On-Time Flight Arrivals.ipynb

{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "You can create additional projects and notebooks as you work with Azure Notebooks. You can create notebooks from scratch, or you can upload existing notebooks.\n\nJupyter notebooks are highly interactive, and since they can include executable code, they provide the perfect platform for manipulating data and building predictive models from it.\n\nEnter the following command into the first cell of the notebook:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "!curl https://topcs.blob.core.windows.net/public/FlightData.csv -o flightdata.csv",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n                                 Dload  Upload   Total   Spent    Left  Speed\n100 1552k  100 1552k    0     0   177k      0  0:00:08  0:00:08 --:--:--  337k\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\n\ndf = pd.read_csv('flightdata.csv')\ndf.head()",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\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]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "markdown",
      "source": "### Clean and prepare data"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Before you can prepare a dataset, you need to understand its content and structure. In the previous lab, you imported a dataset containing on-time arrival information for a major U.S. airline. That data included 26 columns and thousands of rows, with each row representing one flight and containing information such as the flight's origin, destination, and scheduled departure time. You also loaded the data into a Jupyter notebook and used a simple Python script to create a Pandas DataFrame from it.\n\nA DataFrame is a two-dimensional labeled data structure. The columns in a DataFrame can be of different types, just like columns in a spreadsheet or database table. It is the most commonly used object in Pandas. In this exercise, you will examine the DataFrame — and the data inside it — more closely."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.shape",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "(11231, 26)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "Getting a row and column count\n\nNow take a moment to examine the 26 columns in the dataset. They contain important information such as the date that the flight took place (YEAR, MONTH, and DAY_OF_MONTH), the origin and destination (ORIGIN and DEST), the scheduled departure and arrival times (CRS_DEP_TIME and CRS_ARR_TIME), the difference between the scheduled arrival time and the actual arrival time in minutes (ARR_DELAY), and whether the flight was late by 15 minutes or more (ARR_DEL15).\n\nHere is a complete list of the columns in the dataset. Times are expressed in 24-hour military time. For example, 1130 equals 11:30 a.m. and 1500 equals 3:00 p.m.\n\nTABLE 1\nColumn\tDescription\nYEAR\tYear that the flight took place\nQUARTER\tQuarter that the flight took place (1-4)\nMONTH\tMonth that the flight took place (1-12)\nDAY_OF_MONTH\tDay of the month that the flight took place (1-31)\nDAY_OF_WEEK\tDay of the week that the flight took place (1=Monday, 2=Tuesday, etc.)\nUNIQUE_CARRIER\tAirline carrier code (e.g., DL)\nTAIL_NUM\tAircraft tail number\nFL_NUM\tFlight number\nORIGIN_AIRPORT_ID\tID of the airport of origin\nORIGIN\tOrigin airport code (ATL, DFW, SEA, etc.)\nDEST_AIRPORT_ID\tID of the destination airport\nDEST\tDestination airport code (ATL, DFW, SEA, etc.)\nCRS_DEP_TIME\tScheduled departure time\nDEP_TIME\tActual departure time\nDEP_DELAY\tNumber of minutes departure was delayed\nDEP_DEL15\t0=Departure delayed less than 15 minutes, 1=Departure delayed 15 minutes or more\nCRS_ARR_TIME\tScheduled arrival time\nARR_TIME\tActual arrival time\nARR_DELAY\tNumber of minutes flight arrived late\nARR_DEL15\t0=Arrived less than 15 minutes late, 1=Arrived 15 minutes or more late\nCANCELLED\t0=Flight was not cancelled, 1=Flight was cancelled\nDIVERTED\t0=Flight was not diverted, 1=Flight was diverted\nCRS_ELAPSED_TIME\tScheduled flight time in minutes\nACTUAL_ELAPSED_TIME\tActual flight time in minutes\nDISTANCE\tDistance traveled in miles\nThe dataset includes a roughly even distribution of dates throughout the year, which is important because a flight out of Minneapolis is less likely to be delayed due to winter storms in July than it is in January. But this dataset is far from being \"clean\" and ready to use. Let's write some Pandas code to clean it up.\n\nOne of the most important aspects of preparing a dataset for use in machine learning is selecting the \"feature\" columns that are relevant to the outcome you are trying to predict while filtering out columns that do not affect the outcome, could bias it in a negative way, or might produce multicollinearity. Another important task is to eliminate missing values, either by deleting the rows or columns containing them or replacing them with meaningful values. In this exercise, you will eliminate extraneous columns and replace missing values in the remaining columns."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "One of the first things data scientists typically look for in a dataset is missing values. There's an easy way to check for missing values in Pandas. To demonstrate, execute the following code in a cell at the end of the notebook:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.isnull().values.any()",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "True"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### Checking for missing values\n##### The next step is to find out where the missing values are. To do so, execute the following code:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.isnull().sum()",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Number of missing values in each column\n\nCuriously, the 26th column (\"Unnamed: 25\") contains 11,231 missing values, which equals the number of rows in the dataset. This column was mistakenly created because the CSV file that you imported contains a comma at the end of each line. To eliminate that column, add the following code to the notebook and execute it:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = df.drop('Unnamed: 25', axis=1)\ndf.isnull().sum()",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The DataFrame with column 26 removed\n\nThe DataFrame still contains a lot of missing values, but some of them aren't useful because the columns containing them are not relevant to the model that you are building. The goal of that model is to predict whether a flight you are considering booking is likely to arrive on time. If you know that the flight is likely to be late, you might choose to book another flight.\n\nThe next step, therefore, is to filter the dataset to eliminate columns that aren't relevant to a predictive model. For example, the aircraft's tail number probably has little bearing on whether a flight will arrive on time, and at the time you book a ticket, you have no way of knowing whether a flight will be cancelled, diverted, or delayed. By contrast, the scheduled departure time could have a lot to do with on-time arrivals. Because of the hub-and-spoke system used by most airlines, morning flights tend to be on time more often than afternoon or evening flights. And at some major airports, traffic stacks up during the day, increasing the likelihood that later flights will be delayed.\n\nPandas provides an easy way to filter out columns you don't want. Execute the following code in a new cell at the end of the notebook:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = df[[\"MONTH\", \"DAY_OF_MONTH\", \"DAY_OF_WEEK\", \"ORIGIN\", \"DEST\", \"CRS_DEP_TIME\", \"ARR_DEL15\"]]\ndf.isnull().sum()",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The output shows that the DataFrame now includes only the columns that are relevant to the model, and that the number of missing values is greatly reduced:"
