100-python-exercises

Udemy Course "Solve 100 Python Exercises to Boost your Python Skills" by Ardit Sulce

100days-of-code

100 days of Python Practice

aircraft-performance

Aircraft performance of a twin engine electric aircraft

analyticalskillsbook

Code for the book Analytical Skills for AI and Data Science

apod-api

Astronomy Picture of the Day API service

artificial-intelligence-a-z

This repository contains my projects from the Artificial intelligence A-Z course from superdatascience.com

attritionanalysis

Attrition Analysis on data using Python

attritionanalysis-1

backend

bokeh

Interactive Web Plotting for Python

books

cgdn-ebooks

codingninjas

colah.github.io

condition_monitoring

Condition monitoring of hydraulic systems

dash

Interactive, Reactive Web Apps for Python. Dash Is Productive™

data-analysis

Data Analysis Using Python

data-structures-and-algorithmic-thinking-with-python

Data Structure And Algorithmic Thinking With Python

data-structures_algorithms_coding-ninjas_full-course

All the quizzes, content and coding assignment solutions of Data Structures and Algorithms course of Coding Ninjas.

data_engineering

Data Engineering practice

deep_learning-python

Deep Learning using python

deepreinforcementlearning

A replica of the AlphaZero methodology for deep reinforcement learning in Python

dl-nlp-readings

My Reading Lists of Deep Learning and Natural Language Processing

dsfs

Data Science From Scratch

e-books

my E-books collection

ebooks_pdfs

just a fork

example_repo

An example repo for learning purposes

git_practice

github-slideshow

A robot powered training repository :robot:

hr-analysis-employee-attrition-classification

The project aims to predict if an employee will leave the company or not using two datasets. Dataset A is provided by IBM and dataset B is provided by another company. They have different number of samples and use unlike features. To solve the attrition classification problem, I use three machine learning methods, i.e. Logistic Regression, Random Forest and Adaboost. In each method, I use cross-validation to choose the best parameters and use the best parameters to predict the attrition of unseen employees. Logistic regression performs best on dataset A while random forest performs best on dataset B. I get a good predictive score on dataset B but a ‘bad’ score on dataset A. By comparing the features and predictive scores of two datasets, I give some conjecture about the results.