A SciKit-Learn style feature selector using best subsets and stepwise regression.

Stepwise regression fits a logistic regression model in which the choice of predictive variables is carried out by an automatic forward stepwise procedure.

This is a group project for MTH416A: Regression Analysis at IIT Kanpur

Design of a model with appropriate feature engineering, that estimates one target temperature rotor temperature (“pm”) in a causal manner, based on the data set that records the rotor temperatures of a permanent magnet synchronous motor (PMSM) in real-time

Predictive modeling

Classifying customers using Logistic Regression

Analysis of real estate sales data. Tasks include understanding dataset structure, variable conversion, descriptive analysis, pairwise comparisons, linear relationship analysis, multiple regression modeling, feature selection using stepwise methods, final model summary, assumptions checking, and LASSO variable selection. Results are documented.

EPS forecast with Lasso, Elastic Net and multiple linear regression

This project was made as part of the Statistical and Machine Learning Approaches for Marketing course at IESEG School of Management

The given dataset contains electricity consumer household information. This information has been used to predict the amount to be paid by the consumer with the help of regression model selection and validated with feature importance.

Lead scoring is an effective lead prioritization method used to rank prospects based on the likelihood of converting them to customers. This repository aimed to develop an automatic lead scoring through logistic regression technique. Stepwise selection approach is used to identify and select important variables for the model.

Multiple regression model with stepwise elimination using SAS

Applying stepwise selection in R Studio to forecast credit balances and stock market behavior.