Added Shelter Outcome

Author: Rupak Chakraborty

Shelter Animal Outcomes/Data-Shelter Animal Outcomes _ Kaggle.pdf

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+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.ensemble import BaggingClassifier
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.neural_network import MLPClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.naive_bayes import BernoulliNB
+from sklearn.naive_bayes import MultinomialNB
+from sklearn import metrics
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import Imputer
+from sklearn.model_selection import train_test_split
+
+
+def get_naive_bayes_models():
+    gnb = GaussianNB()
+    mnb = MultinomialNB()
+    bnb = BernoulliNB()
+    classifier_list = [gnb,mnb,bnb]
+    classifier_name_list = ['Gaussian NB','Multinomial NB','Bernoulli NB']
+    return classifier_list,classifier_name_list
+
+
+def get_neural_network(hidden_layer_size=50):
+    mlp = MLPClassifier(hidden_layer_sizes=hidden_layer_size)
+    return [mlp], ['MultiLayer Perceptron']
+
+
+def get_ensemble_models():
+    rf = RandomForestClassifier(n_estimators=51,min_samples_leaf=5,min_samples_split=3)
+    bagg = BaggingClassifier(n_estimators=71,random_state=42)
+    extra = ExtraTreesClassifier(n_estimators=57,random_state=42)
+    ada = AdaBoostClassifier(n_estimators=51,random_state=42)
+    grad = GradientBoostingClassifier(n_estimators=101,random_state=42)
+    classifier_list = [rf,bagg,extra,ada,grad]
+    classifier_name_list = ['Random Forests','Bagging','Extra Trees','AdaBoost','Gradient Boost']
+    return classifier_list,classifier_name_list
+
+
+def label_encode_frame(dataframe):
+    columns = dataframe.columns
+    encoder = LabelEncoder()
+    for column in columns:
+        if type(dataframe[column][0]) is str:
+            dataframe[column] = encoder.fit_transform(dataframe[column].values)
+    return dataframe
+
+
+def print_evaluation_metrics(trained_model,trained_model_name,X_test,y_test):
+    print '--------- For Model : ', trained_model_name
+    predicted_values = trained_model.predict(X_test)
+    print metrics.classification_report(y_test,predicted_values)
+    print "Accuracy Score : ",metrics.accuracy_score(y_test,predicted_values)
+    print "---------------------------------------\n"
+
+
+filename = 'train.csv'
+shelter_frame = pd.read_csv(filename)
+target_variable = 'OutcomeType'
+columns_to_drop = ['AnimalID','Name','DateTime','OutcomeType','OutcomeSubtype']
+class_labels = list(shelter_frame[target_variable].values)
+shelter_frame.drop(columns_to_drop,axis=1,inplace=True)
+encoded_frame = label_encode_frame(shelter_frame)
+X_train,X_test,y_train,y_test = train_test_split(encoded_frame,class_labels,test_size=0.2,random_state=42)
+classifier_list,classifier_name_list = get_ensemble_models()
+for classifier,classifier_name in zip(classifier_list,classifier_name_list):
+    classifier.fit(X_train,y_train)
+    print_evaluation_metrics(classifier,classifier_name,X_test,y_test)