolution, and confidence score.
+
+## Project Structure
+
+```
+ft_linear_regression/
+│
+├── datasets/
+│   ├── data.csv                # Default dataset
+│   ├── big_data.csv
+│   ├── negative_data.csv
+│   ├── nonlinear_data.csv
+│   ├── perfectpositive_data.csv
+│   ├── small_data.csv
+│   └── variance_data.csv
+│
+├── model.py                    # Main file: trains, saves, and visualizes the model
+├── estimate.py                 # Script to estimate a price given a mileage
+├── confidence.py               # Calculates R² confidence score
+└── thetas.csv                  # Saved learned parameters after running model.py
+```
+
+## Usage
+
+### 1. Train the Model
+
+Simply run:
+
+```bash
+python model.py
+```
+
+- Trains the linear regression model on `datasets/data.csv`.
+- Saves the learned parameters (`thetas.csv`).
+- Displays a plot with the data, regression line, confidence score (R²), and cost vs epochs.
+
+### 2. Estimate Car Price
+
+After training, estimate a car price by mileage:
+
+```bash
+python estimate.py
+```
+
+- Enter the mileage when prompted.
+- Outputs the predicted price.
+
+### 3. Confidence Score
+
+The R² (confidence) score is printed on the regression plot, indicating how well the model fits the data.
+
+## Custom Datasets
+
+You can swap out the dataset by changing the `data_path` variable in `model.py` or by providing your own CSV file in the `datasets/` folder. The CSV must have columns: `km,price`.
+
+## Requirements
+
+- Python 3.x
+- pandas
+- numpy
+- matplotlib
+
+Install dependencies with:
+
+```bash
+pip install pandas numpy matplotlib
+```
+
+## Notes
+
+- The model uses simple linear regression (1 feature: km).
+- Data is normalized for stable and faster learning.