Various Classification models used are Logistic regression, K-NN, Support Vector Machine, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification using R
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Updated
Jan 18, 2018 - R
Various Classification models used are Logistic regression, K-NN, Support Vector Machine, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification using R
One Data Set with multiple Algorithms
Analytical tool to help the company decide whether the employee will stay or not
Classification on the Kobe Bryant Shot Selection dataset (https://www.kaggle.com/c/kobe-bryant-shot-selection/data) using Decision Trees
The cancer like lung, prostrate, and colorectal cancers contribute up to 45% of cancer deaths. So it is very important to detect or predict before it reaches to serious stages. If cancer predicted in its early stages, then it helps to save the lives. Statistical methods are generally used for classification of risks of cancer i.e. high risk or l…
Custom implementation of Random Forest based on rpart library.
Prediction whether the economic crisis will occur in Africa countries
Optimising Kaggle Dataset to classify drivers
In this project, I analyzed the NLP data of all the pitches in 'Sharktank' and generated a model to predict the significance of keywords in getting a deal using Random Forest & Decision trees.
Bootcamp-R Study
Heart Failure Prediction for Harvard University Professional Certificate in Data Science Capstone Project, 2nd Capstone Project using R programming
This is a type of decision tree algorithm( C 5.0) popularly used in machine learning applied on credits dataset.
KNN, Naive Bayes and Trees - Wine UCI Dataset
Parkinson’s disease classification using speech signal features; comparison of various multiclass classification algorithms
Water potability classification using decision tree classifier
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