Regular practice on Data Science, Machien Learning, Deep Learning, Solving ML Project problem, Analytical Issue. Regular boost up my knowledge. The goal is to help learner with learning resource on Data Science filed.

python ETL framework

For this project I am creating an ETL (Extract, Transform, and Load) pipeline using Python, RegEx, and SQL Database. The goal is to retrieve data from different sources, clean and transform it into a useful format and finally load the data into an SQL database where the data is ready for further analysis. The result is an established automated p…

3NF-normalize Yelp data on S3 with Spark and load it into Redshift - automate the whole thing with Apache Airflow

Implementation of an ETL process for real-time sentiment analysis of tweets with Docker, Apache Kafka, Spark Streaming, MongoDB and Delta Lake

This is a PHP project which combines ETL with different strategies to extract data from multiple databases, files, and services, transform it and load it into multiple destinations.

This project repository provides a headless module to enrich location data in a database table using the Google Maps Geocode API.

a data warehouse for an online course shop

Sugar candy for data scientist. Easy manipulation in time-series data analytics works.

This is a sentimental analysis project that aims to provide a better insight on customers' satisfaction based on comments gathered (scrapped) from social media using google's Bert classification model.

Dynamic website scraper and email notifier.

Udacity nd027 Data Modeling with Postgres

Scraping BooksToScrape (P2 OC D-A Python) : Utiliser les bases de Python pour l'analyse de marché

I made various data normalization operations with python scripts. Target data in CSV format

Extractor of Ethereum data to Dgraph format, utilities to analyse the indexed data.

We examine two data sets relate with the music Industry. We Extract, transform and load the data sets in order to create a data base and identify insides and trends about the music Industry.

A Case Study of Extract, Transform, Load. Documentaion includes sources of data, types of data wrangling performed (data cleaning, joining, filtering, and aggregating) and the schemata used in the final production database. Technologies used include Pandas, PostgreSQL, Jupyter Notebook.

An ETL process for a fictitious streaming service, Amazing Prime, was developed in Jupyter Notebook. The code was then refactored into a Python script to automate the ETL process.

ETL and analysis of trends in product review data from Amazon Vine.

This repository contains OLTP, ETL process (using Pentaho Data Integration), and OLAP of credit card dataset. The dataset is taken from Kaggle (https://www.kaggle.com/rikdifos/credit-card-approval-prediction) and part of author Capstone Project.