final (copy).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.py:41: DeprecationWarning:\n",
      "\n",
      "This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import sqlite3\n",
    "import numpy as np\n",
    "import mysql.connector\n",
    "import pandas as pd\n",
    "import getpass\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly\n",
    "plotly.tools.set_credentials_file(username='mainakchain', api_key='B9m4DVrYYmsoW3jDSiok')\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly import __version__\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "import cufflinks as cf\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.figure_factory as ff\n",
    "from scipy import stats\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from datetime import timedelta\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import datetime\n",
    "import holidays\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import median_absolute_error\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.svm import LinearSVR\n",
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "toll_time = pd.read_csv('toll_time.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_toll_time = toll_time[toll_time['origin'] == 'Jamshedpur'][toll_time['destination'] == 'HOSUR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "toll_time.drop(['Unnamed: 0'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "toll_time['route'] = toll_time['origin'] + ' ' + toll_time['destination']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_route_to_dummy(df):\n",
    "    num = 1\n",
    "    dict_route = {}\n",
    "    for r in df['route'].unique().tolist():\n",
    "        df['route'] = df['route'].replace({r: num})\n",
    "        dict_route[num] = r\n",
    "        num += 1\n",
    "    return dict_route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_route = change_route_to_dummy(toll_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6425, 18)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toll_time.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_toll_time = toll_time.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "today_date = datetime.datetime.now()\n",
    "today_date.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "today = today_date.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "new_toll_time['today'] = datetime.datetime.strptime(today, '%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_toll_time['start_date'] = new_toll_time['start_date'].apply(lambda x:datetime.datetime.strptime(x, '%Y-%m-%d %H:%M:%S') )\n",
    "new_toll_time['days_from_now'] = new_toll_time['today'] - new_toll_time['start_date'] \n",
    "new_toll_time['days_from_now'] = new_toll_time['days_from_now'].apply(lambda x: x.days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_toll_time['unloading_in_time'] = new_toll_time['unloading_in_time'].apply(lambda x:datetime.datetime.strptime(x, '%Y-%m-%d %H:%M:%S') )\n",
    "new_toll_time['avg_travel_time'] = new_toll_time['start_date'] + (new_toll_time['unloading_in_time'] - new_toll_time['start_date'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_toll_time['month_of_travel'] = new_toll_time['avg_travel_time'].apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_toll_time['ist_timestamp'] = new_toll_time['ist_timestamp'].apply(lambda x:datetime.datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))\n",
    "new_toll_time['time_of_travel'] = new_toll_time.ist_timestamp.apply(lambda x: x.hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_toll_time['time_of_travel'] = new_toll_time['time_of_travel'].replace({3:'midnight',4:'midnight',5:'midnight',6:'midnight'})\n",
    "new_toll_time['time_of_travel'] = new_toll_time['time_of_travel'].replace({7:'morning',8:'morning',9:'morning',10:'morning'})\n",
    "new_toll_time['time_of_travel'] = new_toll_time['time_of_travel'].replace({11:'noon',12:'noon',13:'noon',14:'noon'})\n",
    "new_toll_time['time_of_travel'] = new_toll_time['time_of_travel'].replace({15:'afternoon',16:'afternoon',17:'afternoon',18:'afternoon'})\n",
    "new_toll_time['time_of_travel'] = new_toll_time['time_of_travel'].replace({19:'evening',20:'evening',21:'evening',22:'evening'})\n",
    "new_toll_time['time_of_travel'] = new_toll_time['time_of_travel'].replace({23:'night',0:'night',1:'night',2:'night'})\n",
    "\n",
    "new_toll_time['time_of_travel'] = new_toll_time['time_of_travel'].replace({'midnight':1,'morning':2,'noon':3,'afternoon':4,'evening':5,'night':6})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_toll_time['day_of_travel'] = new_toll_time['avg_travel_time'].apply(lambda x: x.weekday())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_toll_time['season_of_travel'] = new_toll_time['month_of_travel'].replace({12:'Winter',1:'Winter',2:'Winter'})\n",
    "new_toll_time['season_of_travel'] = new_toll_time['season_of_travel'].replace({3:'Summer',4:'Summer',5:'Summer'})\n",
    "new_toll_time['season_of_travel'] = new_toll_time['season_of_travel'].replace({6:'Rainy',7:'Rainy',8:'Rainy',9:'Rainy'})\n",
