Skip to content
View YugamdeepGoyal's full-sized avatar
7 followers · 4 following

Block or report YugamdeepGoyal

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Content in all repositories owned by your account will be closed.
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
YugamdeepGoyal/README.md

Yugamdeep Goyal

Artificial Intelligence and Data Science Student currently in second year of BTech/BE. Focused on applied machine learning and data analysis, with ongoing work in deep learning.

Areas of Focus

  • Machine Learning: classification, regression, model evaluation and tuning
  • Data Analysis: exploratory data analysis, data cleaning, feature engineering
  • Deep Learning: currently learning NLP with deep learning

Technical Skills

Python, scikit-learn, pandas, NumPy, XGBoost, Jupyter Notebook, seaborn/matplotlib, tensorflow, keras

Selected Projects

healthcare-dataset-eda Exploratory data analysis and ML preprocessing pipeline on 20,000 patient records spanning 32 diseases, including handling of structured missing data, ordinal encoding, and multi-label symptom binarization.

PremiumPulse Prediction of medical insurance charges using ten regression models, ranging from linear regression to XGBoost, with systematic hyperparameter tuning and reusable scikit-learn pipelines.

beyond_accuracy Stroke risk prediction pipeline using logistic regression with SMOTE oversampling to address class imbalance, with classification threshold optimization.

titanic_prediction Titanic survival prediction using logistic regression and support vector machines, implemented with scikit-learn pipelines.

Python_Data_Analysis_Project Exploratory data analysis on 7,668 IMDb movies from 1980 to 2020 using Python, pandas, and seaborn.

Pinned Loading

  1. titanic_prediction titanic_prediction Public

    Titanic survival prediction using Logistic Regression and SVM with sklearn pipelines

    Jupyter Notebook

  2. PremiumPulse PremiumPulse Public

    Predicting medical insurance charges using 10 regression models - from linear regression to XGBoost. Includes systematic hyperparameter tuning, reusable scikit-learn pipelines, and some feature eng…

    Jupyter Notebook

  3. beyond_accuracy beyond_accuracy Public

    A machine learning pipeline that predicts stroke risk from patient health data. Uses logistic regression with SMOTE oversampling to handle class imbalance, and optimizes the classification threshol…

    Jupyter Notebook

  4. mnist_dataset_model mnist_dataset_model Public

    Fully connected neural network for MNIST digit classification, tuned via Keras Tuner Hyperband search. Achieves ~98.58% test accuracy. Includes reproducible training pipeline, confusion matrix anal…

    Jupyter Notebook

  5. healthcare-dataset-eda healthcare-dataset-eda Public

    EDA and ML preprocessing pipeline on 20,000 Indian patient records across 32 diseases. Handles structured missing data, ordinal encoding, multi-label symptom binarization and feature engineering.

    Jupyter Notebook

  6. Python_Data_Analysis_Project Python_Data_Analysis_Project Public

    Exploratory data analysis on 7,668 IMDb movies (1980–2020) using Python, pandas, and seaborn

    Jupyter Notebook