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 Enthusiast | Predictive Modeler | Turning Insights into Strategies

Autoregressor: simple and robust time series model selection

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.

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.

Data Science Project (Logistic Regression M7)

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

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Data Science 2023-24

Bank Customer Churn Prediction with MLflow and MLOps

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.

This repository contains the Plant Ecosystem Analysis project, utilizing R to investigate the relationship between native plant species richness and ecological factors within diverse geographical gradients.

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

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.

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