Otobi1

Otobi1's Stars

• Asabeneh/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

  Language:Python43.7k8271398.4k

• Python-World/python-mini-projects

  A collection of simple python mini projects to enhance your python skills

  Language:Python15.2k2911845k

• plasma-umass/scalene

  Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals

  Language:Python12.3k92477402

• Datalux/Osintgram

  Osintgram is a OSINT tool on Instagram. It offers an interactive shell to perform analysis on Instagram account of any users by its nickname

  Language:Python10k2021.1k2.2k

• megadose/holehe

  holehe allows you to check if the mail is used on different sites like twitter, instagram and will retrieve information on sites with the forgotten password function.

  Language:Python7.8k121107888

• prakhar1989/Algorithms

  :computer: Data Structures and Algorithms in Python

  Language:Python3.1k14611828

• eddwebster/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.

  Language:Jupyter Notebook2k844278

• sspaeti-com/practical-data-engineering

  Practical Data Engineering: A Hands-On Real-Estate Project Guide

  Language:Jupyter Notebook49912385

• Bunsly/HomeHarvest

  Python package for scraping real estate property data

  Language:Python368157082

• microsoft/fabric-samples

  Samples and data for Microsoft Fabric Learn content

  Language:Jupyter Notebook290280205

• EcZachly/cumulative-table-design

  This repository helps teach people how to correctly define and create cumulative tables!

  Language:Python2235025

• airscholar/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.

  Language:Python21647101

• curiousily/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.

  Language:Jupyter Notebook1714062

• m-kovalsky/Fabric

  Useful code for fabric notebooks

  Language:Python14112729

• LearnWeb3DAO/BasicFrontEndTutorial

  Language:HTML1187046

• realaisles/BerlinSalaryTrends

  This was a Salary Trends repo.

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• ergest/metricsplaybook

  Language:Shell32204

• djouallah/aemo_fabric

  example of a Microsoft Fabric Solution

  Language:Python311013

• Carsoncantcode/Sports-Betting-API

  Language:Python26204

• misraturp/Pandas-dataframe-reshaping

  Language:Jupyter Notebook16207

• edseldim/steam_prices_data_engineering

  Language:Python13100

• juanitorduz/ml_prod_tutorial

  Explore tips and tricks to deploy machine learning models with Docker.

  Language:Python13144

• gurokeretcha/Fish-Weight-Prediction-Beginners

  Language:Jupyter Notebook72

• cyberomin/NSEFinance-PHP

  PHP Library for NSEFinance

  Language:PHP3401

• Carsoncantcode/REScraper

  Language:Python2100

• skepticalchemist/churn_prediction

  The probabilities calculated by the model BG-NBD are used to define the target variable to predict customer churn.

  Language:Jupyter Notebook21

• dbrownems/SqlToOneLake

  Language:C#10

• ola0x/single_chicken_detector

  Language:Jupyter Notebook110

• osule/lard

  An operator for performing the Extract and Load bit of the ELT data pipeline in an Apache Airflow workflow.

  Language:Python120

• skepticalchemist/crime_rate_by_regression_trees

  Predicting crime rate using DecisionTreeRegressor(), analyzing the importance of specific features, and reducing the complexity of the model.

  Language:Jupyter Notebook1