    "new_toll_time['season_of_travel'] = new_toll_time['season_of_travel'].replace({10:'Autumn',11:'Autumn'})\n",
    "\n",
    "new_toll_time['season_of_travel'] = new_toll_time['season_of_travel'].replace({'Winter':1,'Summer':2,'Rainy':3,'Autumn':4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_vehicle_no_to_dummy(df):\n",
    "    num = 1\n",
    "    dict_vehicle_no = {}\n",
    "    for truck in df['vehicle_no'].unique().tolist():\n",
    "        df['vehicle_no'] = df['vehicle_no'].replace({truck: num})\n",
    "        dict_vehicle_no[num] = truck\n",
    "        num += 1\n",
    "    return dict_vehicle_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_toll_booth_to_dummy(df):\n",
    "    num = 1\n",
    "    dict_toll_both = {}\n",
    "    for toll in df['toll_both'].unique().tolist():\n",
    "        df['toll_both'] = df['toll_both'].replace({toll: num})\n",
    "        dict_toll_both[num] = toll\n",
    "        num += 1\n",
    "    return dict_toll_both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_toll_both = change_toll_booth_to_dummy(new_toll_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_delivery_status_to_dummy(df):\n",
    "    df['delivery_status'] = df['delivery_status'].replace({'early':1,'on-time':2,'late':3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_toll_time['loading_out_time'] = new_toll_time['loading_out_time'].apply(lambda x:datetime.datetime.strptime(x, '%Y-%m-%d %H:%M:%S') )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_toll_time = new_toll_time.sort_values(['route','vehicle_no','time_taken'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_between_toll(df):\n",
    "    for truck in df.vehicle_no.unique().tolist():\n",
    "        for route in df.route.unique().tolist():\n",
    "            if df[(df.vehicle_no == truck) & (df.route == route)].shape[0] == 1:\n",
    "                continue\n",
    "            elif df[(df.vehicle_no == truck) & (df.route == route)].shape[0] > 1 :\n",
    "                df['time_btn_toll'][(df['vehicle_no'] == truck) & (df['route'] == route)] = df['time_taken'][(df['vehicle_no'] == truck) & (df['route'] == route)].diff()\n",
    "            else:\n",
    "                continue\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "new_toll_time['time_btn_toll'] = 0\n",
    "new_toll_time = time_between_toll(new_toll_time)\n",
    "new_toll_time['time_btn_toll'] = new_toll_time['time_btn_toll'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_toll_time['loading_out_time'] = new_toll_time['loading_out_time'].apply(lambda x: x.hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_toll_time.late_by_hrs = new_toll_time.late_by_hrs.round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['origin',\n",
       " 'destination',\n",
       " 'toll_both',\n",
       " 'vehicle_no',\n",
       " 'loading_out_time',\n",
       " 'odometer_km',\n",
       " 'ist_timestamp',\n",
       " 'distance_from_toll',\n",
       " 'loading_in_time',\n",
       " 'start_date',\n",
       " 'eta',\n",
       " 'unloading_in_time',\n",
       " 'slug',\n",
       " 'delivery_status',\n",
       " 'distance_travelled',\n",
       " 'late_by_hrs',\n",
       " 'time_taken',\n",
       " 'route',\n",
       " 'today',\n",
       " 'days_from_now',\n",
       " 'avg_travel_time',\n",
       " 'month_of_travel',\n",
       " 'time_of_travel',\n",
       " 'day_of_travel',\n",
       " 'season_of_travel']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_toll_time.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = new_toll_time[new_toll_time['vehicle_no'].isin(['OD04D5891','PB10CH3759','NL02N3814']) == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = new_toll_time[new_toll_time['vehicle_no'].isin(['OD04D5891','PB10CH3759','NL02N3814']) == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((42, 25), (6383, 25))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape, train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "if set(test_df.vehicle_no.unique().tolist()) < set(train_df.vehicle_no.unique().tolist()):\n",
    "    final_train_df = train_df[['route','toll_both','vehicle_no','odometer_km','loading_out_time','distance_travelled','time_of_travel','day_of_travel','season_of_travel','days_from_now','time_taken']]\n",
    "    final_test_df = test_df[['route','toll_both','vehicle_no','odometer_km','loading_out_time','distance_travelled','time_of_travel','day_of_travel','season_of_travel','days_from_now','time_taken']]\n",
    "    train_dict_vehicle_no = change_vehicle_no_to_dummy(final_train_df)\n",
    "    test_dict_vehilce_no = change_vehicle_no_to_dummy(final_test_df)\n",
    "    \n",
    "else:\n",
    "    final_train_df = train_df[['route','toll_both','odometer_km','loading_out_time','distance_travelled','time_of_travel','day_of_travel','season_of_travel','days_from_now','time_taken']]\n",
    "    final_test_df = test_df[['route','toll_both','odometer_km','loading_out_time','distance_travelled','time_of_travel','day_of_travel','season_of_travel','days_from_now','time_taken']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['route',\n",
