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.

Logistic regression model focusing on significant features extraction using different methods

Customer Attrition Prediction with Python

isye6414_group_project

Boston house price prediction using Linear Regression.

Verificar Hipóteses da Regressão Linear

Lead_Scoring Case Study using Logistic Regression

an R project of manipulating and fittingdata into regression with 95.5% R-Square, involving Automated Selection, detecting outliers, influential observations and multicollinearity

Statistical study and analysis of Multiple linear regression model using Scalation tool

Linear Regression to identify the important physicochemical properties of the substrate that influence the aerial biomass production in the Cape Fear Estuary.

This is a Linear Regression Project, we have created multiple models using different feature selection techniques to predict the future demands for a bike company.

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.

Quantify multicollinearity by VIF

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

A boat-sharing system is a service in which boats are made available for city tour. Required to model the demand for open boats with the available independent variables. 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 le…

Consider a real estate company that has a dataset containing the prices of properties in the Delhi region. It wishes to use the data to optimise the sale prices of the properties based on important factors such as area, bedrooms, parking, etc.

Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in t