Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

1. WELCOME TO OUR
PRESENTATION

2. A CASE STUDY ON PREDICT THE
ONSET OF DIABETES BASED ON
DIAGNOSTIC MEASURES

3. Presented By
Hasan Ahmed Khan 152-15-5587
Foysal Ahmed 152-15-5738
Nadim Mahmud Nayem 152-15-5772
Nazim Uddin Hridoy 152-15-5895
Syed Tanvir Anjum 151-15-5158

4. Four Classification algorithm
– Naïve Bayes
– LMT Trees
– Random Forest Trees
– Random Trees

5. NAÏVE BAYES
■ Naive Bayes classifiers are a collection of
classification algorithms based on Bayes’
Theorem. It is not a single algorithm but a
family of algorithms where all of them share
a common principle, i.e. every pair of features
being classified is independent of each other.

6. LOGISTIC MODEL TREES
■ The Logistic Model Tree (LMT) algorithm is a
supervised training algorithm that combines the
basic technique of decision tree learning with the
standard Logistic Regression functions at the leaves.
LMT is a kind of decision tree that have logistic
regression models at the leaves.

7. RANDOM FOREST TREES
■ Random Forest is a supervised learning algorithm. it creates a
forest and makes it somehow random. The „forest“ it builds, is
an ensemble of Decision Trees, most of the time trained with
the “bagging” method. The general idea of the bagging method
is that a combination of learning models increases the overall
result.
■ Random forest builds multiple decision trees and merges them
together to get a more accurate and stable prediction.

8. RANDOM TREES
■ Random trees usually refers to randomly built trees
which have nothing to do with machine learning.
However the popular machine learning framework
Weka uses the term to refer to a decision tree built
on a random subset of columns.

9. NAÏVE BAYES

10. LOGISTIC MODEL TREES

11. RANDOM FOREST TREEES

12. RANDOM TREES