delay 1

delay_time_Detection/.ipynb_checkpoints/delay_time-checkpoint.ipynb

+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

delay_time_Detection/Trains.csv

+Weater_Status,Wind_Speed,Average_Speed,Distance,Delay_time
+Mostly_cloudy,3.22,54,84.437,-1
+Mostly_cloudy,3.0107,60,120.737,4
+Drizzle,8.05,25,6.873,9.32
+partialy_cloudy,3.22,53,96.081,7.32
+partialy_cloudy,2.9785,25,1.9,6.4
+Sunny,3.22,60,109.446,-1
+Mostly_cloudy,0,42,8.676,3.56
+Sunny,3.1717,52,73.827,-0.3
+Sunny,8.05,57,96.081,-0.05
+partialy_cloudy,3.22,49,90.239,11.55
+Sunny,4.6529,28,3.668,2.36
+partialy_cloudy,5.6833,55,105.537,8.14
+Sunny,7.6153,32,6.873,-0.23
+Rainy,3.4615,51,109.446,14.52
+Mostly_cloudy,9.4024,43,15.546,3.55
+Drizzle,9.2414,45,73.827,5.5
+Rainy,3.9767,47,90.239,15
+partialy_cloudy,4.83,38,27.538,6.36
+Sunny,9.2092,35,6.873,-0.56
+Rainy,2.2379,52,114.851,15.58
+Rainy,0.483,47,84.437,13.57
+partialy_cloudy,4.9588,54,114.851,12.4
+partialy_cloudy,0.483,41,37.478,6.53
+Mostly_cloudy,0,46,22.522,2.69
+Rainy,0,23,3.668,8.54
+partialy_cloudy,3.1073,46,73.827,8.36
+partialy_cloudy,1.6905,26,1.9,7.7
+partialy_cloudy,0,52,109.446,8.49
+partialy_cloudy,4.7978,40,37.478,7.35
+Mostly_cloudy,0,33,6.873,3.48
+partialy_cloudy,0,25,1.9,6.2
+Mostly_cloudy,3.0268,29,6.873,0
+partialy_cloudy,0,40,22.522,4.54
+Mostly_cloudy,0,42,50.27,4.16
+Drizzle,3.1556,52,109.446,9.55
+partialy_cloudy,7.9695,37,22.522,3.57
+Mostly_cloudy,3.381,30,6.873,0
+Sunny,7.2933,45,8.676,3.36
+partialy_cloudy,3.4293,31,6.873,0.55
+Mostly_cloudy,2.9141,50,73.827,3.44
+partialy_cloudy,1.7066,27,1.9,6.31
+Rainy,4.0894,51,114.851,15.3
+Sunny,7.6475,48,37.478,-0.2
+Mostly_cloudy,9.7566,51,73.827,2.1
+Mostly_cloudy,9.177,49,27.538,2.2
+Rainy,9.1126,48,90.239,14.52
+Drizzle,7.4543,24,1.9,8.4
+Sunny,8.4203,56,101.069,2.15
+partialy_cloudy,2.9302,27,6.873,8.52
+Drizzle,5.0071,56,114.851,10
+partialy_cloudy,14.007,50,90.239,8.25
+partialy_cloudy,15.6331,40,50.27,4.4
+Drizzle,16.5991,49,90.239,7.15
+Sunny,16.5991,53,84.437,-0.25
+partialy_cloudy,13.7333,58,114.851,4.45
+partialy_cloudy,12.1555,50,84.437,6.05
+Rainy,9.4507,43,73.827,14.33
+Mostly_cloudy,9.3219,55,96.081,2.24
+Rainy,10.3523,37,15.546,5.56
+Rainy,10.9641,52,114.851,13.3
+Drizzle,13.9587,36,27.538,5.3
+Drizzle,13.7333,25,3.668,7.22
+Rainy,12.4614,22,1.9,8.51
+Rainy,12.397,50,109.446,9.52
+partialy_cloudy,12.0106,55,105.537,7.2
+partialy_cloudy,14.0231,40,37.478,5.15
+Mostly_cloudy,12.1877,40,15.546,3.48
+Sunny,11.7691,53,90.239,2.12
+partialy_cloudy,12.3487,50,84.437,6.55
+Rainy,9.4185,50,96.081,12.15
+Mostly_cloudy,4.7495,52,90.239,0.5
+Drizzle,9.66,26,1.9,7
+Mostly_cloudy,6.5044,62,120.737,-1
+partialy_cloudy,6.2307,49,96.081,6
+Rainy,0.3703,45,73.827,11.55
+Mostly_cloudy,0,61,120.737,3.2
+Mostly_cloudy,2.0769,45,37.478,3.3
+Rainy,0.3703,35,27.538,10.5
+Rainy,0,26,6.873,9.43
+Mostly_cloudy,3.0268,46,37.478,3.16
+Rainy,0.3703,44,73.827,11.4
+Drizzle,8.05,55,120.737,8.48
+Rainy,4.9749,36,27.538,14.3
+partialy_cloudy,4.7173,50,90.239,4.25
+partialy_cloudy,4.8944,35,8.676,7.59
+Mostly_cloudy,7.1162,41,22.522,0.8
+Mostly_cloudy,9.3702,51,90.239,-0.1
+Rainy,8.6618,30,8.676,9.34
+Drizzle,8.7745,26,3.668,6.14
+partialy_cloudy,4.8461,50,90.239,2.26
+Sunny,11.0607,53,90.239,-0.11
+Mostly_cloudy,9.5473,29,3.668,2.06
+Rainy,7.8246,54,120.737,14.34
+Drizzle,0.2576,51,96.081,7.35
+Mostly_cloudy,1.9642,53,96.081,2.3
+partialy_cloudy,0,41,37.478,4.15
+Sunny,0,47,22.522,-0.2
+partialy_cloudy,0.4186,53,105.537,9.5
+Mostly_cloudy,0,55,96.081,3.29