       " 'toll_both',\n",
       " 'odometer_km',\n",
       " 'loading_out_time',\n",
       " 'distance_travelled',\n",
       " 'time_of_travel',\n",
       " 'day_of_travel',\n",
       " 'season_of_travel',\n",
       " 'days_from_now',\n",
       " 'time_taken']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_test_df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JQPSBfH46Kft/xblVi58uYfsF4AVJr1UceAPtbUl24Lbvk/QY0LBzqxYrnbLKLu359SgI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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f89b4576c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = new_toll_time.corr()\n",
    "\n",
    "# plot the heatmap\n",
    "sns.heatmap(corr, \n",
    "        xticklabels=corr.columns,\n",
    "        yticklabels=corr.columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = final_train_df.drop(['time_taken'], axis=1)\n",
    "train_y = final_train_df['time_taken']\n",
    "\n",
    "test_X = final_test_df.drop(['time_taken'], axis=1)\n",
    "test_y = final_test_df['time_taken']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(train_X,train_y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale= MinMaxScaler()\n",
    "X_train_scaled = scale.fit_transform(X_train)\n",
    "X_test_scaled = scale.fit_transform(X_test)\n",
    "\n",
    "# y_train_std = final_train_df['time_btn_toll']\n",
    "# y_test_std = final_test_df['time_btn_toll']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters = {\n",
    "#     'hidden_layer_sizes':[15,20,30,40],\n",
    "#     'activation':['logistic','relu','identity'],\n",
    "#     'solver':['adam'],\n",
    "#     'learning_rate':['invscaling','constant']\n",
    "#              }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reg_mlp = MLPRegressor( hidden_layer_sizes=(100,50),  activation='logistic', solver='adam', alpha=1, batch_size='auto',\n",
    "#     learning_rate='constant', learning_rate_init=0.01, power_t=0.01, max_iter=5000, shuffle=False, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True,\n",
    "#     early_stopping=False,random_state=6, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\"C\": [1e1, 1e2, 1e3, 1e4],\n",
    "                               \"gamma\": np.logspace(-2, 2, 5)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_svr = SVR(kernel='rbf', gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid = GridSearchCV(reg_svr, param_grid=parameters, n_jobs=-1,\n",
    "#                    scoring='r2',\n",
    "#                    verbose=1)\n",
    "grid = GridSearchCV(reg_svr, cv=5,\n",
    "                   param_grid=parameters, verbose=1, n_jobs=-1, scoring='r2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5106, 9), (5106,))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   48.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  8.4min finished\n"
     ]
    }
   ],
   "source": [
    "grid_result = grid.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters set found on development set:\n",
      "SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=10.0,\n",
      "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)\n",
      "Grid scores on development set:\n",
      "0.168 (+/-0.008) for {'C': 10.0, 'gamma': 0.01}\n",
      "0.324 (+/-0.011) for {'C': 10.0, 'gamma': 0.10000000000000001}\n",
      "0.435 (+/-0.011) for {'C': 10.0, 'gamma': 1.0}\n",
      "0.452 (+/-0.016) for {'C': 10.0, 'gamma': 10.0}\n",
      "0.107 (+/-0.004) for {'C': 10.0, 'gamma': 100.0}\n",
      "0.288 (+/-0.010) for {'C': 100.0, 'gamma': 0.01}\n",
      "0.391 (+/-0.011) for {'C': 100.0, 'gamma': 0.10000000000000001}\n",
      "0.500 (+/-0.011) for {'C': 100.0, 'gamma': 1.0}\n",
      "0.649 (+/-0.026) for {'C': 100.0, 'gamma': 10.0}\n",
      "0.353 (+/-0.007) for {'C': 100.0, 'gamma': 100.0}\n",
      "0.326 (+/-0.012) for {'C': 1000.0, 'gamma': 0.01}\n",
      "0.431 (+/-0.010) for {'C': 1000.0, 'gamma': 0.10000000000000001}\n",
      "0.554 (+/-0.011) for {'C': 1000.0, 'gamma': 1.0}\n",
      "0.712 (+/-0.020) for {'C': 1000.0, 'gamma': 10.0}\n",
      "0.400 (+/-0.012) for {'C': 1000.0, 'gamma': 100.0}\n",
      "0.391 (+/-0.011) for {'C': 10000.0, 'gamma': 0.01}\n",
      "0.459 (+/-0.009) for {'C': 10000.0, 'gamma': 0.10000000000000001}\n",
      "0.599 (+/-0.009) for {'C': 10000.0, 'gamma': 1.0}\n",
      "0.660 (+/-0.019) for {'C': 10000.0, 'gamma': 10.0}\n",
      "0.400 (+/-0.012) for {'C': 10000.0, 'gamma': 100.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/sklearn/model_selection/_search.py:761: DeprecationWarning:\n",
      "\n",
      "The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameters set found on development set:\")\n",
    "print(grid_result.best_estimator_)\n",
    "print(\"Grid scores on development set:\")\n",
    "for parameters, mean_score, scores in grid_result.grid_scores_:\n",
    "    print(\"%0.3f (+/-%0.03f) for %r\"\n",
    "          % (mean_score, scores.std() / 2, parameters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reg_sv = SVR(C=1500, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=10.0,\n",
