Arpit Singh

Passionate and detail-oriented Data Science and Business Analytics enthusiast with hands-on experience in real-world projects involving machine learning, data visualization, and business intelligence. Proficient in Python, SQL, Power BI, and statistical modeling, with a strong foundation in data preprocessing, predictive analytics, and dashboard development. Adept at translating data into actionable insights that drive strategic decision-making.

SPOT Award (Issued by: Datamatics Global Services Limited)

Recognized for exceptional performance and contributions to the organization

• E-Commerce Purchase Propensity & Recommender System
  Developed a machine learning model with 87% accuracy to predict customer churn using transactional and behavioral data from around 50K users. Applied feature engineering, trained models like Logistic Regression and XGBoost, used SHAP for interpretability, and delivered insights via a Power BI dashboard.
• Medical Insurance Fraud Detection System
  Built a model to detect fraudulent medical insurance claims using anomaly detection and supervised learning. Processed real-world datasets, implemented ensemble models, applied SHAP for explainability, and deployed the results through a Streamlit app.
• Twitter & Reddit Sentiment Analyzer using NLP & Machine Learning
  Performed sentiment analysis on over 20000 product reviews using NLP. Built classification models like Naive Bayes and SVM, and visualized sentiment trends to support product strategy decisions.
• Employee Performance & Attrition Prediction System
  Designed a predictive system to identify employees at risk of attrition. Processed around 15000 records, built models like Random Forest and XGBoost with 85% F1 score, used SHAP for feature insights, and deployed via Streamlit for HR teams.
• Loan Default Risk Prediction
  Developed machine learning models including Logistic Regression, Random Forest, and Gradient Boosting to predict loan default risk. Addressed missing data, outliers, and class imbalance with SMOTE. Optimized pipelines with feature selection and saved the best-performing models.
• Academic Projects | Data Science & Business Analytics (Great Learning)
  Completed hands-on projects across marketing, finance, HR, and operations domains involving SQL, clustering, machine learning, and EDA. Worked on real-life use cases like cancellation prediction, customer segmentation, OTT viewership forecasting, and sales analysis.

• PGP in Data Science and Business Analytics, Great Learning
• Complete SQL and Databases Bootcamp, Udemy
• Data Science and Machine Learning Bootcamp with R, Udemy
• 2025 Deploy ML Model in Production with FastAPI and Docker, Udemy
• Microsoft Power BI Data Analyst, Coursera
• Certified Business Analysis Professional (CBAP), Coursera

• Cricket
• Trekking
• Video Games
• Reading