dsir-code-challenge.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# DSIR Code Challenge\n### Christopher Messier"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Part 1"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Step 1: Import the packages"
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "import numpy as np\nimport scipy as sp\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n% matplotlib inline",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "/usr/local/Cellar/python3/3.6.3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n  return f(*args, **kwds)\n/usr/local/Cellar/python3/3.6.3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n  return f(*args, **kwds)\n/usr/local/Cellar/python3/3.6.3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n  return f(*args, **kwds)\n"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Step 2: Read in the data\n\n_Note: One thing I think would help people think like data scientists would be to provide some background for the data that is being used. Not only does it provide context, but it can also present various sources of where one can find data to practice with._\n\n\nIn this lesson, we'll be looking at marketing data from a bank in Portugal to determine whether a customer will sign up for a term deposit account.\nThe data source can be found [here](https://archive.ics.uci.edu/ml/datasets/bank+marketing).\nThe data used in this excercise is from the [University of California Irvine's Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php), a collection of datasets for machine learning tasks.\nThis is an excellent source if you would want to practice using the models we present throughout the course.\n\n_Fun fact: I used this data to write one of my (forthcoming...) blog posts!_"
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "! ls  # use ls to see the contents of the directory",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "README.md                 bank-names.txt            dsir-code-challenge.ipynb\r\nbank-full.csv             coding-challenge.md\r\n"
    }
   ]
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "df = pd.read_csv('bank-full.csv')  # read in the data from bank-full.csv",
   "execution_count": 3,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "df.head()  # take a quick look at the data",
   "execution_count": 4,
   "outputs": [
    {
     "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>age;\"job\";\"marital\";\"education\";\"default\";\"balance\";\"housing\";\"loan\";\"contact\";\"day\";\"month\";\"duration\";\"campaign\";\"pdays\";\"previous\";\"poutcome\";\"y\"</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>58;\"management\";\"married\";\"tertiary\";\"no\";2143...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44;\"technician\";\"single\";\"secondary\";\"no\";29;\"...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33;\"entrepreneur\";\"married\";\"secondary\";\"no\";2...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>47;\"blue-collar\";\"married\";\"unknown\";\"no\";1506...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>33;\"unknown\";\"single\";\"unknown\";\"no\";1;\"no\";\"n...</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "  age;\"job\";\"marital\";\"education\";\"default\";\"balance\";\"housing\";\"loan\";\"contact\";\"day\";\"month\";\"duration\";\"campaign\";\"pdays\";\"previous\";\"poutcome\";\"y\"\n0  58;\"management\";\"married\";\"tertiary\";\"no\";2143...                                                                                                  \n1  44;\"technician\";\"single\";\"secondary\";\"no\";29;\"...                                                                                                  \n2  33;\"entrepreneur\";\"married\";\"secondary\";\"no\";2...                                                                                                  \n3  47;\"blue-collar\";\"married\";\"unknown\";\"no\";1506...                                                                                                  \n4  33;\"unknown\";\"single\";\"unknown\";\"no\";1;\"no\";\"n...                                                                                                  "
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### _Uh oh..._\n\nThat doesn't look right.\nWhat you'll notice is that the items in the rows aren't separated by a comma ( `,` ), as they usaually are.\nInstead we see that they are separated with a semi-colon ( `;` ).\nWe refer to the characters separating the fields of fields of an an array as _\"delimiters\"_.\nTo get `pandas` to read the data in properly, we'll have to specify that the delimiter is a semi-colon."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "df = pd.read_csv('bank-full.csv', delimiter=';')  # specifying that the delimiter is a semi-colon",
   "execution_count": 5,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "df.head()  # that looks better!",
   "execution_count": 6,
   "outputs": [
    {
     "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>age</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>balance</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>day</th>\n      <th>month</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>poutcome</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>58</td>\n      <td>management</td>\n      <td>married</td>\n      <td>tertiary</td>\n      <td>no</td>\n      <td>2143</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>may</td>\n      <td>261</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44</td>\n      <td>technician</td>\n      <td>single</td>\n      <td>secondary</td>\n      <td>no</td>\n      <td>29</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>may</td>\n      <td>151</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>entrepreneur</td>\n      <td>married</td>\n      <td>secondary</td>\n      <td>no</td>\n      <td>2</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>may</td>\n      <td>76</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>47</td>\n      <td>blue-collar</td>\n      <td>married</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>1506</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>may</td>\n      <td>92</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>33</td>\n      <td>unknown</td>\n      <td>single</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>1</td>\n      <td>no</td>\n      <td>no</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>may</td>\n      <td>198</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   age           job  marital  education default  balance housing loan  \\\n0   58    management  married   tertiary      no     2143     yes   no   \n1   44    technician   single  secondary      no       29     yes   no   \n2   33  entrepreneur  married  secondary      no        2     yes  yes   \n3   47   blue-collar  married    unknown      no     1506     yes   no   \n4   33       unknown   single    unknown      no        1      no   no   \n\n   contact  day month  duration  campaign  pdays  previous poutcome   y  \n0  unknown    5   may       261         1     -1         0  unknown  no  \n1  unknown    5   may       151         1     -1         0  unknown  no  \n2  unknown    5   may        76         1     -1         0  unknown  no  \n3  unknown    5   may        92         1     -1         0  unknown  no  \n4  unknown    5   may       198         1     -1         0  unknown  no  "
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Step 3: Exploratory Data Analysis\n\nNow that we have our data read in, and assigned to the variable `df`, it's time to take a look at our data.\nIn general, this process is referred to as __Exploratory Data Analysis (EDA)__ (shout out to [John Tukey](https://en.wikipedia.org/wiki/John_Tukey)).\nThe purpose of this process is to understand how our data is organized.\nThis is important, because it allows you to account for problems with our data -- and correct those problems! -- before we try to feed it into a model.\nThe following cells will walk you through this process."
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Step 3a: Read the data dictionary\n\nIf you're lucky, your data will come with a data dictionary.\nA data dictionary is generally a text file that describes what the data looks like.\nIt will identify the fields, the values that are in that field, and may include some summary statistics.\nWe're lucky enough to have one here, found in `bank-names.txt`.\nYou can open it and take a look, but I was also nice enough to copy it below."