    "#   kernel='sigmoid', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reg_sv = SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.01,\n",
    "#   kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=10.0,\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=10.0,\n",
    "#   kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reg_rf = RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
    "#            max_features='auto', max_leaf_nodes=50,\n",
    "#            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "#            min_samples_leaf=1, min_samples_split=4,\n",
    "#            min_weight_fraction_leaf=0.0, n_estimators=1000, n_jobs=-1,\n",
    "#            oob_score=False, random_state=42, verbose=1, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reg_lin = LinearRegression(fit_intercept=True, normalize=True, copy_X=True, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # summarize results\n",
    "# print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.69953703379571719"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_result.best_estimator_.fit(X_train_scaled, y_train)\n",
    "grid_result.best_estimator_.score(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(grid_result.best_estimator_, X_train_scaled, y_train, scoring='r2', n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.67 (+/- 0.03)\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_predict_scaled = MinMaxScaler().fit_transform(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = grid_result.best_estimator_.predict(X_predict_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3TvP9rYE8oqdZd8mbHNYALwJfTpc/mJZdPJWNSjoceB/w0YrilcCtki4Htk/1vc06nfuc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f89b44f51d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## The line / model\n",
    "plt.scatter(test_y, predictions)\n",
    "plt.ylabel('predictions')\n",
    "plt.xlabel('actual')\n",
    "# plt.axis('equal')\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((42, 25), (42,))"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape, predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_df = test_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "show_df['predicted'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_df['time_taken_delta'] = show_df['time_taken'].apply(lambda x: timedelta(hours=x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_df['predict_delta'] = show_df['predicted'].apply(lambda x: timedelta(hours=x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_df['predicted_ist_timestamp'] = show_df['ist_timestamp'] - show_df['time_taken_delta'] + show_df['predict_delta']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_df['route'] = test_df['route'].replace(dict_route)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_df['toll_both'] = show_df['toll_both'].replace(dict_toll_both)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_df = show_df.sort_values(['route','vehicle_no','time_taken'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>route</th>\n",
       "      <th>vehicle_no</th>\n",
       "      <th>toll_both</th>\n",
       "      <th>time_taken</th>\n",
       "      <th>predicted</th>\n",
       "      <th>ist_timestamp</th>\n",
       "      <th>predicted_ist_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4690</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Sergrarh</td>\n",
       "      <td>63.595833</td>\n",
       "      <td>63.929903</td>\n",
       "      <td>2017-11-24 19:44:00</td>\n",
       "      <td>2017-11-24 20:04:02.649709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4601</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Panikholi</td>\n",
       "      <td>66.002222</td>\n",
       "      <td>64.151447</td>\n",
       "      <td>2017-11-24 22:08:23</td>\n",
       "      <td>2017-11-24 20:17:20.208063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4473</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Manguli</td>\n",
       "      <td>67.488056</td>\n",
       "      <td>62.286564</td>\n",
       "      <td>2017-11-24 23:37:32</td>\n",
       "      <td>2017-11-24 18:25:26.631366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4132</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Gudipada (Old Gangapada)</td>\n",
       "      <td>68.857500</td>\n",
       "      <td>62.432214</td>\n",
       "      <td>2017-11-25 00:59:42</td>\n",
       "      <td>2017-11-24 18:34:10.970111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4177</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Gurapalli</td>\n",
       "      <td>71.043611</td>\n",
       "      <td>62.548083</td>\n",
       "      <td>2017-11-25 03:10:52</td>\n",
       "      <td>2017-11-24 18:41:08.097006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3929</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Bellupada</td>\n",
       "      <td>85.191111</td>\n",
       "      <td>62.857815</td>\n",
       "      <td>2017-11-25 17:19:43</td>\n",
