This repository houses a diverse collection of projects developed using Jupyter Notebooks, focusing on testing various machine learning pipelines, neural network models, and statistical machine learning approaches. Through exploration of different datasets, the projects delve into predictive modeling, classification tasks, and in-depth analyses.

My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings 👍

Complite Machine Learning With Python!

Using R Markdown for Data Analysis, Machine Learning

Mobile Phone Price Category Classification Model with SVM and a Flask App to Create API Endpoints.

Deep Learning Projects

Practices and Assignments from the Advanced Machine Learning Class

The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

Data fetched by wafers is to be passed through the machine learning pipeline and it is to be determined whether the wafer at hand is faulty or not apparently obliterating the need and thus cost of hiring manual labour.

In this section we will be predicting diabetes using classification machine-learning algorithms

In this section, we will examine classification algorithms in machine learning.

Code for "Designing Decision Support Systems Using Counterfactual Prediction Sets". ICML 2024.

My portfolio website regarding data science projects. Some visualization and analysis projects reflect work for PITAPOLICY clients.

This repository shows the source code for detection of COVID-19 in medical images of the lung with high precision. Also, this code identifies the areas of the lung with lesions caused by the disease and quantify them in relation to the total lung parenchyma.

This repository contains all the projects that I have made.

Machine Learning Library for Classification Tasks

Fake news is a significant issue in today's digital landscape. This project aims to tackle this problem by developing a system that can effectively detect fake news using machine learning techniques.

📸 Upload your mammogram image and answer a few questions. 🧠 Our app, powered by TensorFlow machine learning models, predicts whether your results are benign or malignant. 🚀 Utilizing image processing and extensive dataset training, BreastOnco ensures accurate and reliable diagnoses.

Demo DVC project training a classification model on tabular data

Machine learning library for classification tasks