AutoPricePred - Car Price Prediction Project

AutoPricePred is a data analysis and predictive modeling project aimed at understanding the factors that influence car prices and building a predictive model to estimate car prices based on various independent variables. This project is part of a team effort by a group of four members to delve into the realm of data science and machine learning.

Problem Statement

The main objectives of this project are:

Task 1: Data Analysis Report

In Task 1, we perform a comprehensive data analysis on the provided dataset. The dataset contains information about various attributes of cars, such as make, fuel type, engine specifications, dimensions, and more. Through exploratory data analysis (EDA), statistical analysis, and data visualization techniques, we aim to uncover patterns, trends, correlations, and insights within the data. The insights obtained from this analysis will provide a deep understanding of the dataset, which will serve as the foundation for the subsequent predictive modeling.

Task 2: Predictive Model for Car Prices

Task 2 involves the creation of a predictive model that estimates car prices based on the available independent variables. By applying data science techniques and machine learning algorithms, we aim to develop a model that accurately predicts car prices. This model will aid management in understanding how different attributes impact car prices, allowing them to adjust car design, business strategies, and pricing strategies accordingly.

Datasets

The provided dataset consists of the following attributes:

1. symboling: -3, -2, -1, 0, 1, 2, 3.
2. normalized-losses: continuous from 65 to 256.
3. make: alfa-romero, audi, bmw, chevrolet, dodge, honda, ...
4. fuel-type: diesel, gas.
5. aspiration: std, turbo.
6. num-of-doors: four, two.
7. body-style: hardtop, wagon, sedan, hatchback, convertible.
8. drive-wheels: 4wd, fwd, rwd.
9. engine-location: front, rear.
10. wheel-base: continuous from 86.6 to 120.9. ... (and more attributes)

The dataset includes various continuous and categorical attributes that describe different aspects of cars. These attributes will be used to build the predictive model.

Project Structure

The project repository is organized as follows:

• data/: This directory contains the dataset files.
• notebooks/: Jupyter notebooks for data analysis, model development, and evaluation.
• src/: Source code for data preprocessing, model training, and evaluation.
• autoprime_image.jpg: An image related to the project.

Getting Started

To explore and contribute to the AutoPricePred project, you can follow these steps:

1. Clone the repository: git clone https://github.com/Dishantkharkar/AutoPricePred_Capstone.git
2. Navigate to the project directory: cd AutoPricePred_Capstone
3. Set up your development environment and install the required dependencies.
4. Explore the dataset, Jupyter notebooks, and source code in their respective directories.
5. Contribute to the project by creating branches, making changes, and submitting pull requests.

Acknowledgments

We would like to express our gratitude to our team members for their collaboration and efforts in making this project a reality. Additionally, we appreciate the guidance and support from mentors and instructors who have contributed to our learning journey.

Feel free to explore the project, provide feedback, and join us in this exciting data science endeavor!

Result:

Based on Our results, it's clear that the XGBRegressor with hyperparameter tuning outperformed the initial model. The mean absolute error and root mean squared error were both reduced, indicating improved predictive accuracy. The R-squared score also increased slightly, indicating a better fit to the data. Therefore, the final model choice would be the XGBRegressor with hyperparameter tuning due to its superior performance on this dataset.

• Team ID - PTID-CDS-AUG-23-1609
• Team Members mail_id - [email protected], [email protected], [email protected], [email protected], [email protected].

Thank You