       "      <td>2017-11-24 18:59:43.133946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4425</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Madapam</td>\n",
       "      <td>89.519722</td>\n",
       "      <td>63.650202</td>\n",
       "      <td>2017-11-25 21:39:26</td>\n",
       "      <td>2017-11-24 19:47:15.727832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4051</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Chilakapalem</td>\n",
       "      <td>90.272500</td>\n",
       "      <td>63.684234</td>\n",
       "      <td>2017-11-25 22:24:36</td>\n",
       "      <td>2017-11-24 19:49:18.241700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4381</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Laxmipuram</td>\n",
       "      <td>91.336389</td>\n",
       "      <td>63.985636</td>\n",
       "      <td>2017-11-25 23:28:26</td>\n",
       "      <td>2017-11-24 20:07:23.289011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4554</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Nathavalasa/ Vizianagaram</td>\n",
       "      <td>91.336389</td>\n",
       "      <td>63.987893</td>\n",
       "      <td>2017-11-25 23:28:26</td>\n",
       "      <td>2017-11-24 20:07:31.413844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4823</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Vempadu</td>\n",
       "      <td>97.708056</td>\n",
       "      <td>63.817303</td>\n",
       "      <td>2017-11-26 05:50:44</td>\n",
       "      <td>2017-11-24 19:57:17.290729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4337</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Krishnavaram</td>\n",
       "      <td>102.237500</td>\n",
       "      <td>63.723366</td>\n",
       "      <td>2017-11-26 10:22:30</td>\n",
       "      <td>2017-11-24 19:51:39.116901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4243</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Kalaparru</td>\n",
       "      <td>107.043333</td>\n",
       "      <td>63.758414</td>\n",
       "      <td>2017-11-26 15:10:51</td>\n",
       "      <td>2017-11-24 19:53:45.290888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4648</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Pottipadu</td>\n",
       "      <td>107.693333</td>\n",
       "      <td>63.901099</td>\n",
       "      <td>2017-11-26 15:49:51</td>\n",
       "      <td>2017-11-24 20:02:18.955700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4285</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Kaza</td>\n",
       "      <td>109.140000</td>\n",
       "      <td>63.860845</td>\n",
       "      <td>2017-11-26 17:16:39</td>\n",
       "      <td>2017-11-24 19:59:54.041921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3963</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Bolapalli</td>\n",
       "      <td>111.742778</td>\n",
       "      <td>63.863421</td>\n",
       "      <td>2017-11-26 19:52:49</td>\n",
       "      <td>2017-11-24 20:00:03.314063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4776</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Tangutur</td>\n",
       "      <td>113.518056</td>\n",
       "      <td>64.064473</td>\n",
       "      <td>2017-11-26 21:39:20</td>\n",
       "      <td>2017-11-24 20:12:07.102557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4733</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Sunambatti (Musunur)</td>\n",
       "      <td>122.180000</td>\n",
       "      <td>64.157915</td>\n",
       "      <td>2017-11-27 06:19:03</td>\n",
       "      <td>2017-11-24 20:17:43.494477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4867</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Venkatachallam &amp;#160;(Old Nellore)</td>\n",
       "      <td>123.666111</td>\n",
       "      <td>64.225120</td>\n",
       "      <td>2017-11-27 07:48:13</td>\n",
       "      <td>2017-11-24 20:21:45.430617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4007</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Budanam</td>\n",
       "      <td>124.570278</td>\n",
       "      <td>64.151898</td>\n",
       "      <td>2017-11-27 08:42:28</td>\n",
       "      <td>2017-11-24 20:17:21.834552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4092</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Gaddurur</td>\n",
       "      <td>134.004722</td>\n",
       "      <td>64.184485</td>\n",
       "      <td>2017-11-27 18:08:32</td>\n",
       "      <td>2017-11-24 20:19:19.146868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4515</th>\n",
       "      <td>Jamshedpur HOSUR</td>\n",
       "      <td>OD04D5891</td>\n",
       "      <td>Mulbagal</td>\n",
       "      <td>134.925278</td>\n",
       "      <td>64.178803</td>\n",
       "      <td>2017-11-27 19:03:46</td>\n",
       "      <td>2017-11-24 20:18:58.690432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5037</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Pundag</td>\n",
       "      <td>16.748611</td>\n",
       "      <td>39.231565</td>\n",
       "      <td>2017-11-30 07:47:54</td>\n",
