Rohit Bathija (robathija)

robathija

Geek Repo

Company:Kroll

Location:Toronto

Home Page:http://robathija.github.io

Twitter:@robathija

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Rohit Bathija's repositories

Clustering-Customers-with-k-means

Python code to cluster retail customers using k-means algorithm and RFM (recency, frequency and monetary) principle

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PolyBase-SQLServer-AzureBlobStorage

Using PolyBase to query Azure Blobs from SQL Server

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PythonFlaskWebApp-DownloadSQLDataAsCSV

A Simple web app to download a dataset as CSV built with Python 3 using Flask and Jinja

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abot

C# web crawler built for speed and flexibility. Please star this project! +1

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atlas-v2

Apache Atlas v2 Rest API

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azure-sql-db-python-rest-api

REST API using Python, Flask and Azure SQL

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AzureFunctionUploadToSQL

Azure function to upload a CSV file to Azure SQL automatically via Azure Blob Store

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CheatSheets

Repository of various cheat sheets found in the Internet

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mongodb-python-flask-rest-api

A simple CRUD REST API built using Python Flask and MongoDB Atlas with Azure SQL as a data source

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pysprak-SQLtoCSVtoParquet-Azure

A PySpark script to load data from SQL/NoSQL Dbs to a CSV, perform ETL and clean up and finally load into Azure Data Lake Gen2/HDFS

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customer-behavior-analytics-IBMWatson

Customer Behavior Analytics using Python

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DatabricksDEBoilerplate

Boilerplate stuff for Data Engineering in Azure Databricks

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devportfolio

A lightweight, customizable single-page personal portfolio website template built with JavaScript and Sass

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dice-rolling-simulator

This is a simple dice rolling simulator built in Pyton 3

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leetcode-and-hackerrank-sql

Solutions to some hard problems on Leetcode and HackerRank

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Machine-Learning-with-Python

Python code for common Machine Learning Algorithms

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robathija.github.io

My GitHub.io Resume

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twitter-sentiment-analysis

R shiny web application to scrape tweets based on user-defined search keyword and perform sentiment analysis of the tweets. Sentiment analysis of tweets consists of classifying tweets into emotion classes (i.e., anger, disgust, fear, joy, sadness and surprise) and also polarity classes (i.e., negative, neutral and positive) using naïve Bayes classifier. The tweets are scraped, classified into sentiment classes and visualized in R using twitteR, sentiment and ggplot2 packages, respectively.

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