Top Rating
- Top Contributors
  Discover the Top Open Source contributors by country or by language
- Interviews
  Discover real stories from Open Source developers
Discover

Discover your Favorite Language
Discover the top trending repositories and projects on Github. Explore the latest trends in your preferred languages.

Zig

Lua

Crystal

Objective-C

Scala

Go

PHP

Perl

More Languages
Awesome

Awesome repositories
Discover the most awesome repositories and projects of your favorite languages. Inspired by the Awesome-* lists trend in GitHub.

C

Rust

Elixir

Java

C++

Scala

Go

PHP

More Languages
By Country

Rankings by Country
Discover the community of talented open source contributors in each country.

🇷🇼 Rwanda

🇰🇵 North Korea

🇧🇦 Bosnia and Herzegovina

🇦🇼 Aruba

🇮🇶 Iraq

🇩🇿 Algeria

🇭🇰 Hong Kong

🇳🇿 New Zealand

All Countries Compare Countries

Orkhan-Bakir/Fifa-Analysis

Stars
1
Language
Jupyter Notebook
Created about 3 years ago
Updated about 3 years ago

Orkhan-Bakir/Fifa-Analysis

Orkhan-Bakir

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

The Federation Internationale de Football Association (FIFA)

Movie-Recommendation-with-ML

In this Data Science project, you will see how to build a basic model of simple as well as content-based recommendation systems.

Jupyter Notebook

Pfizer-Analysis

The dataset that I am using for the task of Pfizer vaccine sentiment analysis is downloaded from Kaggle, which was initially collected from Twitter when people were sharing their opinions about the Pfizer vaccine. Let’s start the task of Pfizer vaccine sentiment analysis by importing the necessary Python libraries and the dataset:

Jupyter Notebook

Employee-Attrition

Predicting Employee Attrition in Python.

Jupyter Notebook

Ecommerce_and_customers-ML-using-Sklearn-

Analysis and prediction using ML (Sklearn)

Jupyter Notebook

Heart-Disease-Prediction-with-ML.

Heart disease is one of the biggest causes of morbidity and mortality among the population of the world. Prediction of cardiovascular disease is regarded as one of the most important subjects in the section of clinical data science.

Jupyter Notebook

Using-PyCaret-for-ML-models-

Compare different models, fixing the imbalance problem, building the best model according to best metric and perform hyperparameter tuning for accuracy, using Logistic Regression, CatBoost Classifier and LightGBM algorithms.

Jupyter Notebook

USA_Housing-Linear-Regression-ML-

Here we have data "USA Housing" , which I took from Kaggle.com. I statistically analyzed and also used Linear Regression analysis using Machine Learning and made predictions.

Jupyter Notebook