       "      <td>2017-12-01 06:16:52.632207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5052</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Rasoiya Dhamna</td>\n",
       "      <td>20.642778</td>\n",
       "      <td>44.631195</td>\n",
       "      <td>2017-11-30 11:41:33</td>\n",
       "      <td>2017-12-01 11:40:51.302307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5081</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Saukala</td>\n",
       "      <td>39.560000</td>\n",
       "      <td>53.553754</td>\n",
       "      <td>2017-12-01 06:36:35</td>\n",
       "      <td>2017-12-01 20:36:12.514422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5067</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Sasaram</td>\n",
       "      <td>42.858056</td>\n",
       "      <td>51.218944</td>\n",
       "      <td>2017-12-01 09:54:28</td>\n",
       "      <td>2017-12-01 18:16:07.198807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5019</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Mohania</td>\n",
       "      <td>44.411389</td>\n",
       "      <td>60.614667</td>\n",
       "      <td>2017-12-01 11:27:40</td>\n",
       "      <td>2017-12-02 03:39:51.801752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4926</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Daffi</td>\n",
       "      <td>47.523333</td>\n",
       "      <td>70.652077</td>\n",
       "      <td>2017-12-01 14:34:23</td>\n",
       "      <td>2017-12-02 13:42:06.476786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5001</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Lalanagar</td>\n",
       "      <td>49.730278</td>\n",
       "      <td>86.482703</td>\n",
       "      <td>2017-12-01 16:46:48</td>\n",
       "      <td>2017-12-03 05:31:56.730516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4905</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Allahabad Bypass (Khokhraj, Nawabganj, Soraon,...</td>\n",
       "      <td>52.150556</td>\n",
       "      <td>117.086042</td>\n",
       "      <td>2017-12-01 19:12:01</td>\n",
       "      <td>2017-12-04 12:08:08.751139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4975</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Katoghan</td>\n",
       "      <td>76.688611</td>\n",
       "      <td>111.946069</td>\n",
       "      <td>2017-12-02 19:44:18</td>\n",
       "      <td>2017-12-04 06:59:44.846896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4919</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Badauri</td>\n",
       "      <td>86.420833</td>\n",
       "      <td>67.748295</td>\n",
       "      <td>2017-12-03 05:28:14</td>\n",
       "      <td>2017-12-02 10:47:52.863570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Usaka ( Chamari)</td>\n",
       "      <td>90.806111</td>\n",
       "      <td>89.363782</td>\n",
       "      <td>2017-12-03 09:51:21</td>\n",
       "      <td>2017-12-03 08:24:48.614203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4899</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Ait</td>\n",
       "      <td>91.722778</td>\n",
       "      <td>76.045407</td>\n",
       "      <td>2017-12-03 10:46:21</td>\n",
       "      <td>2017-12-02 19:05:42.466063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5043</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Raksha</td>\n",
       "      <td>96.366389</td>\n",
       "      <td>94.206174</td>\n",
       "      <td>2017-12-03 15:24:58</td>\n",
       "      <td>2017-12-03 13:15:21.226362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4936</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Guna Bypass</td>\n",
       "      <td>113.003889</td>\n",
       "      <td>82.323235</td>\n",
       "      <td>2017-12-04 08:03:13</td>\n",
       "      <td>2017-12-03 01:22:22.644593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4965</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Indore Dewas (Indore side)</td>\n",
       "      <td>122.616389</td>\n",
       "      <td>71.442421</td>\n",
       "      <td>2017-12-04 17:39:58</td>\n",
       "      <td>2017-12-02 14:29:31.715652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4950</th>\n",
       "      <td>Jamshedpur INDORE</td>\n",
       "      <td>PB10CH3759</td>\n",
       "      <td>Indore Dewas  (Indore Bypass)</td>\n",
       "      <td>134.403611</td>\n",
       "      <td>75.754548</td>\n",
       "      <td>2017-12-05 05:27:12</td>\n",
       "      <td>2017-12-02 18:48:15.371978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5329</th>\n",
       "      <td>Jamshedpur KOLKATA</td>\n",
       "      <td>NL02N3814</td>\n",
       "      <td>Debra</td>\n",
       "      <td>35.776111</td>\n",
       "      <td>52.150600</td>\n",
       "      <td>2017-11-19 13:48:33</td>\n",
       "      <td>2017-11-20 06:11:01.159297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5363</th>\n",
       "      <td>Jamshedpur KOLKATA</td>\n",
       "      <td>NL02N3814</td>\n",
       "      <td>Jaladhulagori (Dhulagarh)</td>\n",
       "      <td>38.179722</td>\n",
       "      <td>52.398588</td>\n",
