This project demonstrates iris flower classification using machine learning. It's an accessible introduction to data science and classification techniques. Explore the code and Jupyter Notebook to enhance your data science skills.

This is GEM repo, as it has all the Hands-On ML notebooks

this notebook is just a copy paste of other notebooks that I found online

This repository contains a Jupyter Notebook exploring the adult income dataset. The notebook performs Exploratory Data Analysis (EDA), including visualizations with charts and graphs. Additionally, it implements various classification models to predict income and analyzes their accuracy.

Educational notebooks reviewing machine learning models and concepts.

Loan Eligibility Prediction Model: A machine learning application to predict loan approval based on applicant data. Includes a web interface for submitting loan applications and receiving predictions. Built with Python and Jupyter Notebook.

👩‍💻This repository contains implementations of various machine learning algorithms, along with example datasets and Jupyter Notebook files for demonstration.

This repo consists of code, Jupyter Notebooks from various Learning Tracks hosted on Simplilearn

This notebook uses historical data on animal outcomes in a shelter to create a classification model that will predict whether animals will be adopted or not

Predict tomorrow's rain using machine learning on Australian weather data. Explore, preprocess, and train models in a Jupyter notebook.

Different ML Algorithms both in scripts & Jupyter Notebooks

This project aims to provide an all-in-one Jupyter notebook covering the entire pipeline for football player classification in Snowpark. From data ingestion to model inference.

This notebook explores comprehensive machine learning analysis on a rock dataset, covering attribute distribution analysis, outlier identification using statistical values and visualizations like scatter plots,and applying Multinomial Logistic Regression,Support Vector Machines, Random Forest classifiers,Ensemble learning,hyperparameter optimizatio

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow

Python based Jupyter Notebook Project to Predict Potential Customer for Term Deposit Marketing Campaign in Banking Institution using Logistic Regression, K-NN, Decision Tree & Random Forest Supervised Classification Machine Learning

This repository contains a collection of fundamental topics and techniques in machine learning. It aims to provide a comprehensive understanding of various aspects of machine learning through simplified notebooks. Each topic is covered in a separate notebook, allowing for easy exploration and learning.

Prediction-of-House-Grade-Classification using python ( Jupyter Notebook)

Classification Machine Learning project

Relationship prediction between nodes using Neo4J and Jupiter Notebook

This repository contains a notebook that examines the performance of various classification models on the Kaggle dataset: https://www.kaggle.com/datasets/andrewmvd/heart-failure-clinical-data. The best performing model was a Random Forest Classifier with 86.67% accuracy.