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "The only column that now contains missing values is the ARR_DEL15 column, which uses 0s to identify flights that arrived on time and 1s for flights that didn't. Use the following code to show the first five rows with missing values:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df[df.isnull().values.any(axis=1)].head()",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\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>177</th>\n      <td>1</td>\n      <td>9</td>\n      <td>6</td>\n      <td>MSP</td>\n      <td>SEA</td>\n      <td>701</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>179</th>\n      <td>1</td>\n      <td>10</td>\n      <td>7</td>\n      <td>MSP</td>\n      <td>DTW</td>\n      <td>1348</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>184</th>\n      <td>1</td>\n      <td>10</td>\n      <td>7</td>\n      <td>MSP</td>\n      <td>DTW</td>\n      <td>625</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>1</td>\n      <td>10</td>\n      <td>7</td>\n      <td>DTW</td>\n      <td>MSP</td>\n      <td>1200</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>478</th>\n      <td>1</td>\n      <td>22</td>\n      <td>5</td>\n      <td>SEA</td>\n      <td>JFK</td>\n      <td>2305</td>\n      <td>NaN</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\n177      1             9            6    MSP  SEA           701        NaN\n179      1            10            7    MSP  DTW          1348        NaN\n184      1            10            7    MSP  DTW           625        NaN\n210      1            10            7    DTW  MSP          1200        NaN\n478      1            22            5    SEA  JFK          2305        NaN"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The reason these rows are missing ARR_DEL15 values is that they all correspond to flights that were canceled or diverted. You could call dropna on the DataFrame to remove these rows. But since a flight that is canceled or diverted to another airport could be considered \"late,\" let's use the fillna method to replace the missing values with 1s.\n\nUse the following code to replace missing values in the ARR_DEL15 column with 1s and display rows 177 through 184:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = df.fillna({'ARR_DEL15': 1})\ndf.iloc[177:185]",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\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>177</th>\n      <td>1</td>\n      <td>9</td>\n      <td>6</td>\n      <td>MSP</td>\n      <td>SEA</td>\n      <td>701</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>178</th>\n      <td>1</td>\n      <td>9</td>\n      <td>6</td>\n      <td>DTW</td>\n      <td>JFK</td>\n      <td>1527</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>179</th>\n      <td>1</td>\n      <td>10</td>\n      <td>7</td>\n      <td>MSP</td>\n      <td>DTW</td>\n      <td>1348</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>180</th>\n      <td>1</td>\n      <td>10</td>\n      <td>7</td>\n      <td>DTW</td>\n      <td>MSP</td>\n      <td>1540</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>181</th>\n      <td>1</td>\n      <td>10</td>\n      <td>7</td>\n      <td>JFK</td>\n      <td>ATL</td>\n      <td>1325</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>182</th>\n      <td>1</td>\n      <td>10</td>\n      <td>7</td>\n      <td>JFK</td>\n      <td>ATL</td>\n      <td>610</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>183</th>\n      <td>1</td>\n      <td>10</td>\n      <td>7</td>\n      <td>JFK</td>\n      <td>SEA</td>\n      <td>1615</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>184</th>\n      <td>1</td>\n      <td>10</td>\n      <td>7</td>\n      <td>MSP</td>\n      <td>DTW</td>\n      <td>625</td>\n      <td>1.0</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\n177      1             9            6    MSP  SEA           701        1.0\n178      1             9            6    DTW  JFK          1527        0.0\n179      1            10            7    MSP  DTW          1348        1.0\n180      1            10            7    DTW  MSP          1540        0.0\n181      1            10            7    JFK  ATL          1325        0.0\n182      1            10            7    JFK  ATL           610        0.0\n183      1            10            7    JFK  SEA          1615        0.0\n184      1            10            7    MSP  DTW           625        1.0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The dataset is now \"clean\" in the sense that missing values have been replaced and the list of columns has been narrowed to those most relevant to the model. But you're not finished yet. There is more to do to prepare the dataset for use in machine learning.\n\nThe CRS_DEP_TIME column of the dataset you are using represents scheduled departure times. The granularity of the numbers in this column — it contains more than 500 unique values — could have a negative impact on accuracy in a machine-learning model. This can be resolved using a technique called binning or quantization. What if you divided each number in this column by 100 and rounded down to the nearest integer? 1030 would become 10, 1925 would become 19, and so on, and you would be left with a maximum of 24 discrete values in this column. Intuitively, it makes sense, because it probably doesn't matter much whether a flight leaves at 10:30 a.m. or 10:40 a.m. It matters a great deal whether it leaves at 10:30 a.m. or 5:30 p.m.\n\nIn addition, the dataset's ORIGIN and DEST columns contain airport codes that represent categorical machine-learning values. These columns need to be converted into discrete columns containing indicator variables, sometimes known as \"dummy\" variables. In other words, the ORIGIN column, which contains five airport codes, needs to be converted into five columns, one per airport, with each column containing 1s and 0s indicating whether a flight originated at the airport that the column represents. The DEST column needs to be handled in a similar manner."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "In this exercise, you will \"bin\" the departure times in the CRS_DEP_TIME column and use Pandas' get_dummies method to create indicator columns from the ORIGIN and DEST columns.\n\nUse the following command to display the first five rows of the DataFrame:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.head()",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\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>1905</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>1345</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>940</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>819</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>2300</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          1905        0.0\n1      1             1            5    DTW  MSP          1345        0.0\n2      1             1            5    ATL  SEA           940        0.0\n3      1             1            5    SEA  MSP           819        0.0\n4      1             1            5    SEA  DTW          2300        0.0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The DataFrame with unbinned departure times\n\nUse the following statements to bin the departure times:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import math\n\nfor index, row in df.iterrows():\n    df.loc[index, 'CRS_DEP_TIME'] = math.floor(row['CRS_DEP_TIME'] / 100)\ndf.head()",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "Confirm that the numbers in the CRS_DEP_TIME column now fall in the range 0 to 23:"