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "##### Input variables:\n1. age (numeric)\n2. job : type of job (categorical: \"admin.\",\"unknown\",\"unemployed\",\"management\",\"housemaid\",\"entrepreneur\",\"student\",\"bluecollar\",\"selfemployed\",\"retired\",\"technician\",\"services\")\n3. marital : marital status (categorical: \"married\",\"divorced\",\"single\"; note: \"divorced\" means divorced or widowed)\n4. education (categorical: \"unknown\",\"secondary\",\"primary\",\"tertiary\")\n5. default: has credit in default? (binary: \"yes\",\"no\")\n6. balance: average yearly balance, in euros (numeric) \n7. housing: has housing loan? (binary: \"yes\",\"no\")\n8. loan: has personal loan? (binary: \"yes\",\"no\")\n9. contact: contact communication type (categorical: \"unknown\",\"telephone\",\"cellular\") \n10. day: last contact day of the month (numeric)\n11. month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\")\n12. duration: last contact duration, in seconds (numeric)\n13. campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n14. pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)\n15. previous: number of contacts performed before this campaign and for this client (numeric)\n16. poutcome: outcome of the previous marketing campaign (categorical: \"unknown\",\"other\",\"failure\",\"success\")\n\n##### Output variable (desired target):\n1. y - has the client subscribed a term deposit? (binary: \"yes\",\"no\")"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Step 3b: Data Hygiene\n\n__Data Hygiene__ is the process of cleaning your data, and verfiying that everything looks right.\n\nTo do this, we'll:\n- check the data types\n- look for missing values\n- remove duplicates\n\nWe'll start by looking at the data types."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "df.dtypes  # check the type of the columns in our data",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "age           int64\njob          object\nmarital      object\neducation    object\ndefault      object\nbalance       int64\nhousing      object\nloan         object\ncontact      object\nday           int64\nmonth        object\nduration      int64\ncampaign      int64\npdays         int64\nprevious      int64\npoutcome     object\ny            object\ndtype: object"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "In our data we have 2 data types:\n1. `int64`\n2. `object`\n\nComparing the output from `df.dtypes`, the variables match with what's in the data dictionary.\nThere does appear to be a problem though; the fields that are supposed to be \"binary\" are currently strings of \"yes\" and \"no\": we'll have to fix this.\nFor most of the models we will be using, the data must be numeric values, but `object` refers to strings.\n\nWe can quickly change the `'yes'` and `'no'` strings to `1`, and `0` using the `.replace()` method."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "df = df.replace('yes', 1)  # replace values of 'yes' with 1\ndf = df.replace('no', 0)  # replace values of 'no' with 0",
   "execution_count": 8,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Let's check our data types again."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "df.dtypes",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "age           int64\njob          object\nmarital      object\neducation    object\ndefault       int64\nbalance       int64\nhousing       int64\nloan          int64\ncontact      object\nday           int64\nmonth        object\nduration      int64\ncampaign      int64\npdays         int64\nprevious      int64\npoutcome     object\ny             int64\ndtype: object"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Using `replace()` corrected the data types of our binary variables.\nThere are still some `object` variables in there though.\nFor this data we know this means that they are __categorical__ variables.\nWe'll keep this in the back of our mind as we move forward. "
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "We'll also want to see how large our dataset is."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "df.shape  # get the shape of the dataframe",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "(45211, 17)"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "_Note: `df.shape` is a 2-tuple where the first value is the number of rows, and the second is the number of columns._"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "A common problem we'll face when working with real-world data is having missing values."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "df.isnull().sum()  # check for the number of null values",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "age          0\njob          0\nmarital      0\neducation    0\ndefault      0\nbalance      0\nhousing      0\nloan         0\ncontact      0\nday          0\nmonth        0\nduration     0\ncampaign     0\npdays        0\nprevious     0\npoutcome     0\ny            0\ndtype: int64"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "To quickly explain what the code above is doing; the method `.isnull()` returns a boolean for each value in the dataframe: `True` if it's a null value, and `False` if it isn't.\nFrom this, running the method `.sum()` will then sum all of those values.\nRemember, `True` is 1, and `False` is 0; so this will give us a count of missing values.\nTo return a percent of missing values, use the following code instead."
  },
  {
   "metadata": {
    "scrolled": true,
    "trusted": false
   },
   "cell_type": "code",
   "source": "df.isnull().sum() / df.shape[0]  # get the percent of missing values",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "age          0.0\njob          0.0\nmarital      0.0\neducation    0.0\ndefault      0.0\nbalance      0.0\nhousing      0.0\nloan         0.0\ncontact      0.0\nday          0.0\nmonth        0.0\nduration     0.0\ncampaign     0.0\npdays        0.0\nprevious     0.0\npoutcome     0.0\ny            0.0\ndtype: float64"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Step 3c: Numeric EDA\n\nNow that we've confirmed that there are no missing values, while noting that several of our variables are categorical.\nThe next step is to get a numeric summary of our data, generally in the form of __summary statistics__.\nSummary statistics describe our data by giving us the following values for each variable:\n- count: the number of observations\n- mean: the average of the observations\n- standard deviation: how \"spread out\" the values are\n- min: the lowest observed value\n- median: the value of the \"middle\" observation\n- max: the largest observed value\n\nLuckily, there is a simple pandas method to do this for us: `.describe()`\n"
  },
  {
   "metadata": {
    "scrolled": true,
    "trusted": false
   },
   "cell_type": "code",
   "source": "df.describe()  # get descriptive statistics for the data",
   "execution_count": 13,
   "outputs": [
    {
     "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>age</th>\n      <th>default</th>\n      <th>balance</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>day</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>40.936210</td>\n      <td>0.018027</td>\n      <td>1362.272058</td>\n      <td>0.555838</td>\n      <td>0.160226</td>\n      <td>15.806419</td>\n      <td>258.163080</td>\n      <td>2.763841</td>\n      <td>40.197828</td>\n      <td>0.580323</td>\n      <td>0.116985</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>10.618762</td>\n      <td>0.133049</td>\n      <td>3044.765829</td>\n      <td>0.496878</td>\n      <td>0.366820</td>\n      <td>8.322476</td>\n      <td>257.527812</td>\n      <td>3.098021</td>\n      <td>100.128746</td>\n      <td>2.303441</td>\n      <td>0.321406</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>18.000000</td>\n      <td>0.000000</td>\n      <td>-8019.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>-1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>33.000000</td>\n      <td>0.000000</td>\n      <td>72.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>8.000000</td>\n      <td>103.000000</td>\n      <td>1.000000</td>\n      <td>-1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>39.000000</td>\n      <td>0.000000</td>\n      <td>448.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>16.000000</td>\n      <td>180.000000</td>\n      <td>2.000000</td>\n      <td>-1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>48.000000</td>\n      <td>0.000000</td>\n      <td>1428.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>21.000000</td>\n      <td>319.000000</td>\n      <td>3.000000</td>\n      <td>-1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>95.000000</td>\n      <td>1.000000</td>\n      <td>102127.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>31.000000</td>\n      <td>4918.000000</td>\n      <td>63.000000</td>\n      <td>871.000000</td>\n      <td>275.