Merge pull request #84 from LaraibNoor/laraib-noor

Added Decision Tree problem of Playing tennis

Author: Circinusss

Play Tennis.py

@@ -0,0 +1,30 @@
+# Load libraries
+import numpy as np
+import pandas as pd
+from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation
+df=pd.read_csv("Play Tennis.csv")
+from sklearn import preprocessing
+string_to_int= preprocessing.LabelEncoder() #encode your data
+df=df.apply(string_to_int.fit_transform) #fit and transform it
+#To divide our data into attribute set and Label:
+feature_cols = ['Outlook','Temprature','Humidity','Wind']
+X = df[feature_cols ]        #contains the attribute 
+y = df.Play_Tennis           #contains the label
+
+#To divide our data into training and test sets:
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# perform training 
+from sklearn.tree import DecisionTreeClassifier              # import the classifier
+classifier = DecisionTreeClassifier(criterion="entropy")     # create a classifier object
+classifier.fit(X_train, y_train)                             # fit the classifier with X and Y data 
+#Predict the response for test dataset
+y_pred= classifier.predict(X_test)  
+print(y_pred)
+pred= classifier.predict([[0,1,0,1]])  
+print(pred)
+
+# Model Accuracy, how often is the classifier correct?
+from sklearn.metrics import accuracy_score
+print("Accuracy:",metrics.accuracy_score(y_test, y_pred))