AI Developer Insights Platform
This project demonstrates an end-to-end Machine Learning and Data Analytics workflow to uncover deep demographic and income patterns across global developers. By processing massive datasets, the pipeline extracts actionable business intelligence through clustering and classification.
Core Features
Advanced Data Preprocessing
Handling structural NaNs, categorical encoding, and feature standardization for massive datasets.
Exploratory Data Analysis
Comprehensive visualizations using Seaborn and Matplotlib (Choropleth maps, scatter plots, and boxplots).
K-Means Clustering & PCA
Grouping developers into optimized personas by evaluating Silhouette Scores and visualizing them via Principal Component Analysis.
Ensemble Classification Models
Predicting high-income developers using Logistic Regression, Decision Trees, k-NN, and a final Voting Classifier.
Technical Deep Dive
Unsupervised Learning & Clustering
Implemented K-Means clustering to discover hidden developer personas based on education, experience, and role. Evaluated the optimal K value using the Elbow Method and Silhouette Scores, and reduced dimensionality via PCA for clean visual interpretation.
High-Accuracy Ensemble Classifiers
Built a robust predictive pipeline utilizing multiple classification algorithms. By combining Logistic Regression, Decision Trees, and k-NN into an ensemble Voting Classifier, the system achieves robust performance on hold-out testing data.
Performance Benchmark
"Engineered a complete CRISP-DM machine learning pipeline that clusters developers and predicts high-income earners using ensemble classification models."