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "                age       default        balance       housing          loan  \\\ncount  45211.000000  45211.000000   45211.000000  45211.000000  45211.000000   \nmean      40.936210      0.018027    1362.272058      0.555838      0.160226   \nstd       10.618762      0.133049    3044.765829      0.496878      0.366820   \nmin       18.000000      0.000000   -8019.000000      0.000000      0.000000   \n25%       33.000000      0.000000      72.000000      0.000000      0.000000   \n50%       39.000000      0.000000     448.000000      1.000000      0.000000   \n75%       48.000000      0.000000    1428.000000      1.000000      0.000000   \nmax       95.000000      1.000000  102127.000000      1.000000      1.000000   \n\n                day      duration      campaign         pdays      previous  \\\ncount  45211.000000  45211.000000  45211.000000  45211.000000  45211.000000   \nmean      15.806419    258.163080      2.763841     40.197828      0.580323   \nstd        8.322476    257.527812      3.098021    100.128746      2.303441   \nmin        1.000000      0.000000      1.000000     -1.000000      0.000000   \n25%        8.000000    103.000000      1.000000     -1.000000      0.000000   \n50%       16.000000    180.000000      2.000000     -1.000000      0.000000   \n75%       21.000000    319.000000      3.000000     -1.000000      0.000000   \nmax       31.000000   4918.000000     63.000000    871.000000    275.000000   \n\n                  y  \ncount  45211.000000  \nmean       0.116985  \nstd        0.321406  \nmin        0.000000  \n25%        0.000000  \n50%        0.000000  \n75%        0.000000  \nmax        1.000000  "
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "From the summary statistics, we can begin to get a sense of how our data are distributed.\nSomething is missing though.\nIt seems that `.describe()` left out our categorical variables!\nThe reason for this is kind of obvious.\nIf we look at the `job` feature, we can't find the 'average' job.\nTo get around this, we'll pass the argument `include='all'` to `.describe()`"
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "df.describe(include='all')  # get descriptive statistics for categorical data as well",
   "execution_count": 14,
   "outputs": [
    {
     "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>age</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>balance</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>day</th>\n      <th>month</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>poutcome</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>45211.000000</td>\n      <td>45211</td>\n      <td>45211</td>\n      <td>45211</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211</td>\n      <td>45211.000000</td>\n      <td>45211</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211.000000</td>\n      <td>45211</td>\n      <td>45211.000000</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>NaN</td>\n      <td>12</td>\n      <td>3</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>NaN</td>\n      <td>12</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>NaN</td>\n      <td>blue-collar</td>\n      <td>married</td>\n      <td>secondary</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>cellular</td>\n      <td>NaN</td>\n      <td>may</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>unknown</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>NaN</td>\n      <td>9732</td>\n      <td>27214</td>\n      <td>23202</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>29285</td>\n      <td>NaN</td>\n      <td>13766</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>36959</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>40.936210</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.018027</td>\n      <td>1362.272058</td>\n      <td>0.555838</td>\n      <td>0.160226</td>\n      <td>NaN</td>\n      <td>15.806419</td>\n      <td>NaN</td>\n      <td>258.163080</td>\n      <td>2.763841</td>\n      <td>40.197828</td>\n      <td>0.580323</td>\n      <td>NaN</td>\n      <td>0.116985</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>10.618762</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.133049</td>\n      <td>3044.765829</td>\n      <td>0.496878</td>\n      <td>0.366820</td>\n      <td>NaN</td>\n      <td>8.322476</td>\n      <td>NaN</td>\n      <td>257.527812</td>\n      <td>3.098021</td>\n      <td>100.128746</td>\n      <td>2.303441</td>\n      <td>NaN</td>\n      <td>0.321406</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>18.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-8019.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>-1.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>33.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>72.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>8.000000</td>\n      <td>NaN</td>\n      <td>103.000000</td>\n      <td>1.000000</td>\n      <td>-1.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>39.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>448.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>16.000000</td>\n      <td>NaN</td>\n      <td>180.000000</td>\n      <td>2.000000</td>\n      <td>-1.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>48.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>1428.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>21.000000</td>\n      <td>NaN</td>\n      <td>319.000000</td>\n      <td>3.000000</td>\n      <td>-1.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>95.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>102127.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>31.000000</td>\n      <td>NaN</td>\n      <td>4918.000000</td>\n      <td>63.000000</td>\n      <td>871.000000</td>\n      <td>275.000000</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "                 age          job  marital  education       default  \\\ncount   45211.000000        45211    45211      45211  45211.000000   \nunique           NaN           12        3          4           NaN   \ntop              NaN  blue-collar  married  secondary           NaN   \nfreq             NaN         9732    27214      23202           NaN   \nmean       40.936210          NaN      NaN        NaN      0.018027   \nstd        10.618762          NaN      NaN        NaN      0.133049   \nmin        18.000000          NaN      NaN        NaN      0.000000   \n25%        33.000000          NaN      NaN        NaN      0.000000   \n50%        39.000000          NaN      NaN        NaN      0.000000   \n75%        48.000000          NaN      NaN        NaN      0.000000   \nmax        95.000000          NaN      NaN        NaN      1.000000   \n\n              balance       housing          loan   contact           day  \\\ncount    45211.000000  45211.000000  45211.000000     45211  45211.000000   \nunique            NaN           NaN           NaN         3           NaN   \ntop               NaN           NaN           NaN  cellular           NaN   \nfreq              NaN           NaN           NaN     29285           NaN   \nmean      1362.272058      0.555838      0.160226       NaN     15.806419   \nstd       3044.765829      0.496878      0.366820       NaN      8.322476   \nmin      -8019.000000      0.000000      0.000000       NaN      1.000000   \n25%         72.000000      0.000000      0.000000       NaN      8.000000   \n50%        448.000000      1.000000      0.000000       NaN     16.000000   \n75%       1428.000000      1.000000      0.000000       NaN     21.000000   \nmax     102127.000000      1.000000      1.000000       NaN     31.000000   \n\n        month      duration      campaign         pdays      previous  \\\ncount   45211  45211.000000  45211.000000  45211.000000  45211.000000   \nunique     12           NaN           NaN           NaN           NaN   \ntop       may           NaN           NaN           NaN           NaN   \nfreq    13766           NaN           NaN           NaN           NaN   \nmean      NaN    258.163080      2.763841     40.197828      0.580323   \nstd       NaN    257.527812      3.098021    100.128746      2.303441   \nmin       NaN      0.000000      1.000000     -1.000000      0.000000   \n25%       NaN    103.000000      1.000000     -1.000000      0.000000   \n50%       NaN    180.000000      2.000000     -1.000000      0.000000   \n75%       NaN    319.000000      3.000000     -1.000000      0.000000   \nmax       NaN   4918.000000     63.000000    871.000000    275.000000   \n\n       poutcome             y  \ncount     45211  45211.000000  \nunique        4           NaN  \ntop     unknown           NaN  \nfreq      36959           NaN  \nmean        NaN      0.116985  \nstd         NaN      0.321406  \nmin         NaN      0.000000  \n25%         NaN      0.000000  \n50%         NaN      0.000000  \n75%         NaN      0.000000  \nmax         NaN      1.000000  "
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "For the categorical variables we can see the number of unique values, the most frequent of these values, and the frequency of this category.\nYou'll also notice that it only does this for the categorical variables, not the numeric variables; just as it only gets the numeric statistics for numerical data."
