JVedant / Car_Price-Prediction

Car_Price-Prediction

An app made using Flask to predict the car price using details like year of purchase, purchasing price, number of owners, etc.

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Technique used:

• Decision tree Regressor
• Random Forest Regressor

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the template used can be found on index.html

Also the packages used to run the project can be found in requirements.txt. You can install it using "pip install -r requirements.txt"

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the dataset used here is very small and can be found at car data.csv

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How to run:

1. run create_folds.py from src to create folds in the dataset.
2. run encode_data.py from src to encode the data using pandas dummmy method and to select the features.
3. run model.py to train the model and dump it using joblib at model.pkl
   • you can check the model.pkl also, it is trained on random forest regressor and is shown in EDA.ipynb
4. run app.py