Description
Q1. MLR Stepwise Regression – Household Expense
500 household were surveyed on their monthly expenses. The data is in the file MLR_MonthlyExpense.
For this, use the monthly payment as the dependent variable.
- Begin with family size and iterative add one variable and estimate the resulting regression equation.
- Does adding any explanatory variable lead to a fall in adjusted R-Squared.
- Which variables are added in the final model?
- Interpret the coefficients, R-squared and standard error of estimate for the final model.
- What result do you get if you use mlxtend stepwise regression?
Q2. MLR Feature Selection – Box Office Revenue Prediction
An industry analyst is interested in building a predictive model to understand the impact of various factors and opening week revenue numbers in the overall collections of a movie (Total revenue).
Box Office collection of Bollywood movies were recorded. The data is provided in file:
MLR_MovieBoxOffice_data.csv.
- Identify the variables that can be used to fit a linear regression model.
- How is the revenue impacted by genre of the movie?
- Does the month have any role to play in movie opening?
- Use any variable reduction technique to fit a model using all relevant variables.
- Do you find any outliers in the dataset? What could be the possible reason for those being outliers?
Q3. MLR – Feature Selection – Building Energy Efficiency
A study looked into assessing the heating load and cooling load requirements of buildings (that is, energy efficiency) as a function of building parameters. We perform energy analysis using 12 different building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valued responses (heating load and cooling load). File: MLR_BuildingEffciency.csv 1) Which features impact the heating load?
2) Which features impact the cooling load?
The data files can be found here: https://github.com/Accelerate–AI/Data–ScienceGlobal–Bootcamp/tree/main/ClassAssignment/Assignment07