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Step 3d: Graphical EDA\n\nNow that we have a numeric sense of our data we know all there is to know, right?\n\n_Wrong!_\n\nNo matter how mathematically savy a person is, just seeing the summary statistics for a data set is never enough.\nWhat we need to do is _see_ our data.\nTo do this, we need to plot it.\nLuckily, there is a simple way for us to do this: `sns.pairplot()`"
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "sns.pairplot(df, hue='y');  # create pairplots for the variables, vary colors for y",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "image/png": 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G9ohie6KiOo+7hgsqlcstyeWcnqRWn7NUl7ztf+niGx4I6vuvd3d97/v+L8o5BqaCMQ+gPbwe5Ffwfx23m9l9ku6U9BPn3P8v6bOS/tDMHpX0lvB+W+XyJV2wdoPWPbZdxbLTuse264K1G5TLF5VImAZ6UkpYeJ0w5QolXbD23jHPv1e5fGnaDZJxjZmvr9eOobw++p0N3jVsYm7Wc5wrlHThtbV5vPDaDcoVSi1ZAHS9SNTFuXyxbr27M1fQuse269RXHqBLlx+tef0ZDeXp3ME4s57jcjnoyNm2a0TOSdt2jWjXcIEseixi+9fZb0/ki1p7x5O6dPnReuQzp+jS5Udr7R1PKpcvtmQB0BkNj0NimuNmzEbn8ESDTc1ui4nKN9PtGqWO8Xpl2ZErBL9GrzqmjohZz3CpVA72P2PavReu3aCNW4ZGt1Up/LKrTBoaKWrBnJ4ofMbhh7b0UYytoz7+vfv1wTcdVtV/Vr8/rFQqT7l+ilJdhrbqaHtiql/AO+/rd+uZncP66m2PTTuXo8eYQ3lt2TVSp0+ke+v7iLVxpyNS7WJgGhjzANrA69P1O+cek/SqOtO3S3pz+0u0R7Ynqbs27aiZdtemHcr2pOqeKimbafT8pN739fWj/ytSaZBcdc7SSf9XpLoxU3ntx793vy5dfrRO+tf/nPJ8MLvakeP+nlTdfPWz7dECUamLsw1yfvC8rJa/6qW66K1H6pPX319zWteo/OoLndeOHA8XS9o1UtTFNzwwmsN//ONjlE4llM1QH/soSvvXdmS4L5PU6UsOqqlLP7fiGPVlOBWezxoeh3Rgu0alTVFR73hqJsdQlc79saeZr7RHmt0WE5Vvptu11cs+E1Eqy2TakeFcodRw/3PYfgNa99h2rb3jCa18zSG6cO2Gmvpakm6873cd+4zDD+3IcaM66rD9BkZvN+oPu/Ls46ZcJ/hUf6B1OtmemGhfXy+Pn7w+6Kedbi5zhZJ25gq6+IYH9M33viYybbooiFIbdzqi1i4GmsWYB9Aevv+SP7JyIyUdv3BezbTjF87Tk9tzdb9JlMvXf35upDRpg6TRN0SndNDkScMGMzM0Uqybr6ERfnmH+GhU7z61I6fz/+AwffL6+33+BjdioFyWPv69+8f9YqnM2c691W3711y+NK4u/eT19yuXpy71WcPjELZryzuHJ/tFWbPbYqLyTWVeE51VIEod41EqSxT096S0cctg3e27ccugJOmkVxww7pf+n7z+fp3/B4eNPpfPODqpURtq13Bh9Haj/rBGHfr16gTqD7TbRPv6ifppp5vLbCapg+dlddemHQ33DfXq+274GwvauED8dVufDFAPX2mZDeWystqtb7/v1dq2Y6f+/pYn9OyLeX1uxTG67JZHdNmfLNbgSFHZTFLDhZLKZakvk9AXVi2u+ab95auWKJEIKqbKtzylPQ2SgfCsANuH8rr2jid05iv30QEL9pUbGVS5p1+5QrnuaysH/mPnU2lw5vKl4NSH/Lo1NrKZpP7trCXqt2EleueoPLxLQ66XA1vESsKkK9+1RFntyflu9aroTHN603TuoOOyPUm9ZK+Mbv//jtdLF+yr323dpn++9Wlle8ihrzIJ0xVnLVF+96Dmz52r7Tt3KtM3oExM21D9Pam6GeZb8n7rSyXq5rgvxffBK53D1cdTF554mDQyKPX0S/mclM5Kiap1VS5LhZyUyY57fLLBpmw6qctXLdYFNceEi5UwBf9FP+Y4bWz5lr/qpfroHx4hKWgXjZ/XEmXTybCYE59VoN6yVx8/TkWrjjFz+ZIuOPEwnfSKA3TYfgPauGVQNz+4uamyxMnQSFGPbd2lL6xcrAuvrc1KvljW8le9dHTAqFplIGnZovk1WZjUBJluWivnBa9lM0l9bsUx+uG9T4d9WfNl+SGpJ6W7Pv5aZfvnqCynRz5zijZuGdSXbt2oG+/73WjHfb3p9eqEVtRlQDMm+wJevf1ZZXC+Xi7LZad8saQet1vK9Ev5IVm6f7TuzOVL2rZrRMcvnKcv3bpRn1txzJgzGC5WQsFfvSRNcoUhKdMvNzKor/3Xs/rCLzbG9kyHQbtqybi2zpT3fwAir9KeGHu2Qfp7Y6BckvJDUs9AcPyd6ZcSbNd6aNG2Wrks5bYqcd250pyXaMGb/lyX/clilYYH9a17ntVzL45ocLioHUN5HTS3T4PDxdED8wtOPEz/9q7jNNCb0tBIcbTRMfbg/QsrF6svlVC57DSUL+raO57QuccOKHvje6Qn10mHLJNbsVrZ/gXjGjP/+MfH6J9ufqTmwL5eB88XVi3W/GxGySQH3HFQKpU0RzlZbqfUM6Bkbrvm9M1VoTSgJJ0qiIl0QurL75Rd/17pyXVKHrJM/SuuVqlvX+XyRV144mE645X7jA5Mff+B5zU4XIza/7gixvKFoj7/RwuVHnle0nwdlBrU5/9oofKFonoz6U4XD9Pg5NRf3Km9fxTUOwsOWabimVermJrf6aLNCjIcT4VSWdlCbY4LZ1ytQmrfrj8WGNs5fOGJh+nDr1sgG9oaDFIObpHLztVIco4y6aTyhZJ68ttl1507elymt6+WsgukRGJKg02ZZEL/cOYrdfC8rLa8OKx0MqFz16yvf3r/dFJXnHWsduYKOmhun3YM5WuOG68461hddfZSZXvGD7JPdgrrel84uOKsYyW35wsHfamEdhfLdQfxJ/sSQTP6UgmtfPUhdY+Ju1FfKqk3HL5AX7v9cX3+7cfowLl92r5rWMlCTgfss4/+5fSXa6RYrJu13flS8HcOU/3CRdi/oQaZbkor5wXv7S6U9fDvdur81y1QopCTzKShbdJPPqoFu55VecVqfemOF0YHID+34hgdtqBfK19ziL52++O6fMz0Va95Wd2BOwb50G4TfUGwv6df57/+Jfryf23W28IMf2HlYqVTCV1x1rGjfbSVL8gNF0oqFkvKFnfKbnjvnj7ft69Wvme+CqXgi+Tz+jP6v+9crP/z7Q267JZH9A9nvlKHzM/quReG9Xc/+bWee3FEV5y1RHuVnx9toyQPWaZzl1+pR7furxvve3b0rzD6M/HpH0kkTPP7M6N/Y8SP2oD4yeVL+sG9T+vS5UePfnnqB/c+rXe/4VDN6aV96a1ySRp+Udq9MxjcH9oWTOvdi4H+Osy5+J2OZzqWLl3q1q9fP/MZjQxKa1dKA/tJb/5L6YcfHm2EjZx+lYYz81QsS9/61RM6+3ULNac3PfrtY0n66B8eoUPmZ/Xk9pzmZtNKJxO6+rbHRr/luWu4oIGelPLFsobyRc3r79GzW7fqwJveI226bU85Fp4gt2qtRqxPhbJTf0/wxYF0wpRJ1zZsBkeKet8162saoMsWzQ++cMDgV0XkV8JEGXb5IVluu/SDD+3pUDn9y3LZ+bJMf5tLilZp8tdRkc+wNLO62A2/KLv2nePrwpXfVj6RVXpkuxLX7+lULK9YrVxqrpRI8CsOf0Q+xxNluJzPKZHbNq4uLmf3VSKTbXNJ0QpuZJds7aq6bTDrmVPvJZHPsNQ4x2Q4nuKY45Yd2yn45VvlP9BV2F33M/BiYh8d+9nbte5jr9GCH50zbl2WV66VegY0XChpaKQ47tf1lYHvscdlN3/kjbr0xodqjtM++pbD9e43HKr+nlTN/C5dfrRufnDzuF8HvueERaPtnOq2oyQdcclNKladnjeVMP3m705RwkzlstOu4YJ25go6eF5W2wZHZKbRM89dcOJh4wbeJ1oWKTjGnM7/DQ8OF3Xbo1u07OX7aq++tF7cXdC6327TCYfvp4HeabXhIp9hqXGOh/NFZdJJHXHJTfrJBSfo3x/cHH7p/7yaL/2/kNhHH/zWvTVfjMhmguOF3tQUBzkq/RtjMq1V1wa/qmlGK+cFyYMcT1QXl0plWXG3ErvH9FOc9kXp538jDW7R06d8VW/4l7skBfXHV846ViYpmTD1ppP67dYh3fzg5qBOnGBgkjNXRpYXG6HZNsXYL7ldeOJh+vBr9pZV9UXkll+pT928WTfe96yWLZqvfzjzlervSWpeNqMduYLWb9quZS/fV3N6U7L8kBLXjm+nFf/k23rXNx6u+bHWQCal3kxSQyNFfe32x3XZzx4dfcn9F79Oe33/XePmU/mcpRKmRz5zirYPjfCjr+ZEPsdTzfDCT/1kyvPc9Nm3zaRIiJbIZ1hqnOPcSFE7cnl9/Hv31/zIdV42oyx9vf7KD0l1xrKUnR8M+o/nRY5nC0lvtUw2CN4HbgsG+CuNp023qecH71N65Vqpd0DnvO5QXfPfwbePv7jqVfrXMw6T9fRr246d+th3HtGifQf0wde/RMnMgFa8ch/9bONWzcumNZQvaU5vWvlSebQz5/D99g3es9qT66RMv0aGS7rmvx7XY9uG9JG3BF8gyI2U1JdO7OngcdL+e/XUvLzyP2e5AqcwiwVXljZcK536eWnfI6VtjwT3l32w0yXDNI3reN01ornZtOb0pruyw6JcdrKeAWnOS6QL75P2OSRoEAw/L2X6lSnkgoPqqjo5cf256l+1Vi7TuFORDiG0krlS3brYqIv9lekP6p0PrduzTW+7rNFBh/fIcExl+hseS3SFMacPL6ezyhXKo7+gqx6Uf/SvTgg6GqraE/rBhzRn1Vo98plTlDDXcF0ecclNk/66fuwpfg/bb6DmLzKef/559Q4MKF8MBuaLJae1dzypdY9t18sX9Ov0JQeNO1VlXzoRLmbtoMPdf/GHdX/pPTRS1JzetHKFkj7wzXtGH7/tE3+gT1x3/+j9k15xgC68dkPjMwG08H+we9MJHfeyefrgN++