A machine learning model designed to predict the selling price of used cars based on various features such as brand, age, mileage, and fuel type.

• Source: Car price prediction Dataset (provided by CodeAlpha)
• Features: Year, Present Price, Kms Driven, Fuel Type, Selling Type, Transmission, Owner.
• Target: Selling Price

1. Data Loading: Handled CSV data using Pandas.
2. Preprocessing: - Calculated Car_Age to represent the car's age.
   • Encoded categorical features (Fuel Type, Transmission, etc.) into numeric values.
3. Model Selection: Used Random Forest Regressor for its high accuracy and robustness.
4. Evaluation: Achieved a high R2 score, indicating a very reliable model.

• Model R2 Score: 0.9625
• Accuracy: 96.26%

• Pandas & NumPy
• Scikit-learn
• Matplotlib & Seaborn

Syed Fazeel Ahmed — Data Science Intern at CodeAlpha