Mounesh Gouda's repositories
Learn_DSA
This repository explores fundamental data structures and their implementations. Learn how to organize and manipulate data efficiently for various programming tasks. (Feel free to add your specific focus areas here, e.g., algorithms, interview prep)
Python_Tutorial
Python is a high-level, interpreted programming language known for its simplicity and readability.Python emphasizes code readability and allows programmers to express concepts in fewer lines of code compared to languages like C++ or Java.
Emotion-based-Music-Player-
"Emotion-based-Music-Player" is a unique project designed to enhance music listening experiences. By analyzing user emotions, this player dynamically selects music tracks that match the user's mood. Through innovative algorithms, it tailors playlists to evoke desired emotional responses, offering a personalized journey through music.
Deep-learning
This repository contains Jupyter Notebooks showcasing different techniques for price prediction using Artificial Neural Networks (ANNs). Topics covered include early stopping, batch normalization, dropout regularization, and deep neural networks. These notebooks offer practical implementations and insights for improving model training
Top-10-AI-Projects
This repository curates a collection of the top 10 artificial intelligence projects from various domains. From cutting-edge research to practical applications, these projects showcase the latest advancements in AI technology.
Real-Time-Face-Emotion-Detection-Application
"Real-Time-Face-Emotion-Detection-Application" is a publicly available project aimed at detecting facial expressions in real-time. Leveraging advanced computer vision techniques, it accurately identifies emotions such as happiness, sadness, anger, and surprise from live video feeds. With its intuitive interface,
Machine_Learning
ChatGPT This repository features a diverse set of resources covering essential topics in machine learning and data analysis. From understanding and visualizing confusion matrices to exploring feature selection techniques and addressing multicollinearity, it offers comprehensive insights into model evaluation and feature engineering. Additionally
Insurance-claim
Prediction of Auto Insurance Claim detection • Problem statement is related is to insurance domain • Performed a key role in Machine learning : Data gathering, cleaning ,Feature engineering ,Feature Selection ,Data visualization Model building ,Hyper parameter tunning • It’s a Classification problem evaluated model using confusion matrix and model