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# ft_linear_regression
A simple linear regression implementation using gradient descent to predict car prices based on mileage.
## Requirements
- Python 3
- matplotlib (for visualization only)
```
pip install matplotlib
```
## Usage
### Train the model
```
python3 train.py <learning_rate> <iterations>
```
Example:
```
python3 train.py 1.0 1000
```
This trains the model on `data.csv` and saves the resulting parameters (θ0, θ1) to `thetas.csv`.
### Predict a price
```
python3 predict.py
```
Prompts for a mileage value and outputs the estimated price. Loops until Ctrl+C.
If no trained model is found, θ0 and θ1 default to 0.
### Visualize
```
python3 visualize.py
```
Displays a scatter plot of the dataset. If a trained model exists, the regression line is drawn on top.
## How it works
The model fits a linear function:
```
estimatePrice(mileage) = θ0 + θ1 * mileage
```
Parameters are found via gradient descent with min-max normalization on the input data. After training, thetas are denormalized so they work directly on raw mileage values.
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