Feature Selection Examples

This is a project demonstrating Logistic Regression method using Python. An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.

A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price.

This assignment is a programming assignment wherein we have to build a multiple linear regression model for the prediction of demand for shared bikes.

Build a multiple linear regression model for the prediction of demand for shared bikes.

We are required to build a regression model using regularization in order to predict the actual value of the prospective properties and decide whether to invest in them or not.

Objective: To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.

A comprehensive ML framework to detect heart disease using the Cleveland dataset

Bank Customer Behaviour Prediction

Explored data using data visualisation and exploratory data analysis. Used Logistic Regression to create a basic prediction model. Improved model using recursive feature elimination.

HDB flats resale price prediction. Neural network in Python. Machine learning models in R. Data pre-processing, feature engineering and feature selection mainly in R.

Building logistic classifier model (RFE)

Student grade prediction using different machine learning models

Customer Attrition Prediction with Python

Data warehouse and analytics project to predict bike theft prediction from TPS data

This project tackles BoomBikes' post-Covid revenue decline by predicting shared bike demand after the lockdown. A consulting company identifies key variables impacting demand in the American market. The goal is to model demand, aiding BoomBikes in adapting its strategy to meet customer expectations and navigate market dynamics.

Hospitals contain large databases. We can use that data to discover new useful and potentially life saving knowledge. Here we use datamining especially to predict type 2 diabetes mellitus.Predicting the percentage of chance of occurrence of Diabetes mellitus type 2 with less time complexity and high accuracy.

In this repository, We are going to be working on upGrad's lead score data set and to see how can we solve the problem using Exploratory data analysis techniques and using supervised machine learning models.

upGrad's Telecom Churn Case Study hosted on Kaggle platform