This project involved the implementation of several Machine Learning algorithms, such as KNN, Logistic Regression, and Random Forest. The focus was on learning and applying various steps in the Machine Learning process. Data preprocessing and exploratory data analysis (EDA) were performed on a dataset containing over 7000 records. These steps helped enhance the overall efficiency of the models developed in the project.

The project focused on developing a deepfake detection system using deep learning techniques. A deepfake analyzer was created using a neural network that could detect deepfake images and provide a confidence score associated with each prediction. The frontend of the system was built using Anvil, a web-based platform for building user interfaces. The goal of the project was to effectively identify and flag manipulated images using advanced deep learning algorithms and provide a user-friendly interface for interacting with the system.

The project focused on credit card fraud detection using various machine learning models such as Logistic Regression, Random Forest, Support Vector Machine, and Naïve Bayes. These models were utilized to identify fraudulent transactions within a credit card dataset. The project also involved deploying the models on Flask, a web framework, and creating a frontend using HTML/CSS. This allowed users to interact with the system and input credit card transaction details for fraud detection. The objective of the project was to build an efficient and user-friendly application that could effectively detect and prevent credit card fraud.

The project involved creating a weather information website using HTML, CSS, and JavaScript. It utilized the Open Cage API to fetch weather data based on the user's location. The website was developed as a personal project, demonstrating a passion for programming and an interest in web development. The final product was deployed on Netlify, a platform for hosting and deploying websites. The objective of the project was to provide users with an intuitive and visually appealing interface to access real-time weather information based on their location.

The project involved the development of a news app using Java and Android Studio. The app featured a user interface that provided information from over five different fields. A REST API was integrated to fetch and display search results within the app. Object-oriented programming concepts were utilized to create efficient and reusable code. This personal project reflected a passion for programming and an interest in software engineering. The goal of the project was to create a user-friendly and functional news app that delivered up-to-date information across various fields to the users.