Car Price Prediction using Linear Regression

Problem Statement:

Geely Auto, a Chinese automobile company, plans to enter the US market by setting up local manufacturing to compete with US and European car manufacturers. They hired an automobile consulting company to identify the factors influencing car pricing in the American market, which may differ from the Chinese market.

Preparation : The dataset includes a CarName variable, which contains the car company's name and the car model. Use only the car company name as an independent variable for model building.

Objectives:

Identify significant variables in predicting car prices. Assess how well these variables describe car prices.

Business Goals:

Develop a model to predict car prices based on available independent variables. This model will help management understand price variations and inform design and strategy decisions to meet specific price targets. Additionally, it will provide insights into the pricing dynamics of the US market.

Solution:

A Linear Regression Model will be used to identify significant variables affecting car prices and assess their predictive power. The model will help understand price variations and inform strategic decisions for entering the US market.

What is Linear Regression?

Linear regression is a statistical technique used to model and analyze the relationship between a dependent variable and one or more independent variables. It predicts the value of the dependent variable based on the linear combination of the independent variables. The goal is to find the best-fitting line that minimizes the sum of the squared differences between the observed and predicted values.

Libraries used:

1. Numpy (for data manipulation)
2. Pandas (for data manipulation)
3. Matplotlib (for data visualization)
4. Seaborn (for data visualization)
5. Statsmodels (for data modeling)
6. Scikit-Learn (for data modeling)

Contents:

• Importing the required libraries
• Importing and reading dataset
• Understanding and Visualizing the data
• Exploratory Data Analysis (EDA)
• Preparing the data for modeling
• Training the Model
• Predicting the training data
• Residual Analysis
• Predicting the test data
• Getting the model accuracy
• Sharing Solution to the objectives