Shreya Prasad's Projects
The code creates a chatbot using NLTK for sentiment analysis and TF-IDF for word embedding. It reads and visualizes dialogues, performs text normalization, and employs cosine similarity to match user queries with dataset responses. The chatbot handles low-confidence cases and allows user interaction until typing 'exit.'
This Python project uses Isolation Forest for credit card fraud detection, analyzing transaction data and visualizing distributions. It employs PyCaret for model comparison, highlighting Random Forest. The project is concise and effective for fraud detection.
The DNA Classification for E.Coli project, built on Django, uses an MLPClassifier to predict E.Coli presence in DNA sequences. The web app allows users to input sequences and view classification results. The project is structured for reproducibility and deployment.
Django app leveraging neural networks for emotion recognition using scikit-learn MLP model trained on RAVDESS dataset. Clean UI allows users to upload audio for emotion prediction. Repository includes model, Django components, and management script. Clone and run at http://localhost:8000.
This repository contains three AI and machine learning projects completed during the internship at Encryptix: Face Detection and Recognition System, Netflix Recommendation System, and Tic-Tac-Toe. Each project includes data preprocessing, model building, and evaluation, using OpenCV, deep learning, collaborative filtering, and game AI algorithms.
This repository contains a Python face detection application built using Streamlit and OpenCV. The application leverages the power of real-time face detection through a webcam or video stream and includes interactive elements like a sidebar for profile details and user preferences.
This project was developed during a 3-month internship program at Feynn Labs. The repository contains a DNA classification app for detecting E. Coli, leveraging machine learning techniques.
This project was developed during a 3-month internship program at Feynn Labs. The project analyzes the feasibility of an Electric Vehicle (EV) startup in India, focusing on the 2-wheeler and 4-wheeler markets. It explores market growth, consumer needs, and differentiation strategies for success.
This project was developed during a 3-month internship program at Feynn Labs. The project replicates a McDonald's case study using Python. It involves data analysis, PCA for dimensionality reduction, KMeans clustering, segment profiling, and decision tree classification for marketing segmentation,
This repository features my first project completed during my winter internship at Nexus Info. Using advanced Natural Language Processing methods, the project focuses on building a business-purpose chatbot to improve user engagement and sentiment analysis.
This repository showcases my second project developed during my internship at Nexus Info. The project centers around building a specialized chatbot tailored for college-related queries, enhancing user engagement and providing informative responses.
This repository highlights my third project developed during my internship at Nexus Info. The project focuses on creating a robust system for detecting multiple diseases using advanced machine learning techniques, aiming to assist healthcare professionals in accurate diagnosis and treatment planning.
Config files for my GitHub profile.
This repository includes a Python Text Detection App using TesseractOCR and OpenCV to extract and process text from images. It manages basic text extraction and advanced image preprocessing for improved accuracy. Also, it features a Flask web app with a user-friendly interface for uploading images and viewing extracted text.
Discover the possibilities of Augmented Reality (AR) with Unity-AR-App. This demo application demonstrates basic AR capabilities through interactive 3D models in a virtual environment. Deployed on Android, with visuals provided for a preview.
The voice assistant is a Python project that employs speech recognition and text-to-speech functionalities. it greets users based on the time of day and is capable of answering questions using Wikipedia, opening specific websites like YouTube or Google and providing the current time. This project showcases a basic but functional voice assistant