aymanhaque001 Goto Github PK

advanced-internet-programming

aymanhaque001

Config files for my GitHub profile.

aymanhaque001-project

data-engineering-nanodegree

notebooks produced throughout the Udacity's Nanodegree Data Engineering Course

data-mining-final-project-identifying-optimal-subgroups-of-tbi-patient-with-k-means-clustering-

drowsiness-detection-using-haar-cascades-and-cnn

fake-reviews-detection-using-word2vec-and-deeplearning

fetch_rewards_project

Image Pixels generation from Corner Points

fetching-financial-data

Fetching financial data for technical & fundamental analysis and algorithmic trading from a variety of python packages and sources.

gesture-recognition-for-hci-systems

google-interview-university

A complete daily plan for studying to become a Google software engineer.

hci-project--evaluating-overall-efficiency-between-two-systems

ibm-hr-analytics-attrition-dataset

Business Problem IBM HR Analytics Employee Attrition & Performance. Predict attrition of your valuable employees. Attrition is a problem that impacts all businesses, irrespective of geography, industry and size of the company. Employee attrition leads to significant costs for a business, including the cost of business disruption, hiring new staff and training new staff. As such, there is great business interest in understanding the drivers of, and minimizing staff attrition. In this context, the use of classification models to predict if an employee is likely to quit could greatly increase the HR’s ability to intervene on time and remedy the situation to prevent attrition. While this model can be routinely run to identify employees who are most likely to quit, the key driver of success would be the human element of reaching out the employee, understanding the current situation of the employee and taking action to remedy controllable factors that can prevent attrition of the employee. This data set presents an employee survey from IBM, indicating if there is attrition or not. The data set contains approximately 1500 entries. Given the limited size of the data set, the model should only be expected to provide modest improvement in indentification of attrition vs a random allocation of probability of attrition. While some level of attrition in a company is inevitable, minimizing it and being prepared for the cases that cannot be helped will significantly help improve the operations of most businesses. As a future development, with a sufficiently large data set, it would be used to run a segmentation on employees, to develop certain “at risk” categories of employees. This could generate new insights for the business on what drives attrition, insights that cannot be generated by merely informational interviews with employees. Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’. This is a fictional data set created by IBM data scientists. Education 1 'Below College' 2 'College' 3 'Bachelor' 4 'Master' 5 'Doctor' EnvironmentSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' JobInvolvement 1 'Low' 2 'Medium' 3 'High' 4 'Very High' JobSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' PerformanceRating 1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding' RelationshipSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' WorkLifeBalance 1 'Bad' 2 'Good' 3 'Better' 4 'Best' IBM HR Analytics Employee Attrition & Performance Predict attrition of your valuable employees IBM HR Analytics Employee Attrition & Performance IBM HR Analytics Employee Attrition & Performance

implementing-id3-decision-tree

interview

Everything you need to prepare for your technical interview

literate-octo-happiness

mle-py

movie_ratings_2016_17

Contains a dataset with movie ratings for some of the most popular movies for 2016 and 2017 (IMDB, Fandango, Metacritic, Rotten Tomatoes)

noising-and-denoising-images-with-autoencoders-on-cifar10-dataset

ny-311-service-request-dataset

python-guide

Python best practices guidebook, written for humans.

social-media-fastapi

social_media_app

stock-forcasting-using-lstm-rnn-