LINEAR REGRESSION

Table of Content

1. Problem Statement
   
2. Data Loading and Description
   
3. Exploratory Data Analysis
   
4. Introduction to Linear Regression
   
   • 4.1 Linear Regression Equation with Errors in consideration
     
     • 4.1.1 Assumptions of Linear Regression
       
   • 4.2 Preparing X and y using pandas
     
   • 4.3 Splitting X and y into training and test datasets
     
   • 4.4 Linear regression in scikit-learn
     
   • 4.5 Interpreting Model Coefficients
     
   • 4.3 Using the Model for Prediction
     
5. Model evaluation
   
   • 5.1 Model evaluation using metrics
     
   • 5.2 Model Evaluation using Rsquared value.
     
6. Feature Selection
   
7. Handling Categorical Features
   

1. Problem Statement

Sales (in thousands of units) for a particular product as a function of advertising budgets (in thousands of dollars) for TV, radio, and newspaper media. Suppose that in our role as Data Scientist we are asked to suggest.

• We want to find a function that given input budgets for TV, radio and newspaper predicts the output sales.

• Which media contribute to sales?

• Visualize the relationship between the features and the response using scatter plots.