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TravelTimePredictionNN.py

+# %%
+import pandas as pd
+
+# %%
+data = pd.read_csv('BusTravelData.csv')
+
+
+# %%
+data['Day'].unique()
+
+# %%
+data['Day'].map({'Sunday':0,'Monday':1,'Tuesday':2})
+
+# %%
+data['Day'] = data['Day'].map({'Sunday':0,'Monday':1,'Tuesday':2})
+
+# %%
+data.head()
+
+# %%
+data['Special'] = data['Special'].map({'No':0,'Yes':1})
+
+# %%
+data.tail()
+
+# %%
+data['Weather'].unique()
+
+# %%
+data.columns
+
+# %%
+X = data.drop(['Date','Travel Time'],axis=1)
+
+
+# %%
+y = data['Travel Time']
+
+
+# %% [markdown]
+# Train/test split
+# 
+# 1. split data into two parts 
+# 
+#     training data set
+#     testing data set
+# 
+# 2. Train the models on training set
+# 
+# 3. Test the models on testing data set
+
+# %%
+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,random_state=42)
+
+# %%
+y_train
+
+# %%
+from sklearn.linear_model import LinearRegression
+from sklearn.svm import SVR
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.neural_network import MLPClassifier
+
+
+# %%
+lr = LinearRegression()
+NN = MLPClassifier()
+lr.fit(X_train,y_train)
+NN.fit(X_train,y_train)
+
+# %%
+y_pred1 = lr.predict(X_test)
+
+# %%
+df1 = pd.DataFrame({'Actual':y_test, 'Lr Results':y_pred1})
+
+# %%
+df1
+
+# %%
+y_pred2 = NN.predict(X_test)
+df2 = pd.DataFrame({'Actual':y_test, 'NN Results':y_pred2, 'LR Results':y_pred1})
+df2
+
+# %%
+import matplotlib.pyplot as plt
+
+# %%
+plt.subplot(221)
+plt.plot(df1['Actual'].iloc[0:11],label='Actual')
+plt.plot(df1['Lr Results'].iloc[0:11],label='Lr Results')
+plt.legend()
+
+# %% [markdown]
+# 	Time	Day	Rush	Special	Congestion	Drving Speed	Stops	Weather	Distance	Travel Time
+
+# %%
+plt.subplot(221)
+plt.plot(df1['Actual'].iloc[0:11],label='Actual')
+plt.plot(df2['NN Results'].iloc[0:11],label='NN Results')
+plt.legend()
+
+# %% [markdown]
+# Prediction with Current parameters (Predict Time with trained Model)
+
+# %%
+data = {'Time':6,
+        'Day':1,
+        'Special':0,
+        'Congestion':8,
+        'Drving Speed':40,
+        'Stops':14,
+        'Weather':23,
+        'Distance':14.7
+        }
+df = pd.DataFrame(data,index=[0])
+df
+
+
+# %%
+new_pred = lr.predict(df)
+NN_pred = NN.predict(df)
+
+
+# %%
+data = {'Time':17,
+        'Day':1,
+        'Special':0,
+        'Congestion':5,
+        'Drving Speed':40,
+        'Stops':20,
+        'Weather':24,
+        'Distance':14.7
+        }
+ddf = pd.DataFrame(data,index=[0])
+ddf
+
+# %%
+resultsNN = NN.predict(ddf)
+resultsNN
+
+# %%
+def GetPrediction(time,date,special,congestion,dspeed,stops,weather,distance):
+    data = {'Time':time,
+        'Day':date,
+        'Special':special,
+        'Congestion':congestion,
+        'Drving Speed':dspeed,
+        'Stops':stops,
+        'Weather':weather,
+        'Distance':distance
+        }
+    ddf = pd.DataFrame(data,index=[0])
+    resultsNN = NN.predict(ddf)
+    resultsNN
+    print('Travel Time Prediction :-',*resultsNN,'Min')
+
+# %%
+def GetDateCode(date):
+    if('Sunday' == date ):
+        return 0
+    elif('Monday' == date):
+        return 1
+    elif('Tuesday' == date):
+        return 2
+    elif('Wednesday' == date):
+        return 3
+    elif('Thursday' == date):
+        return 4
+    elif('Friday' == date):
+        return 5
+    elif('Saturday' == date):
+        return 6
+    else:
+        return 9
+    
+import testingWeather
+
+city = "malabe"
+city = city+" weather"
+ 
+time= 6  #this time come from semins data/member of route planning
+day ='Friday'  #date come from UI 
+date = GetDateCode(day)
+special = 0 # Special Day or Not 1/0
+Congestion= 8 # This is depend on the depature time---- heavy traffic =9 / No Traffic = 0 
+drivingspeedAVG = 40 #this is come from GPS data
+stops =14 #total Bus stops/holts between the travel --- data come from the Seminas data/member of route planning 
+weather =testingWeather.weather(city)  #Get weather in travel area
+Distance =14.7 #Distance data come from seminas ----- data come from the Seminas data/member of route planning
+
+
+GetPrediction(time,date,special,Congestion,drivingspeedAVG,stops,weather,Distance)
+
+
+
+
+