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

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

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 Science Project (Logistic Regression M7)

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.

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.

Data Science 2023-24

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.

Bank Customer Churn Prediction with MLflow and MLOps

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.

End-to-end Predictive Analytics ML Project

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

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

Predictive models identifying the major factors contributing to employee attrition and the state of attrition .

Assignments for the Computational Intelligence course, Department of Computer Science and Engineering, University of Ioannina.