This code demonstrates how to integrate Apache Beam with scikit-learn datasets and perform simple data transformations. It loads the Linnerud dataset from scikit-learn, converts it into a Pandas DataFrame for easier manipulation.

Developed a feed-forward neural network in PyTorch to classify breast cancer as malignant or benign. Applied data scaling, trained a two-layer model, and achieved high test accuracy. Demonstrates skills in deep learning, binary classification, and medical ML analysis.

A project predicting housing prices using the California Housing Dataset with Linear Regression, featuring data preprocessing pipelines, model training, and future prediction capabilities.

This repository is dedicated to storing the code developed during the "Machine Learning Model Deployment with Streamlit" course on Udemy. The course covers basic to advanced techniques for deploying machine learning models using Streamlit.