Task-1: Movie Genre Classification Create a machine learning model that can predict the genre of a movie based on its plot summary or other textual information. You can use techniques like TF-IDF or word embeddings with classifiers such as Naive Bayes, Logistic Regression, or Support Vector Machines.
Task-2: Credit Card Fraud Detection Build a model to detect fraudulent credit card transactions. Use a dataset containing information about credit card transactions, and experiment with algorithms like Logistic Regression, Decision Trees, or Random Forests to classify transactions as fraudulent or legitimate.
Task-3: Customer Churn Prediction Develop a model to predict customer churn for a subscription-based service or business. Use historical customer data, including features like usage behavior and customer demographics, and try algorithms like Logistic Regression, Random Forests, or Gradient Boosting to predict churn.
Task-4: Spam SMS Detection Build an AI model that can classify SMS messages as spam or legitimate. Use techniques like TF-IDF or word embeddings with classifiers like Naive Bayes, Logistic Regression, or Support Vector Machines to identify spam messages.
Task-5: Handwritten Text Generation Implement a character-level recurrent neural network (RNN) to generate handwritten-like text. Train the model on a dataset of handwritten text examples, and let it generate new text based on the learned patterns.