       "      <td>2017-11-19 16:12:46</td>\n",
       "      <td>2017-11-20 06:25:53.915922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5328</th>\n",
       "      <td>Jamshedpur KOLKATA</td>\n",
       "      <td>NL02N3814</td>\n",
       "      <td>Debra</td>\n",
       "      <td>40.685833</td>\n",
       "      <td>63.634792</td>\n",
       "      <td>2017-11-05 07:58:31</td>\n",
       "      <td>2017-11-06 06:55:27.251109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5330</th>\n",
       "      <td>Jamshedpur KOLKATA</td>\n",
       "      <td>NL02N3814</td>\n",
       "      <td>Debra</td>\n",
       "      <td>47.644444</td>\n",
       "      <td>63.687593</td>\n",
       "      <td>2017-12-06 16:17:42</td>\n",
       "      <td>2017-12-07 08:20:17.334556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   route  vehicle_no  \\\n",
       "4690    Jamshedpur HOSUR   OD04D5891   \n",
       "4601    Jamshedpur HOSUR   OD04D5891   \n",
       "4473    Jamshedpur HOSUR   OD04D5891   \n",
       "4132    Jamshedpur HOSUR   OD04D5891   \n",
       "4177    Jamshedpur HOSUR   OD04D5891   \n",
       "3929    Jamshedpur HOSUR   OD04D5891   \n",
       "4425    Jamshedpur HOSUR   OD04D5891   \n",
       "4051    Jamshedpur HOSUR   OD04D5891   \n",
       "4381    Jamshedpur HOSUR   OD04D5891   \n",
       "4554    Jamshedpur HOSUR   OD04D5891   \n",
       "4823    Jamshedpur HOSUR   OD04D5891   \n",
       "4337    Jamshedpur HOSUR   OD04D5891   \n",
       "4243    Jamshedpur HOSUR   OD04D5891   \n",
       "4648    Jamshedpur HOSUR   OD04D5891   \n",
       "4285    Jamshedpur HOSUR   OD04D5891   \n",
       "3963    Jamshedpur HOSUR   OD04D5891   \n",
       "4776    Jamshedpur HOSUR   OD04D5891   \n",
       "4733    Jamshedpur HOSUR   OD04D5891   \n",
       "4867    Jamshedpur HOSUR   OD04D5891   \n",
       "4007    Jamshedpur HOSUR   OD04D5891   \n",
       "4092    Jamshedpur HOSUR   OD04D5891   \n",
       "4515    Jamshedpur HOSUR   OD04D5891   \n",
       "5037   Jamshedpur INDORE  PB10CH3759   \n",
       "5052   Jamshedpur INDORE  PB10CH3759   \n",
       "5081   Jamshedpur INDORE  PB10CH3759   \n",
       "5067   Jamshedpur INDORE  PB10CH3759   \n",
       "5019   Jamshedpur INDORE  PB10CH3759   \n",
       "4926   Jamshedpur INDORE  PB10CH3759   \n",
       "5001   Jamshedpur INDORE  PB10CH3759   \n",
       "4905   Jamshedpur INDORE  PB10CH3759   \n",
       "4975   Jamshedpur INDORE  PB10CH3759   \n",
       "4919   Jamshedpur INDORE  PB10CH3759   \n",
       "5094   Jamshedpur INDORE  PB10CH3759   \n",
       "4899   Jamshedpur INDORE  PB10CH3759   \n",
       "5043   Jamshedpur INDORE  PB10CH3759   \n",
       "4936   Jamshedpur INDORE  PB10CH3759   \n",
       "4965   Jamshedpur INDORE  PB10CH3759   \n",
       "4950   Jamshedpur INDORE  PB10CH3759   \n",
       "5329  Jamshedpur KOLKATA   NL02N3814   \n",
       "5363  Jamshedpur KOLKATA   NL02N3814   \n",
       "5328  Jamshedpur KOLKATA   NL02N3814   \n",
       "5330  Jamshedpur KOLKATA   NL02N3814   \n",
       "\n",
       "                                              toll_both  time_taken  \\\n",
       "4690                                           Sergrarh   63.595833   \n",
       "4601                                          Panikholi   66.002222   \n",
       "4473                                            Manguli   67.488056   \n",
       "4132                           Gudipada (Old Gangapada)   68.857500   \n",
       "4177                                          Gurapalli   71.043611   \n",
       "3929                                          Bellupada   85.191111   \n",
       "4425                                            Madapam   89.519722   \n",
       "4051                                       Chilakapalem   90.272500   \n",
       "4381                                         Laxmipuram   91.336389   \n",
       "4554                          Nathavalasa/ Vizianagaram   91.336389   \n",
       "4823                                            Vempadu   97.708056   \n",
       "4337                                       Krishnavaram  102.237500   \n",
       "4243                                          Kalaparru  107.043333   \n",
       "4648                                          Pottipadu  107.693333   \n",
       "4285                                               Kaza  109.140000   \n",
       "3963                                          Bolapalli  111.742778   \n",
       "4776                                           Tangutur  113.518056   \n",
       "4733                               Sunambatti (Musunur)  122.180000   \n",
       "4867                 Venkatachallam &#160;(Old Nellore)  123.666111   \n",
       "4007                                            Budanam  124.570278   \n",
       "4092                                           Gaddurur  134.004722   \n",
       "4515                                           Mulbagal  134.925278   \n",
       "5037                                             Pundag   16.748611   \n",
       "5052                                     Rasoiya Dhamna   20.642778   \n",
