Description

Regression is a supervised learning technique in the field of machine learning, specifically used for predicting numerical data. Linear regression, also known as least squares regression, is a method used to model the relationship between a dependent/target variable (Y) and one or more independent/predictor variables (X). Linear regression is considered one of the simplest algorithms in machine learning. In this portfolio, we will predict car prices using multiple linear regression with three variables. The variables will be determined based on their highest correlation with the price.

The steps involved in the process are as follows:

• Importing Libraries
• Importing Dataset
• Exploratory Data Analysis
• Data Pre-processing
• Data Visualization
• Training/Testing Data
• Model Training
• Model Evaluation

A. Pre-processing

• Import dataset and library 

• Reading data using pandas

• Check the amount of missing data in each column

• Replace the symbol “?” with NaN and delete NaN values

• Changing variables with numeric values ​​from integer type to float and normalizing data with Standardscale

• Look for the correlation value between each variable and the target variable and display the correlation value in order

B. Modelling

• Perform training data with split test data and create multiple linear regression models and train models with training data

C. Evaluation

• Calculating the mean squared error (MSE) and calculating the coefficient of determination (R-squared)