This notebook explores fraud detection using various machine learning techniques.

Different machine learning approaches on classifying customers who are most likely to purchase an offer. Made with Jupyter Notebook, scikit-learn, and other helpful python packages.

Here you will find a Notebook with examples of various Machine Learning algorithms (ML), more specifically, Supervised and Unsupervised Learning examples. All of the code is followed by explanations and everything is easy to use and to understand thanks to the documentation.

ML models implemented using Python libraries. Each notebook is designed to demonstrate fundamentals of ML concepts, and it can serve as a learning resource for beginners.

This repository is made following the course by Sir Jose Portilla, and focuses on Supervised Machine Learning algorithms. I studied all these concepts in December 2023

The Jupyter notebook repository covering a wide variety of machine-learning models and algorithms ranging from regression to classification.

This project showcases iris flower classification using machine learning. It's a beginner-friendly example of data science and classification techniques. Explore the code, Jupyter Notebook, and enhance your data science skills.

Jupyter notebook for IoT threat detection using ensemble machine learning. Features data preprocessing, model training (Logistic Regression, Decision Trees, Neural Networks, etc.), and ensemble techniques for enhanced accuracy.

Project made in Jupyter Notebook with Kaggle Credit Card Fraud Detection Dataset 2023, which aims at selection of best supervised machine learning model for capturing credit card frauds.

A trained ML model for prediction of house prices in IOWA using Random Forest Regression technique

"Heart Disease Prediction Project: Employing a Decision Tree algorithm, this machine learning project achieves 83.6% accuracy in predicting heart disease risk. Valuable for early intervention in cardiovascular health, it offers a user-friendly Jupyter Notebook for usage and welcomes contributions to enhance its effectiveness."

🌲 Learning the different implementation approaches of decision trees using Python (Jupyter Notebook).

In this repository, an attempt was made to practice the supervised machine learning techniques, in particular the classification based technique, i.e., Decision Trees and Random Forests. The csv sheet contains the data which is loaded in the jupyter notebook ("training.ipynb") from which further analysis was done.

This repository contains all learning materials for become a data scientist. It have notebooks realted to tensorflow, scikit-learn, and deep learnings like gradient descent etc. 😄

This repository focuses on using machine learning algorithms, such as decision trees, gradient boosting, and random forest, to advance exoplanet detection. Explore data analysis, Jupyter notebooks, and code implementations, and contribute.

Welcome to the Machine Learning Repository! This repository is a collection of notebooks showcasing various machine learning projects and implementations. It incluedes Decision tree algorithm, Random forest , Support vector machine etc.

A list of machine leaarning tasks carried out in a set of series spread across 3 Colab Notebooks

In this repo you shall find the jupyter notebooks of the different phases of the project for Introduction to Data Science course I took in Fall 2022 quarter at Seattle University.

A Deep Learning Diabetes Prediction Model implemented as a Jupyter Notebook that employs various machine learning techniques to predict the likelihood of diabetes based on a set of features.