Mohit Dagar

I’m a Computer Science & Engineering student (B.Tech) building end-to-end data solutions—from data cleaning and ETL to modeling and dashboards. I focus on turning messy records into decisions people can act on.

In my AgroMind project, I processed and analyzed 3000+ agricultural records to build a Random Forest crop recommendation system (92% prediction accuracy). I also deployed a Flask-based web app for real-time predictions and used feature analysis to identify key yield drivers.

In the Recruitment Funnel Intelligence System, I engineered an analytics pipeline on 1,470+ HR records to simulate hiring funnel stages. I used SQL-based analytics plus a Random Forest model (AUC-ROC test: 0.78; cross-validation: 0.82) and created an interactive dashboard to spot conversion drop-offs and early-tenure attrition.

Through projects like Customer Segmentation (ETL + K-Means clustering) and my training at Cipher Schools, I’ve strengthened my Data Analytics and Machine Learning foundations. I’m excited to apply this toolkit in roles where data quality, experimentation, and business impact matter.