Build a regression model to understand the factors on which the demand for bike sharing systems vary on and help a company optimise its revenue.

A machine learning project that predicts the future price of Ethereum (ETH) using the price data gathered from coincodex.com.

The Crop Recommendation with Machine Learning project is designed to assist farmers and agricultural professionals in selecting the most suitable crops for their specific environmental conditions.

Build end-to-end DL pipeline for computer vision (Image classification) for “Chest Disease Classification from Chest CT Scan Images” and deploy Flask web app to AWS EC2 with Docker and CI/CD tool: Jenkins

website forcasting bukit darmo property

Website Prediksi Pembuatan beer Menggunakan Beberapa Model

A lung cancer decision tree is a type of machine learning model that can predict the likelihood of a patient having lung cancer based on various features such as smoking habits, age, and symptoms such as yellow fingers, anxiety, fatigue, etc.

International Sports Events and Repression in Autocracies. Statistical Analysis with Python

Supervised learning model that predicts whether an individual makes more than $50000 per year (accuracy score of 0.87) .

Using ML methods, develop a data analytics-based strategy to help classify if prospective borrowers would be a risk to a bank. With the use of predictive modeling, a bank could determine whether an applicant is approved or rejected for a loan by analyzing their credit risk. (Final Group Project for CIND119)

Iniciando estudos sobre o dataset de distribuição de bolsas do ProUni

machine learning practitioner， android and python

UCLA Computer Science Summer Institute; CS97 - Introduction to Data Science

This report is generated of what cryptocurrencies are available on the trading market and how they can be grouped using classification.

An NLP analysis on the impact of Star Trek: The Next Generation's character spoken lines and how it affects the rating of the episode.

utilize the sklearn library to train models on a set of data and use to make predictions

Simple API, built with FAST API, with advanced linear regression of California home prices.

Testing different machine learning methods on advertising dataset. With a simple RESTAPI using flask