Telecom Churn Prediction Model using ANN

Analyzing 1,470 IBM employees to find why they leave — Python, SQL, Interactive Dashboard

📊 Interactive Tableau dashboard analyzing 1,470 IBM employees — uncovering attrition risks, pay gaps, and workforce patterns through data storytelling.

Responsible AI dashboard for employee attrition analysis using ML, fairness metrics, and explainability

Power BI workforce analytics dashboard to analyze employee attrition and retention risk.

IBM HR Analytics Dashboard - A comprehensive Power BI dashboard analyzing employee attrition patterns across departments, demographics, and tenure. Features interactive visualizations revealing key insights into workforce retention challenges, with a 16.12% attrition rate affecting 237 employees across various job roles and experience levels.

This project explores customer churn trends for a company in California using an IBM dataset. Built in a Jupyter Notebook, it employs pandas, NumPy, matplotlib, seaborn, plotly, and scipy to clean, analyze, and visualize data. SKlearn predictive model was trained using three main algorithms Decision Tree, Naive Bayes, and Random Forest

Attrition_causes

XYlofy-AI Internship Week 2: Predicts whether an employee is likely to leave the company using HR analytics data.

Excel analytics project uncovering churn patterns across 7,043 telecom customers — month-to-month contracts churn at 42.7% vs 2.8% for two-year plans. Features an interactive customer lookup tool, 5 summary tables, and a full KPI dashboard built with INDEX/MATCH, nested IFS, COUNTIFS, and AVERAGEIFS.

An automated analytics engine built to process the IBM Telco Customer Churn dataset. It identifies leading operational indicators of customer drop-off and generates visual dashboards to guide proactive retention strategies.

IBM Telco customer churn prediction