pGbDuTXfnAESh7PTs9pyOXzhPL1/Qrw+87iVKf/edwQ8NPnCbtO+Rsp1PaK85c0bPCvHUjpwSCWm4WFI6Yeop7Zbr7ZdNdsr8Sv9GtSfXBdOb1cp5wXuFUlk9qtNPcd93pVP/SbrpE3rpgn1Hn3/Xph2a05vWWVffoX8485VKmOnmBzfr9CUHjX55pZFEwkb7tejfwmwb++txjQzKqgfpN92m7I3n6Z/f8W1JCf30gc06eF5WZ119h648+zit37Rdx71snr7+35t0+pKDdNDc+u20ZO+ALl1+9OhfVly4doOuOnuphkaK6u9J6fJfbKx5yZy99q573FL5nFX+3vXSGx8Kf/RljT9X/PUKgIjozSS1cdOu0bNjDw4Xdc+TO/TGI/brdNEwE4xlNYU9cKvlc8E3S/Y9sm4jzDL9OuLTN+kD37xbK447WKvPPk5vXZhS4tpVsr9doAU/OkeX/dGBOv91C5T6zjtlf7tAB970Hr3rmH4lEtLFNzygIz990+j/S3/p1o0qjwwG71ntkGX63ZZt+sA3gvf56+VHS5Kck4byRW3P5fW+a9briEtu0vu+vl4XnXSklr/qpaMvrzTuspngVFFtUS4H3+534XW53J737QbprLT0HCnZG9xP9gb303So+Gq4WNKukeJonXDxDQ9o10hRw8VSp4vWEblCSSrslt5yafAFqxvOkwa3SnsdGAz2pxt1KvYrl6+/ziqd4aN15TXrtX0o3746EfFDXRw/heHgzE0//YT0mf2C6zf/ZTA9jshwPOWH6h5LKD/UmfK0U+X04WtXSn+7QFq7Uja0VV+77TEdcclN2vLiiNbe8aQuXX60HvnMKZN+IaI03OC4bOs2FctO6x7brg988x7JpITZuLOmVU7xW/HcC7v1+T9aqINSg0rIaZ57Xn3FIX3jvzfpiEtu0vu/cbdOX3KQlr/qpRocKeqT1weD8JX3+uT194+2c3KFktbe8cTosgz0BP9fuWzRfKUSFvyd28rFMgWn45eTTn7F/rr5I2/Ub//+VM3NZmq+GN7oP98rg/hjl0Xa89cEzdpdKI1+oaCybBdeu0G7C93Z7u3vSelff/YbXb5ysXYM5ZXsDb/oWr0/euA6Wbmgl83v0wvP79Rlt/yP/s+3Nygpaa/S80p+J+h/0NqVwWegcuw99pi8MNygfsg1X/BKX0kr5gXvlZ2kdJ/0mvPCztvfSA//WHrVO4JTsb75L7XzxRdHn1/po7pr0w4dPC+rT15/v056xQE19RwQFZUvliTMlOip33ZI9Q7ostNfrkc/c7KUH9QHf/9QmUwnveIAZVIJvfeERfrhvU+rPLxL+vSWYHD+FSuC14d9vpfe+JAuemvQn3vXph3qyyS1fQdwOPoAACAASURBVDCvoZHiuH1wo+OW5198UcsWzdfnVhyjL926seZHX3XVaTvV7EcAoI3yhZKOOmBvvf8bd48eHx11wN7Kd+lxQmzQ99QUBvlbrJzOyq1YLe18ovYA9hUrpPPvkky686LXaMGctC763n06YWE2OH30ptukclHadJvsunOV2L1zz7SB/ZTID2lef0bfeNdR+s1nTtau4YKOXzhPN973O/3XE7uD91x4gpRISQtPUG75lfrcL57Suse266Lv3VdzmqWeVEIXrq3tJPn49+7X35x2tH7796fqlxe9SV985xLd/OBmPbk9155BLRqJs6s4LBVHpB9dEDTmf3RBcL8Y00GILlAuSx//3v3jPsfd+pHpTSWClfL9DwS/Yjrx00HO/3aB7Np3Bv+d+8ZP1r7okGVyI0MN/4+x+r9qK+v4grX3Nj7YBSZDXRw/5dKeX/WG7Tj94EPB9Dgiw7FUSPSNO5ZwK1arkOjrdNFmX2Eo+H/w6mOx68/Vnx47X8Wy00Fz+7TiuIN16Y0P6chP36Ryg0H88vCgjvz0TfrZxl3j1mV5xWr9fOPg6NMn+jV7Np3UFWcdq19e9Cb99u9P1UuyTuniYM1nzvK79M6l+9UM5P/NaUdrr760vrhqsTb+P/bOPDyu6krwv1urVCXZli2Z3ZidOOw4gHvYQjpsHcBEBGxwTNKAWcJAT4aEpEM66QBN0yHpgaGBBBya1TLB7JNlurM00GPWxCxNYjCbIQQseZNUe9W788d5r+rVJllGlqqk8/u++qrqrffdd965Z7nv3u8cxdvXncQbf3cUJ+03U6YZAFrDAb70F7uyx1RLwFhMNoHj5Ll10SG8fu1J3L74UAoWLrj7xWJH8JP234GdprVgDOQdh++d9slix/A16waHTOJ782CXdSLYynmw49FQzQ4F8Un6Rm4yU2D3zjiRUJDLe1bxQW8fHPu3pZEE55wGB56J6TkHc3UX0x9bzI2f25mLj5nNtHCuNLqV1249eJ68kVnLJ88OwFn3VOkHJxxjMJMfWZwgHIMzyp8PzliqwbpJSsBYSKyH5YvchOPXJMH/0gPSyWxVD2Fji/rDS0D6k/1eZ6PJqguUJqFOByeb6ZeXu67pItizkKN2hKff+Ii9v/VzLr73d+QLBS6c207ogXNKSfkTr4OvribTfVcx5nvlipf5yqf3LI7Gs8v0GOlcgVsXHcLzf3scb//90bx93Umi81f1VPkt01qC3PCFA7jh/67msZc+KHvpqya5ZJXtVGxHFEVRxpicY2t2Bs7pC1rNjcaeRoQm+UeZZM7h5mc3U2jbrhTg2f8L8nbp45e5Tva5/OMJO7D9lAimTo9Ops+WIadPvF56Wrr7hpafjUn2Yh