Problem Statement:

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts.

They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know:

• Which variables are significant in predicting the price of a car?
• How well those variables describe the price of a car?

Based on various market surveys, the consulting firm has gathered a large dataset of different types of cars across the Americal market.

Business Goal:

we are required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with respect to the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. The model will act as a better medium for management to understand the pricing dynamics of a new market.

Following steps will be followed to reach our goal:

1. Importing libraries
2. Reading the concerned dataset
3. Data Understanding
4. Data handling
5. Data visualization
6. Data preparation
7. Splitting the Data and feature scaling
8. Building a linear regression model
9. Residual analysis of the train data
10. Making Predictions Using the Final Model
11. Model Evaluation
12. Conclusion