       "5081                                            Saukala   39.560000   \n",
       "5067                                            Sasaram   42.858056   \n",
       "5019                                            Mohania   44.411389   \n",
       "4926                                              Daffi   47.523333   \n",
       "5001                                          Lalanagar   49.730278   \n",
       "4905  Allahabad Bypass (Khokhraj, Nawabganj, Soraon,...   52.150556   \n",
       "4975                                           Katoghan   76.688611   \n",
       "4919                                            Badauri   86.420833   \n",
       "5094                                   Usaka ( Chamari)   90.806111   \n",
       "4899                                                Ait   91.722778   \n",
       "5043                                             Raksha   96.366389   \n",
       "4936                                        Guna Bypass  113.003889   \n",
       "4965                         Indore Dewas (Indore side)  122.616389   \n",
       "4950                      Indore Dewas  (Indore Bypass)  134.403611   \n",
       "5329                                              Debra   35.776111   \n",
       "5363                          Jaladhulagori (Dhulagarh)   38.179722   \n",
       "5328                                              Debra   40.685833   \n",
       "5330                                              Debra   47.644444   \n",
       "\n",
       "       predicted       ist_timestamp    predicted_ist_timestamp  \n",
       "4690   63.929903 2017-11-24 19:44:00 2017-11-24 20:04:02.649709  \n",
       "4601   64.151447 2017-11-24 22:08:23 2017-11-24 20:17:20.208063  \n",
       "4473   62.286564 2017-11-24 23:37:32 2017-11-24 18:25:26.631366  \n",
       "4132   62.432214 2017-11-25 00:59:42 2017-11-24 18:34:10.970111  \n",
       "4177   62.548083 2017-11-25 03:10:52 2017-11-24 18:41:08.097006  \n",
       "3929   62.857815 2017-11-25 17:19:43 2017-11-24 18:59:43.133946  \n",
       "4425   63.650202 2017-11-25 21:39:26 2017-11-24 19:47:15.727832  \n",
       "4051   63.684234 2017-11-25 22:24:36 2017-11-24 19:49:18.241700  \n",
       "4381   63.985636 2017-11-25 23:28:26 2017-11-24 20:07:23.289011  \n",
       "4554   63.987893 2017-11-25 23:28:26 2017-11-24 20:07:31.413844  \n",
       "4823   63.817303 2017-11-26 05:50:44 2017-11-24 19:57:17.290729  \n",
       "4337   63.723366 2017-11-26 10:22:30 2017-11-24 19:51:39.116901  \n",
       "4243   63.758414 2017-11-26 15:10:51 2017-11-24 19:53:45.290888  \n",
       "4648   63.901099 2017-11-26 15:49:51 2017-11-24 20:02:18.955700  \n",
       "4285   63.860845 2017-11-26 17:16:39 2017-11-24 19:59:54.041921  \n",
       "3963   63.863421 2017-11-26 19:52:49 2017-11-24 20:00:03.314063  \n",
       "4776   64.064473 2017-11-26 21:39:20 2017-11-24 20:12:07.102557  \n",
       "4733   64.157915 2017-11-27 06:19:03 2017-11-24 20:17:43.494477  \n",
       "4867   64.225120 2017-11-27 07:48:13 2017-11-24 20:21:45.430617  \n",
       "4007   64.151898 2017-11-27 08:42:28 2017-11-24 20:17:21.834552  \n",
       "4092   64.184485 2017-11-27 18:08:32 2017-11-24 20:19:19.146868  \n",
       "4515   64.178803 2017-11-27 19:03:46 2017-11-24 20:18:58.690432  \n",
       "5037   39.231565 2017-11-30 07:47:54 2017-12-01 06:16:52.632207  \n",
       "5052   44.631195 2017-11-30 11:41:33 2017-12-01 11:40:51.302307  \n",
       "5081   53.553754 2017-12-01 06:36:35 2017-12-01 20:36:12.514422  \n",
       "5067   51.218944 2017-12-01 09:54:28 2017-12-01 18:16:07.198807  \n",
       "5019   60.614667 2017-12-01 11:27:40 2017-12-02 03:39:51.801752  \n",
       "4926   70.652077 2017-12-01 14:34:23 2017-12-02 13:42:06.476786  \n",
       "5001   86.482703 2017-12-01 16:46:48 2017-12-03 05:31:56.730516  \n",
       "4905  117.086042 2017-12-01 19:12:01 2017-12-04 12:08:08.751139  \n",
       "4975  111.946069 2017-12-02 19:44:18 2017-12-04 06:59:44.846896  \n",
       "4919   67.748295 2017-12-03 05:28:14 2017-12-02 10:47:52.863570  \n",
       "5094   89.363782 2017-12-03 09:51:21 2017-12-03 08:24:48.614203  \n",
       "4899   76.045407 2017-12-03 10:46:21 2017-12-02 19:05:42.466063  \n",
       "5043   94.206174 2017-12-03 15:24:58 2017-12-03 13:15:21.226362  \n",
       "4936   82.323235 2017-12-04 08:03:13 2017-12-03 01:22:22.644593  \n",
       "4965   71.442421 2017-12-04 17:39:58 2017-12-02 14:29:31.715652  \n",
       "4950   75.754548 2017-12-05 05:27:12 2017-12-02 18:48:15.371978  \n",
       "5329   52.150600 2017-11-19 13:48:33 2017-11-20 06:11:01.159297  \n",
       "5363   52.398588 2017-11-19 16:12:46 2017-11-20 06:25:53.915922  \n",
       "5328   63.634792 2017-11-05 07:58:31 2017-11-06 06:55:27.251109  \n",
       "5330   63.687593 2017-12-06 16:17:42 2017-12-07 08:20:17.334556  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_df[['route','vehicle_no','toll_both','time_taken','predicted','ist_timestamp','predicted_ist_timestamp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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