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HUSTLE BUSTLE
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HUSTLE BUSTLE
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Shriram-Vibhute/README.md

Welcome 👋🏻 to my GitHub profile! I’m Shriram Vibhute, a passionate Machine Learning Engineer based in Pune, Maharashtra. With a strong background in programming and a keen interest in solving real-world problems through data, I love exploring new technologies and enhancing my skills. Feel free to connect with me or explore my projects below!

📚 Technical Skills

  • Programming Languages and Tools: Python 🐍 | SQL (MySQL) 💾 | Git 🛠️ | GitHub 🧑‍💻
  • Libraries & Frameworks: Scikit-learn 🤖 | TensorFlow 🔍 | Keras 🍁 | Pandas 📊 | Numpy 🔢 | Matplotlib 📈 | Beautiful Soup 🍲
  • Data Science Tools: Data Collection 📥 | Data Preprocessing 🔧 | Data Visualization 📉 | Data Wrangling 🤝
  • Machine Learning: Linear & Logistic Regression 📊 | KNN 🔍 | Decision Tree 🌳 | Random Forest 🌲 | SVM 🧩 | K Means 🧪 | Gradient Boosting & XGBoosting 🚀
  • Mathematics: Statistics 📈 | Probability 🎲

💼 Experience

Machine Learning Projects

Movie Recommendation System - Web Application 🎬

  • Libraries & Frameworks: NumPy | Pandas | Scikit-Learn | NLTK | Streamlit
  • Developed a content-based movie recommender system with a dataset of 5,000 movies 🎥.
  • Conducted data preprocessing and feature engineering, utilizing “Bag of Words” and “Cosine Similarity” 📐.
  • Employed NLTK for text normalization, including stemming techniques for tags creation 📚.
  • GitHub Repository: Movie Recommendation System

MNIST Digit Classification 🧮

  • Libraries & Frameworks: NumPy | Pandas | Scikit-Learn | Matplotlib
  • Developed and evaluated multiple models (RandomForestClassifier, SGDClassifier, KNN) for digit classification, achieving an F1-Score of 0.93 📊.
  • Optimized classifiers using precision-recall and ROC curves, achieving 90% precision by adjusting decision thresholds 🎯.
  • GitHub Repository: MNIST Digit Classification

House Price Prediction 🏠

  • Libraries & Frameworks: NumPy | Pandas | Scikit-Learn | Matplotlib
  • Built a comprehensive pipeline for house price prediction, including data visualization, preprocessing, and model evaluation 🔍.
  • Employed Random Forest modeling with cross-validation and GridSearchCV, achieving 81% accuracy (R2 score) 💡.
  • GitHub Repository: House Price Prediction

Data Analysis Projects

WhatsApp Chat Analysis - WebApp 🗪

  • Libraries & Frameworks: NumPy | Pandas | Seaborn | Plotly | Word Cloud | Streamlit
  • Description: Developed a Streamlit app that enables users to upload and analyze WhatsApp chat files, both group and one-on-one. The app provides insightful trends, sentiment analysis, and key highlights, transforming conversations into meaningful data 💡.
  • GitHub Repository: WhatsApp Chat Analysis

Among Us Exploratory Data Analysis 🎮

  • Libraries & Frameworks: NumPy | Pandas | Plotly | EDA
  • Description: Exploratory data analysis (EDA) of gameplay statistics from the popular multiplayer game, Among Us. Originally released in 2018, the game surged in popularity during the summer of 2020, resulting in a wealth of data regarding player performance and game dynamics 💡.
  • GitHub Repository: Among Us Exploratory Data Analysis

📫 Get In Touch

Thanks for visiting my profile! 🚀

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  1. Deep-Learning Deep-Learning Public

    Welcome to the Deep Learning Repository! This repository is designed to provide comprehensive resources and practical implementations related to deep learning. It includes foundational concepts, ad…

    Jupyter Notebook 1

  2. Machine_Learning Machine_Learning Public

    This repository features both original implementations of machine learning algorithms and their modern sklearn counterparts, providing a comprehensive resource for exploring and comparing algorithm…

    Jupyter Notebook 1

  3. Movie_Recommendation_System Movie_Recommendation_System Public

    Movie Recommendation System - Web Application using Machine Learning: The system utilizes pre-processed movie data and a cosine similarity matrix to suggest similar movies

    Jupyter Notebook 1

  4. House_Price_Prediction House_Price_Prediction Public

    This project aims to predict Prices of House. It involves several key stages, including data preprocessing, feature engineering, model selection, and evaluation. The goal is to develop a model that…

    Jupyter Notebook 1

  5. Among-Us-Exploratory-Data-Analysis Among-Us-Exploratory-Data-Analysis Public

    This Notebook contains an exploratory data analysis (EDA) of gameplay statistics from the popular multiplayer game, Among Us. Originally released in 2018, the game surged in popularity during the s…

    Jupyter Notebook 1

  6. Digit_Classification Digit_Classification Public

    This project demonstrates various machine learning techniques for classifying handwritten digits from the MNIST dataset. It covers data preprocessing, model training, evaluation, and advanced class…

    Jupyter Notebook 1