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "The DataFrame with binned departure times\n\nNow use the following statements to generate indicator columns from the ORIGIN and DEST columns, while dropping the ORIGIN and DEST columns themselves:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = pd.get_dummies(df, columns=['ORIGIN', 'DEST'])\ndf.head()",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\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  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "The DataFrame with indicator columns\n\nUse the File -> Save and Checkpoint command to save the notebook.\n\nThe dataset looks very different than it did at the start, but it is now optimized for use in machine learning."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Build Machine Learning Model"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "To create a machine learning model, you need two datasets: one for training and one for testing. In practice, you often have only one dataset, so you split it into two. In this exercise, you will perform an 80-20 split on the DataFrame you prepared in the previous lab so you can use it to train a machine learning model. You will also separate the DataFrame into feature columns and label columns. The former contains the columns used as input to the model (for example, the flight's origin and destination and the scheduled departure time), while the latter contains the column that the model will attempt to predict — in this case, the ARR_DEL15 column, which indicates whether a flight will arrive on time.\n\nSwitch back to the Azure notebook that you created in the previous section. If you closed the notebook, you can sign back into the Microsoft Azure Notebooks portal , open your notebook, and use the Cell -> Run All to rerun the all of the cells in the notebook after opening it.\n\nIn a new cell at the end of the notebook, enter and execute the following statements:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.model_selection import train_test_split\ntrain_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)",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The first statement imports scikit-learn's train_test_split helper function. The second line uses the function to split the DataFrame into a training set containing 80% of the original data, and a test set containing the remaining 20%. The random_state parameter seeds the random-number generator used to do the splitting, while the first and second parameters are DataFrames containing the feature columns and the label column.\n\ntrain_test_split returns four DataFrames. Use the following command to display the number of rows and columns in the DataFrame containing the feature columns used for training:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_x.shape",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "(8984, 14)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "Now use this command to display the number of rows and columns in the DataFrame containing the feature columns used for testing:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "test_x.shape",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "(2247, 14)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "How do the two outputs differ, and why?\n\nCan you predict what you would see if you called shape on the other two DataFrames, train_y and test_y? If you're not sure, try it and find out."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_y.shape",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": "(8984,)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_x.shape",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 18,
          "data": {
            "text/plain": "(8984, 14)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "There are many types of machine learning models. One of the most common is the regression model, which uses one of a number of regression algorithms to produce a numeric value — for example, a person's age or the probability that a credit-card transaction is fraudulent. You'll train a classification model, which seeks to resolve a set of inputs into one of a set of known outputs. A classic example of a classification model is one that examines e-mails and classifies them as \"spam\" or \"not spam.\" Your model will be a binary classification model that predicts whether a flight will arrive on-time or late (\"binary\" because there are only two possible outputs).\n\nOne of the benefits of using scikit-learn is that you don't have to build these models — or implement the algorithms that they use — by hand. Scikit-learn includes a variety of classes for implementing common machine learning models. One of them is RandomForestClassifier, which fits multiple decision trees to the data and uses averaging to boost the overall accuracy and limit overfitting."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Execute the following code in a new cell to create a RandomForestClassifier object and train it by calling the fit method."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.ensemble import RandomForestClassifier\n\nmodel = RandomForestClassifier(random_state=13)\nmodel.fit(train_x, train_y)",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/home/nbuser/anaconda3_501/lib/python3.6/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "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=None,\n            oob_score=False, random_state=13, verbose=0, warm_start=False)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "# Training the model\n\nNow call the predict method to test the model using the values in test_x, followed by the score method to determine the mean accuracy of the model:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "predicted = model.predict(test_x)\nmodel.score(test_x, test_y)",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": "0.8602581219403649"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Testing the model\n\nThe mean accuracy is 86%, which seems good on the surface. However, mean accuracy isn't always a reliable indicator of the accuracy of a classification model. Let's dig a little deeper and determine how accurate the model really is — that is, how adept it is at determining whether a flight will arrive on time.\nThere are several ways to measure the accuracy of a classification model. One of the best overall measures for a binary classification model is Area Under Receiver Operating Characteristic Curve (sometimes referred to as \"ROC AUC\"), which essentially quantifies how often the model will make a correct prediction regardless of the outcome. In this unit, you'll compute an ROC AUC score for the model you built previously and learn about some of the reasons why that score is lower than the mean accuracy output by the score method. You'll also learn about other ways to gauge the accuracy of the model.\n\nBefore you compute the ROC AUC, you must generate prediction probabilities for the test set. These probabilities are estimates for each of the classes, or answers, the model can predict. For example, [0.88199435, 0.11800565] means that there's an 89% chance that a flight will arrive on time (ARR_DEL15 = 0) and a 12% chance that it won't (ARR_DEL15 = 1). The sum of the two probabilities adds up to 100%."
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "Run the following code to generate a set of prediction probabilities from the test data:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.metrics import roc_auc_score\nprobabilities = model.predict_proba(test_x)",
      "execution_count": 23,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "Now use the following statement to generate an ROC AUC score from the probabilities using scikit-learn's roc_auc_score method:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "roc_auc_score(test_y, probabilities[:, 1])",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": "0.6743824904998539"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Generating an AUC score - 67%\n\nWhy is the AUC score lower than the mean accuracy computed in the previous exercise?\n\nThe output from the score method reflects how many of the items in the test set the model predicted correctly. This score is skewed by the fact that the dataset the model was trained and tested with contains many more rows representing on-time arrivals than rows representing late arrivals. Because of this imbalance in the data, you're more likely to be correct if you predict that a flight will be on time than if you predict that a flight will be late.\n\nROC AUC takes this into account and provides a more accurate indication of how likely it is that a prediction of on-time or late will be correct.\n\nYou can learn more about the model's behavior by generating a confusion matrix, also known as an error matrix. The confusion matrix quantifies the number of times each answer was classified correctly or incorrectly. Specifically, it quantifies the number of false positives, false negatives, true positives, and true negatives. This is important, because if a binary classification model trained to recognize cats and dogs is tested with a dataset that is 95% dogs, it could score 95% simply by guessing \"dog\" every time. But if it failed to identify cats at all, it would be of little value.