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

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

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.

project for the practice of webscraping, APIs, machine learning, feature selection

Build a classification model for reducing the churn rate for a telecom company

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

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts. They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to …

Regression Model using regularisation to predict the actual value of the prospective properties and decide whether to invest in them or not.

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

In this project we built a model to predict whether a person will remain in a hypothetical trade union called the United Data Scientists Union (UDSU).

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.

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

Building a model to predict demand of shared bikes. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels.

[Codenation] Feature Selection w/ Recursive Feature Elimination (aka RFE) and Dimensionality Reduction using Principal Component Analysis (aka PCA)