Customer churn prediction model: A telecommunications company wants to identify customers who are most likely to cancel their subscription. You could build a predictive model using historical customer data like age, subscription length, monthly spend, service issues etc. to classify customers into high, medium and low churn risk. This would help the company focus its retention programs. You would need to clean, explore and preprocess the customer data, engineer relevant features, select and train different classification algorithms (logistic regression, random forests, neural networks etc.), perform model evaluation, fine-tuning and deployment.
Market basket analysis for retail store: A large retailer wants insights into purchasing patterns and item associations among its vast product catalog. You could apply market basket analysis or association rule mining on the retailer’s transactional data over time to find statistically significant rules like “customers who buy product A also tend to buy product B and C together 80% of the time”. Such insights could help with cross-selling, planograms, targeted promotions and inventory management. The project would involve data wrangling, exploratory analysis, algorithm selection (apriori, eclat), results interpretation and presentation of key findings.
Customer segmentation for banking clients: A bank has various types of customers from different age groups, locations having different needs. The bank wants to better understand its customer base to design tailored products and services. You could build an unsupervised learning model to automatically segment the bank’s customer data into meaningful subgroups based on similarities. Variables could include transactions, balances, demographics, product holdings etc. Commonly used techniques are K-means clustering, hierarchical clustering etc. The segments can then be profiled and characterized to aid marketing strategy.
predicting taxi fare amounts: A ride-hailing company wants to optimize its dynamic pricing strategy. You could collect trip data like pickup/drop location, time of day, trip distance etc and build regression models to forecast fare amounts for new rides. Linear regression, gradient boosting machines, neural networks etc. could be tested. Insights from the analysis into factors affecting fares can help set intelligent default and surge pricing. Model performance on test data needs to be evaluated.
Predicting housing prices: A property investment group is interested in automated home valuation. You could obtain datasets on past property sales along with attributes like location, size, age, amenities etc and develop regression algorithms to predict current market values. Both linear regression and more advanced techniques like XGBoost could be implemented. Non-linear relationships and feature interactions need to be captured. The fitted models would allow estimate prices for new listings without an appraisal.
Fraud detection at an e-commerce website: Online transactions are vulnerable to fraudulent activities like payment processing and identity theft. You could collect data on past orders with labels indicating genuine or fraudulent class and build supervised classification models using machine learning algorithms like random forest, logistic regression, neural networks etc. Features could include payment details, device specs, order metadata, shipping addresses etc. The trained models can then evaluate new transactions in real-time and flag potentially fraudulent activities for manual review. Model performance, limitations and scope for improvements need documentation.
These are some examples of data-driven projects a student could undertake as part of their capstone coursework. As you can see, they involve applying the data analytics workflow – from problem definition, data collection/generation, wrangling, exploratory analysis, algorithm selection, model building, evaluation and reporting insights. Real-world problems from diverse domains have been considered to showcase the versatility of data skills. The key aspects covered are – clearly stating the business objective, selecting relevant datasets, preprocessing data, feature engineering, algorithm selection basis problem type, model building and tuning, performance evaluation, presenting results and scope for improvement. Such applied, end-to-end projects allow students to gain hands-on experience in operationalizing data analytics and communicate findings to stakeholders, thereby preparing them for analytics roles in the industry.