The aim of this data science project is to predict the price of used cars in major Indian metro cities. The prediction is based on various car features, including the car's manufacturer, model, variant, fuel type, color, kilometers driven, body style, transmission type, manufacture date, model year, CNG kit availability, ownership history, dealer details, and a quality score. The project utilizes data analysis and machine learning techniques to provide insights and price predictions, ultimately helping both buyers and sellers in the used car market.
Here is a detailed data dictionary for the columns in the "Indian IT Cities Used Car Dataset 2023":
| Column Name | Description |
|---|---|
| ID | Unique ID for each listing |
| Company | Name of the car manufacturer |
| Model | Name of the car model |
| Variant | Name of the car variant |
| Fuel Type | Fuel type of the car |
| Color | Color of the car |
| Kilometer | Number of kilometers driven by the car |
| Body Style | Body style of the car |
| Transmission Type | Transmission type of the car |
| Manufacture Date | Manufacture date of the car |
| Model Year | Model year of the car |
| CngKit | Whether the car has a CNG kit or not |
| Price | Price of the car |
| Owner Type | Number of previous owners of the car |
| Dealer State | State in which the car is being sold |
| Dealer Name | Name of the dealer selling the car |
| City | City in which the car is being sold |
| Warranty | Warranty offered by the dealer |
| Quality Score | Quality score of the car |
In the course of this data science project aimed at predicting used car prices in major Indian metro cities, several significant insights have emerged through exploratory data analysis and machine learning techniques. These insights shed light on both the demand and pricing dynamics within the Indian used car market, offering valuable information to both prospective buyers and sellers.
Demand and Price Trends: One of the most notable observations is the strong correlation between demand and price within the used car market. Lower-priced used cars enjoy a significantly higher demand, indicating a preference among consumers for budget-friendly options. In contrast, luxury car manufacturers such as MG, Mercedes Benz, BMW, Volvo, and KIA command the highest prices. This highlights a trend where consumers are more inclined to opt for new luxury cars instead of pre-owned ones.
Fuel Type Influence: Fuel type plays a pivotal role in pricing, with the market predominantly comprising petrol and diesel cars. Diesel cars, while not overwhelmingly prevalent, do exhibit slightly higher pricing compared to their petrol counterparts.
Color Preferences: Car color also contributes to the pricing dynamics, as common colors like white, grey, silver, and black are in high demand, while more exotic colors such as burgundy, riviera red, dark blue, and black magic tend to fetch higher prices in the used car market.
Kilometer Reading Impact: An analysis of the odometer readings reveals that most cars listed for sale have covered fewer than 10,000 kilometers. Unsurprisingly, cars with lower mileage tend to command higher prices, underlining the importance of mileage in pricing determination.
Body Style Preferences: Body style preferences vary among consumers, with hatchbacks, SUVs, and sedans being the top choices. Conversely, MPVs and luxury SUVs are typically more expensive.
Age and Resale Value: The age of a car is a pivotal factor influencing its resale value. Newer cars, typically those under 5 years old, tend to have higher prices, reflecting a demand for relatively younger used vehicles.
Geographic Price Variation: Significant variation in car prices across different regions has been observed. Delhi, Maharashtra, and Rajasthan stand out as the top states with the highest car prices. Additionally, specific dealers like Car Estate, Star Auto India, and Car Choice consistently list cars at higher prices.
Owner Type and Warranty Impact: Buyers often prefer cars with a 1st owner type, leading to increased demand and higher prices for such vehicles. Additionally, cars offering warranties tend to command slightly higher prices, as warranties provide assurance to potential buyers.
Quality Score and Pricing: The quality score of a car has a direct bearing on its price. Cars with higher quality scores generally command higher prices in the market.
In terms of machine learning models, decision tree regressor and random forest regressor models were employed to predict car prices. Among these, the random forest regressor model demonstrated superior performance. An examination of feature importance revealed that car age, body style, and car manufacturer are the key drivers affecting car prices.
This data-driven project equips both buyers and sellers in the Indian used car market with valuable insights, empowering them to make informed decisions and facilitating more accurate price predictions.