Data Science - Multi Linear Regression Work

Multiple Regression model building with Sklearn and statsmodels and analysis of relevant predictors using P-values and VIF

📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.

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 and Consider only the some columns and prepare a prediction model for predicting Price.

This Jupyter notebook demonstrates a dimension reduction method by dropping high variance-inflation-factor (VIF) features recursively.

Regression models for predicting customer acquisition costs (CAC) and the effectiveness of univariate and lasso feature selection techniques in improving the accuracy.

Prevendo Customer Churn em Operadoras de Telecom

Logistic regression model build on lead score data to score leads on the basis of their probability of conversion.

First project implementing Logistic Regression

Insurance charges calculation

R programming - Statistical Modelling II

Classification problem using multiple ML Algorithms

Analysis will help Jamboree in understanding what factors are important in graduate admissions and how these factors are interrelated among themselves. It will also help predict one's chances of admission given the rest of the variables.

This project predicts stock price of Infosys using machine learning. It involves data collection, data preprocessing, feature engineering, model building, hyperparameter tuning and model evaluation.

By leveraging ensemble learning, this program can be used to analyze the Linkage Disequilibrium between SNPs in each Indonesian rice chromosomes. Developed using Python 3.9.12.

Analyze the data of INN Hotels to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.

Forecasting the likelihood of a customer defaulting their auto loan using classification models

Prediction of Miles per gallon (MPG) Using Cars Dataset