car-price-prediction-and-analytics

Built 5 machine learning models to analyze and predict car price, model success, sales trends, market segments, and advertising impact using real-world automotive data. Tools used include Python, scikit-learn, statsmodels, and Excel.

🚗 Predictive Analytics & Segmentation in the Automotive Industry

This project explores advanced ML techniques to predict and understand various aspects of car data: price prediction, model success, clustering, sales forecasting, and mixed-effect modeling.

🧠 Key Models Built:

1. Random Forest Regression – Predict car prices
2. Logistic & Random Forest Classifier – Predict model success
3. KMeans Clustering – Market segmentation
4. ARIMA, Holt’s, Exp Smoothing – Weekly car sales forecasting
5. Mixed Effects Model – Impact of price and advertising on sales

📊 Tools Used:

• Python: pandas, scikit-learn, statsmodels, matplotlib, seaborn
• Jupyter Notebook
• Excel (raw data source)

🧰 Files:

• Car_Assignment.xlsx: Raw dataset
• Rajkumar_car_predictive_model_project.ipynb: ML model code
• Rajkumar_car_predictive_model_presentation.pdf: Final report & visualizations

📌 Summary:

This project reflects real-world automotive business problems and how ML models can help predict outcomes and segment data for business insights.

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👨‍💻 Author: Rajkumar K P

• 📧 Email: [email protected]
• 🔗 LinkedIn