Pinned Repositories
10-steps-to-become-a-data-scientist
📢 Ready to learn! ✌ you will learn 10 skills as data scientist: Machine Learning, Deep Learning, Data Cleaning, EDA, Learn Python, Learn python packages such as Numpy, Pandas, Seaborn, Matplotlib, Plotly, Tensorfolw, Theano...., Linear Algebra, Big Data, Analysis Tools and solve some real problems such as predict house prices.
2018-cs109b
Harvard CS109b Public Repository
a-2017
Public Repository for cs109a, 2017 edition
adult
adult dataset
ai-project-fraud-detection
Credit card fraud detection using kafka,scikit,flask,cassandra
air-passengers-arima
ARIMA forecasts on the AirPassengers dataset built in Python and Tableau
Amazing-Feature-Engineering
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
Cookbook
The Data Engineering Cookbook
Machine-Learning-with-Python
Python codes for common Machine Learning Algorithms
mmejdoubi's Repositories
mmejdoubi/Amazing-Feature-Engineering
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
mmejdoubi/aws-machine-learning-university-accelerated-tab
mmejdoubi/c9-python-getting-started
Sample code for Channel 9 Python for Beginners course
mmejdoubi/colabcode
Run VSCode (codeserver) on Google Colab or Kaggle Notebooks
mmejdoubi/COMS4995-s20
COMS W4995 Applied Machine Learning - Spring 20
mmejdoubi/cp-ia-intro-ml-2022
Notebooks pour le module "Fondamentaux du Machine Learning" de la formation Chef de Projet IA (2022)
mmejdoubi/credit_risk_model
A comprehensive credit risk model and scorecard using data from Lending Club
mmejdoubi/Data-Science-for-Marketing-Analytics-Second-Edition
mmejdoubi/Data-Visualization
Data Visualization with Python
mmejdoubi/DataEngineeringProject
mmejdoubi/docker-fundamentals
mmejdoubi/DS_Production
mmejdoubi/e01
mmejdoubi/Home-Credit-Default-Risk-Recognition
The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.
mmejdoubi/in_SAS_
mmejdoubi/iv-and-woe-python
This repository contains analysis of churn in telephone service company (using IV and WOE), comparison of effect size and information value and quick tutorial how to use information value module (created for this analysis).
mmejdoubi/Learning-PySpark
Code repository for Learning PySpark by Packt
mmejdoubi/Linux-command
linux command notes
mmejdoubi/machine-learning-book
Code Repository for Machine Learning with PyTorch and Scikit-Learn
mmejdoubi/marjane_api
mmejdoubi/ML-API
Guide on creating an API for serving your ML model
mmejdoubi/mlflow_fraud_ecom
mmejdoubi/optbinning
Optimal binning: monotonic binning with constraints
mmejdoubi/pandas_ui
pandas_ui helps you wrangle & explore your data and create custom visualizations without digging through StackOverflow. All inside your Jupyter Notebook ( alternative to Bamboolib ).
mmejdoubi/Python-Machine-Learning
Tous les codes utilisés dans la série YouTube Python Spécial Machine Learning !
mmejdoubi/rfm-analysis-python
This repository contains RFM analysis applied to identify customer segments for global retail company and to understand how those groups differ from each other.
mmejdoubi/The-Zimnat-Insurance-Assurance-Challenge-by-ZindiWeekendz
mmejdoubi/tutorial-assets
Assets used in Cloudera Tutorials
mmejdoubi/webstep-sklearn-quickstart-2019
Scikit-learn quickstart tutorial for Webstep
mmejdoubi/XuniVerse
xverse (XuniVerse) is collection of transformers for feature engineering and feature selection