Iris dataset

1. Dr Asmita Titre

2.  Best known database to be found in the
pattern recognition literature.
 Data set- Iris flower data set(Donated date -
1988-07-01), also known as Fisher's Iris data
set and Anderson's Iris data set b/c Edgar
Anderson collected the data.
 It is multivariate(more than 2 dependent
variable) data set Study of three related Iris
flowers species. Data set contain 50 sample
of each species(Iris-Setosa, Iris-Virginica, Iris-
Versicolor)

3.  Sepal length in cm
 Sepal width in cm
 Petal length in cm
 Petal width in cm

4.  One class is linearly separable from the other
2; the latter are NOT linearly separable from
each other
 Missing Attributes Values : None

5.  Class Distribution: 33.3% for each of 3
classes.

7. Classify a new flower as belonging to one of
the 3 classes given the 4 features

9.  What is data saying ?
( Exploratory data analysis).
 We will try to find the answer of the
following questions with the help of all
available asset

10. 1. Descriptive statistics- SD, Min, Max etc
2. Class Distribution (Species counts are
balanced or imbalanced) – Balanced
3. Univariate Plots:- Understand each attribute
better.

11. # Box and whisker plots(Give idea about
distribution of input attributes)

13.  3.2 Distribution of attribute through their
bin, we find the distribution of attribute
follow Gaussian or other distributions

14. Understand the relationships between
attributes & species better. (Which attributes
contributes a lot in classifying species)

16. 1.Using Sepal_Lenght & Sepal_Width features,
we can only distinguish Setosa flower from
others
2.Seperating Versicolor & Virginica is much
harder as they have considerable overlap
3.Hence, Sepal_Lenght & Sepal_Width features
only work well for Setosa

18. 1.Using Petal_Lenght & Petal_Width features,
we can distinguish Setosa, Versicolor &
Virginica fairly
2.There are slightly overlap of Versicolor &
Virginica.
3.Graph shows that Petal (Length and Width)
features are best contributor for Iris Species
as compare to Sepal (Length and Width)

22. (Here comes the beauty of machine)

23. 1 Import Library
2 Create Correlation Matrix
3 Spliting the Data Set
But keep in mind;

24. 3.1 Take all the data features
3.2 Take only Sepal Features(Length & Width)
3.3 Take only Petal Features(Length & Width)
3.4 Take all relevant Features from correlation
Matrix

25.  4 Evaluate by using 6 different
Algorithms(Cross Validation)
Here,
4.1 Logistic Regression (LR)
4.2 Linear Discriminant Analysis(LDA)
4.3 K-Nearest Neighbour(KNN)
4.4 Classification and Regression Tree(CART)
4.5 Gaussion Naive Bayes(NB)
4.6 Support Vector Machine

26.  5 Final Evalution (Compare all model
according to features selection and accuracy)
 6 Deep Learning

27. Note:-
Testing dataset size(validation size) is small

28. Case Features used Best
Model
Train
Accuracy
Test
Accuracy
Missclassified
1 All features in SVM .9899 .9555 2 classes
2 Sepal only SVM .8472 .7111 12
3 Petal only SVM .9899 .9333 3
4 PetalWidth,Sepal
(Len,Wid)
SVM/LDA .9809 .9111 4
5 PetalLen,Sepal
(Len,Wid)
SVM .9700 .9111 4