Real-Time Projects
Every project here goes the full distance — pulling real open-source data, cleaning it, training a model, evaluating it properly, and tying the result back to an actual business use case. Built for students who want more than a tutorial: working code you can run, extend and put on your resume.
Predict house prices using California housing data and the classic regression workflow — feature correlation analysis, train/test split, linear regression with scikit-learn, and evaluation with RMSE and R². Used in real estate pricing engines and mortgage risk tools.
Predict which telecom customers are about to cancel their subscription using logistic regression on real customer data. Covers handling categorical variables, class imbalance, and evaluation with precision, recall and ROC-AUC — the exact workflow used by subscription businesses to reduce churn.
Predict loan default risk using a Random Forest classifier on real lending data. Covers feature importance analysis, hyperparameter tuning with GridSearchCV, and confusion matrix evaluation. The same approach banks and fintech companies use for credit scoring.
Group retail customers into meaningful segments based on purchasing behaviour using K-Means clustering. Covers the elbow method for choosing K, feature scaling, and PCA visualisation. Directly applicable to marketing teams running targeted campaigns.
Build a spam classifier on real SMS message data using Multinomial Naive Bayes. Covers text preprocessing, TF-IDF vectorisation, and evaluation with precision/recall — the foundational technique behind every email and SMS spam filter in production today.
Our Machine Learning training programme covers exactly this workflow — live, hands-on, with a capstone project of your own.
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