Predict heart disease by using Adaboost and Random Forest Classifier
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Updated
Nov 4, 2018 - Jupyter Notebook
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
Data Science Case Study
🔱 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.
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.
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.
Performing analyses on New York City Airbnb and developing business intelligence for both the hosts and the guests
Analyzing factors leading to customers churning; predicting which customers' will churn?
Analysing the telecom customer churn data
To Detect Sepsis Disease using six Classifiers on clinical data
App to Detect Parkinson's Disease
Scala routines to estimate classifications methods based on the Dataframe API machine learning classes.
Write a code to implement AdaBoost algorithm using decision stump to learn strong classifier
Implementation of decision trees for binary categorical data using numpy. Includes regular decision trees, random forest, and boosted trees.
Face Detection by AdaBoost learning. Conformal Geometric Algebra is applied for feature extraction.
A classification project to determine the eligibility of getting a loan after filling an online form
CART, K-Means, Apriori, Adaboost, RFE; models using Anti-cancer peptides vs Human proteins
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.
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