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Auto Analytics: Advanced Estimation & Deployment 🛠️

car_price_prediction

This project focuses on using machine learning algorithms to estimate car prices. Various regression algorithms were implemented, including:

  • Linear Regression
  • Lasso Regression
  • Ridge Regression
  • Decision Tree
  • Random Forest
  • XGBoost

Model evaluation, grid-search, and cross-validation were performed, resulting in the following scores:

Model R2 MAE RMSE MAPE
XGBoost 0.921 2123.94 3373.07 0.132
Random Forest 0.921 2252.57 3374.97 0.150
Lasso 0.831 2818.00 4954.25 0.192
Linear Regression 0.830 2818.65 4957.25 0.192
ElasticNet 0.830 2817.18 4959.12 0.192
Decision Tree 0.816 3467.44 5157.75 0.221

The final models chosen were Random Forest and XGBoost. Feature importance was determined separately for each model to reduce feature counts. The models were saved using Pickle and converted into a Streamlit file for deployment outside the notebook environment. The Streamlit file was published on both AWS EC2 instances and the Streamlit website, enabling users to make predictions interactively.

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