Used supervised machine learning classification algorithms such as Decision Tree and Random Forest to predict whether or not water pumps in Tanzania require repair.

Classify whether the text is a spam or not.

Learning tradicional machine learning with scikit-learn.

Example showing how Scikit-learn can be used to recognize images of hand-written digits.

Intro to Machine Learning. Uses random forest and logistic regression models to compare accuracy on loan approvals.

Predicting whether a given passenger would have survived during the titanic crash using scikit-learn.

Space Titanic Data Project

Binary classfication with scikit-learn and Random Forest models.

Simple anti-spam detector for text messages

Our objective is to predict the eligibility for an employee to be promoted or not. The dataset is taken from live hackathon in Analytics Vidhya

Using this notebook we can learn simple SCIKIT-LEARN for Data science and Machine learning

Using Machine Learning for Creating a Movie Recommendation System

python-data-science-machine-learning

PennState STAT508 course model examples in Python (sklearn)

A Flight price prediction application that predicts fares of flights for a particular date based on various parameters like Source, Destination, Stops & Airline. Data used in this project is scraped from an online ticket booking website 'Ease my Trip' using a Python module name BeautifulSoup. The dataset goes through Data Cleaning, Data Wrangling,

Welcome to this repository! This project uses data science and machine learning to predict retail product sales prices. It includes a robust data preprocessing pipeline, handles outliers, and features an ensemble model. With real-time predictions through a user-friendly Flask app and API, it's a game-changer for businesses seeking accurate sales.

A Flight price prediction application that predicts fares of flights for a particular date based on various parameters like Source, Destination, Stops & Airline. A web application is created using Flask through which users can interact and get accurate predictions of flight fares.

This repository contains all of the code taught in the textbook called "Introduction to Machine Learning with Python: A Guide for Data Scientists" by Andreas Müller and Sarah Guido.

Boston House Prices Data Project