On-Time Flight Arrivals.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>YEAR</th>\n      <th>QUARTER</th>\n      <th>MONTH</th>\n      <th>DAY_OF_MONTH</th>\n      <th>DAY_OF_WEEK</th>\n      <th>UNIQUE_CARRIER</th>\n      <th>TAIL_NUM</th>\n      <th>FL_NUM</th>\n      <th>ORIGIN_AIRPORT_ID</th>\n      <th>ORIGIN</th>\n      <th>...</th>\n      <th>CRS_ARR_TIME</th>\n      <th>ARR_TIME</th>\n      <th>ARR_DELAY</th>\n      <th>ARR_DEL15</th>\n      <th>CANCELLED</th>\n      <th>DIVERTED</th>\n      <th>CRS_ELAPSED_TIME</th>\n      <th>ACTUAL_ELAPSED_TIME</th>\n      <th>DISTANCE</th>\n      <th>Unnamed: 25</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>DL</td>\n      <td>N836DN</td>\n      <td>1399</td>\n      <td>10397</td>\n      <td>ATL</td>\n      <td>...</td>\n      <td>2143</td>\n      <td>2102.0</td>\n      <td>-41.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>338.0</td>\n      <td>295.0</td>\n      <td>2182.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>DL</td>\n      <td>N964DN</td>\n      <td>1476</td>\n      <td>11433</td>\n      <td>DTW</td>\n      <td>...</td>\n      <td>1435</td>\n      <td>1439.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>110.0</td>\n      <td>115.0</td>\n      <td>528.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>DL</td>\n      <td>N813DN</td>\n      <td>1597</td>\n      <td>10397</td>\n      <td>ATL</td>\n      <td>...</td>\n      <td>1215</td>\n      <td>1142.0</td>\n      <td>-33.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>335.0</td>\n      <td>300.0</td>\n      <td>2182.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>DL</td>\n      <td>N587NW</td>\n      <td>1768</td>\n      <td>14747</td>\n      <td>SEA</td>\n      <td>...</td>\n      <td>1335</td>\n      <td>1345.0</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>196.0</td>\n      <td>205.0</td>\n      <td>1399.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>DL</td>\n      <td>N836DN</td>\n      <td>1823</td>\n      <td>14747</td>\n      <td>SEA</td>\n      <td>...</td>\n      <td>607</td>\n      <td>615.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>247.0</td>\n      <td>259.0</td>\n      <td>1927.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 26 columns</p>\n</div>",
            "text/plain": "   YEAR  QUARTER  MONTH  DAY_OF_MONTH  DAY_OF_WEEK UNIQUE_CARRIER TAIL_NUM  \\\n0  2016        1      1             1            5             DL   N836DN   \n1  2016        1      1             1            5             DL   N964DN   \n2  2016        1      1             1            5             DL   N813DN   \n3  2016        1      1             1            5             DL   N587NW   \n4  2016        1      1             1            5             DL   N836DN   \n\n   FL_NUM  ORIGIN_AIRPORT_ID ORIGIN     ...       CRS_ARR_TIME ARR_TIME  \\\n0    1399              10397    ATL     ...               2143   2102.0   \n1    1476              11433    DTW     ...               1435   1439.0   \n2    1597              10397    ATL     ...               1215   1142.0   \n3    1768              14747    SEA     ...               1335   1345.0   \n4    1823              14747    SEA     ...                607    615.0   \n\n   ARR_DELAY  ARR_DEL15  CANCELLED  DIVERTED  CRS_ELAPSED_TIME  \\\n0      -41.0        0.0        0.0       0.0             338.0   \n1        4.0        0.0        0.0       0.0             110.0   \n2      -33.0        0.0        0.0       0.0             335.0   \n3       10.0        0.0        0.0       0.0             196.0   \n4        8.0        0.0        0.0       0.0             247.0   \n\n   ACTUAL_ELAPSED_TIME  DISTANCE  Unnamed: 25  \n0                295.0    2182.0          NaN  \n1                115.0     528.0          NaN  \n2                300.0    2182.0          NaN  \n3                205.0    1399.0          NaN  \n4                259.0    1927.0          NaN  \n\n[5 rows x 26 columns]"
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('Data/flightdata.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": "(11231, 26)"
