yashashree1111,Yashashree Fulsundar,github

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Name: Yashashree Fulsundar

Type: User

Bio: Passionate about leveraging AI and data science to unravel the complexities of the digital world. With over a year of hands-on experience, my journey involves

I'm a Data Scientist and AI enthusiast on a mission to decode the mysteries of artificial intelligence and uncover the hidden stories within data. 🤖💡 Armed with a B.Tech in Data Science and a knack for turning complex datasets into strategic assets, I'm your go-to person for all things AI and machine learning. When I'm not busy crafting code or diving into neural networks, you'll find me embracing the chaos of data with a dash of humor and a cup of coffee. I'm the Sherlock Holmes of data, always on the hunt for insights and patterns that others might miss🚀✨

Yashashree Fulsundar's Projects

cat-vs-dog-classification-using-cnn

Cat vs Dog Classification project powered by Convolutional Neural Networks (CNNs), developed using Keras, TensorFlow, and Streamlit

end-to-end-sms-email-spam-classification

The SMS/Email Spam Classification project is a machine learning application that utilizes the Naive Bayes algorithm to accurately classify and filter spam messages in both SMS and email formats.

iris-flower-classification-with-decision-trees

Iris Flower Classification with Decision Trees using machine learning

movie-recommender-system

The Movie Recommender project is an machine learning project that leverages the IMDB 5000 movies dataset to provide personalized movie recommendations to users.

password-generator

this python program will take input from user , like number of characters , numbers and special symbols in the password which you want to generate. will return a very strong and unique password for all of your sites.

sentiment-analysis-on-imdb-movie-reviews-using-lstm-with-word2vec-embeddings

This project focuses on sentiment analysis using natural language processing (NLP) techniques applied to the IMDB Movie Reviews dataset. The pipeline includes data preprocessing steps such as lowercasing, removal of HTML tags, punctuation, URLs, tokenization, stop word removal, and stemming.