Hi there,

I am Ansu John, and am working as Data Engineer. I joined Github to publish data science projects exploring various analytics tools and techniques, and to identify best practices in the process. If you are interested, please find the project details below.

Do message me in LinkedIn for any comments / suggestions.

ETL Verification - verification of loaded data after ETL using scala.

Big Data Analysis - demonstrates the various Spark RDD / Dataframe operations using PySpark 3.0.1 library.

The below repositories are build to explore Scikit-learn / Spark 3.0.1 API functionalities using Python :

Extarcting, transforming and selecting features from data - explore various ways of extarcting, transforming and selecting features from data for machine learning using PySpark 3.0.1 library.

Scikit-learn Regression - demonstrates the various machine learning regression models using Scikit-learn 0.23.2 library.

Scikit-learn Classification - demonstrates the various machine learning classification models using Scikit-learn 0.23.2 library.

Linear Regression with Spark - demonstrates the implementation of Linear Regression using PySpark 3.0.1 library.

Logistic Regression with Spark - demonstrates the implementation of Logistic Regression using PySpark 3.0.1 library.

Spark MLlib Clustering - demonstrates the various clustering algorithms using PySpark 3.0.1 library.

Spark MLlib Classification - demonstrates the various classification algorithms using PySpark 3.0.1 library.

Movie Recommender System - implementation of movie recommendation systems using Apache Spark 3.0.1 ML alternating least squares (ALS)

Natural Language Processing - explore the different NLP options using Python.

Credit Card Fraud Detection - predict fraudulent credit card transactions using TensorFlow, Keras, K Neighbors, Decision Tree, SVM Regression and Logistic Regression classfiers .

IBM HR Analytics Employee Attrition - predict using LogisticRegression and RandomForestClassifier.

AB Testing - exploration of data from database using python libraries : pyodbc and matplotlib libraries.

Customer Segmentation - demonstrates the various python machine learning clustering models.

Retail Sales Forecasting - predicting sales using various python machine learning models.

Study Notes - consolidated notes.

Introduction to Python - demonstrates the basics of Python coding.