code for Iris data

Author: Faizgeeky

Iris-Model/irismodel.py

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+# -*- coding: utf-8 -*-
+"""
+
+@author: FaizMohammad
+"""
+
+#importing all the required Modules
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.metrics import confusion_matrix,accuracy_score
+from sklearn.cross_validation import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+
+
+#insert iris_data from sklearn.datasets and separate features and target
+#let's take X = features and y =  Target 
+
+iris = load_iris()
+X = iris.data
+y= iris.target
+
+#Now let's divide the data for traning and testing with test-size= 20%
+X_train,X_test,y_train,y_test= train_test_split(X,y,test_size=0.2,random_state=0)
+
+#Select Decision tree Classifier algorithm for model fitting
+classifier = DecisionTreeClassifier()
+classifier.fit(X_train,y_train)
+
+#Now check the Model prediction with testing data
+predict = classifier.predict(X_test)
+
+#and at last check the Model Accuracy 
+cm= accuracy_score(pred,y_test)
+
+#plot (X VS y )graph where X-label is sepal length(cm) and y-label is sepal width(cm)
+feature1=0
+feature2=1
+formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])
+plt.figure(figsize=(5,4))
+plt.scatter(X[:,feature1],X[:,feature2],c=y)
+plt.colorbar(ticks=[0,1,2],format=formatter)
+plt.xlabel(iris.feature_names[0])
+plt.ylabel(iris.feature_names[1])
+plt.show()