Welcome to my collection of curated Machine Learning notebooks, covering end-to-end workflows from exploratory data analysis (EDA) to predictive modeling. This repository showcases my hands-on work using real-world datasets and demonstrates my skills in Python, data storytelling, and machine learning fundamentals.
Notebooks >Analysis >Prediction
- Goal: Predict survival of passengers based on socio-demographic & ticket data.
- Techniques: Logistic Regression, Random Forest, feature engineering.
- Highlight: Custom pipelines for preprocessing and evaluation.
- Goal: Classify sonar signals as rocks or mines (military-grade binary classification).
- Techniques: KNN, SVM, Decision Trees.
- Highlight: High-dimensional data handling & model comparison.
- Goal: Predict diabetes occurrence from medical diagnostic data.
- Techniques: Data scaling, Logistic Regression, ROC analysis.
- Highlight: Balanced accuracy evaluation and clinical feature analysis.
- Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn)
- Jupyter Notebooks / Google Colab
- Model selection, hyperparameter tuning, and evaluation metrics
- Clean, modular notebook structure for readability and reusability
This repo reflects:
- My ability to analyze and model real-world data
- Clean coding habits and reproducible workflows
- Readiness to work in data-driven teams — from exploration to deployment
I'm actively looking for roles in:
- Data Analytics
- Data Science
- Machine Learning Engineering
- Internships or entry-level data roles
📧 Email: sohamwaghe47@gmai.com 🔗 LinkedIn: https://www.linkedin.com/in/sohamwaghe/
⚡ Thanks for visiting! Feel free to fork, star, or reach out if you’re hiring or collaborating.