This project analyzes car prices using features like make, model, year, mileage, horsepower, and more. The goal is to understand key drivers of car prices and predict pricing using machine learning and data visualization.
- Size: 11,914 rows, 16 columns.
- Key Features: Make, Model, Year, Engine HP, Fuel Type, Transmission, Price, etc.
- Top Brands: Chevrolet, Ford, Toyota, Volkswagen, Nissan.
- Price Range: $200 to $2,065,902.
- Top Manufacturing Years: 2015, 2016, 2017.
- Expensive Cars: Maybach, Lamborghini, Bugatti.
- Horsepower: Most cars range between 130-310 HP.
- Price Trends: Higher horsepower, cylinders, and newer models increase prices.
- Fuel Efficiency: Larger cars have lower city MPG.
- Price Drivers: Horsepower, manufacturing year, and fuel type significantly impact car prices.
- Market Trends: Compact and midsize cars dominate the market, with sedans and SUVs being the most popular styles.
- Fuel Efficiency: Smaller cars have better city MPG, while larger cars are less fuel-efficient.
- Top Brands: Chevrolet, Ford, and Toyota have the highest number of cars in the dataset.
- Target High-Performance Segments: Focus on marketing high-horsepower and luxury cars (e.g., Lamborghini, Bugatti) to premium buyers.
- Optimize Fuel Efficiency: Promote smaller, fuel-efficient cars to eco-conscious consumers.
- Leverage Popular Models: Increase inventory for popular models from top brands like Chevrolet, Ford, and Toyota.
- Pricing Strategy: Use predictive models to set competitive prices based on features like horsepower, year, and fuel type.
- Expand Electric and Hybrid Options: Invest in electric and hybrid cars to cater to the growing demand for sustainable vehicles.
- Python Libraries: Pandas, NumPy, Matplotlib, Seaborn, Plotly.
- Tools: Jupyter Notebook,GitHub.