CarDekho Used Car Price Prediction

Problem Statement :

The primary objective of this project is to create a data science solution for predicting used car prices accurately by analyzing a diverse dataset. The dataset includes various features such as car model, number of owners, age, mileage, fuel type, kilometers driven, additional features, and location. The goal is to build a machine learning model that provides users with accurate predictions for the current valuations of used cars.

This project utilizes the following key technologies and skills:

1.Python 2. Numpy: 3. Pandas: 4. Scikit-Learn: 5. Matplotlib: 6. Seaborn: 7. Pickle: 8. Streamlit:

Dataset Overview :

The dataset is organized into multiple Excel files, with each file representing a specific city. Each Excel file provides a comprehensive overview of cars, including details, specifications, and available features. Below is a guide on how to handle and analyze these files:

Excel File Structure

Each Excel file follows a similar structure with columns providing information about different aspects of the cars. Here's an overview of the typical structure:

1. Car Details Sheet:

   • Overview details about the cars, including ignition type, fuel type, body type, kilometers driven, transmission type, number of previous owners, ownership details, original equipment manufacturer (OEM), car model, year of manufacture, central variant ID, variant name, price, actual price (if available), price saving information (if available), fixed price details, and trending car information.

2. Car Overview Sheet:

   • Provides an overview of the cars, including headings, top details such as registration year, insurance validity, fuel type, etc., and additional bottom data (if available).

3. Car Feature Sheet:

   • Contains information about the features of the cars, including headings, top features, and detailed feature information categorized by comfort, interior, exterior, safety, etc.

4. Car Specs Sheet:

   • Presents specifications of the cars, including headings, top specifications like mileage, engine, max power, torque, etc., and detailed engine and transmission information, dimensions, capacity, and miscellaneous details.

Data Extraction :

1. New Car Detail: To extract specific features from the 'new_car_detail' column in the dataset, create new columns for 'Fuel_Type,' 'Body_Type,' 'Mileage_km,' 'Transmission type,' 'Number owner,' 'OEM,' 'Car model,' 'modelYear,' 'centralVariantId,' 'variantName,' and 'price'
2. New Car Specs : To extract specific features from the 'new_car_detail' column in the dataset reate new columns for mileage, engine, max power, torque,Seats.

Handling Null Values: The dataset may contain missing values that need to be addressed. The choice of handling these null values, whether through mean, median, or mode imputation, depends on the nature of the data and the specific feature.

Encoding and Data : Encode categorical variables using suitable techniques, such as ,mapping method based on their nature and relationship with the target variable.

Outliers Handling: Outliers can significantly impact model performance. We tackle outliers in our data by using the Interquartile Range (IQR) method.

Regression:

Algorithm Selection: After thorough evaluation, Random Forest Regressor, demonstrate commendable testing accuracy. Upon checking for any overfitting issues in both training and testing, both models exhibit strong performance without overfitting concerns. I choose the Random Forest Regressor for its ability to strike a balance between interpretability and accuracy, ensuring robust performance on unseen data.

Hyperparameter Tuning with GridSearchCV and Cross-Validation: To fine-tune our model and mitigate overfitting, we employ GridSearchCV with cross-validation for hyperparameter tuning. This function allows us to systematically explore multiple parameter values and return the optimal set of parameters. {'max_depth': 20, 'max_features': log2, 'min_samples_leaf': 1, 'min_samples_split': 2,n_estimators =50}.

Contributing

Contributions to this project are welcome! If you encounter any issues or have suggestions for improvements, please feel free to submit a pull request.

License:

This project is licensed under the MIT License. Refer to the LICENSE file for more details.

Contact

📧 Email: thangamani1128@gmail.com

For any further questions or inquiries, feel free to reach out. We are happy to assist you with any queries.