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": "YEAR                       0\nQUARTER                    0\nMONTH                      0\nDAY_OF_MONTH               0\nDAY_OF_WEEK                0\nUNIQUE_CARRIER             0\nTAIL_NUM                   0\nFL_NUM                     0\nORIGIN_AIRPORT_ID          0\nORIGIN                     0\nDEST_AIRPORT_ID            0\nDEST                       0\nCRS_DEP_TIME               0\nDEP_TIME                 107\nDEP_DELAY                107\nDEP_DEL15                107\nCRS_ARR_TIME               0\nARR_TIME                 115\nARR_DELAY                188\nARR_DEL15                188\nCANCELLED                  0\nDIVERTED                   0\nCRS_ELAPSED_TIME           0\nACTUAL_ELAPSED_TIME      188\nDISTANCE                   0\nUnnamed: 25            11231\ndtype: int64"
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": "YEAR                     0\nQUARTER                  0\nMONTH                    0\nDAY_OF_MONTH             0\nDAY_OF_WEEK              0\nUNIQUE_CARRIER           0\nTAIL_NUM                 0\nFL_NUM                   0\nORIGIN_AIRPORT_ID        0\nORIGIN                   0\nDEST_AIRPORT_ID          0\nDEST                     0\nCRS_DEP_TIME             0\nDEP_TIME               107\nDEP_DELAY              107\nDEP_DEL15              107\nCRS_ARR_TIME             0\nARR_TIME               115\nARR_DELAY              188\nARR_DEL15              188\nCANCELLED                0\nDIVERTED                 0\nCRS_ELAPSED_TIME         0\nACTUAL_ELAPSED_TIME    188\nDISTANCE                 0\ndtype: int64"
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Drop the empty column 25\n",
        "df = df.drop('Unnamed: 25', axis=1)\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": "MONTH             0\nDAY_OF_MONTH      0\nDAY_OF_WEEK       0\nORIGIN            0\nDEST              0\nCRS_DEP_TIME      0\nARR_DEL15       188\ndtype: int64"
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create a new DataFrame containing only the relevant features\n",
        "df = df[[\"MONTH\", \"DAY_OF_MONTH\", \"DAY_OF_WEEK\", \"ORIGIN\", \"DEST\", \"CRS_DEP_TIME\", \"ARR_DEL15\"]]\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# Fill empty ARR_DEL15 values with 1s for late arrivals (all represent canceled flights)\n",
        "df = df.fillna({'ARR_DEL15': 1})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MONTH</th>\n      <th>DAY_OF_MONTH</th>\n      <th>DAY_OF_WEEK</th>\n      <th>ORIGIN</th>\n      <th>DEST</th>\n      <th>CRS_DEP_TIME</th>\n      <th>ARR_DEL15</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>ATL</td>\n      <td>SEA</td>\n      <td>19</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>DTW</td>\n      <td>MSP</td>\n      <td>13</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>ATL</td>\n      <td>SEA</td>\n      <td>9</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>SEA</td>\n      <td>MSP</td>\n      <td>8</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>SEA</td>\n      <td>DTW</td>\n      <td>23</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   MONTH  DAY_OF_MONTH  DAY_OF_WEEK ORIGIN DEST  CRS_DEP_TIME  ARR_DEL15\n0      1             1            5    ATL  SEA            19        0.0\n1      1             1            5    DTW  MSP            13        0.0\n2      1             1            5    ATL  SEA             9        0.0\n3      1             1            5    SEA  MSP             8        0.0\n4      1             1            5    SEA  DTW            23        0.0"
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# \"Bin\" departure times into hours 0-23\n",
        "import math\n",
        "\n",
        "for index, row in df.iterrows():\n",
