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

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.

Data Enthusiast | Predictive Modeler | Turning Insights into Strategies

Data Science Project (Logistic Regression M7)

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.

Autoregressor: simple and robust time series model selection

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

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

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.

This GitHub repository hosts code for analyzing time series air pollution data in the United States. Utilizing a dataset from the U.S. EPA, the code conducts preprocessing, exploratory data analysis, feature selection, and model evaluation to uncover insights into air pollutant trends and correlations across various locations.

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End-to-end Predictive Analytics ML Project

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

This project aims to predict the success of mobile applications on the Google Play Store using machine learning. By analyzing various features such as app category, rating, number of installs, size, type (free or paid), and content rating, the model can classify whether an app is likely to be successful or not.

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.

Bank Customer Churn Prediction with MLflow and MLOps

Data Science 2023-24

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