SmartCards

The AI Flashcard Generator is an innovative tool that creates personalized flashcards and multiple-choice questions (MCQs) based on your prompts. Perfect for students and lifelong learners, this AI-powered solution enhances your study sessions with ease and efficiency.

AI-Powered Generation: Instantly generate flashcards and MCQs tailored to your input.
User Authentication: Securely save and manage your content with personalized accounts.
Interactive Learning: Engage with your materials through quizzes and dynamic flashcards.
User-Friendly Interface: Enjoy a seamless and intuitive experience for all your learning needs.

The Celebrity Image Classification project is a sophisticated web application designed to identify celebrities in uploaded images using advanced machine learning techniques. By leveraging Support Vector Machine (SVM) algorithms, this application accurately detects faces and eyes, allowing it to determine which celebrity an individual most closely resembles.

Face Detection: Our application employs robust face detection algorithms to locate and isolate faces within the uploaded images.
Eye Detection: In addition to face detection, we also identify the location of eyes, which plays a crucial role in determining the similarity to a specific celebrity.
Celebrity Matching: Using the detected facial features and our trained SVM model, we match the uploaded image to the most similar celebrity in our database.
Similarity Score: Along with the identified celebrity, we provide a similarity score that indicates how closely the individual in the image resembles the matched celebrity.
Flask Server Integration: The application is deployed using a Flask server, ensuring seamless integration between the front-end and back-end components.

The Job Application Tracker is a Google Chrome extension designed to streamline your job search process. This tool enables users to easily add and manage job applications, providing a clear and organized list of positions applied for. With user authentication and Firestore storage, you can securely track your applications, edit details, or remove entries as needed.

User Authentication:Ensure your data is secure with a personalized account, allowing you to access your job applications anytime.
Firestore Storage: All your application data is stored efficiently in Firestore, providing reliable access and management of your job applications.
Easy Job Management: Add jobs you’ve applied to with just a few clicks, and view them in a neatly organized list that you can edit or remove at any time.
Real-Time Updates: Keep your job application status up to date, ensuring you never miss an opportunity to follow up or prepare for interviews.
User-Friendly Interface: The extension features a clean and intuitive design, making it simple for users to navigate and manage their job applications.

AI Chatbot Service

The AI Chatbot is an an intelligent conversational agent designed to answer any questions or prompts from users. This innovative tool leverages advanced natural language processing to provide accurate and relevant responses, making it an invaluable resource for users seeking information or assistance. With user authentication and chat history storage, the AI Chatbot ensures a personalized and secure experience.

Intelligent Responses: The AI Chatbot utilizes state-of-the-art algorithms to understand user prompts and provide informative answers, making it capable of handling a wide range of inquiries.
Chat History Storage: All interactions with the chatbot are securely stored, allowing users to revisit previous conversations and easily track their inquiries over time.
User Authentication: Ensure your data is protected with a secure user authentication system, providing personalized access to your chat history and settings.
User-Friendly Interface: The chatbot features an intuitive interface that makes it easy for users to engage in conversations and get the information they need quickly.

The Falcon 9 Predictor is a predictive model designed to forecast the landing success of the Falcon 9 rocket. By utilizing comprehensive data collection methods, including the SpaceX API and web scraping, this project aims to enhance the understanding of the factors influencing rocket landings. Through meticulous data visualization, feature engineering, and machine learning techniques, the Falcon 9 Predictor provides insights into the reliability of Falcon 9 landings.

Data Collection:The project gathers essential data from the SpaceX API and employs web scraping techniques to compile a robust dataset necessary for accurate predictions.
Data Visualization: Graphical representations of the data help identify trends and patterns, allowing for a clearer understanding of the factors affecting landing success.
Feature Engineering: Key features that influence landing outcomes are carefully selected and prepared for machine learning, ensuring the model is trained on the most relevant data.
Model Building: Various machine learning algorithms are implemented to develop the predictive model, with accuracy scores evaluated (89%) to determine the best-performing approach for forecasting landing success.
Performance Evaluation: The model's effectiveness is assessed using multiple metrics, providing insights into its predictive capabilities and reliability.

The NBA Score Predictor is a machine learning model designed to forecast the outcomes of NBA matches. By utilizing the Ridge Classification Model, this tool analyzes extensive historical data to predict which team is likely to win. With a robust data collection process and meticulous feature selection, the NBA Score Predictor offers accurate predictions to enhance your understanding of game outcomes.

Data Collection:The model is built upon nine years' worth of NBA data, collected through web scraping techniques to ensure a comprehensive dataset for analysis.
Data Preprocessing: The collected data undergoes thorough manipulation and cleaning to make it suitable for machine learning, ensuring high-quality input for the model.
Feature Selection: Relevant features that significantly influence game outcomes are carefully selected, optimizing the model's ability to predict winners effectively.
Model Building: The Ridge Classification Model is employed, achieving an accuracy score of 78%. This model is trained on the processed data to provide reliable predictions for NBA match outcomes.