Focused on enhancing the reliability of predictive models, this project applies both traditional Bootstrap and Bag of Little Bootstraps (BLB) techniques, optimized for large datasets. We conduct a …

A comprehensive ETL pipeline and sales analysis project leveraging Microsoft Azure and PySpark, designed to optimize e-commerce sales by providing actionable insights through detailed data analysis.

This project demonstrates an ETL pipeline using Microsoft Azure for IMDb Movie Rating Dataset analysis. It covers data extraction from Azure Blob Storage, transformation with Azure Databricks, and …

The project uses various classification models like Logistic Regression, Random Forest, and Decision Tree, to analyze and predict whether a borrower will default on a loan based on features to assi…

Predict flight prices using machine learning. This project involves data preprocessing, exploratory analysis, feature engineering, and model training with various regression algorithms to accuratel…

A comprehensive analysis of customer retention in online food sales using various machine learning models and data preprocessing techniques.

This is a customer churn prediction project using machine learning algorithms like Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machine, XGBoost, and Gradient Boosting. T…

Dive into the world of baseball with an in-depth analysis of players using the Lahman Baseball Database. Explore comprehensive player statistics and insights to gain a deeper understanding of playe…

Made by Rohit, Sabina and Subham