Future Interns Projects
3 production-grade ML projects built during January 2026 internship
Internship Overview
Completed an intensive Machine Learning internship at Future Interns in January 2026, building three production-grade ML systems across different domains: NLP-based recruitment screening, text classification for customer support, and time-series demand forecasting.
The internship culminated in a Letter of Recommendation and Certificate of Completion (CIN: FIT/JAN26/ML4913), recognizing work that demonstrated end-to-end ML pipeline design, real-world data handling, and deployment-ready code architecture.
Project 1: AI Resume Ranking System
Built an AI-powered resume screening system that processes 1000+ resumes using TF-IDF vectorization for candidate-job matching and skill gap analysis. The system transforms both resumes and job descriptions into high-dimensional vector representations, then ranks candidates by cosine similarity.
- Processes 1000+ resumes with vectorized NLP operations
- TF-IDF vectorization for meaningful skill representation
- Cosine similarity scoring for objective candidate ranking
- Automated skill gap identification for recruiter feedback
- Batch processing with structured CSV output reports
Project 2: Support Ticket Classifier
Developed an NLP-based support ticket classification and prioritization system. The system automatically categorizes incoming support tickets into predefined categories and assigns priority levels using ensemble machine learning models trained on TF-IDF features.
- Multi-class text classification for support ticket categorization
- TF-IDF with n-gram features for capturing phrase-level patterns
- Ensemble methods (Random Forest + Gradient Boosting) for robust predictions
- Automatic priority assignment based on ticket content and category
- Evaluation using precision, recall, F1-score, and confusion matrix analysis
Project 3: Sales Demand Forecasting
Created a sales demand forecasting model using Random Forest Regressor trained on real-world sales data. The system performs time-series feature engineering — extracting temporal patterns, seasonal components, and lag variables — to predict future sales volumes.
- Time-series feature engineering with rolling statistics and lag variables
- Random Forest Regressor for non-linear demand pattern capture
- Temporal feature extraction (day of week, month, quarter, year)
- Rolling mean and rolling standard deviation features for trend capture
- Visualizations using Matplotlib for trend analysis and forecast comparison
- R-squared and MAE evaluation metrics for regression performance
Achievements
- Letter of Recommendation — Received from Future Interns recognizing exceptional work across all three projects.
- Certificate of Completion — Official ML internship certification (CIN: FIT/JAN26/ML4913).
- Production-Grade Code — All three projects follow clean code practices with proper documentation, modular design, and reproducible pipelines.