Python code for common Machine Learning Algorithms

A curated list of gradient boosting research papers with implementations.

A fast xgboost feature selection algorithm

Extension of the awesome XGBoost to linear models at the leaves

Tuning XGBoost hyper-parameters with Simulated Annealing

All codes, both created and optimized for best results from the SuperDataScience Course

XGBoost implementation from scratch with Numpy using Adam and RSMProp optimizers.

Machine Learning Project using Kaggle dataset

The python notebook is on googles new collabatory tool. Its a churn model being run on 3 different algorithms to compare.

XGBoost, LightGBM, LSTM, Linear Regression, Exploratory Data Analysis

Introduction to XGBoost with an Implementation in an iOS Application

The objective of this project is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.

Club Mahindra Data Olympics

Extreme Gradient Boosting (XGBoost) with R and H2o for Stroke Prediction

In this project, I have predicted Housing sales price prices for King County,USA which includes Seattle. It includes homes sold between May 2014 and May 2015. It has 19 house features plus the price and the id columns, along with 21613 observations. In this project I have done the implementation of different Boosting regression machine learning models such as Gradient Boosting, eXtreme Gradient Boosting (XGB) and Adaboost. In this project, I have also used Permutation Importance for filtering the irrelevant features of the dataset. In this project, I have predicted Housing sales price prices for King County,USA which includes Seattle. It includes homes sold between May 2014 and May 2015. It has 19 house features plus the price and the id columns, along with 21613 observations. In this project I have done the implementation of different Boosting regression machine learning models such as Gradient Boosting, eXtreme Gradient Boosting (XGB) and Adaboost. In this project, I have also used Permutation Importance for filtering the irrelevant features of the dataset. Maximum Accuracy achieved around 98.59%.

Machine learning Based Minor Project, which uses various classification Algorithms to classify the news into FAKE/REAL, on the basis of their Title and Body-Content. Data has been collected from 3 different sources and uses algorithms like Random Forest, SVM, Wordtovec and Logistic Regression. It gave 94% accuracy.

Kaggle competition dataset anlaysis and prediction using machine learning algorithms

A text analysis challenege on Hackerearth by Infosys where data was highly imbalanced.

Machine Learning Project on Imbalanced Data in R

This repository is for Fake News Detection using Deep Learning models

The Complete Journey Dataset: Churn Prediction

As an early diagnosis step machine learning classifiaction algorithms could be used in finding if the patient is prone to parkinsons disease.

Project for IST 718: Big Data Analytics

Building regression model

Predictive analysis of Taxi duration

After watching a couple of them, we asked ourselves, what makes the videos popular !!! and what if we could predict the popularity !!! After all, we want to help the struggling YouTuber/influencer community by providing them with valuable insights on trending. At the same time, predicting popularity would help the advertising firms to identify the best videos to invest upon. Above all, we want to know, if there are any interesting insights underlying the trending patterns? This project is all about our journey towards finding these answers. Stay tuned !!!

Code library

R codes for common Machine Learning Algorithms

Capstone project for the Udacity MLND on the prediction of customer acquisition based on regional demographics and customer attributes. With the help of supervised and unsupervised learning techniques.