Noza's repositories

Stargazers:0Issues:0Issues:0

feature-selection-for-machine-learning

Code Repository for the online course Feature Selection for Machine Learning

License:NOASSERTIONStargazers:0Issues:0Issues:0

feature-engineering-for-machine-learning

Code Repository for the online course Feature Engineering for Machine Learning

License:NOASSERTIONStargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0
Stargazers:0Issues:0Issues:0

powerbi-jupyter

A Custom Jupyter Widget Library for Power BI

License:MITStargazers:0Issues:0Issues:0

TaxiFareWebsite

Prediction app for taxi

Stargazers:0Issues:0Issues:0

ml_hacks

Приёмы в машинном обучении

Stargazers:0Issues:0Issues:0

noza_ml_project

The package desribes the ML project that I undertook at Le Wagon Data Science Bootcamp

Language:MakefileStargazers:0Issues:0Issues:0

dotfiles

Default configuration for Le Wagon's students

Language:ShellStargazers:0Issues:0Issues:0

zero-to-mastery-ml

All course materials for the Zero to Mastery Machine Learning and Data Science course.

Stargazers:0Issues:0Issues:0

causallift

CausalLift: Python package for causality-based Uplift Modeling in real-world business

License:NOASSERTIONStargazers:0Issues:0Issues:0

Hands-On-Gradient-Boosting-with-XGBoost-and-Scikit-learn

Hands-On Gradient Boosting with XGBoost and Scikit-learn Published by Packt

License:MITStargazers:0Issues:0Issues:0

Pricing-optimization-Model

Optimize revenues through pricing algorithm in python - Demand with uniform distribution

Stargazers:0Issues:0Issues:0

DataAnalyst-NanoDegree

Data Science Portfolio - DAND - Udacity - Projects -Data Analytics

Stargazers:0Issues:0Issues:0

Programming-for-Data-Science-with-Python-Udacity-Nanodegree

This repository contains projects did for Udacity Programming For Data Science With Python Nanodegree.

Stargazers:0Issues:0Issues:0

pycaret-demo-tfmeetup

Introducing PyCaret 1.0 in TF Meetup

Stargazers:0Issues:0Issues:0
Stargazers:0Issues:0Issues:0

Customer-Analytics-in-Python

I use various Data Science and machine learning techniques to analyze customer data using STP framework. I preprocessed the data, performed segmentation, hierarchical clustering, k-means, PCA techniques with a lot of visualizations to segment and understand customer data. I have performed Purchase Analytics (both descriptive analysis and predictive analysis). Used deep learning to enhance my model.

Stargazers:0Issues:0Issues:0

fundamentals

Fundamentals of data exploration, data manipulation, data cleaning, and data analysis

Stargazers:0Issues:0Issues:0

flask_api

Creating a Machine Learning API using Flask - Repository for AV Article

License:MITStargazers:0Issues:0Issues:0

Hands-On-Data-Science-for-Marketing-1

Hands-On Data Science for Marketing, published by Packt

License:MITStargazers:0Issues:0Issues:0

machine_learning_examples

A collection of machine learning examples and tutorials.

Stargazers:0Issues:0Issues:0