        "    df.loc[index, 'CRS_DEP_TIME'] = math.floor(row['CRS_DEP_TIME'] / 100)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MONTH</th>\n      <th>DAY_OF_MONTH</th>\n      <th>DAY_OF_WEEK</th>\n      <th>CRS_DEP_TIME</th>\n      <th>ARR_DEL15</th>\n      <th>ORIGIN_ATL</th>\n      <th>ORIGIN_DTW</th>\n      <th>ORIGIN_JFK</th>\n      <th>ORIGIN_MSP</th>\n      <th>ORIGIN_SEA</th>\n      <th>DEST_ATL</th>\n      <th>DEST_DTW</th>\n      <th>DEST_JFK</th>\n      <th>DEST_MSP</th>\n      <th>DEST_SEA</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>19</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>13</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>9</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>8</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>23</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   MONTH  DAY_OF_MONTH  DAY_OF_WEEK  CRS_DEP_TIME  ARR_DEL15  ORIGIN_ATL  \\\n0      1             1            5            19        0.0           1   \n1      1             1            5            13        0.0           0   \n2      1             1            5             9        0.0           1   \n3      1             1            5             8        0.0           0   \n4      1             1            5            23        0.0           0   \n\n   ORIGIN_DTW  ORIGIN_JFK  ORIGIN_MSP  ORIGIN_SEA  DEST_ATL  DEST_DTW  \\\n0           0           0           0           0         0         0   \n1           1           0           0           0         0         0   \n2           0           0           0           0         0         0   \n3           0           0           0           1         0         0   \n4           0           0           0           1         0         1   \n\n   DEST_JFK  DEST_MSP  DEST_SEA  \n0         0         0         1  \n1         0         1         0  \n2         0         0         1  \n3         0         1         0  \n4         0         0         0  "
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# One-hot-encode the ORIGIN and DEST columns\n",
        "df = pd.get_dummies(df, columns=['ORIGIN', 'DEST'])\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_x, test_x, train_y, test_y = train_test_split(df.drop('ARR_DEL15', axis=1), df['ARR_DEL15'], test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "/home/nbuser/anaconda3_420/lib/python3.5/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n  from numpy.core.umath_tests import inner1d\n"
        },
        {
          "data": {
            "text/plain": "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n            max_depth=None, max_features='auto', max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n            oob_score=False, random_state=13, verbose=0, warm_start=False)"
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = RandomForestClassifier(random_state=13)\n",
        "model.fit(train_x, train_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": "0.8602581219403649"
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.score(test_x, test_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": "0.6743824904998539"
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "probabilities = model.predict_proba(test_x)\n",
        "roc_auc_score(test_y, probabilities[:, 1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": "array([[1882,   54],\n       [ 260,   51]])"
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "confusion_matrix(test_y, model.predict(test_x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": "0.9972375690607734"
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import precision_score\n",
        "\n",
        "train_predictions = model.predict(train_x)\n",
        "precision_score(train_y, train_predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": "0.8650159744408946"
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import recall_score\n",
        "\n",
        "recall_score(train_y, train_predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "/home/nbuser/anaconda3_420/lib/python3.5/site-packages/matplotlib/font_manager.py:281: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n  'Matplotlib is building the font cache using fc-list. '\n"
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0,0.5,'True Positive Rate')"
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fe0273061d0>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "fpr, tpr, _ = roc_curve(test_y, probabilities[:, 1])\n",
        "plt.plot(fpr, tpr)\n",
