WeightedAverageEnsemble2

WeightedAverageEnsemble2.py

##### Decision Tree
DecisionTree = DecisionTreeClassifier()
DecisionTree.fit(data, label)
DecisionTree_pred = DecisionTree.predict(Test)
#make submission table
DecisionTreePrediction = pd.DataFrame(
  {'QuoteNumber':df, 
   'QuoteConversion_Flag':DecisionTree_pred})
#save file
DecisionTreePrediction.to_csv('DecisionTree1.csv', 
                              index = False)

##### KNN
KNN = KNeighborsClassifier(n_neighbors=2)
KNN.fit(data, label)
KNN_pred = KNN.predict(Test)
#make submission table
KNNPrediction = pd.DataFrame(
  {'QuoteNumber':df, 
   'QuoteConversion_Flag':KNN_pred})
#save file
KNNPrediction.to_csv('KNN1.csv', 
                     index = False)

##### MLPC
MLPC = MLPClassifier(random_state=1, max_iter=300)
MLPC.fit(data, label)
MLPC_pred = MLPC.predict(Test)
#make submission table
MLPCPrediction = pd.DataFrame(
  {'QuoteNumber':df, 
   'QuoteConversion_Flag':MLPC_pred})
#save file
MLPCPrediction.to_csv('MLPC1.csv', 
                      index=False)

##### Random Forest
RandomForest = RandomForestClassifier()
RandomForest.fit(data,label)
RandomForest_pred = RandomForest.predict(Test)
#make submission table
RandomForestPrediction = pd.DataFrame(
  {'QuoteNumber':df, 
   'QuoteConversion_Flag':RandomForest_pred})
#save file
RandomForestPrediction.to_csv('RandomForest1.csv', 
                              index=False)

##### XGB Classifier
XGB = XGBClassifier()
XGB.fit(data,label)
XGB_pred = XGB.predict(Test)
#make submission table
XGBPrediction = pd.DataFrame(
  {'QuoteNumber':df, 
   'QuoteConversion_Flag':XGB_pred})
#save file
XGBPrediction.to_csv('XGB1.csv', 
                     index=False)