\n\nUse the following code to produce a confusion matrix for your model:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.metrics import confusion_matrix\nconfusion_matrix(test_y, predicted)",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": "array([[1882,   54],\n       [ 260,   51]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Generating a confusion matrix\n\nBut look at the second row, which represents flights that were delayed. The first column shows how many delayed flights were incorrectly predicted to be on time. The second column shows how many flights were correctly predicted to be delayed. Clearly, the model isn't nearly as adept at predicting that a flight will be delayed as it is at predicting that a flight will arrive on time. What you want in a confusion matrix is large numbers in the upper-left and lower-right corners, and small numbers (preferably zeros) in the upper-right and lower-left corners.\n\nOther measures of accuracy for a classification model include precision and recall. Suppose the model was presented with three on-time arrivals and three delayed arrivals, and that it correctly predicted two of the on-time arrivals, but incorrectly predicted that two of the delayed arrivals would be on time. In this case, the precision would be 50% (two of the four flights it classified as being on time actually were on time), while its recall would be 67% (it correctly identified two of the three on-time arrivals). You can learn more about precision and recall from https://en.wikipedia.org/wiki/Precision_and_recall\n\nScikit-learn contains a handy method named precision_score for computing precision. To quantify the precision of your model, execute the following statements:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.metrics import precision_score\n\ntrain_predictions = model.predict(train_x)\nprecision_score(train_y, train_predictions)",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": "0.9972375690607734"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "Examine the output. What is your model's precision? -99%"
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "Measuring precision\n\nScikit-learn also contains a method named recall_score for computing recall. To measure you model's recall, execute the following statements:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.metrics import recall_score\n\nrecall_score(train_y, train_predictions)",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": "0.8650159744408946"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": "What is the model's recall? - 86%"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Use the File -> Save and Checkpoint command to save the notebook.\n\nIn the real world, a trained data scientist would look for ways to make the model even more accurate. Among other things, they would try different algorithms and take steps to tune the chosen algorithm to find the optimum combination of parameters. Another likely step would be to expand the dataset to millions of rows rather than a few thousand and also attempt to reduce the imbalance between late and on-time arrivals. But for our purposes, the model is fine as-is."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Visualize Output of Model\n\nIn this unit, you'll import Matplotlib into the notebook you've been working with and configure the notebook to support inline Matplotlib output.\n\n1. Switch back to the Azure notebook that you created in the previous section. If you closed the notebook, you can sign back into the Microsoft Azure Notebooks portal , open your notebook, and use the Cell -> Run All to rerun the all of the cells in the notebook after opening it.\n\n2. Execute the following statements in a new cell at the end of the notebook. Ignore any warning messages that are displayed related to font caching:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nsns.set()",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/home/nbuser/anaconda3_501/lib/python3.6/site-packages/matplotlib/font_manager.py:229: 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",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The first statement is one of several magic commands supported by the Python kernel that you selected when you created the notebook. It enables Jupyter to render Matplotlib output in a notebook without making repeated calls to show. And it must appear before any references to Matplotlib itself. The final statement configures Seaborn to enhance the output from Matplotlib.\n\n3. To see Matplotlib at work, execute the following code in a new cell to plot the ROC curve for the machine-learning model you built in the previous lab:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.metrics import roc_curve\n\nfpr, tpr, _ = roc_curve(test_y, probabilities[:, 1])\nplt.plot(fpr, tpr)\nplt.plot([0, 1], [0, 1], color='grey', lw=1, linestyle='--')\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 29,
          "data": {
            "text/plain": "Text(0, 0.5, 'True Positive Rate')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAESCAYAAAAFYll6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzt3Xl8ldW96P9PEjKQOWSAEBJCphUI86AgswwqSEVAGVTs5Dn2nF/Pr71tz+npvfX09Axt723vPbetPVpre6xWRa3VMlgUBZmVQIAwLWKAjJCJkHnY2fu5fzwbG2kge4c97+/79crL7Idn5/kuA893r7Wetb4hhmEghBBCOCPU2wEIIYTwP5I8hBBCOE2ShxBCCKdJ8hBCCOE0SR5CCCGcJslDCCGE0yR5CCGEcJokDyGEEE6T5CGEEMJpkjyEEEI4TZKHEEIIpw3zdgAuFAnMAi4DVi/HIoQQ/iIMSAeOAD2OvimQkscsYJ+3gxBCCD81H9jv6MmBlDwuAzQ3d2CzOb9TcHJyLE1N7S4PypdJm4ODtDk4DLXNoaEhJCXFgP0e6qhASh5WAJvNGFLyuP7eYCNtDg7S5uBwm212arhfJsyFEEI4TZKHEEIIp0nyEEII4TSPzHkopX4MrAWygUla61MDnBMG/BS4FzCAH2qtf+WJ+IQQQjjHUz2Pt4AFQMUtznkEyAPygTnA95RS2e4PTQghhLM8kjy01vu11lWDnLYeeE5rbdNaN2AmnIfcH50QQghn+dKjull8tmdSCWR6KRYhhPBp3b19nCxvovjURS7XXeGrj68gLS7CY9f3peThEsnJsUN+b2pqnAsj8Q/S5uAgbQ4Mnd0WPj5Tx8GTtRw9W0dcSCNZUbXkZ4xn3Oh4oqPCPRaLLyWPSmAs5v4q8Jc9EYc0NbUPaaFMamocDQ1tTr/Pn0mbg4O02b91dlsoKWvkqG7g1MUm+qwGabF9zJuUSWFGJnljlpCYmER0VPiQ2hwaGjKkD92+lDxeB55QSr0JJAOrMSfZhRAiqLR3WSgpa+CobuD0xatYbQZJcZEsmpLOiNDL1FaWs2zqJEaOTPdajJ56VPenwBpgFLBLKdWktS5SSu0AntJaFwMvAncCZfa3fV9rfcET8QkhhLe1dfZSUtbIkXP1nKtoxmozSEmIYtnMTGYUppKZGs2bv38ZIyGR9esfIyZm6EP0ruCR5KG1/jvg7wY4vqLf91bgK56IRwghfEFLRy/HzjdQfK4eXXkNm2GQmhjF8jsymanSyB4VR19fH7W11USEJ7Bkyb2kpKQREhLi7dB9athKCCEC3rX2Ho7qBo7qenTVNQwDRiYN577ZWcwqTCMzLfbT5FBdXcmePe8xevQYsrKySU0d6eXo/0yShxBCuNnV1m6O6gaKdT2fVLdgAOnJ0ay6K5uZKo2M1Ji/6E1ofYaPPjrAwoVLGTt2nHcCvwVJHkII4QaNLV2fJozymlYAxqTG8MC8ccwoTCMjJWbA9128+AkJCYmMG5fLuHG5REREejJsh0nyEEIIF6m/1sVRXU/xuQYuXjYTRlZaLGsW5DBDpZKePHDCAOjs7GDfvt00NTWwZMl9Pps0rpPkIYQQt6HuaifF9oRRUWeus8geFce6RbnMUKmMTIoe9GcYhsHWrW+SlZXNkiX3MGyY5xb7DZUkDyGEcNLlpg6Kz9VTrBuoqjdLv+aMjufhxXnMUKmkJg536Oe0tbVy6tQJZs+ex5o1GwgP9/2kcZ0kDyGEGIRhGNQ2dlCszcdqaxo7AMgbk8CGJfnMKEglOSHKqZ93+vQJPv74EFOmTMcwDL9KHCDJQwghBmQYBlX17RTbH6u93NRJCJCfmcimpfnMUGkkxQ1tXqKq6hJan2X16ocZMSLZtYF7iCQPIYSwMwyDyrp2+xxGPXXNXYSEgMpMZMmMMcwoSCUhdmgJw2azcfx4McOHR1NYWERmZrZPLPYbKkkeQoigZhgGl660ceScmTAaW7oJDQmhcGwi99yZxfT8VOJjbm+r88bGBnbv3klk5HAWLVrq10njOkkeQoigYzMMLtS2UnyunqO6nqbWHsJCQxifncT9d2UzLT+FuGjX1cY4ceIokyZNQ6kJAZE4QJKHECJI2AyDT6pbKNb1HNUNNLeZCaNo3AhWz89han4KMS6sh3H5ci2HDu1lxYoHWLLkXpf9XF8hyUMIEbBsNoOy6mscOVfP0fMNtLT3MiwslEk5I1i3MJcpeSlER7n2Nmix9HL48H7Ky8uYP38xUVGOPbbrbyR5CCECitVm48T5BnZ9XMExXU9rp4XwYaFMzklmRmEqU3JTGB7pnlufzWajs7MTq9XKhg2bAzZxgCQPIUQA6LPaOFfZTPG5Bo6db6C9y0JEeCiTc1OYqVKZnJtMVIT7bnfd3V0cPLiX8PBw5s+/m0WLlrntWr5CkocQwi/1WW2cuXSV4nMNlJQ10NHdR2REGFPzUrh7VhZZKdFEhoe5PY4LFz5h3773ycnJ584757n9er5CkocQwm9Y+qycvthMsa6npKyRrp4+hkeaCWOmSmNizgjCh4V5pIZ5b28PERGR9Pb2sHz5/aSnZ7j1er5GkocQwqf1WqycuniVYl3P8bJGunutREcOY3p+CjML05iQPYLwYaEei8cwDLQ+w6FD+1izZj2FhUUeu7YvkeQhhPA5PRYrpeVNFOt6TnzSRI/FSkzUMGYVpjGzMI3xY5MYFua5hHFdd3cX7723g66uLlatWkNCQpLHY/AVkjyEED6hu7ePk+VNFJ+r5+SFJnotNuKiw5ldNJKZKg2VleiVhAFmb6O9vY3o6BhycvIpLCwiLMz98ym+TJKHEMJrunr6OPFJI0fO1XPq4lUsfTbiYyKYOymdmSqNgswEwkK9kzCua26+yu7d7xIXF8+yZSsoKprs1Xh8hSQPIYRHdXZbKClr5Khu4NTFJvqsBomxESyYMpqZKpX8MYmEhvrGFh5nz57i0KG9zJp1FxMnTvF2OD5FkocQwu3auyyUlDVwVDdw+uJVrDaDpLhIFk8bw8zCVHIzEgj1oT2fGhvrSUhIIi1tJA899ChxcfHeDsnnSPIQQrhFW2cvJWXmkNS5imasNoOUhCiWzcxkRmEq49LjfSphAPT1WThy5DDnzp1m5crVpKWN8nZIPkuShxDCZVo6ejl23qy2pyuvYTMMUhOjWH5HJjNVGtmj4nx2V9m+PguvvfY7kpNTWL/+MaKjY7wdkk+T5CGEuC3X2ns4aq+2p6uuYRgwMmk4983OYlZhGplpsT6bMAB6e3upra0iOzuX5ctXkpKS6u2Q/IIkDyGE0662dnNUN1Cs6/mkugUDSE+OZtVd2cxUaWSkxvh0wriuouIiH364i6yscYwdmyOJwwmSPIQQDmls6fo0YZTXtAIwJjWGB+aNY0ZhGhkp/jXMc+7caYqLD7N48XIyM8d6Oxy/I8lDCHFT9de6OGqv533xsrlXVFZaLGsW5DBDpZKe7F8JwzAMysvLSEoaQU5OPrm5BYSHu64AVDCR5CGE+Iy6q50U63qKzzVQUWcmjOxRcaxblMsMlcrIpGgvRzg0HR3t7N37PteuNbN06X1ERLiuzGwwkuQhhOByUwfF5+op1g1U1bcDkDM6nocX5zFDpZKa6N9FjQzDYNu2PzBuXA7Ll68kLExufbfLY/8HlVIFwAtAMtAEbNZal91wThrwGyATiAA+AP5Oa93nqTiFCAaGYVDb2GGWZ9UN1DR2AJCXkcCGJfnMKEglOSHKy1HevtbWFkpLjzNnznzWrt3AsGEyROUqnky/zwBPa61fUko9CjwL3H3DOd8BzmqtVyqlwoH9wBrgNQ/GKURAMgyDqvp2iu2P1V5u6iQEyM9MZNPSfGaoNJLiIr0dpkvYbDZOnDjG0aOHmTZtFoAkDhfzSPKw9yimA9drM74C/Fwplaq1buh3qgHEKaVCgUjM3keNJ2IUIlC1dvTy9v6LHD59hbrmLkJCQGUmsmTGGGYUpJIQGxgJo78LFy5w8WIZa9ZsJDExeLdNdydP9TwygRqttRVAa21VStXaj/dPHv8C/B64DMQAP9daH3DmQsnJsUMOMjU1bsjv9VfS5sDV3NbNW3vK2XHwIt29Vqbkp7BuSQGzJ6aTGCA9jP6sVisHDhwgNjaWadOmkZub6xdrTVzJk3+3fW3W6CHgJLAEiAPeUUqt01q/4egPaGpqx2YznL6wJ8pW+hppc2BqbuvhncMVfHiilj6rjQVTx7B0+mgyUs0PVpbuXhq6e70cpWvV119h9+53iYmJY+HCpYSEhAT87/lGQ/27HRoaMqQP3Z5KHlVAhlIqzN7rCANG24/391Xgi1prG9CilHobWAw4nDyECFaN17rYcbiC/aWXMQyYUzSKFXPGMkmNDNgbqWEYhISEcOrUCaZNm0V+fmHQ9Ta8xSPJQ2tdr5Q6DmwEXrL/t+SG+Q6Ai8C9wMdKqQhgKfCmJ2IUwl/VXe1k+6EKDp2+QkgIzJuUzorZY0nx88drB1NTU8WhQ3u5//613H33Pd4OJ+h4ctjqSeAFpdRTQDOwGUAptQN4SmtdDHwNeEYpVQqEAbuB5zwYoxB+o6ahne2HKvjobB3DwkJZPC2De+/MYkS8/z9ieyu9vT0cOrSPS5cusGDBEqKiAru9vspjyUNrfQ64c4DjK/p9X86fn8gSQgyg4kob2w5d4qhuIDI8jHvuyOKeO7JIiAn8FdNWq5Xu7m5CQkLYsOFxIiMDb+LfXzidPJRSaVrrencEI4S4uQu1rWw9cJET5U0Mjwzj/ruyWTZzDHHRgZ80urq62L9/N1FRUcyffzcLFizxdkhBz6HkoZRKAH4GPAxYgRil1Cpgptb6n9wYnxBB73zVNbYeuMjpS83ERA3jwfnjWDJjDNFRwbHorbz8PHv3fkBBwXjuuOMub4cj7Bztefwn0AHkYz5KC/AR8BNAkocQLmYYBmcqmtl64BLnq64RHx3OQ4tyWTQtg+GRvvaEvXt0d3cTFRVFX18fK1Y8wMiR6d4OSfTj6N/CpcAYrXWvUsqAT5+gGum+0IQIPoZhcLK8iW0HL1Fe20pibAQbl+SzYOpoIsPDvB2eRxiGwdmzpRw+fIC1azeg1ARvhyQG4GjyaAVGAFeuH1BKZQJ17ghKiGBjMwxKzjew9eAlKuvaSY6P4rF7FPMmpRM+LNTb4XlMV1cX7767DYullwceWEdCgmwt4qscTR6/Bl5XSn0HCFVKzQJ+gLm5oRBiiGw2g4/P1bH9YAU1jR2kJQ3nCysKmVM0imFhwZM0bDYb7e1txMTEUlAwHqUmEBoaPO33R44mjx8AvcDzQBTwMmbi+D9uikuIgNZntXH4dB3bD12irrmL0Skx/NWqCcwan0ZYkN00m5oa2b37XZKSRrBkyb2MHz/R2yEJBziaPJK11j8Gftz/oFIqBWh0eVRCBChLn40DpZfZcbiCxpZuMtNi+ZvVE5muUgkNwm01zpw5yeHDB5g9ey7jx0/ydjjCCY4mjwtA/ADHz2POhQghbqHXYmXviVre+aiS5rYexqXHsWlpAVPykoNyL6b6+iskJSUzatRoHn74UWJjg2On40DiaPL4i7/dSqlYwObacIQILN29fewpqeVPH1fS2tFL/pgEvrCikKLsEUGZNCwWCx9/fJDz58+ycuWDpKXJA5v+6pbJQyl1EbNA03Cl1IUb/jgFs/aGEOIGnd19vH+smveOVNHeZWFCdhKrHihCZQXv00MWi4XXXnuRtLRRbNjwOMOHB/bGjYFusJ7HlzF7HX8Enuh33ADqtNan3RWYEP6ovcvCe0eq2HW0mq6ePibnJnP/XdnkZSR4OzSv6enpoaamkpycfO677wFGjEj2dkjCBW6ZPLTW7wMopUZprVs9E5IQ/qe1o5edH1fyQUkNPb1WpheksuqubMaOCu6x/EuXyvnww/cZNy6XcePyJHEEEIfmPLTWrUqpicB8zOGqkH5/9n03xSaEz2tu6+FPH1Xy4fEaLH02Zo1P4/67shmTOvRyyIHi7NlTHDv2MUuX3kdGRqa3wxEu5ujGiF/C3Bjxfcwt09/DLBW71X2hCeG7Glu6eOdwJftO1mKzwZyikayYM5b05Bhvh+ZVhmFQVnaOESNSyMsrIC9PER4eHBs4BhtHn7b6NrBCa71HKdWstV6llLofWOPG2ITwOXXN9qp9p8ydeuZNTue+2WNJC/CqfY5oa2tj795dtLW1sXTpfYSHB/5W8cHM0eQxUmu9x/69TSkVCmwHfgt80R2BCeFLahs72HboEh+dMav2LZqWwX1BULXPUYZhsGPHW+Tm5nPvvZ8jLCw4NnEMZo4mj2ql1FitdQVQBqzEXFlucVtkQviAyro2th00q/aFh4dyz6ws7rkjk4RYqWAHcO1aM6Wlx5k7dyHr1m0kLCw4tosXjiePnwATgQrgX4HXgXDgv7kpLiG86uLlVrYeuMTxTxqJighjxZyxLJ+VGRRV+xxhs9k4ceIoJSVHmDHDrC4tiSO4OPq01fP9vt+mlEoCIrXWLW6LTAgvOF91jW0HL3Hq4lViooaxet44lswcQ0yQVO1zVHV1BVVVFaxdu4mEhERvhyO8YEgfFbTW3UqpGKXUD7TW/+jqoITwJMMwOGuv2qerrhEXHc66RbksDqKqfY6wWvs4evQjYmPjGD9+EpmZ2UG5xYowDfovQyn1ODAVc67jl0A08F3gSeCgW6MTwo0Mw6D0QhNbD16ivKaVhNgINizJZ+GU0URGyIRvf1eu1LJ797skJiZRVDRFkoYYdG+r/wk8hpkkNgKzgTnAUWCe1vqE2yMUwsXMqn2NbDt4iYq6NpLjI3lseQHzJqcTPkySRn+GYRASEsLZs6eYNesucnPzJXEIYPCexwZggda6TCk1HjgNbNRab3F/aEK4ls1msK+kht/tPEtNQwdpicP5/H2F3DUxuKr2OaqqqoJDh/bxuc+tY/Hi5d4OR/iYwZJHota6DEBrfVYp1SmJQ/gbq+161b4KrlztJD05mifun8AdE4Kvap8jenq6OXDgQ6qrK1m4cClRUbKWRfylwZJHiFIqkz/vZdV3w2u01pXuCk6I29FnNav2bT9kVu0bkxrLP2yeSf6oOEJDZehlIFZrH729vURERLBhw+NERMijyWJggyWPGOASny0GVdHvewOQQWLhU3otVvadNEu9Nrf1kD0qjo1L85mSl8LItHgaGtq8HaLP6ezsYN++3URHRzN//t3Mm7fY2yEJHzdY8pCH24Xf6Om1srukhp0fV9LS0UvemAS+cF8hReOCs2qfo8rKzrF//x4KC4uYNWu2t8MRfmKweh5WTwUixFB19fTxwbFqdn5sVu0bPzaJv/5cESorUZLGLXR1dTF8+HAMw5CSsMJpsgJK+K32Lgu7iqvYVVxNZ08fk3KSWXVXNnljgrdqnyMMw+DUqRMcOXKQtWs3UVAw3tshCT/kseShlCoAXgCSgSZg8/UnuW4472HMRYghmHMqS7XWdZ6KU/i+1o5e3j1SxQfHqunutTItP4VVc7PJHhXv7dB8XmdnJzt3bsVms7F69XrZWkQMmSd7Hs8AT2utX1JKPQo8C9zd/wSl1Ezge8DdWusrSqkEoMeDMQofdq3drNq3p+TPVftWzskmM02q9g3GZrPR1tZCXFwCEyZMIj+/kFB5TFncBoeTh1JqGDALyNBav6GUGg6gte5y4L1pwHTMKoQArwA/V0qlaq0b+p36deDHWusr9p8tGy8Kmlq62fFRBftOXMZmM5hdNJKVUrXPYVeuXOHNN/9AcnIqd999D0pN8HZIIgA4Woa2CHjb/nIU8AZmGdpHMLctGUwmUHN9Al5rbVVK1dqP908eE4CLSqm9QCzwJvBvWmvDkThFYKm3V+07aK/aN3fSKFbMHktaUrSXI/Mfp06doLj4ELNnz5ekIVzK0Z7HfwL/qrX+L6VUs/3YHsyhKFfHMxmzhxIB/AmoxKxY6JDk5KEPYaSmxg35vf7KF9tc29DOq+9pPiypISw0hHvnZLNmcZ7LkoYvttnVqqqqGDlyJBMnKmbOnEJcXOC3+UbB8Hu+kSfb7GjymIQ52Q3mJDZa63allKP/mquADKVUmL3XEQaMth/vrwJ4Q2vdA/Qopd4G7sCJ5NHU1I7N5nxHJTU1LugWj/lam22Gwa7iat7YU05oKCydMYZ778wiMTYS+qwuidXX2uxqFksvhw/vp7y8jJUrHyQ1NY24uMBu80AC/fc8kKG2OTQ0ZEgfuh1NHhXANODY9QP2ye1yR96sta5XSh3HHOJ6yf7fkhvmOwBeBlYopV60x7YEc4hMBLimlm5+veMsZyuamZKbzOP3FZpJQzjMYrGwZcuLpKdnsGHDZqKihns7JBHAHE0eTwHblVK/ACKUUt8C/hb4ihPXehJ4QSn1FNAMbAZQSu0AntJaFwOvAjOBM4AN2Ak8P/CPE4HAMAwOn67jpffOY7MZfP6+QuZPTpfFfU7o7u6iurqKvLwC7r9/DYmJSd4OSQSBEMNwbIhHKTULeAIYiznc9Eut9cdujM1Z2cBFGbZynLfb3N5l4bd/OkexbiAvI4Ev3z/e7ZPh3m6zq5WXn2ffvt3k5Snmzl04YNINtDY7QtrsuH7DVuMw9zJ0iKNPWyVprY8AR5yOTIgBnCxv4jfvnKW908LahTncd+dY2enWSWfOlHL8eDH33HM/6ekZ3g5HBBlHh61qlFK7gN8Bf3RkbYcQA+nptbJl9yfsKakhIyWGrz80hayRwfdUzFAZhoHWZ0hJSSU/v5CCgvEMGya7DAnPc/Rv3ThgPeYivufsT0G9DLwrmycKR5XXtPCrbWeob+7injsyWbMgR8q+OqG1tYU9e3bR3d3FkiX3EB4um14L73Eoedj3lvop8FOlVA6wCfgxkALIVpzilvqsNv544BLbD11iRFwk39o4jcKxMqnrDMMweOedP5Kfr5gyZQZhYZJ0hXcNpb+bYP+KAzpcG44INLWNHTy39QwVdW3MnTiKjUsLiI6SYRZHXb3aRGnpcebPX8y6dZskaQif4eiEeQHm2oxNmInjdWCD1vqgG2MTfsxmGLxfXM0bH5YTGR7G3z44kRkqzdth+Q2r1UpJSTEnTx5l1qy7CAkJkY0MhU9x9CPgEeAPwN8Bu2SeQ9zK1dZunt9uLvibnJvMF+4rJEEW/DmlpqaSK1dqeOihR4mLk63mhe9xNHmM1Fp3uzUS4fcMw+DwmTpeetdc8Pf4vYoFU0bLgj8H9fVZOHLkMPHxCRQVTSYzM1v+3wmfddPkoZTaqLV+xf7yYaXUgOdprR3ed0oErvYuC7/dqSk+V++xBX+BpLa2mj173iM5OZUpU6YDSOIQPu1WPY/PY9bdAHNl+UAMnNi0UASm0gtN/HqHLPgbCsMwCAkJ4fz5c8yePY+cnHxvhySEQ26aPLTW9/T7fr5nwhH+pKfXymu7P2F3SQ2jU2L42ropjB0lC/4cVVFxkUOH9vLgg+tZtGipt8MRwimOPm11RGs9a4Djh7XWs10flvB15bUt/GqrueBv+axM1i6UBX+O6u7uYv/+PVy+XMOiRcuIjIzydkhCOM3RCfPCmxwvcFUgwj/0WW1sPXCJ7YcqSIyL4JsbpzFeFvw5xDAMrFYrFouF4cOj2bBhM+HhEd4OS4ghuWXyUEr92v5tRL/vr8sGzrojKOGbahs7eG7bGSqutHHXxFFskgV/DuvoaGfv3veJjY1n/vzFzJ270NshCXFbBvuXX3OT7w3gKLDF5REJn2MzDN4/alb4iwwP429WT2RmoSz4c5TWZzh48EOKiiYzY8ad3g5HCJe4ZfLQWn8XPp3b2O6ZkIQvudpqVvg7c8lc8Pd5qfDnsM7ODqKjYwgLC2PVqnWkpKR6OyQhXOZW6zzmaq0P2F+2KaUWDHSe1nqvWyITXnf4zBVe2nkeq81g872KhbLgzyE2m43S0uMcPXqYtWs3kZc38BopIfzZrXoez/PnifLf3eQcA8hyaUTC69q7LLz0rubjs/XkZsTz5fsnMFIW/Dmks7ODd955m7CwMNas2UhCQqK3QxLCLW61zqOw3/eZnglHeNsp+4K/tk4LaxbkcN/sLMJkQ75BWa1WWltbSEhIZPLk6eTlKemliYA2pEdllFLzgT6t9SEXxyO8pMdi5fXdn/DBMXPB3/8vC/4cVld3hd2732XUqHQWLVpGfv7NnmwXInA4ukhwD/BdrfU+pdQ3gX8A+pRS/6G1/pE7AxTud6G2lee2naHuaifLZ5kV/iLCZcGfI0pLSygu/oi5cxdK0hBBxdGexyTgei/jr4FFQBuwD5Dk4af6rDbe2neBbQfNBX/f2jCV8dkjvB2WX6itrSY1NY3MzGxycxXR0TInJIKLo8kjFLDZS9AO01qfBlBKyZ3GT11u6uDff3eMT6quMadoFI8syyc6SmpiD6a3t4eDB/dRUXGBlSsflMdvRdByNHkcBP4DGI1ZFAp7ImlyU1zCTWyGwQdHq3l9TzlREbLgzxkWi4UtW14kM3MsGzY8TmSkrHcRwcvR5PF54FuABn5oPzYB+JkbYhJu0tzWw6+3n+H0pWYm5STzzcdmYu2xeDssn9fV1Ul1dSX5+YV87nPr5PFbIXAweWitG4C/v+HYNmCbO4ISrnd9wV+fzcbmexQLp45mRHwUDQ2SPG7GMAw++USzf/8elJpAXp6SxCGEnaNPWw0D/hF4DMjA3OfqReCHWmu5+/iwzyz4G21f8DdCJncdceZMKaWlJaxY8QAjR6Z7OxwhfIqjw1Y/AuYCXwMqgLHA/wASgW+4JzRxuxqudfGTV4/T1NrNgwtyWCEL/gZlGAZnzpSSljaSgoLxFBZOICxMdg4W4kaO/qt4GJimtW60vz6tlDoCHEeSh0+qbmjnJ1uO09dn4x8emU5eRoK3Q/J5LS3N7N79Hn19FtLTMwgPl6fPhLgZR5NHGGC74ZgNkP0XfFB5TQv/8foJwoeF8u1HppORGuvtkHyeYRjs3LmNgoIJTJ48jVDpoQlxS44mjzeAPyql/gmoxBy2egr4vbsCE0Nz+uJVfvbmSRJjIvnGhqmkJg73dkg+rampkZMnj7Fw4VLWrt1EWJisrBfCEY4mj28B/4Q3bSXmAAAbr0lEQVS50246UAu8CvyzoxdSShUALwDJmOtDNmuty25yrgJKgF9orb/p6DWCXfG5ep7942nSk2P4xvopJEjdjZuyWq0cPfoRp06dYPbsuYSEhEhvQwgnOPqobg/wHfvXUD0DPK21fkkp9SjwLHD3jScppcLsf/bWbVwr6Hx4vIbf7tTkZiTwtXWTZbX4IGpqKmlsrOfhhx8lNlY2gBTCWYPVMM/H7G1MBI4BX9RaVzp7EaVUGjAdWGY/9Arwc6VUqn0NSX/fxlw/Emv/EoPYcbiCN/aUMyknmb95cCKRsqnhgCwWCzt37iQiIoaJE6eQmZkt26YLMUSD9Tx+jrmm48fAJswtStYM4TqZQI3W2gqgtbYqpWrtxz9NHkqpycA9wGLgu0O4DsnJQ883qan+9QnUMAxe2H6G3+8pZ8G0DL62YTrhw5wbevG3Ng/VxYsX2bp1K2PGjGH+/PlBt5FhsPye+5M2u9dgyWMGkKm17lJK7QbOuSsQpVQ48BzwBXtyGdLPaWpqx2YznH5famocDQ1tQ7qmN9hsBr/deY69Jy6zeHoGjywr4Fpzh1M/w9/aPBSGYRASEsKxYyeYM2chs2ZNoaGhjY6OwG53f8Hwe76RtNlxoaEhQ/rQPdjH1AitdReA1roNGOqjO1VAhn0+4/q8xmj78evSgVxgh1LqEuaCxCeUUr8c4jUDlqXPxjNvn2Lvicvcf1c2jy4rIFSGX/7CxYvlvPrqC/T0dLNgwRKys3O8HZIQAWOwnkekUuqpfq+H3/AarfX3B7uI1rpeKXUc2Ai8ZP9vSf/5DvtcSsr110qp7wGx8rTVZ3X39vH0m6WcvtTMhiX5LJ8lFYJv1NXVyb59u2loqGPRomVERkZ5OyQhAs5gyeM1IL/f6zdueO3M+NCTwAv25NMMbAZQSu0AntJaFzvxs4JSe5eF/3j9BJcut/GlleOZO0n2W+rPMAz6+vro67MSH5/A4sXLZZW4EG4SYhjOzw/4qGzgYqDOeTS39fCTLcepb+7iKw8UMa3g9osQ+XqbndHW1saHH+4iMTGJefMW3fS8QGqzo6TNwcEFcx7jgEuOvk92fPMDdc2d/OTV47R1Wfj6w1MYPzbJ2yH5lLNnT3Ho0F4mT57OtGmzvB2OEEFBkoePq6xr43+/dgKbzeDvN05jXHq8t0PyGR0d7cTExBIREcHq1Q8zYkTK4G8SQriE7Mfgw8qqr/Gjl0sICw3hHx+dLonDzmazUVJyhC1bfktrawu5uQWSOITwMOl5+KiT5U384g+lJMVH8c31U0lOkCeGADo7O9i+/S0iIyNZu3YT8fGy1bwQ3uBw8lBKLQY2ACO11quVUtOBOK31h26LLkgdPnOF57edZUxqLF9/eArxMRHeDsnrrNY+WlpaSExMYvr0WeTk5MvWIkJ4kUPDVkqpv8Hc46oKc+sQgF7g39wUV9Dafaya5/54hryMBP5+0zRJHMCVK7W89tpLlJYeJzQ0lNzcAkkcQniZoz2PbwBLtdYXlFLXKweeBca7J6zgYxgG2w5V8Ie9F5ial8KTDxQRIRsccvLkMY4dO8K8eYvJzc0f/A1CCI9wNHnEYdYuhz8vDByG2fsQt8lmGLz2wSe8e6SKOUWj+MKKQoaFBfezDNXVlaSljWLs2HEUFIwnKkqKWgnhSxy9Q+0Hbtwm5G8Bme+4TVabjd9sP8u7R6pYOmMMX7p/fFAnju7ubj74YCcffLCT1tYWEhKSJHEI4YMc7Xl8FdimlHoCiFNKncbsdaxwW2RBwNJn5Zm3T1NS1sjq+eNYdVdw15ewWCy89tqLZGfnsGHD40REyHyPEL7K0UqCNUqpGcAcIAtz4vzQ9focYmheevc8JWWNPLKsgCUzxng7HK/p7OygqqoCpSbw4IPriYuT9SxC+DqHH9XVWtuAA/YvcZuO6gb2nbzMyjljgzZxGIaB1mc5dOhDxo+fhGEYkjiE8BMOJQ+l1EVusoOu1lqKJDipua2H/3rnLGNHxfHAvHHeDsdrTp8+yZkzJ7n//jWkpo70djhCCCc42vP48g2v0zHnQV5xbTiBz2YY/Hr7GSxWG3+1akLQTY4bhsGpUycYOTKdwsIJjB8/kbAweSRZCH/j6JzH+zceU0q9D+zArGsuHLTrSBWnLzWz+R5FenKMt8PxqObmq+zZ8x6GYTBmTBbDhkmtDSH81e3sbdUFyJCVE6rq23njw3Km5qWwcOpob4fjUYZhsGvXDpQqYtKkqUH9VJkQgcDROY+nbjgUDawE3nV5RAHK0mfll1tPExMVzudXFAbNzbOhoZ6TJ4+xePFy1q7dRGhocA3TCRGoHO153LgvRAfwNPBfLo0mgL2+p5yahg5zo8PowF+/0NfXR3HxYc6eLWXOnAWEhIQETcIUIhgMmjyUUmHAe8BrWutu94cUeE5daGJXcTVLZoxhUk6yt8PxiNraaq5du8r69ZuJjg6uuR0hgsGgyUNrbVVK/Uxr/VtPBBRoWjt7eX77WTJSYnhoUa63w3Eri6WXw4f3k5Q0gokTp5KVle3tkIQQbuLoAPR2pZRsReIkwzB44Z1zdHRbeGLVhIDeJbey8hKvvvpbLBYLeXnK2+EIIdzM0TmPUOBNpdR+zK1JPl0wqLX+ojsCCwR7T9RSUtbI+rvzyBoZ5+1w3MJmsxEaGkpl5UUWLlwqvQ0hgoSjyaMM+F/uDCTQXLnaySvvlzEhO4llszK9HY5blJef56OPDrB27SbmzVs8+BuEEAHjlslDKbVRa/2K1vq7ngooEPRZbfzyj6cJDwvlSysnEBpgTxl1dnawd+/7XL3axOLFy4mMjPR2SEIIDxus5/EssgWJ097ef5FLV9r4m9UTSYoLnBurYRj09Vmw2WyMGJHC0qUrGDbsdtaZCiH81WD/8gPrI7MHnK+6xo5DFcybnM7MwjRvh+Myra0t7Nmzi+TkZObOXcQdd9zl7ZCEEF40WPIIU0ot5hZJRGv9gWtD8l+d3X08t/UMqYnD2bQ0cOptnz59ko8+2s/UqTOZOnWmt8MRQviAwZJHJPA8N08eBrK/1adeek/T3NbDPz42nagI/x/OaWtrIzY2luHDo3nwwQ0kJY3wdkhCCB8x2B2uQ+p1OObwmSscPl3H6nnjyB2d4O1wbovVaqWkpJiTJ4+ybt0j5OTkeTskIYSP8f+Pxz6gqaWbF3eeJzcjnpV3jfV2OLels7ODrVvfJCYmhoceelQq+wkhBuSxCXOlVAHwApAMNAGbtdZlN5zzXWAD0Gf/+o7WeqerYnAHm83gV9vOYDMMnlhVRJif7hrb12ehpeUaSUnJ3HHHXWRn58hGhkKIm7rlnU5r7cpl0c8AT2utCzB35H12gHM+BmZpracAXwS2KKWGuzAGl9t5pBJddY1NS/NJS/TpUG+qtraaLVte5MyZUkJDQxk3LlcShxDiljwybKWUSgOmA8vsh14Bfq6UStVaN1w/74ZexknMnk8yUO2JOJ1VWdfGmx9eYEZBKvMmpXs7nCE5ceIox48XM3/+EpnbEEI4zFNzHplAjdbaCp/u1FtrP95wk/dsBsq11j6ZOHotVn659Qyx0eFsvlf53Sf1yspLxMcXkJ2di1JFREVFeTskIYQf8ckJc6XUQuBf+HNPxWHJybFDvm5qquOjdL98q5Taxg7++Yk55Iz1nxodnZ2d7Ny5k8rKSjIzR5KXF5j7bt2KM7/nQCFtDg6ebLOnkkcVkKGUCrP3OsKA0fbjn6GUmgO8BDygtdbOXqipqR2bzRj8xBukpsbR0NDm0LmnLjSxdd8Fls4YQ2bycIff520Wi4WXX/4NubkFPPTQY6SljfCb2F3Fmd9zoJA2B4ehtjk0NGRIH7o98miQ1roeOA5stB/aCJT0n+8AUErNArYA67TWxzwRm7Pa7MWdRqfEsM5Pijt1dLRz9uwpwsPDWbt2I/PmLSI8PNzbYQkh/Jgnnyt9EviqUuo88FX7a5RSO5RS1/e8+AUwHHhWKXXc/jXJgzHekmEY/PZPmvYuC3/lB8WdDMPgzJlStmx5kba2VgzDIDY2+LryQgjX89ich9b6HHDnAMdX9Pt+lqfiGYr9pZc5er6Bhxbn+kVxp9OnT3L27Ck+97l1pKSkejscIUQA8ckJc19U39zJy7vKKMxK5J5ZWd4O56ZsNhulpSWMGpVBYWEREyZMItRPFy4KIXyXJA8HPb/9LKEhIWZxp1DffCz36tVGdu9+l7CwYWRn50itDSGE28jdxQF1Vzspq25hw915JCf45noIm83G++/vZMKESUyYMMnv1p0IIfyLjGc44ER5EwDTCnxv3qCu7grvvbcDgHXrNlFUNFkShxDC7aTn4YDS8kbSk6NJ9aG9qywWC0eOHETrs8ydu5CQkBBJGkIIj5HkMYju3j501TWWzBjj7VA+4/LlGtrb29mwYTPDh0d7OxwhRJCR5DGIs5ea6bMaTM5N8XYo9PT0cOjQPpKTk5k0aRpZWdneDkkIEaRkzmMQJy80ERURRv4Y71YHvHTpAq+++gJgUFAwwauxCCGE9DxuwTAMTpY3UZQ9gmFh3smzNpuN0NBQamqqWLr0XjIyfHeNiRAieEjP4xaqGzpobuthcq7nd801DIOysnO8/PJv6OnpYe7chZI4hBA+Q3oet3CyvBGAiTmeTR4dHe3s2bOLtrYWli1bQWRkpEevL4QQg5HkcQul5U1kjYwlKc4zN2/DMLBYegEYOTKde+9dRViYb2++KIQITjJsdRMd3RY+qWn12FNWLS3NvP326xw5cpiYmFhmzrxTEocQwmdJz+MmTl+8is0wPDLfUVp6nCNHDjJjxp1MmjTN7dcTQojbJcnjJk6WNxE7PJyc9Hi3XaO1tYW4uHji4uJZu3YTCQmJbruWEEK4kgxbDcBmGJReaGJizgi37KBrtfbx8ccHeeONl2lrayU7O0cShxDCr0jPYwAXL7fS1mlhshuesuroaGfr1t8TH5/Aww8/KpX9hBB+SZLHAI6dbyAsNIRJLpzvsFgstLQ0k5ycypw5C8jKypaNDIUQfkuGrW5gGAbHdAOFWYnERIW75GdWV1eyZctvOXfuDCEhIYwdO04ShxDCr0nP4wa1jR3UNXex/A7XrOYuKTlCaelxFixYQnZ2jkt+phBCeJskjxscPd9ACDAt//bWd1y6dIH09AxycwsoKppMRISsEhdCBA4ZtrrBsfMN5GTEkxg7tJt9Z2cHO3du48CBPXR0tBMfnyCJQwgRcCR59NNwrYvKunZmFKQN6f0WSy+vv/474uPjWb/+MUaM8PyGikII4QkybNVPyfkGAKYXODdk1dbWSlXVJSZMmMxDDz1CdHSMO8ITQgifIT2Pfo6db2BMagxpSY6VdTUMg1OnTvD667+js7MLwzAkcQghgoL0POw6uy2UVbew8q5sh99z6tQJzp8/y+rVD8sQlRAiqEjysKuub8cAskfdesW3zWbj+PFiMjIymTBhIkVFkwkNlQ6cECK4SPKwq65vByA9+eZDVo2NDezevZPIyCjy8hRhYfK/TwgRnOTuZ1dd30ZYaAipicMH/HObzcaePe8xceJUCguLZIW4ECKoyXiLXXV9O2lJwxkW9tn/JZcv17Jz5zYA1q7dyPjxEyVxCCGCnvQ87Krq2hg14s9DVhZLL4cPH6C8/Dzz5y8mJCREkoYQQth5LHkopQqAF4BkoAnYrLUuu+GcMOCnwL2AAfxQa/0rd8fWZ7VxubHj0y3YDcPgypXL9PR0s2HDZqKiBh7KEkKIYOXJYatngKe11gXA08CzA5zzCJAH5ANzgO8ppbLdHVhjSzdWm0Fq/DA++GAnpaXHycwcy9Kl90niEEKIAXgkeSil0oDpwCv2Q68A05VSqTecuh54Tmtt01o3AG8BD7k7voZrXSQOu0ZZyQ6GDRtGYWGRuy8phBB+zVPDVplAjdbaCqC1tiqlau3HG/qdlwVU9HtdaT/HYcnJsU4HF17dSlJkN2vWrEUV5Dr9fn+Wmhp8lQylzcFB2uxeATdh3tTUjs1mOPUelRHHj/77l2hv7aKhoc1Nkfme1NS4oGovSJuDhbTZcaGhIUP60O2pOY8qIMM+IX59Yny0/Xh/lcDYfq+zBjjH5UJCQhgeGXB5VAgh3MYjyUNrXQ8cBzbaD20ESuzzGv29DjyhlAq1z4esBn7viRiFEEI4zpNPWz0JfFUpdR74qv01SqkdSqmZ9nNeBC4AZcBh4Pta6wsejFEIIYQDPDZWo7U+B9w5wPEV/b63Al/xVExCCCGGRrYnEUII4TRJHkIIIZwmyUMIIYTTAun51DAwn1keqtt5r7+SNgcHaXNwGEqb+70nzJn3hRiGcwvqfNg8YJ+3gxBCCD81H9jv6MmBlDwigVnAZcDq5ViEEMJfhAHpwBGgx9E3BVLyEEII4SEyYS6EEMJpkjyEEEI4TZKHEEIIp0nyEEII4TRJHkIIIZwmyUMIIYTTJHkIIYRwWiBtTzIopVQB8AKQDDQBm7XWZTecEwb8FLgXMIAfaq1/5elYXcXBNn8X2AD02b++o7Xe6elYXcWRNvc7VwElwC+01t/0XJSu5WiblVIPA98FQjD/fi/VWtd5MlZXcfDvdhrwGyATiAA+AP5Oa93n4XBvm1Lqx8BaIBuYpLU+NcA5Hrt/BVvP4xngaa11AfA08OwA5zwC5AH5wBzge0qpbI9F6HqOtPljYJbWegrwRWCLUmq4B2N0NUfafP0f2rPAWx6MzV0GbbO96Nr3gGVa64mYW/q0eDJIF3Pk9/wd4KzWejIwCZgBrPFciC71FrAAqLjFOR67fwVN8rB/ApkOvGI/9Aow3V7utr/1wHNaa5u9TO5bwEOei9R1HG2z1nqn1rrT/vIk5qfSZI8F6kJO/J4Bvg1sA857KDy3cKLNXwd+rLW+AqC1btFad3suUtdxos0GEKeUCsXcwigCqPFYoC6ktd6vta4a5DSP3b+CJnlgdltr7NUKr1ctrLUf7y+Lz2b2ygHO8ReOtrm/zUC51rraA/G5g0NtVkpNBu4B/o/HI3Q9R3/PE4AcpdRepdQxpdT/UEr569azjrb5X4ACzD3vrgA7tdYHPBmoh3ns/hVMyUMMQim1EPMf20Zvx+JOSqlw4Dngyes3nyAxDJgMLAMWAvcBj3k1Ivd7CLM3nQ5kAAuUUuu8G1JgCKbkUQVk2Me5r493j7Yf768SGNvvddYA5/gLR9uMUmoO8BKwWmutPRqlaznS5nQgF9ihlLoEfA14Qin1S8+G6jKO/p4rgDe01j1a6zbgbeAOj0bqOo62+avA7+zDOC2YbV7s0Ug9y2P3r6BJHlrreuA4f/5UvREosY8L9vc65o0k1D5+uhr4vecidR1H26yUmgVsAdZprY95NkrXcqTNWutKrXWK1jpba50N/AfmOPFfeTxgF3Di7/bLwHKlVIi997UEOOG5SF3HiTZfxHzyCKVUBLAU+IunlAKIx+5fQZM87J4EvqqUOo/5ieRJAKXUDvuTKAAvAheAMuAw8H2t9QVvBOsijrT5F8Bw4Fml1HH71yTvhOsSjrQ50DjS5leBeuAM5o33NPC8F2J1FUfa/DVgvlKqFLPN5zGHLP2OUuqnSqlqYAywSyl12n7cK/cvqechhBDCacHW8xBCCOECkjyEEEI4TZKHEEIIp0nyEEII4TRJHkIIIZwmyUP4PaXUS0qp73k7jsEopbRSav4t/vxdpdQjnoxJiKEKqi3ZhW+zr/YeCfTfMqRAa13rhVheAh4Geu1fxcD/p7Ue8iaKWmvV7+f/KzBGa/35fn++fMgB34RSahhgAToxNwm8hrmJ4D9orW0OvH8p8Cv7YkohPiU9D+FrVmmtY/t9eTxx9PPvWutYzI3lrgK/9mIst6vI3pa7MfezetzL8Qg/Jz0P4fPs22m/hll/IgpzpfBXtNZnBzg3Dfgv4C7ABpzSWi+w/9kY4Gf2n9OOuT3504NdX2vdoZR6BbPwEEqpKOB/Ym66Z8Pc2uXbWuveQa5fDTwKxAJ/D4TYN+nTWusZSqn9wK/sP68OuENrfc7+3lGYW22M0Vo3KaU+h7mJ5VjM7TaeHKg40ABtOa+UOghM7ff/7MvANzBXLtcDP9Ba/0oplQBsBSKVUu3203OARszt7L8EJAC7MH8fzYNdXwQO6XkIf7ENs8DNKMyb5Ys3Oe9bmNszpNrP/S58unHeNuAI5u6qy4BvKaWWDHZhpVQcsAmz4iDAU8BMzB1qpwFzgX+81fX701pvw0w+v7P3rmbc8OddmHUY+u9uvB543544ZmFusfFlzLorvwbetu/dNFhbxtvj/aTf4TpgJRAPPAH8TCk12b6R4Cqgsl9PsB74b/bzF2AmnA7M6nUiiEjPQ/iat5RS10uE7tFar7aPzf/X9RPsk+MNSqkYrXXHDe+3YO6Ym6W1Lgc+tB+fDcRrrf/d/voTpdTzmOV3379JLN9WSn0N6AI+wqyyCGa1tieub8KnlPo+8H+Bf77F9Z31MuYN+Z/srzfZrwHwV5hlc4/YX/9aKfXfgVnAzWpVnLQn0Gjgd/Sruqe13trvvA+UUu8D8zG3Mh/IXwNf1lrXwKe/j0+UUo87Mo8iAoMkD+FrVmutd/U/YL/p/QBYB6RgDgdh//7G5PFDzJv4+0opK/CM1vp/YQ7vZCmlrvU7NwzYc4tYfqi1/t4Ax9P5bMGdCszezK2u76xdQKJSagbmJHcR5nbiYLblEaXU1/udH9EvhoFMxtyuez3wr5hJpBdAKXU/Zg8pH3M0Ihqzh3YzWcBWpVT/RGEAaZgFl0QQkOQh/MFmYAXmZG8F5lBNA2a53M/QWrdillv9un1n4N1KqY8xaxqUaa3HuyCey5g38Ot1T7Kwlza92fW11jf2QG65I6nWuk8p9Trm0FUL8Ha/XlYV8M9a6x85E7S9V/CKUmo18D+Ab9pr1b+B2QPbrrW2KKW28ef/twPFWQ1s0lp/5Mz1RWCR5CH8QRzQAzRhfir+t5udqJRahbnl+AXMm67V/nUY6FVKfQN4GnN4aQIQobU+6mQ8rwBPKaWOYd5kv4tZSOtW179RHeZW4SFa65slkpcxt1FvB77Z7/gvgdeVUh9gPkIcg1ng6IMBhvEG8gNgv1LqR5j/HyIwk7HV3gtZYv+51+NMUUrF2QtIATwD/LtS6gta60r7QwKztdZ/dODaIkDIhLnwB7/BrE9di1mD4uAtzlXAB5g33APA/9Va79da92H2Xu4ALmE+MfQs5iSxs/4Zs4hSKea8wEeYN+SbXn+An7EF86Z91d4zGshBoA9z8v3d6wftn/i/Avwn0IxZo+JRR4PXWh8HDgHf1Fpfw+wp/QHzceR1mA8WXD/3FGYxoUtKqWv2RPG/gT9hDs212eOc5ej1RWCQeh5CCCGcJj0PIYQQTpPkIYQQwmmSPIQQQjhNkocQQginSfIQQgjhNEkeQgghnCbJQwghhNMkeQghhHCaJA8hhBBO+38L8ILvBoL6PAAAAABJRU5ErkJggg==\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "ROC