'tobi's starred repositories
e2e-data-engineering
An end-to-end data engineering pipeline that orchestrates data ingestion, processing, and storage using Apache Airflow, Python, Apache Kafka, Apache Zookeeper, Apache Spark, and Cassandra. All components are containerized with Docker for easy deployment and scalability.
30-Days-Of-Python
30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
football_analytics
📊⚽ A collection of football analytics projects, data, and analysis by Edd Webster (@eddwebster), including a curated list of publicly available resources published by the football analytics community.
practical-data-engineering
Practical Data Engineering: A Hands-On Real-Estate Project Guide
aemo_fabric
example of a Microsoft Fabric Solution
HomeHarvest
Python package for scraping real estate property data
cumulative-table-design
This repository helps teach people how to correctly define and create cumulative tables!
fabric-samples
Samples and data for Microsoft Fabric Learn content
BerlinSalaryTrends
This was a Salary Trends repo.
NSEFinance-PHP
PHP Library for NSEFinance
Algorithms
:computer: Data Structures and Algorithms in Python
python-mini-projects
A collection of simple python mini projects to enhance your python skills
ml_prod_tutorial
Explore tips and tricks to deploy machine learning models with Docker.
crime_rate_by_regression_trees
Predicting crime rate using DecisionTreeRegressor(), analyzing the importance of specific features, and reducing the complexity of the model.
churn_prediction
The probabilities calculated by the model BG-NBD are used to define the target variable to predict customer churn.
Machine-Learning-from-Scratch
Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.