Car Price Prediction Using Random Forest

Built a machine learning model to predict used car prices based on technical features, brand, and vehicle specifications. Performed data cleaning, feature engineering, and applied Random Forest Regression to achieve reliable price estimates.

Tools & Technologies Used

• Python
• Pandas
• Scikit-learn (Random Forest Regressor)
• Tableau
• Jupyter Notebook

File Structure

.
├── Car_Price_Prediction_Model.ipynb   # Data analysis & model notebook
├── Car_Price_Tableau.twb              # Tableau dashboard for visualization
├── Car_Price_Prediction_Presentation.pptx  # Presentation deck
├── Resources/
│   └── CarPrice_Assignment.csv        # Original dataset

Skills Demonstrated

• Data cleaning and preprocessing of raw automotive data
• Exploratory Data Analysis (EDA) and feature importance analysis
• Building and tuning Random Forest Regressor
• Visualizing key data trends and model insights in Tableau
• Communicating project findings through presentation

Key Findings

• Analyzed 2,000+ used car listings with brand, engine size, transmission, and mileage data.
• Random Forest model achieved R² score of 0.959, outperforming the project benchmark of 0.80.
• Identified brand and mileage as the two strongest predictors of used car price.
• Tableau dashboard visualized price trends across brands, transmission types, and engine size.