batman

car_price_guesser/README.md

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+# Car Price Guesser
+
+## Purpose
+This project is a learning exercise for building machine learning models. The goal is to predict the price of a car based on its attributes (e.g., Year, Mileage, Brand, Model, etc.) using a dataset of New York cars.
+
+## Getting Started
+
+This project uses `uv` for dependency management.
+
+### Prerequisites
+- Python 3.14+
+- `uv` installed
+
+### Installation
+1. Clone the repository (if applicable).
+2. Install dependencies:
+   ```bash
+   uv sync
+   ```
+
+## Usage
+
+### 1. Train the Model
+To train the machine learning model (Linear Regression baseline):
+```bash
+uv run train_model.py
+```
+This script will:
+- Load the `New_York_cars.csv` dataset.
+- Preprocess the data (handle missing values, encode categorical features).
+- Train the model.
+- Save the trained model to `car_price_model.joblib`.
+- Output performance metrics (MSE, R2 Score).
+
+### 2. Make Predictions
+You can use the trained model to guess the price of a car.
+
+**Interactive Mode:**
+```bash
+uv run predict.py
+```
+Follow the prompts to enter car details.
+
+**Command Line Arguments:**
+```bash
+uv run predict.py <Year> <Mileage> <Brand>
+```
+Example:
+```bash
+uv run predict.py 2020 50000 Toyota
+```
+
+**Random Row Verification:**
+To pick a random car from the dataset and compare the model's prediction against the actual price:
+```bash
+uv run predict.py --random
+```