added more features to the models for improving accuracy.

Author: dhiren

fbmetrics.py

@@ -4,8 +4,10 @@
 import matplotlib.pyplot as plt
 from matplotlib import style
 from sklearn.linear_model import LinearRegression,SGDRegressor,Ridge,Lasso,LogisticRegression
-from adspy_shared_utilities import plot_fruit_knn
+from adspy_shared_utilities import plot_fruit_knn,plot_feature_importances,plot_decision_tree
 import graphviz
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import r2_score
 
 
 
@@ -34,6 +36,24 @@ def Type(x,type_of):
 
 plotdf = df.drop(df.columns[7:15],axis =1)
 
+def Weekday(x):
+    if x == 1:
+        return 'Su'
+    elif x== 2:
+        return 'Mo'
+    elif x == 3:
+        return 'Tu'
+    elif x == 4:
+        return 'We'
+    elif x == 5:
+        return 'Th'
+    elif x ==6:
+        return 'Fr'
+    elif x == 7:
+        return "Sa"
+
+df['Weekday'] = df['Post Weekday'].apply(lambda x: Weekday(x))
+
 fig, ax = plt.subplots()
 paid = df[df['Paid']==1]
 free = df[df['Paid']==0]  #seperated free users
@@ -47,24 +67,66 @@ def Type(x,type_of):
 ax.legend(labels=['Paid','Free'])
 #plt.show()
 
-x = df[['Page total likes','Post Month', 'Post Weekday',
-        'Post Hour', 'Paid','Photo','Video','Link','Status','Cat_1','Cat_2']]
-y = df['Lifetime Engaged Users']
+#print(df.head())
+
+dayDf = pd.get_dummies(df['Weekday'])
+df = pd.concat([df,dayDf],axis=1)
+hours = list(range(0,18))
+#hours
+for i in hours:
+    hours[i] = str(hours[i])
+    hours[i]='hr_'+ hours[i]
+
+hourDf = pd.get_dummies(df['Post Hour'],prefix='hr_')
+df = pd.concat([df,hourDf],axis=1)
+monthDf = pd.get_dummies(df['Post Month'],prefix='Mo')
+df = pd.concat([df,monthDf],axis=1)
+
+df['Video'] = pd.get_dummies(df['Type'])['Video']
+x = df[['Page total likes','Paid','Video','Status','Photo',
+        'Cat_1','Cat_2','Mo','Tu','Sa',"We",'Th','Fr',
+        'hr__17','hr__1','hr__2','hr__3','hr__4','hr__5', 'hr__6','hr__7','hr__8',
+        'hr__9','hr__10','hr__11','hr__12','hr__13','hr__14','hr__15','hr__16','Mo_1',
+        'Mo_2','Mo_12','Mo_4','Mo_5','Mo_6','Mo_7','Mo_8','Mo_9','Mo_11','Mo_10']]
+y = df['like']
+
+x_train,x_test,y_train, y_test = model_selection.train_test_split(x,
+                                                                  y, test_size=0.1,
+                                                                  random_state=42)
+
+reg = LinearRegression(normalize=True)
+lasso = Lasso(normalize=True)
+reg.fit(x_train,y_train)
+lasso.fit(x_train,y_train)
+
+reg.fit(x_test,y_test)
+#print(reg.score(x_test,y_test))
 
-x_train,x_test,y_train,y_test = model_selection.train_test_split(x,y, test_size=0.2)
+predicted_train = reg.predict(x_train)
+predicted_test = reg.predict(x_test)
+test_score = r2_score(y_test, predicted_test)
+print(test_score)
+#x_train,x_test,y_train,y_test = model_selection.train_test_split(x,y,test_size=0.2)
+# clf = DecisionTreeClassifier(max_depth = 4, min_samples_leaf = 8,
+#                               random_state = 0).fit(x_train, y_train)
 
+
+
+#clf= LinearRegression()
+
+#cv_scores= model_selection.cross_val_score(clf,x,y)
 # scaler= preprocessing.MinMaxScaler()
 # scaler.fit(x_train)
 # x_test= scaler.transform(x_test)
 # x_train= scaler.transform(x_train)
 #Clf=Lasso(alpha=8.0,max_iter=10000)
 #clf= neighbors.KNeighborsRegressor(n_neighbors=5)
+#print(cv_scores)
 
-clf=LogisticRegression()
-clf.fit(x_train,y_train)
+#clf.fit(x_train,y_train)
 
-accuracy= clf.score(x_test,y_test)
-print(accuracy*100)
+#accuracy= clf.score(x_test,y_test)
+#print(accuracy*100)
 
 # x_train.plot()
 # y_train.plot()
@@ -76,3 +138,9 @@ def Type(x,type_of):
 # sgd = sgd.fit(x,y)
 
 #print(sgd.score(x_test,y_test))
+
+
+#plt.figure(figsize=(10,4))
+#plot_feature_importances(clf,['Page total likes','Post Month', 'Post Weekday',
+          #                    'Post Hour', 'Paid','Photo','Video','Link','Status','Cat_1','Cat_2'])
+#plt.show()