curve generated with Matplotlib\n\nThe dotted line in the middle of the graph represents a 50-50 chance of obtaining a correct answer. The blue curve represents the accuracy of your model. More importantly, the fact that this chart appears at all demonstrates that you can use Matplotlib in a Jupyter notebook.\n\nThe reason you built a machine-learning model is to predict whether a flight will arrive on time or late. In this exercise, you'll write a Python function that calls the machine-learning model you built in the previous lab to compute the likelihood that a flight will be on time. Then you'll use the function to analyze several flights.\n\nEnter the following function definition in a new cell, and then run the cell."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "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]",
      "execution_count": 30,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "This function takes as input a date and time, an origin airport code, and a destination airport code, and returns a value between 0.0 and 1.0 indicating the probability that the flight will arrive at its destination on time. It uses the machine-learning model you built in the previous lab to compute the probability. And to call the model, it passes a DataFrame containing the input values to predict_proba. The structure of the DataFrame exactly matches the structure of the DataFrame we used earlier."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Note: Date input to the predict_delay function use the international date format dd/mm/year.\n\n2. Use the code below to compute the probability that a flight from New York to Atlanta on the evening of October 1 will arrive on time. The year you enter is irrelevant because it isn't used by the model."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "predict_delay('1/10/2018 21:45:00', 'JFK', 'ATL')",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 31,
          "data": {
            "text/plain": "0.6"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Predicting whether a flight will arrive on time\n\n3. Modify the code to compute the probability that the same flight a day later will arrive on time:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "predict_delay('2/10/2018 21:45:00', 'JFK', 'ATL')",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 37,
          "data": {
            "text/plain": "0.8"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "How likely is this flight to arrive on time? If your travel plans were flexible, would you consider postponing your trip for one day?\n\n4. Now modify the code to compute the probability that a morning flight the same day from Atlanta to Seattle will arrive on time:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "predict_delay('2/10/2018 10:00:00', 'ATL', 'SEA')",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 38,
          "data": {
            "text/plain": "1.0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Is this flight likely to arrive on time?\n\nYou now have an easy way to predict, with a single line of code, whether a flight is likely to be on time or late. Feel free to experiment with other dates, times, origins, and destinations. But keep in mind that the results are only meaningful for the airport codes ATL, DTW, JFK, MSP, and SEA because those are the only airport codes the model was trained with.\n\n4. Execute the following code to plot the probability of on-time arrivals for an evening flight from JFK to ATL over a range of days:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\n\nlabels = ('Oct 1', 'Oct 2', 'Oct 3', 'Oct 4', 'Oct 5', 'Oct 6', 'Oct 7')\nvalues = (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'))\nalabels = np.arange(len(labels))\n\nplt.bar(alabels, values, align='center', alpha=0.5)\nplt.xticks(alabels, labels)\nplt.ylabel('Probability of On-Time Arrival')\nplt.ylim((0.0, 1.0))",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 39,
          "data": {
            "text/plain": "(0.0, 1.0)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEBCAYAAACAIClPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAGr1JREFUeJzt3XucXWV97/HPZAiJGeKFEBRCNKWSnwcFLHKzijcEPdTbsXA0R0mVIxaOhlpaORUljVpbz8HSahMFUZSLIiqKR8VLOV5pxRugKPITkWuAkAZUSEgImekfawV2xpk9a83stbN35vN+vea113722mt9M5mZ317rWet5BkZGRpAkqY4Z2zuAJKn/WDwkSbVZPCRJtVk8JEm1WTwkSbVZPCRJtVk8JEm1WTwkSbXt1I2dRMT7gD8FFgH7ZebPxlhnEPgA8GJgBHhvZn6kG/kkSfV068jjUuA5wC1t1nkN8GRgH+CZwIqIWNR8NElSXV0pHpl5RWbeNsFqrwLOyczhzFxLUXCObT6dJKmurpy2quiJbHtkciuwsMb7ZwEHA3cCWzqYS5J2ZIPAHsAPgU1V39RLxWOqDga+u71DSFKfOhy4ourKvVQ8bgWeRFH94PePRCZyJ8C9965neHj7jRQ8b94urFt3/3bb/2SYub0HHhrmu1ffPqVtzHnULDY8UPlD3bgO/6O9eNRO3emq9OeiO7Z35hkzBnjc44ag/BtaVS8Vj88AJ0TE54B5wCsoOtmr2gIwPDyyXYvH1gz9xszj27JlhPs3bJ7SNkYGZrB+itvYmmV4Rvf+r/y56I4eyVzrdH9XPsJExAci4nZgL+DyiPh52X5ZRBxUrnYB8GvgBuBK4F2Z+etu5JMk1dOVI4/MPBk4eYz2o1uWtwAndSOPJGlqvMNcklSbxUOSVJvFQ5JUm8VDklSbxUOSVJvFQ5JUm8VDklSbxUOSVJvFQ5JUm8VDklSbxUOSVJvFQ5JUm8VDklSbxUOSVJvFQ5JUm8VDklSbxUOSVNu4MwlGxDDQbmLdAWAkMwc7nkqS1NPaTUO7T9dSSJL6yrjFIzNv7GYQSVL/aHfksY2IOBp4LrAbxSkrADLz+AZySZJ6WKUO84h4B/BxYA6wBFgPvBTY0FgySVLPqnq11RuAIzJzGfBg+fhSYK/GkkmSelbV4rFrZl5bLj8YETMz80rg+Q3lkiT1sKrF48aI2Ldc/jlwQkQsAX7TTCxJUi+r2mG+HNgduA44DbgI2AU4qaFckqQeVql4ZOYXW5a/ByxqKpAkqfdVKh4R8VngE8CXM/PBZiNJknpd1T6P7wOnA2si4qMRcUREDEz0JknSjqlS8cjMMzLzQOCZwGrgLGB1RJzZZDhJUm+qNapuZl6fmcuB/wb8DPiLRlJJknpaneFJFgGvprjDfAFwCXBEM7EkSb2saof594CnAl8C3gF8NTM3NxlMktS7qh55rAQuzcz1TYaRJPWHqvd5fKLpIJKk/tFuJsHfZeajy+WxZhWsNZNgRCwGzgPmAeuApZl5w6h1dgc+BiwEdga+AZycmQ9V++dIkrqh3dVWB7Qs7wMsHvW1ta2qs4BVmbkYWAWcPcY6pwG/yMz9gf2AZwCvrLEPSVIXtJtJ8CaAiBik+EP/J5m5aTI7KY8oDgSOLJsuAlZGxPzMXNuy6ggwNyJmALMojj5WT2afkqTmTNjnkZlbImIfWmYPnISFwOrM3NKyzTvK9tbi8W6KS4DvBIaAlZn5b3V2NG/eLlOI2Rnz58/tyn7uW/8gGzZN/Yzemns2wGCls49tzZm1E3OHdm67Tj9mXnPPBoaGZk15X53YxuzZM5m/65y26/Tj97iTuvX710n9mLnq1VZ/C6yKiOWZ2eSRwLHATynuH5kLfCUijsnMz1bdwLp19zM8PLp7pnvmz5/L2rX3dWVfGzYP8/Urb57ydoaGZrF+/aQOKrdx1GGL2Lih/Xb6MfPGzcNT3len8m7cuHnCn69+/B53Sjd//zple2eeMWNgUh+6qxaPc8vH10XE1rY6Hea3AQsiYrA86hgE9izbWy0Djs/MYeC3EfEFigmnKhcPSVLzqg5Psg+/32leucM8M+8GrqG4O53y8epR/R0ANwEvBoiInYEXUgyDIknqIRMeeXSiw7x0InBeRCwH7gWWltu/DFiemT8C3gKcFRHXAoPAN4FzprBPSVIDutVhTmZeDxw6RvvRLcs38sgVWZKkHlX1tNXWDvMFTYaRJPWHbnWYS5J2IFWLxz6NppAk9ZWqAyPeOLotIh4DvAr4vdckSTu2ypNBAZTDhryY4kqpl1NcWvvhBnJJknpY1cmg9qcoGK+huPN7JvDqzPx8g9kkST2q7dVWEfGWiLga+CGwL/BW4AnAPUCtMackSTuOiY48zqSYe+M1reNLtVxxJUmahiYqHkdRnK76eEScSTGU+if5/YmhJEnTSNvTVpl5eWYuBR5PcaPgIcBV5fM3RMRjm48oSeo1VS/VXU8xPezHImIRxdHI6ylm/tv+E2hIkrqq1qW6AJl5M/Au4F0R8ayOJ5Ik9byqY1uNqe4sf5KkHcOUiockaXqyeEiSarN4SJJqqzo8yc7A2ymmj90tM3eNiCOBJ2fmh5oMKEm9aAuwafPwlLez5p4NbOzAdmbNnEE358eoerXVmcAi4HjgS2XbL4B/AiwekqadTZuH+fqVN095O0NDs1i/fiozfBeOOmwRc2Z272RS1T39KcVAiFcAwwCZeTuwV1PBJEm9q2rx2Dx63YjYjWKAREnSNFO1eHyW4u7yhQARMR/4AHBxU8EkSb2ravF4G3An8EvgscCtFKPtrmgmliSpl1Ud22oT8OaIWEYxn8eazJz65QGSpL5UeWyriJgN7E0xEOLCrXN6ZOYPmokmSepVVe/zeA2wqnz6QMtLI8CenQ4lSeptVY883gcsycyvNBlGktQf6lyqe3mTQSRJ/aNq8VgBnOHMgZIkqH7a6mfAcmBZRGwp2waAkczcuZFkkqSeVbV4XAhcRHFT4AMTrCtJ2sFVLR7zgdMyc6TJMJKk/lC1z+N8iuHYJUmqfORxAHBSRLwdWNP6Qma+oOOpJEk9rWrxuKD8kiSp8thWH53qjiJiMXAeMI9iUMWlmXnDGOv9d+B0yqu5gBdm5prR60mStp9xi0dELMnMi8rlpeOtl5nnV9zXWcCqzLwwIl4LnA1sc8orIg6iuKfkBZl5V0Q8Bpj6FFuSpI5qd+TxOorLcwFOGGedEYrO9LYiYnfgQODIsukiYGVEzM/MtS2r/iXwvsy8CyAzfzvRtiVJ3Tdu8cjMF0XEEzLzrsw8fIr7WQiszswt5ba3RMQdZXtr8dgXuCkivkMxeu/ngPd4ibAk9ZaJ+jx+CTy6G0FKOwH7Uxyh7Ax8lWLiqaqnxpg3b5dJ7fi+9Q+yYdNDk3pvqzX3bIDBwSlvZ86snZg71P7m/TX3bGBoaNaU9wV0ZDuzZ89k/q5z2q4znTP3W17oXmZ//7rzfe6kiYrHQIf2cxuwICIGy6OOQYqh3G8btd4twGfLyac2RcQXgEOoUTzWrbuf4eH6ByobNg/z9Stvrv2+0YaGZrF+/dS7aY46bBEbN7TfzsbNwx3ZV6cyb9y4mbVr72u/zjTN3G95obuZ/f3rzvd5LDNmDEzqQ/dENwl25HRRZt4NXMMjNxouAa4e1d8B8EngqIgYiIiZwBHATzqRQZLUORMdeezSMhDiaFsHRqx6jHgicF5ELAfuBZYCRMRlwPLM/BHwKeAg4DpgGPgaMOXLhCVJnTVR8dhAcXf5lGXm9cChY7Qf3bI8DJxSfkmSetRExWM4M2/sShJJUt+YqM+jUx3mkqQdyETF4yWjGyJir4aySJL6RNvikZnfHqP5uoaySJL6RNX5PCRJethkiof9IJI0zdUuHpk5t4kgkqT+UXUyKCIiKO752OY+9sw8t9OhJEm9rVLxiIjTgOUUQ4VsaHlpBLB4SNI0U/XI4y3AIZn50ybDSJL6Q9U+jweA65sMIknqH1WPPE4H/iUiVgDbzCdejkclSZpGqhaPj5ePb2hpG6Do85j6zCuSpL5StXj8QaMpJEl9pVLxyMxbImI28Djg3szc2GwsSVIvm7DDPCIOjYhvAfcBtwP3RcR3IuKwpsNJknpT2+IREc8ELqcYDPFIYN/y8efAv5avS5KmmYlOW/0dcFpm/ktLWwLfiojrgPcAL2gqnCSpN0102uogxr+D/GPl65KkaWai4jECzBzntZnl65KkaWai4vFt4JRxXjsF+E5n40iS+sFEfR5vA66IiAOBzwB3AnsAxwJ/DDy72XiSpF400TS01wEHA/cC7wW+XD7eSzFQolPSStI0NOFNgpl5I3BcF7JIkvqEc5hLkmqzeEiSarN4SJJqG7d4RMTFLcuv704cSVI/aHfk8aKIGCiX39+NMJKk/tDuaqvvAt+LiF8CsyPi/LFWysyljSSTJPWsdsXjWOAY4EkUw5Dc2JVEkqSeN27xKCd8uhAgImZm5ju7lkqS1NOqziS4IiL2AZYAC4DVwEWZeUOT4SRJvanSpboR8VLgx8BTgHuAAH4UES9rMJskqUdVOvIA/h54eWZ+c2tDRDwPWAn8vwZySZJ6WNXisRfF1VetrijbK4mIxcB5wDxgHbB0vNNeERHA1cAHM/Ovq+5DktQdVe8wvwb4q1Ftp5TtVZ0FrMrMxcAq4OyxVoqIwfK1S2tsW5LURVWPPE4CvhgRfwHcBiwE1gOV+jwiYnfgQODIsukiYGVEzM/MtaNW/xvgS8Au5ZckqcdUvdrq+oj4L8BhwJ7AHcD3M3Nzxf0sBFZn5pZye1si4o6y/eHiERH7Ay8Cng+cXvlf0WLevMnVmzX3bGBoaNak3jtaJ7Yze/ZM5u86p+06Zu6vzP2WF8zcTj9m7qSqRx5k5kMU/RyNiIiZwDnA68viMqntrFt3P8PD9adW37h5mPXrN01qn62GhmZ1ZDsbN25m7dr72q9j5ilvp1uZ+y0vmHlHzDyWGTMGJvWhu1uj6t4GLCj7M7b2a+xZtm+1B/CHwGURcTPwFuCEiPhwlzJKkiqqfOQxFZl5d0RcQ3GT4YXl49Wt/R2ZeSuw29bnEbEC2MWrrSSp93RzPo8TgWXlQIvLyudExGURcVAXc0iSpqjSkUdEnAmcn5l1Ls3dRmZeDxw6RvvR46y/YrL7kiQ1q+ppq5nA1yJiLXAB8InMvL25WJKkXlbptFVmLqPo4P4b4OnALyLi8ohYGhHeiyFJ00zlPo/M3JKZX8rMJRT3e8wHPg7cFREfiYgFDWWUJPWYyldbRcSjKSaIei2wP3AJ8L+AWymGLvlK2S5J2sFV7TD/LMWd39+hGKPq0szc1PL6KcBvG0koSeo5VY88rgTenJl3jfViZg5HxOM7F0uS1Muq9nkcPlbhiIjPbV3OzA0dSyVJ6mlVi8fzx2l/XodySJL6SNvTVhHxrnJx55blrfYGbmkklSSpp03U57GwfJzRsgwwQjGo4YoGMkmSelzb4pGZrweIiH/PzHO6E0mS1OvGLR4RsSgzby6f/v+I2Hus9TLz100EkyT1rnZHHtcCc8vlX1GcqhoYtc4IMNhALklSDxu3eGTm3Jblbg7dLknqcRYFSVJt7fo8vktxWqqtzHxORxNJknpeuz6Pj3QthSSpr7Tr8zivm0EkSf2j3Wmr4zLzgnL5+PHWy8xzmwgmSepd7U5bLaGYchbguHHWGQEsHpI0zbQ7bXV0y/J4AyNKkqahOjMJPhb4E4q5zO8AvpyZv2kqmCSpd1W6zyMiXgDcDJwMHAwsA26OiCOaiyZJ6lVVjzxWAm/MzE9vbYiIY4FVwFOaCCZJ6l1V7zDfE7hkVNvngSd0No4kqR9ULR7nA28a1XZS2S5JmmaqDk8yAzgpIk4FVgMLgMcDVzaeUJLUc+oMT+JkUJIkwOFJJEmTUOc+j8cDhwC70TIplMOTSNL0U6l4RMQrgAuBG4CnAj8HngZcgcOTSNK0U/Vqq78DXp+ZfwSsLx/fCPy4sWSSpJ5VtXg8MTM/M6rtPGBph/NIkvpA1eJxd9nnAcWwJM8E/hAYbCaWJKmXVe0wPwd4NsVd5v8EfBMYBv6x6o4iYjHF0co8YB2wNDNvGLXO6cCrgYfKr9My82tV9yFJ6o5KRx6Z+X8y85Jy+XxgMfCMzDy9xr7OAlZl5mKKMbHOHmOdHwAHZ+YBwPHAxRHxqBr7kCR1QZ1LdQeBw3hkSPbKd5dHxO7AgcCRZdNFwMqImJ+Za7euN+oo46cUlwTPA26vui9JUvOqDsm+P8Vlup8B3lo+3hARB1Tcz0JgdWZuASgf7yjbx7MUuDEzLRyS1GOqHnmcS3Gq6czMHImIAeAvy/ZndDpURDwXeDePHKlUNm/eLpPa55p7NjA0NGtS7x2tE9uZPXsm83ed03YdM/dX5n7LC2Zupx8zd1LV4rEY+OfMHAEoC8j7gRUV338bsCAiBjNzS3kKbM+yfRvllVwXAi/PzKy4/YetW3c/w8MjE684ysbNw6xfv6n2+0YbGprVke1s3LiZtWvva7+Omae8nW5l7re8YOYdMfNYZswYmNSH7qqX6l4GvGxU20uBL1d5c2beDVwDLCmblgBXt/Z3AETEwcDFwDGZeVXFbJKkLms3JPsFPDIk+yDwqYj4McXRwkKK01VfqLGvE4HzImI5cC/lDYYRcRmwPDN/BHwQeBRwdkRsfd9xmXltjf1IkhrW7rTVr0Y9/1nL8nVArfsvMvN64NAx2o9uWT64zjYlSdtHuyHZ39nNIJKk/lHnPo/nA8dRzCK4GrgwM7/RVDBJUu+qep/HGyg6su8CPgfcCXwyIk5oMJskqUdVPfI4FTgyM3+ytSEiLqYY68rpaSVpmql6qe48ik7yVgns2tk4kqR+ULV4XAGcGRFzACJiCDgD+PemgkmSelfV4nEisB/w24hYA/wGOAD486aCSZJ614R9HuU4Vo8CXgg8gXJUXQcslKTpa8LiUY5jdS0wtywYFg1Jmuaqnra6mmJwREmSKl+q+y3gqxHxcYqxrR4etjYzz+18LElSL6taPJ4F3AQ8d1T7CMWcHpKkaaRS8cjM5zcdRJLUP9oWj/K+jncATwOuAv4hM6c+a4kkqa9N1GG+kmLSp+uBY4D3NZ5IktTzJioe/xU4KjNPLZdf0nwkSVKvm6h4DGXmnQCZeRvwmOYjSZJ63UQd5juV83gMjPMc5/SQpOlnouJxN9teirtu1PMRYO9Oh5Ik9ba2xSMzF3UphySpj1QdnkSSpIdZPCRJtVk8JEm1WTwkSbVZPCRJtVk8JEm1WTwkSbVZPCRJtVk8JEm1WTwkSbVZPCRJtVk8JEm1WTwkSbVZPCRJtVk8JEm1TTQZVMdExGLgPGAexaRSSzPzhlHrDAIfAF5MMdHUezPzI93KKEmqpptHHmcBqzJzMbAKOHuMdV4DPBnYB3gmsCIiFnUtoSSpkq4ceUTE7sCBwJFl00XAyoiYn5lrW1Z9FXBOZg4DayPiUuBY4IwKuxkEmDFjYKL1xn7z4AC7zJk5qfe2mjN7JgMjw1PezuDgwIT/FjP3T+Z+ywtm3hEzj6XlPYN13jcwMjJSe2d1RcQzgPMz86ktbdcBr83Mq1rargWOz8wfls9PBfbKzJMr7ObZwHc7m1ySpo3DgSuqrty1Po8u+CHFP/5OYMt2ziJJ/WIQ2IPib2hl3SoetwELImIwM7eUHeN7lu2tbgWexCP/iCcCt1TcxyZqVE1J0sNurPuGrnSYZ+bdwDXAkrJpCXD1qP4OgM8AJ0TEjIiYD7wCuKQbGSVJ1XXzaqsTgWUR8UtgWfmciLgsIg4q17kA+DVwA3Al8K7M/HUXM0qSKuhKh7kkacfiHeaSpNosHpKk2iwekqTaLB6SpNp2pJsEOyoiZgF/T3G58GbgAeCdmXnpBO9bBByVmR8e5/UFwIUUw7XckJkHjbVej2V+ObAcmAUMAOdm5j/2eOanA+dSfECaCfwbsCwzN/Vq5pb1ZgNXARs68fPR4Pf4ecBlwC/Lpk2ZeehU8zaZuVzn6RQDsO5WNv1VZn6lVzNHxMnA8S1NewMfycxTppp5KjzyGN8Hgb2Ap2bmU4DjKMbjes4E71sEvLHN6/cDf0sxCGSnNZX5LuClmfk04I+BkyLi8A7kheYyJ3BYZj4d2I9iNOc/n3pcoLnMW70H+N6UEm6rybzXZebTy6+OFI5SI5kjYgj4HHBqZu4L7A/8oCOJG8qcmR/Y+j0GDgY2Ap/sTOTJ81LdMUTEk4CfA0/MzHta2k8CjsnMI8rnbwP+BzAMrKcYX+ta4A8oPo39KjOPGWcfzwPe16kjj25kbtnmF4GLM/PCfshcfiL8PHBZZq7s5cxlUX4rcCYd+PloMm+nf4a7lPkE4PDMXNovmUft55XAiszcv5P5J8Mjj7HtR/GfeM+o9iuBAwAi4s+AlwHPyswDKD6ZDwNv4pFPY23/CPdj5oh4CnAY8I1ezxwRe0bENcB/APcBbU8Xbe/M5afifwZO6kDOxvOWFkfEVRHx/XI7vZ55X2BzeXPyNRHx0Yh4XI9nbnU8xenY7c7iMbYq4xq/BPhQZv4OIDPXNRtpQo1njog9gC8Ab8rMO+pH/D2NZs7MO8pD/ScAOwOvnFTKbTWZ+QyKOW9WTzbcGJrMexWwMDMPBF4NLI+IF04u5jaazLwTcATwPyn6He8DOtF/163fvxdQ9JludxaPsV0LPDkidh3Vfhjw03J5chOHNKfRzOWcLJcDZ2Tmpye7nVG68n3OzPXAxXSmn6nJzM+m+AN8M/ApYL+I+Gnbd0yssbyZ+bvM/G25fBNwKfCsyQZt0eT3+BbgG5l5Z/mp/5PAIZPcVqtu/Cz/GcWp1/+Y4nY6wuIxhsy8mWKQxg+VV74QEU8D3g68s1ztixQdx3PL1+eV7b8DHtPVwDSbuVzvX4GV2cFpgRvOvHdE7Fwu7wy8nOIXvGczZ+b+mbkoMxdRfJK/dqrnthv+Hu8REQPl8q7AURQDoE5Jw79/nwYO2fo+iimvf9Ljmbd6HT1yygrsMB9X+QPwDxR/dB6kuMLh3Zl5Sfn6APA2ik+zD1Ec/j6HoiBfSnEFxfVjdNgNUnz6mUXxA3M3xWV3K3o48xnAmymuYNrq/Zn5sR7O/Frgf1N0TA4C3wb+OjMf6NXMo/bxPDrUGd3g9/jNFP0zmylOB52fmf93qnmbzFy+dylwKsXPxk3AGzNzTY9nfhZF4XtiZvbEfEUWD0lSbZ62kiTVZvGQJNVm8ZAk1WbxkCTVZvGQJNVm8ZAk1WbxkCTVZvGQJNX2n4XtEeINi7PCAAAAAElFTkSuQmCC\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Probability of on-time arrivals for a range of dates\n\n3. Modify the code to produce a similar chart for flights leaving JFK for MSP at 1:00 p.m. on April 10 through April 16. How does the output compare to the output in the previous step?\n\n4. On your own, write code to graph the probability that flights leaving SEA for ATL at 9:00 a.m., noon, 3:00 p.m., 6:00 p.m., and 9:00 p.m. on January 30 will arrive on time. Confirm that the output matches this:\n\n"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "\nlabelsx = ('9:00 a.m', 'noon', '3:00 p.m', '6:00 p.m', '9:00 p.m')\nvaluesx = (predict_delay('30/01/2018 09:00:00', 'SEA', 'ATL'),\n          predict_delay('30/01/2018 12:00:00', 'SEA', 'ATL'),\n          predict_delay('30/01/2018 15:00:00', 'SEA', 'ATL'),\n          predict_delay('30/01/2018 18:00:00', 'SEA', 'ATL'),\n          predict_delay('30/01/2018 21:00:00', 'SEA', 'ATL'))\n\nalabelsx = np.arange(len(labelsx))\n\nplt.bar(alabelsx, valuesx, align='center', alpha=0.5)\nplt.xticks(alabelsx, labelsx)\nplt.ylabel('Probability of On-Time Arrival')\nplt.ylim((0.0, 1.0))",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 41,
          "data": {
            "text/plain": "(0.0, 1.0)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Probability of on-time arrivals for a range of times\n\nIf you are new to Matplotlib and would like to learn more about it, you will find an excellent tutorial at https://www.labri.fr/perso/nrougier/teaching/matplotlib/. There is much more to Matplotlib than what was shown here, which is one reason why it is so popular in the Python community."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python36",
      "display_name": "Python 3.6",
      "language": "python"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "name": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6",
      "file_extension": ".py",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}