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Sushrut Gaikwad's Projects

ai-driven-customer_support_system icon ai-driven-customer_support_system

This project implements an intelligent customer support system leveraging Streamlit and a fine-tuned Llama large language model (LLM) from Hugging Face. It empowers users to interact with the system through text input, speech recognition, and tailored responses based on the chosen assistance type (Agriculture, Healthcare, Education)

articletextminingengine icon articletextminingengine

The objective of this assignment is to extract textual data articles from the given URL and perform text analysis to compute variables that are explained below.

emotion-detection icon emotion-detection

The Emotions Detection Project utilizes advanced machine learning algorithms to analyze facial expressions, voice tones, and physiological responses, deciphering human emotions accurately. By integrating computer vision and natural language processing.

emotion-detection-in-audio- icon emotion-detection-in-audio-

"Real-time Emotion Detection System: Analyzes Audio data frame by frame to identify and track predominant emotions. Built for seamless integration, enhancing applications with live emotion insights."

emotion-detection-in-streaming- icon emotion-detection-in-streaming-

"Real-time Emotion Detection System: Analyzes streaming video data frame by frame to identify and track predominant emotions. Built for seamless integration, enhancing applications with live emotion insights."

eye_drowsiness icon eye_drowsiness

Developed real-time drowsiness detection system analyzing users' eye state (open/closed) using computer vision. Integrated alert mechanism for timely notifications. Implemented in Python using OpenCV and TensorFlow.

fraud-transaction-detection icon fraud-transaction-detection

Detect fraudulent transactions using machine learning models. Implement anomaly detection techniques to identify suspicious activities. Utilize Python, scikit-learn, and pandas for data preprocessing and model building.

movie-recommendation-system- icon movie-recommendation-system-

This System recommends movies to users based on their preferences. Collaborative Filtering algorithms and libraries like Pandas, Numpy, Sklearn, Ast, Streamlit, Request and Pickle for recommendation systems are used.

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