ganesh10-india Goto Github PK

dt-algorithm-for-employee-dataset

Decision tree is a Machine Learning Algorithm to Predict the Emplyee will Stay or Not !!

effects-of-socio-economic-status-of-obesity-prediction

Effects of Socio-economic Status of Obesity Prediction

employee-compensation-prediction-city-management-and-ethics-.

The objective of this work is to predict the annual base salary of SanFrancisco (SF) city employee.

end-to-end-time-series

This repository hosts code for my Time Series videos part of playlist here - https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK

example-scripts

Quick example scripts to demonstrate different features

fashion-mist-cnn-sample

CNN Sample

free-programming-books

:books: Freely available programming books

future-scaling-in-ml

Feature Scaling is a technique to standardize the independent features present in the data in a fixed range. ... If feature scaling is not done, then a machine learning algorithm tends to weigh greater values, higher and consider smaller values as the lower values, regardless of the unit of the values.

ganesh10-india

ganeshkasturidatascience

getting-job-data-science

Collection of advice and resources for getting a job as data scientist

gradient-boosting-classification-algorithm

gradient-boosting-regressor-using-gridsearchcv

gridsearchcv-on-svm

GridSearchCV perfrom on Support Vector Machine

group-emotion-recognition

Group Emotion Recognition using deep neural networks and Bayesian classifiers.

hospital-readmission-prediction

Predicting Impact on Readmission Rates for Patients Hospitalized with Diabetes

hr_analytics-employee_attrition-classification-models

Uncover the factors that lead to employee attrition using IBM Employee Data

hranalyticsemployeeattrition

Uncover the factors that lead to employee attrition using IBM Employee Data

image-caption-generator-using-deep-learning

Automatically generates captions for an image using Image processing and NLP. Model was trained on Flickr30K dataset.

image-captioning-gui-system

Automatically generates captions for an image using Image processing and NLP. Model was trained on Flickr30K dataset.

imbalanced-learn

Python module to perform under sampling and over sampling with various techniques.

impulse_healthcare

instagram-sentiment-analysis-

🍂Scrapes captions from instagram accounts and performs sentiment analysis on them.

interview-prepartion-data-science

interview-question-data-science-

is-your-salary-is-good-

Web Scraping for Indeed.com and Predicting Salaries.

k-means-algorithm-on-mall-customers

Kmeans Algorithm Kmeans algorithm is an iterative algorithm that tries to partition the dataset into Kpre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group. It tries to make the intra-cluster data points as similar as possible while also keeping the clusters as different (far) as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster’s centroid (arithmetic mean of all the data points that belong to that cluster) is at the minimum. The less variation we have within clusters, the more homogeneous (similar) the data points are within the same cluster. The way kmeans algorithm works is as follows: Specify number of clusters K. Initialize centroids by first shuffling the dataset and then randomly selecting K data points for the centroids without replacement. Keep iterating until there is no change to the centroids. i.e assignment of data points to clusters isn’t changing. Compute the sum of the squared distance between data points and all centroids. Assign each data point to the closest cluster (centroid). Compute the centroids for the clusters by taking the average of the all data points that belong to each cluster.

kentf

A collection of tensorflow helper functions I regularly use

linear-regression-and-random-forest-in-python

lockdown-sentimental-analysis-complete-nlp-text-per-processing-

Lockdown Sentimental Analysis (Complete NLP Text Per-processing):