delay_time_Detection/delay.py

+from flask import *
+import json, time
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+import pickle
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.model_selection import cross_val_score
+from flask_cors import CORS
+
+data = pd.read_csv("Trains.csv")
+
+def input_to_one_hot(data):
+    
+    enc_input = np.zeros(8)
+    
+    enc_input[0] = data['Wind_Speed']
+    enc_input[1] = data['Average_Speed']
+    enc_input[2] = data['Distance']
+    enc_input[3] = data['Weater_Status_Drizzle']
+    enc_input[4] = data['Weater_Status_Mostly_cloudy']
+    enc_input[5] = data['Weater_Status_Rainy']
+    enc_input[6] = data['Weater_Status_Sunny']
+    enc_input[7] = data['Weater_Status_partialy_cloudy']
+              
+    return enc_input
+
+num_cols = [col for col in data.columns if data[col].dtypes != 'O' and col not in "Id"]
+print('Number of Numerical Variables: ', len(num_cols))
+num_cols
+
+X = data[['Weater_Status', 'Wind_Speed', 'Average_Speed', 'Distance']]
+
+X.dropna(inplace=True)
+
+Y = data.loc[:, data.columns != 'gps'].dropna().Delay_time
+X = pd.get_dummies(data=X) 
+
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = .20, random_state = 42)
+
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.metrics import r2_score
+
+gbr =GradientBoostingRegressor()
+gbr.fit(X_train, Y_train)
+
+predicted = gbr.predict(X_test)
+residual = Y_test - predicted
+
+from sklearn.metrics import mean_squared_error
+rmse = np.sqrt(mean_squared_error(Y_test, predicted))
+scores = cross_val_score(gbr, X, Y, cv=12)
+
+# save model to file
+pickle.dump(gbr, open("gbr2.pickle.dat", "wb"))
+
+loaded_gbr = pickle.load(open("gbr2.pickle.dat", "rb"))
+
+app = Flask(__name__)
+CORS(app, resources={r"/delay/*": {"origins": "*"}})
+
+@app.route('/delay/', methods=['GET'])
+def request_page():
+    
+    user_input = {'Wind_Speed':float(request.args.get('wind')), 
+              'Average_Speed':int(request.args.get('avg_speed')), 
+              'Distance':float(request.args.get('distance')), 
+              'Weater_Status_Drizzle':int(request.args.get('drizzle')),
+              'Weater_Status_Mostly_cloudy':int(request.args.get('m_cloudy')),
+              'Weater_Status_Rainy':int(request.args.get('rainy')),
+              'Weater_Status_Sunny':int(request.args.get('sunny')),
+              'Weater_Status_partialy_cloudy':int(request.args.get('p_cloudy')),
+              }
+    
+    a = input_to_one_hot(user_input)
+
+    delay_pred = loaded_gbr.predict([a])
+
+    print(delay_pred[0])
+    
+    data = str(delay_pred[0])
+    
+    json_dump = json.dumps({"data":data})
+
+    return json_dump
+
+if __name__ == '__main__':
+    app.run(host="127.0.0.1", port=7778,debug = True)
+ 
+ #using this type
+ #  http://127.0.0.1:7778/delay/?wind=15.65&avg_speed=55&distance=225.25&drizzle=0&m_cloudy=0&rainy=1&sunny=0&p_cloudy=0
\ No newline at end of file