Machine Learning

This project involves predicting house prices based on features like location, size, number of bedrooms, and amenities. Students will clean the dataset, train a regression model, and evaluate its performance using metrics like accuracy and error rate.

Ames Housing Dataset or Boston Housing Dataset (Kaggle).

Use customer data (such as annual income, spending score, age) to segment customers into different groups. The project will involve preprocessing the data and applying K-means clustering to identify customer segments.

Mall Customer Dataset (Kaggle).

This project focuses on building a recommendation system that suggests movies based on user preferences and behaviour. It demonstrates how platforms like Netflix suggest content.

Using a dataset of tweets, students can build a sentiment analysis model to classify tweets as positive, negative, or neutral. They'll apply techniques like tokenization, stopword removal, and vectorization (e.g., TF-IDF) and use models like Naive Bayes or SVM.

Twitter Sentiment Analysis Dataset (Kaggle or Twitter API).

Using a credit card transactions dataset, students can build a model to detect fraudulent transactions. Techniques for handling imbalanced data, such as SMOTE (Synthetic Minority Over-sampling Technique), can be applied.

Credit Card Fraud Detection Dataset (Kaggle).