AI PROJECTS SECTION 6A
Stock Prediction System Based on Machine Learning Model
Group Members: Muhammad Umer Shifa, Muhammad Oneeb, Salma
Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and WordPress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall.
Real Estate Price Prediction Machine Learning Model
Group Members: Jawad Mohsin, Muhammad Adan Zaqaf, Mobin Ahmed
The project aims to create a comprehensive system for predicting real estate prices based on various input parameters such as the number of rooms, floors, and area. The system will preprocess the data, clean it, and then train a machine learning model to predict house prices. Additionally, a user-friendly interface will be developed using Stream lit to allow users to input property details and get real-time price estimates.
Movie Recommendation System
Group Members: Shahood Rehan, Basil Irfan Rizvi, Muhammad Arqam
The project aims to design and implement a content-based movie recommendation system that provides personalized movie suggestions to users. By leveraging data mining methodologies, the system will analyze movie attributes and user preferences to generate recommendations tailored to individual tastes.
ShoeSense E-Commerce Recommender System
Group Members: Zeeshan Ibrar, Abdul Haseeb Alam, Haseebullah
The scope of this project encompasses the development of an advanced e-commerce recommender system employing artificial intelligence and data mining techniques. The system aims to deliver personalized product recommendations to users based on their preferences, browsing history, and purchasing behavior.
MedicoHUB (Medicine Recommendation System)
Group Members: Rimsha Zahid, Areeba Kabir, Ariba Azam
The system is built using Python Django, ensuring robust, scalable, and maintainable code. Modular design allows for easy updates and integration of additional features in the future. Users can input their symptoms through a user-friendly interface.
Financial Virtual Assistant (AI Chatbot)
Group Members: Kashan, Yahya, Eima
We will use Yahoo Finance and OpenAI API to create an assistant in Python that gives specific financial advice using its knowledgebase. We will use Streamlit for the front end.
Smart Chatbot
Group Members: Amna Hussain, Muhammad Huzaifa, Hania Ahmed
The Smart Chatbot project is a Python-based application designed to interact with users by responding to their inputs with predefined messages. It utilizes a simple natural language processing approach to analyze user messages, identify keywords, and determine the most suitable response based on predefined criteria.
Anime Recommendation System
Group Members: Faiz Ul Amin Khan, Muhammad Qasim
The Anime Recommendation System aims to provide personalized recommendations to users based on their preferences and viewing history. The system will utilize machine learning algorithms and collaborative filtering techniques to analyze user data and suggest anime titles that are likely to be of interest to them.