Regression model building and forecasting in R

This repository contains machine learning projects. The code for each project is provided, and the explanations can be found in the ReadMe.md file of each project !

End-to-end Predictive Analytics ML Project

Data Science 2023-24

Data Enthusiast | Predictive Modeler | Turning Insights into Strategies

Analyzed customer churn using transaction data. Built ML model to predict lapses. Dataset includes customer status, collection/redemption info, and program tenure. Delivered business presentation outlining modeling approach, findings, and churn reduction strategies.

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Full ENM framework with improved tuning, model performance assessment and selection. Based on MaxEnt, but transferrable to any presence-only ML algorithm.

Solution in the form of a tutorial article wherein the key decisions made in conducting a CFA are validated through recent literature and presented within a dynamic document framework.

End-to-end machine learning pipeline to predict daily sales for Rossmann stores using historical, promotional, and store metadata.

Autoregressor: simple and robust time series model selection

Data Science Project (Logistic Regression M7)

Bank Customer Churn Prediction with MLflow and MLOps

A modular AutoML engine for automated model training, tuning, and benchmarking.

Time series analysis on the United States Housing Price Index data using ARIMA models

A Spark Streaming and Kafka-based project for processing health data in real-time. Includes a machine learning pipeline for predictions, Dockerized infrastructure, and scripts for data ingestion, model training, and streaming pipelines.

Detecting fraudulent financial transactions using machine learning. Includes data preprocessing, EDA, model training - Logistic Regression and evaluation using precision, recall, and ROC-AUC to build an accurate fraud detection system.

End-to-end Predictive Analytics ML Project

A machine learning project to predict medical insurance charges based on user features like age, BMI, and smoking status. Used Gradient Boosting Regressor for accurate cost prediction. Streamlit app enables real-time, interactive user input and predictions. Built with Python, Pandas, scikit-learn, and joblib.

Using linear regression models to assess the most important aspects of winning baseball