🚗 Car Price Prediction Using Multiple Linear Regression 🚗

Welcome to the Car Price Prediction repository! This project focuses on predicting car prices using multiple linear regression techniques. The goal is to model the price of cars based on various features to understand the factors influencing car pricing.

📚 Project Overview

In this project, we use a dataset containing information about different cars to build and evaluate a multiple linear regression model. The model helps in predicting car prices by analyzing the relationships between the price and other car attributes.

📂 Repository Contents

• Jupyter Notebook: A well-commented notebook that details the entire process of building and evaluating the multiple linear regression model.
• CSV Files: Contains the dataset used for training and evaluating the model.
• PDF Document: A PDF with important concepts related to multiple linear regression to help you understand the theoretical background.

🗂️ Dataset Description

The dataset used in this project has the following columns:

1. Car_ID: Unique id of each observation (Integer) 🚀
2. Symboling: Assigned insurance risk rating. A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe. (Categorical) 🛡️
3. carCompany: Name of the car company (Categorical) 🏢
4. fueltype: Car fuel type (gas or diesel) (Categorical) ⛽
5. aspiration: Aspiration used in a car (Categorical) 🌬️
6. doornumber: Number of doors in a car (Categorical) 🚪
7. carbody: Body type of the car (Categorical) 🚗
8. drivewheel: Type of drive wheel (Categorical) 🚙
9. enginelocation: Location of the car engine (Categorical) 🏎️
10. wheelbase: Wheelbase of the car (Numeric) 📏
11. carlength: Length of the car (Numeric) 📐
12. carwidth: Width of the car (Numeric) 📏
13. carheight: Height of the car (Numeric) 📏
14. curbweight: The weight of a car without occupants or baggage (Numeric) ⚖️
15. enginetype: Type of engine (Categorical) 🔧
16. cylindernumber: Number of cylinders in the car (Categorical) 🔩
17. enginesize: Size of the engine (Numeric) 🔧
18. fuelsystem: Fuel system of the car (Categorical) ⛽
19. boreratio: Bore ratio of the car (Numeric) 📏
20. stroke: Stroke or volume inside the engine (Numeric) 📏
21. compressionratio: Compression ratio of the car (Numeric) 🧮
22. horsepower: Horsepower (Numeric) 🏋️
23. peakrpm: Peak RPM of the car (Numeric) ⏱️
24. citympg: Mileage in the city (Numeric) 🚦
25. highwaympg: Mileage on the highway (Numeric) 🚗
26. price: Price of the car (Dependent Variable) 💵

🔍 Business Problem

A Chinese automobile company, Geely Auto, is planning to enter the US market. They want to understand the factors that affect car pricing in the American market compared to the Chinese market.

Business Goals

• Identify Significant Variables: Determine which variables are most significant in predicting car prices.
• Understand Pricing Dynamics: Use the model to understand how car prices vary with different independent variables.
• Strategic Planning: Utilize the model to make informed decisions regarding car design, pricing, and business strategies in the new market.

🛠️ Requirements

• Python 3.x
• Jupyter Notebook
• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn

📑 Documentation

For detailed explanations and comments on the notebook, refer to the well-documented Jupyter Notebook included in the repository.

📬 Contact

For any questions or feedback, feel free to reach out to me at akashanandani.56@gmail.com