Poreddy Manvith Reddy

Artificial Intelligence and Machine Learning graduate with strong programming and problem-solving skills. Quick learner dedicated to applying academic knowledge in practical settings. Committed to contributing to innovative projects and advancing in a collaborative environment.

• Certification of Oracle academy database foundations
• Certification of Oracle cloud infrastructure 2023 data science professional
• Microsoft Power Bi data analysis and visualization certification

• Got first prize in a Hackathon for the project Deep Fake Detection
• Got first prize in Poster Presentation conducted by IBM for diabetes prediction system
• Selected for IHub Anubhuti IITD-Foundation Chanakya-Fellowship Program For project Mood Markets

 Diabetes Prediction System with Daily Habits:

This project predicts the likelihood of diabetes based on user inputs such as age, glucose level, BMI, and daily lifestyle habits. The main goal was to create a simple system that can help users understand their health risk at an early stage.

The model was developed using Logistic Regression in Python and trained on healthcare data. Users can input their details and receive instant predictions through a basic interface.

Tech Stack: Python, Scikit-learn, Machine Learning

Mood Markets – Stock Market Technical Tools Web App:

Mood Markets is a web-based application designed to analyze stock market trends using basic technical tools. It helps users understand market movements and make better decisions.

I worked on trend analysis using Linear Regression and Decision Tree models to analyze stock price behavior. I also implemented sentiment analysis on news data to classify market sentiment as positive or negative.

Tech Stack: Python, Machine Learning, Streamlit, Data Analysis

DEEP-SCAN-SECURE – Deep Fake Detection Web App

This project focuses on detecting deepfake content using deep learning techniques. Users can upload images or videos, and the system predicts whether the content is real or manipulated.

The model was built using CNN, GANs, and Neural Networks, and deployed as a Streamlit web application for easy interaction.

Tech Stack: Python, Deep Learning, CNN, GANs, Streamlit

Hospitality Hotel Chatbot

This chatbot is designed to assist users with hotel-related queries such as room details, amenities, location, and contact information. It provides quick responses to improve user experience.

The idea behind this project was to deploy it in 3-star and 5-star hotels or hospital environments, where users can easily access information without manual support.

Tech Stack: Python, NLP (basic), Web Application

Mental Health and Emotional Support Chatbot

This chatbot is built to support users who are feeling stressed or emotionally low. It interacts with users and provides simple suggestions like taking a break, going for a walk, or getting fresh air.

The goal was to create a basic support system that can help users feel better through simple conversations.

Tech Stack: Python, NLP, Chatbot Logic

Zomato Restaurant Analysis (Power BI)

This project involves analyzing Zomato restaurant data and presenting insights through an interactive Power BI dashboard. It highlights trends such as top-rated restaurants, popular cuisines, pricing patterns, and location-based insights.

The dashboard makes it easy to understand data and extract useful insights visually.

Tech Stack: Power BI, Data Visualization, Data Analysis

• Learning about emerging technologies
• Building interactive dashboards for data
• Investment Research