        "plt.plot([0, 1], [0, 1], color='grey', lw=1, linestyle='--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "def predict_delay(departure_date_time, origin, destination):\n",
        "    from datetime import datetime\n",
        "\n",
        "    try:\n",
        "        departure_date_time_parsed = datetime.strptime(departure_date_time, '%d/%m/%Y %H:%M:%S')\n",
        "    except ValueError as e:\n",
        "        return 'Error parsing date/time - {}'.format(e)\n",
        "\n",
        "    month = departure_date_time_parsed.month\n",
        "    day = departure_date_time_parsed.day\n",
        "    day_of_week = departure_date_time_parsed.isoweekday()\n",
        "    hour = departure_date_time_parsed.hour\n",
        "\n",
        "    origin = origin.upper()\n",
        "    destination = destination.upper()\n",
        "\n",
        "    input = [{'MONTH': month,\n",
        "              'DAY': day,\n",
        "              'DAY_OF_WEEK': day_of_week,\n",
        "              'CRS_DEP_TIME': hour,\n",
        "              'ORIGIN_ATL': 1 if origin == 'ATL' else 0,\n",
        "              'ORIGIN_DTW': 1 if origin == 'DTW' else 0,\n",
        "              'ORIGIN_JFK': 1 if origin == 'JFK' else 0,\n",
        "              'ORIGIN_MSP': 1 if origin == 'MSP' else 0,\n",
        "              'ORIGIN_SEA': 1 if origin == 'SEA' else 0,\n",
        "              'DEST_ATL': 1 if destination == 'ATL' else 0,\n",
        "              'DEST_DTW': 1 if destination == 'DTW' else 0,\n",
        "              'DEST_JFK': 1 if destination == 'JFK' else 0,\n",
        "              'DEST_MSP': 1 if destination == 'MSP' else 0,\n",
        "              'DEST_SEA': 1 if destination == 'SEA' else 0 }]\n",
        "\n",
        "    return model.predict_proba(pd.DataFrame(input))[0][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": "1.0"
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predict_delay('2/10/2018 10:00:00', 'ATL', 'SEA')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": "(0.0, 1.0)"
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fe0272c75c0>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "labels = ('Oct 1', 'Oct 2', 'Oct 3', 'Oct 4', 'Oct 5', 'Oct 6', 'Oct 7')\n",
        "values = (predict_delay('1/10/2018 21:45:00', 'JFK', 'ATL'),\n",
        "          predict_delay('2/10/2018 21:45:00', 'JFK', 'ATL'),\n",
        "          predict_delay('3/10/2018 21:45:00', 'JFK', 'ATL'),\n",
        "          predict_delay('4/10/2018 21:45:00', 'JFK', 'ATL'),\n",
        "          predict_delay('5/10/2018 21:45:00', 'JFK', 'ATL'),\n",
        "          predict_delay('6/10/2018 21:45:00', 'JFK', 'ATL'),\n",
        "          predict_delay('7/10/2018 21:45:00', 'JFK', 'ATL'))\n",
        "alabels = np.arange(len(labels))\n",
        "\n",
        "plt.bar(alabels, values, align='center', alpha=0.5)\n",
        "plt.xticks(alabels, labels)\n",
        "plt.ylabel('Probability of On-Time Arrival')\n",
        "plt.ylim((0.0, 1.0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": "(0.0, 1.0)"
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fe0272e0b00>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "labels = ('9:00 AM', 'Noon', '3:00 PM', '6:00 PM', '9:00 PM')\n",
        "values = (predict_delay('30/1/2018 09:00:00', 'SEA', 'ATL'),\n",
        "          predict_delay('30/1/2018 12:00:00', 'SEA', 'ATL'),\n",
        "          predict_delay('30/1/2018 15:00:00', 'SEA', 'ATL'),\n",
        "          predict_delay('30/1/2018 18:00:00', 'SEA', 'ATL'),\n",
        "          predict_delay('30/1/2018 21:00:00', 'SEA', 'ATL'))\n",
        "alabels = np.arange(len(labels))\n",
        "\n",
        "plt.bar(alabels, values, align='center', alpha=0.5)\n",
        "plt.xticks(alabels, labels)\n",
        "plt.ylabel('Probability of On-Time Arrival')\n",
        "plt.ylim((0.0, 1.0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "name": "python",
      "file_extension": ".py",
      "version": "3.5.4",
      "pygments_lexer": "ipython3",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}