🔱 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.

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

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 overal…

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

To Detect Sepsis Disease using six Classifiers on clinical data

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

Analysing the telecom customer churn data

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

Adaboost Inverse Reinforcement Learning

All assignments of Statistical Machine Learning Course

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

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 $5…