Toolkit for Apache Spark ML for Feature clean-up, feature Importance calculation suite, Information Gain selection, Distributed SMOTE, Model selection and training, Hyper parameter optimization and selection, Model interprability.

Source for book "Feature Engineering A-Z"

Anomaly detection and failure prognosis applied to industrial machines

Numpy and Pandas are one of the most important building blocks of knowledge to get started in the field of Data Science, Analytics, Machine Learning, Business Intelligence, and Business Analytics. This Tutorial Focuses to help the Beginners to learn the core Concepts of Numpy and Pandas and get started with Machine Learning and Data Science.

The code for the survey paper of feature extraction

Using machine learning models to predict if patients have chronic kidney disease based on a few features. The results of the models are also interpreted to make it more understandable to health practitioners.

This project focuses on using the AWS open-source AutoML library, AutoGluon, to predict bike sharing demand using the Kaggle Bike Sharing demand dataset.

Project: What factors impact the accuracy of airfare prediction?

A novel feature selection algorithm using ACO-Ant Colony Optimization, to extract feature words from a given web page and then to generate an optimal feature set based on ACO Metaheuristics and normalized weight defined as a learning function of their learned weights, position and frequency of feature in the web page. JAVA based ACO Framework

We will analyze a dataset provided by an e-commerce marketplace called [Olist](https://www.olist.com) to answer the CEO's question: Should Olist remove underperforming sellers from its marketplace? How to increase customer satisfaction (so as to increase profit margin) while maintaining a healthy order volume?

The "Movie Genre Prediction" project is a comprehensive machine learning system designed to forecast a movie's genre by analyzing its attributes. By employing advanced machine learning methods, it strives to improve genre classification accuracy, offering valuable insights to creators, film aficionados, and the entertainment sector.

Explore an ML model with Logistic Regression, SVM, Gradient Boosting, Random Forest, and Decision Tree, enhanced via Hyperparameter Tuning. Experience our GUI-based ML model with 82.49% accuracy. Try it now!

Machine Learning project to predict popularity of NYT articles

This repository contains the code components of work carried out for analyzing the Medical Provider Fraud Detection dataset with the intent to find most important features to crack down the potentially fraud providers.

we aim to predict trends in the Canadian market basket using sentiment analysis techniques. Sentiment analysis involves analyzing text data to determine the sentiment expressed, whether positive, negative, or neutral.

Find Similar Commercial items by using NN with Mixing Textual, Categorical and Numerical features

Zindi competition on predicting the likelihood of credit default of ecommerce clients

This assignment centered around advanced data minipulation: gathering columns, using pipes, and creating new columns with mutate. As in homework 2, Census data on Michigan was used as a base for this assignment. Simple statistics such as mean, median and trimmed mean were used to describe the variables. Visualization was also implemented to help…

Use of kmeans segmentation algorithm to classify dermis, epidermis and tumor infiltration.