1. SMS Spam Detection: Objective: The objective of this project is to develop a model that can classify SMS messages as either spam or ham (non-spam). Techniques Used: Natural Language Processing (NLP), Text Preprocessing, Feature Engineering, Classification Algorithms (e.g., Naive Bayes, SVM).
2. Movie Genre Recommendation: Objective: The goal of this project is to build a recommendation system that suggests movies to users based on their preferences and viewing history. Techniques Used: Collaborative Filtering, Matrix Factorization, Content-Based Filtering, Recommendation Algorithms (e.g., Naive Bayes).
3. Credit Card Fraud Detection: Objective: This project aims to develop a model capable of detecting fraudulent credit card transactions in real-time. Techniques Used: Anomaly Detection, Imbalanced Data Handling, Feature Engineering, Classification Algorithms (e.g., Random Forest, Support Vector Machine).

I left the codes in jupyter notebook in order to show insights such as confusion matrix, scatter plot of dataset.

SMS_Spam_Detection.ipynb: Contains code and resources related to the SMS spam detection project.

Movie_Genre_Recommendation.ipynb: Includes notebooks and data files for the movie genre recommendation project.

Credit_Card_Fraud_Detection.ipynb: Contains scripts and datasets for the credit card fraud detection project.

Documentation: Additional documentation, reports, and resources related to the projects.