:trident: Some recognized algorithms[Decision Tree, Adaboost, Perceptron, Clustering, Neural network etc. ] of machine learning and pattern recognition are implemented from scratch using python. Data sets are also included to test the algorithms.

Predict heart disease by using Adaboost and Random Forest Classifier

Real Time Face Recognition with Python and OpenCV2, Create Your Own Dataset and Recognize that. #FreeBirdsCrew

Face Detection by AdaBoost learning. Conformal Geometric Algebra is applied for feature extraction.

Bill Gates was once quoted as saying, "You take away our top 20 employees and we [Microsoft] become a mediocre company". This statement by Bill Gates took our attention to one of the major problems of employee attrition at workplaces. Employee attrition (turnover) causes a significant cost to any organization which may later on effect its overall efficiency. As per CompData Surveys, over the past five years, total turnover has increased from 15.1 percent to 18.5 percent. For any organization, finding a well trained and experienced employee is a complex task, but it’s even more complex to replace such employees. This not only increases the significant Human Resource (HR) cost, but also impacts the market value of an organization. Despite these facts and ground reality, there is little attention to the literature, which has been seeded to many misconceptions between HR and Employees. Therefore, the aim of this paper is to provide a framework for predicting the employee churn by analyzing the employee’s precise behaviors and attributes using classification techniques.

Multiple Moving objects in a surveillance video were detected and tracked using ML models such as AdaBoosting. The obtained results were compared with the results from Kalman Filter.

Boosted multi-task learning for face verification

Adaboost Neural Network And Cyclopean View For No-reference Stereoscopic Image Quality Assessment

To Detect Sepsis Disease using six Classifiers on clinical data

Adaboost Inverse Reinforcement Learning

Performing analyses on New York City Airbnb and developing business intelligence for both the hosts and the guests

Use patient health data from MIT's GOSSIS(Global Open Source Severity of Illness Score) to do an experiment, in which we want to evaluate the question of which modeling strategy leads to the most effective predictions.

Predict whether income exceeds 50K/yr based on census data.

Data Science Case Study

Analysing the telecom customer churn data

All assignments of Statistical Machine Learning Course

Scala routines to estimate classifications methods based on the Dataframe API machine learning classes.

Implementation of decision trees for binary categorical data using numpy. Includes regular decision trees, random forest, and boosted trees.

App to Detect Parkinson's Disease

No description provided.

Implementation of Ensemble Learning, Decision Tree, Random Forest, SVM, KNN, Logistic Regression, Bagging, Boosting and Stacking approach to analysis and predict the abnormal and normal behavior of Imbalanced Colon Dataset.

This is my college practice work, where i try to learn and cover all the tree based regression algorithms (preferably in python).

In this project, we analyze and compare the performance of various machine learning algorithms (Linear Regression, Decision Tree, AdaBoost, XGBoost, Gradient Boosting and k- Nearest Neighbors) when used to predict hard drive failures using Backblaze data in the year 2018.

CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. After nearly 32,000 letters were sent to people in the community, CharityML determined that every donation they received came from someone that was making more than $50,000 annually. To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. With nearly 15 million working Californians, CharityML has brought you on board to help build an algorithm to best identify potential donors and reduce overhead cost of sending mail. My goal was to evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent.

Analyzing factors leading to customers churning; predicting which customers' will churn?

No description provided.

A classification project to determine the eligibility of getting a loan after filling an online form

Write a code to implement AdaBoost algorithm using decision stump to learn strong classifier

CART, K-Means, Apriori, Adaboost, RFE; models using Anti-cancer peptides vs Human proteins

Machine Learning models compared to find the strongest predictor for credit risk