Iris-Dataset-EDA-and-Algorithms
Applying EDA on Iris Dataset and Implementing Machine learning Algorithms
Iris Data set contains the details of Iris flowers features such as Petal Length and Width and Sepal Length and Width, and these are of 3 categories which are virginic, versicolor and setosa. Our objective is to classify these objects to 3 categories based on the input features.
You can find the Jupyter notebook for the source code here.
Splitting Dataset:
Before implementing any model we need to split the dataset to train and test sets. We use train_test_split class from sklearn.model_selection library to split our dataset.
from sklearn.model_selection import train_test_split
train,test=train_test_split(data,test_size=0.3)
The above code will split the dataset to 70% as train and 30% as test datasets.
train.shape, test.shape
((105, 5), (45, 5))
Now let’s split the train and test sets further as input and output sets.
train_X=train[[‘Sepal Length’,”Sepal Width”,”Petal length”,”Petal Width”]]
train_y=train.Species
test_X=test[[‘Sepal Length’,”Sepal Width”,”Petal length”,”Petal Width”]]
test_y=test.Species
1. Decission Trees Model:
First let’s get started with Decission Trees Model.
Decision tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. It uses Entropy and Information Gain to construct a decision tree.
Entropy
Entropy controls how a Decision Tree decides to split the data. It actually effects how a Decision Tree draws its boundaries.
Information Gain:
Information gain (IG) measures how much “information” a feature gives us about the class.
We need to import the DecisionTreeClassifier from sklearn.tree to implement our decision tree classifier model and also import metrics function to calculate accuracy score of the model.
from sklearn.tree import DecisionTreeClassifier
from sklearn import metrics
Let’s build the Decision tree model on the train set.
dtmodel=DecisionTreeClassifier()
dtmodel.fit(train_X,train_y)
We can predict the output for the test dataset using predict() function. Let’s do that.
dtpredict=dtmodel.predict(test_X)
And calculate the accuracy score of the model.
dtaccuracy=metrics.accuracy_score(dtpredict,test_y)
print(“Decission Tree Model Accuracy is {}”.format(dtaccuracy * 100))
Decission Tree Model Accuracy is 93.33333333333333
Accuracy score of our decision Tree model is 93.33%. Curious about knowing which are the wrongly predicted records?
test_preddf=test.copy()
test_preddf[‘Predicted Species’]=dtpredict
wrongpred=test_preddf.loc[test[‘Species’] != dtpredict]
wrongpred
Petal length Petal Width Sepal Length Sepal Width Species Predicted Species
77 6.7 3.0 5.0 1.7 versicolor virginica
133 6.3 2.8 5.1 1.5 virginica versicolor
129 7.2 3.0 5.8 1.6 virginica versicolor
these are 3 wrongly predicted records As we can see in above pic 77th indexed record is actually versicolor Specie but predicted as virginica and other 2(133th,129th) records are of virginica but predicted as versicolor.
Will discuss about Logistic Regression Model and SVM(Support Vector Machine) Model in the coming posts.