This project is aimed at building a ML model to predict the price of an used car. The project includes data preprocessing, exploratory data analysis (EDA), model training, and deployment using Docker.

The dataset used in this project is stored in the data directory, which include these files:

• train.csv data for training the model
• test.csv test data
• sample_submission.csv result of the test data

The project's main analysis and development are documented in the used_car_prices.ipynb

train.py script is used to train the machine learning model. The trained model is saved in a binary file called model.bin

Script for serving the model through Flask.

First create the venv and install the packages:

python -m venv my-venv
source my-venv/bin/activate
pip install --no-cache-dir -r requirements.txt

Next run the train.py:

python train.py

Build docker image:

docker build -t predict-car-price . --progress=plain

Run the image:

docker run --rm -p 5000:5000 predict-car-price