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.

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.

Source for book "Feature Engineering A-Z"

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

A beginner level Machine Learning pipeline covering all basic steps.

Kaggle's: Machine Learning from Home-Depot. Demonstrates basic machine learning, analysis, and visualization techniques.

An exploratory study of understanding visit patterns of customers at a shopping mall and predicting their visit in the upcoming week.

Build a model to identify the importance and effect of home characteristics on sale prices in the Seattle area.

Ipny and HTML files EDA Case Studies

Machine Learning project to predict popularity of NYT articles

MIT xPro - New York City Taxi Ride Duration Prediction Graded Case Study

A movie recommendation system built with machine learning and NLP to recommend movies based on searched movies

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?

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

This software is for graphic work and can be used to graphically design a website.

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

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

SQL and NoSQL Databases

End-to-end projects: customer churning prediction using the Random Forest Classifier Algorithm with 97% accuracy; performing pre-processing steps; EDA and Visulization fitting data into the algorithm; and hyper-parameter tuning to reduce TN and FN values to perform our model with new data. Finally, deploy the model using the Streamlit web app.