2H2xcfyspvHMdR+8ykz05hw6l341zVS+Gs+4lO3Y6vfHpvTj1wR7abEiWVzVeNAuDHG/rMeyO4YC1/feRuhIOGZc++u8VJLcex4uxb+S4UnNL/dJ5ktrSuLCCgRuK2xTq1kxB2kmaEJwCxaJ0hUKMjD5w2O45j2ZDMgqdPj76iFOT05H3F+TKkz3//vehkN0CZDbSCoWaQcjSHmVUUQHXxRCQah0+cAl9/G76zUb4/cYosn4ioDE9IsgXLzc9u5v2TfoJzVS/vn/QTbn52M9nCJAiM1Hkzv6OjA5A3yK/46UvFoJGJtpGdf3tZkjJ/+h0EgiFev+ZEPrNHO++mWyksWIb9di+FBct4eWOII/boKh6+8m12v/+UzOYJBwNMi4UxBgy1n7nprSVb5Pl3NjClNQzW0sHm4mhw4QfO5ov7x8nlCjjWks87tBc2FdcHehaya0uKZ97sZe9v/Zy+wSz/Y3lFgGzZKvoGs8WEQygQ4NrT9+Ot605mx2kt3LrokLIk/o8WHUKcFFiHQC7BjHiY28+dK50Izp3LjHhkyDmz6/mRtd5IvOy4PUlk6viWE5xQABYcNov2Fun88OZGi50+uyTLNWxhs+I8jprVOsToVrH6PnkgBAt7RKbPWsbNz25m76t+MfJRrgIBiHXBwh74dq98x7p0iOdJStSmqzucPHopzDkFou1wzBW0R4O8fvXx/Gjxofxm9Uf0DmSqkv2TWRcojUFl+1Ulg+EY1t/B6dhvYc+6j0C0XYYhnnNaUU9/Zo/2YhucSQ0SeeSCmnHSCHmuPWUf3vqHk7jhtN3Za2acGxceVBzhJl+whAMwxdmEWSYjt5ies6UjjTcaABRHnLVAwFDsUPPLV/9cf4SMrZx6Zdh6UhRF2Qri0RAXHrMbL3/neN667mRe/s7xXHjMbtoBsNnR2NOIUGkfZVrDAT5/6M6kbIR4vAW+cCcEoxBtgzPvhpcfgPefI2bT/PCsg2QIvO47oWvP0vwSrz0BvaulJ3P3UnjxrrK5m8yD5zFt4TKscYi3FgBL1KbJhmLcs/IdTt53Kp0thr074Pvd+xFy0gQiEe5eNIdgSxvpbJ7ffeNI2qMGInE2btzIfb9bz5p1g2UBndsWHcor72/k0iN3wEQCOOkBsoFWIuHy+SP9c1YPZvL863++zU2/XsNlx+3JgsNmcXnPquLcid//wgHc8MvVfNSf4aaFB5cCPfWMxHCr1JHO8/TxiMTlLYlQq8hiZhDyqckz1+oEJJkp8L8XHsS8PTqZ0hqmP5Vj5Zt9JDMF2loml2pP5gr0PLuW/37k9nDJszBlB1i4TOQ7tQky/TB1Fwlcfvgq/NUPsNF2bCbBb1d/xP4zw+zQ1YnNDJAJxQgHRbd5w8xWzlWbzBZ0Lkdl64jE4b9dDgvuk+BlZgDee051cRNj81nMIedCISsLAiE45FxZHm4Z38JtCyJxOOFa6NitZE9sfFtluMmJRUNMi4XYvj2KMbB9e5RpsRCxCdzWOY4D2QQmGsd85Xn4zTXw6gpZ6U5V8NZ1JwOw/ZQIT/+PT7FjVyeF9CB/6I/wyTPvI9jaLkPoYjD3n4lZu5LArHns2r2Um5/ezI2/liTUnV86lIiT5u3rTsJmEuSDrWQKFsda0tkCWIdWm8LQTqtNkqGVu//fO9z06zW8ce2JNefP9T9zn5o9nbXrk4QLCXb6w11l8xaaF+8ifMTFWNtG2EmVEmpQTCgcv2AZr197EkDNzo2zZsRYfc1JJDI5coUC2dQg8UgH6UQ/8Xg79y3+JCYax+bSmEICM7gROmIwuA7T1kUbAHHaSAOtkElV+XaOYxlM58ikBoh1dJAa3EyhtY2g6/eFA4ZbFx3CpmSOXabHWNefpjUSZMndLxb9zJsWHsSMeHTIebknCjnH0vPcWr585G789KIj2KkjhpNJEfyfr0MoIvKxcJnYvr/4psj22pWYaBs2l5QEu+dju0Ok22wCInFMvekoTIBEJs8F9/xX0Tb2Rrm6/dy5W24bBwLSfkDpW5mcRNyOkgt7ACv/PbnMDECkDZPog9YOWsPtfHHebD5/yM7EwkG++tm96YiFcYAFh8+q0AUH1+1Q5I9d+WNairK1OI500Ms7FgiCtSSyeRnuvihjATKRGUQWLMNEYpDPYJwcGCPTC55ykxzstUcJtYpePPXA7elsi8DiR2HzexAIQvuOorM7ZmHunk/7WffCm79ip53nYg10hbOsHYA1yRx7zmzDZAcJeJ0EoNSR5uR/KrN5nMwg8WgLPzzzQGw2QSDaIrGVsJFnsiIOa7MJzKx5peO6x7HZBCbaXree1ieyXLbs91v0rCqKomwpuVyBOTvKcP2efrlx4UHkcgWikYnrz054NJc1IlTSRxHHseTzeXZszUO0ReaHdhz46dniHM+aJ0n7A8+C5V8UB3rWPOi+A168G548qvT/vx5x3z49D07+Pvz2WulteeJ1EI5hwjFxeFacB2tXMmXWPGz3UhbP2xWT6Yfnboe2HYjsfgwm2gZ9qwm/9jj20HNpjcQwziZYdgmsXcn0WfO4tHspmWgrb113MoPpPIGAIRY2nDA7hHnmVphzCqZzb6KZAe59YQM/e3UdSxcfQitpTLSNmJMkOQht8TYu+W/bM7VV3ljxhksB6GoPE3VS/PNZB/KndX30PPsuXz5qd9qiodpG4tFXYhN9Eoxy68qesRSjvf1HTiEPFuipkMVCXgJBStPREgpw6OzpXHzv78qMmJbQ5Hs2YpEgi46YBZEQtG0HhZwYAOl+aJ0G+aw4xlN3gd2PgTf/A7Piy5ijr+SEQxdjVvx18bkw3UsZCE6jLRouDjNb6YhuzTCzigKIbG5/APScU6GLcxCKDr+/0nAYY0THWPdNF1uAQg4zUdvWQh7atld7YoKRzxdYtH8M84DoptCseSzqvoNcvkAkPPHcRcdxMIneMh+D+beACcDAh3D6bVgjbX0ml+cH8/ciYPNgIBA0HNCRxzxwLqxdSdDbt21m6Y3pF+/i4r+4kK8ctyfZdIJoZkPxXGbWPMLdSwlH24AgrSYD2cHi+uCsebR238GSo2fzleP2wuaSmBOuk4QXyFyEJ1wHuRRvX3cSTnqAfLCVYCFNsKUTpiyBZ39c8itPuxkTiWFBRpCr0WHAROPs9a2f8+9fPYbLj9uT0/efxo5dnXzQ28fDr2xiMJ2nrSVEOGgIJNcz9fELYO1Kulz/01gLkRjGFmSI7ccvc0dWuhJz6GIZTcnz5brvwLx4Nzx5vfw/6x5MIISJxGk3Saa8shSevJ6uWfMoLFxOwBaANqJOkkC4hZxJY2glVEgSC7Vz/+I5xaBPX6YA2UFsNI7NSMI6MEF9xlgkyLl/MRsDfHKHKYTIY/JZwMJgr9vJolc6FJ78A9j5MLGLE70Y3/2g+w7oXYOde650UssMyrzP2UGItEPfH+UFhGwSJxwHC/eefzhr1g3yL79Zw2MvfaCjXClbTz4LB54t8lYplz49YU6/jVAoAraFNlIQaGNWPI+JtuBkEtzy7IesfGs9px64I1/59J5Mj0dI5wo4Vka/8xKtgCYZlVEnmy+QzBboeW4t3YfuTCQUYNmzazlhvx3Yc2Ybg5k8bWFD1ElCtA3jxYm9NxGtAwTgtFvg8z+GzKAMrZ8ewKy8tfgcMP8WePhCsVO6l0qn22ib+Ja++PKs7qWy3NjSSId+1ro2QCAEs+aRPPXHLP3PDznvqN3I938kIwe0by9TwETbIL0Z6+Qw0anFOGzGtGLm3070kQuKz21m/u1Y00plF2d/x5pEJk9Xe7RsKsQRdRLbSgoFh2SuQDwaIpHJ0xoKki442tlHUSYIWcfy4jsbuHXRIWUvwR25VxcaZWtiNJc1Ioy1OjwOwNy5c+0LL7wwsp0cCWRYrzdJJAaJvpLj/JXnJcjhT1zPPkp6af7vg0vLjv0WHHFRqVdKIAThltIxcyn4/X2S5M+nZGiKk78vb/pXHnvhMnHo413Sc98fvDrtZpnf7PAlMu9Zxb6Fs+7jikff5MP+LD8480BmRnOE3vot7HYUpDZCx66w8V1srIM0UVry/eVBgvm3yAgEO8/FtkyFzCD3/q6Pbz/2B049cHv+8YQdiD22pLh98tQf0zJtewKBALl8nlCqr+x49qz7MMvPqSqnXbisbu/Q4e+ZI/Uy8pEBGt7iG0qGbbof8+ZvYbcjJemZ2gRvP43d41hMy5SxLagyKgykc9z59NtF53HNukF++eqf+fKRu9HeEq61S8PLMGydLh7M5InZlARy81lxlMOtkmyLxCUw/syPSg5y9x3wy7+VoUx/9vUqHdN7yl1EY1OKzubHedtD3xYZdRq+8obVxc/cBnM+VzZ6jz3iItXFTYrNpTC5dJmdRGsHNtyCCdecz7zhZRjqy7HaExMTm+6XIVwrbe4F99e7rw0vx0Pr4gFMz8K6fpRt7SBlQ7S0xqGQIeB/xjP9sPyL5ft+4R7Y4xhJqKb7ASvbeP7gKw9W6X32PwP+5VN1/UW74H7xDXMpTHK9+H+ez3XGndIxzPUdrS1gvPMdfaX4etF2OVcmge3ap3Ss9GZ4aEmZ/2ZjMyDcipNNEfB1OMBN4ttIGyYio6vVasPqXsslK2vaWZx5N7RMhYEPAAMPX1Se3It3iu2Wz9TvIHD0ldjDLsD4fdTWDsz6NXDniaWyx7vqJfobXoahvhwnM3kGM3k+2JTgwJkRDG5MxwQovhGd3AjBkMhCLil+cCQu9+3JG+QtztlHwVn3SseQIy4Ue/mghfDIxT55W4qNtNOfD3LRvaXk6PXdMn9z70CGpV+aKwlVtXfHmoav5CF1cTaBcfIid55eSW+GlimiS392RUlOz14unQEerI5vJQ9YxE//kObT+27PlSteZrspUa44YR++9tOXy5L5reEgF9z9QtkobfN2nzEmSUalLg0vwzC0HA+kczz9Ri/H7tNF1Em7b+qnJSYRieOkBwnYAuYBt52+Yo3sWOE7EGmF1ObqGO6vvifbf/oqmdY1m5AERyAoIwEsq2PPbH4folOkY0CN2K+JtvNBbx+/WjPIZ+dszw6tebEF22bKlLGPXlphK3RiQi2QS2IjMTZu7genwLRpHXzQ28cPfvM+PzjrYAKmdEtrvb3vtR2PvfQBoYDh9WtPKttntCkUHNYnsmUjzN644CB6nlvLTe6IS6PQ2afh5XhL42yzv/F/tviY7/zjX32cIimNRcPLMNSX43zBYUON53x6PEIoODE7/E4K0v1QI/bEHseKrVhNU8jxtkIt2RFSTNiEwbgJfePvTfLi3SUDqmPX2r0mO3Yt/d+vW+ZE8vdKmX8LrP457PlZiM4WoT7gLHHOvbkoOveuM7x9TIIrZ90rw/z7hmvkpQdK85ud/H05hufkmwDBQECmEMgmIGggD+z1l5DsK38j4/AltLa0wIYPy95a4ZFL4Kz7YPk5xTpZ1L2U0w/+S+KBrPRCPfmf5LzpfmItU7HZQZxIG8F8SoI23npvmKeTvw9Pfr80lJQ7XKDMWTnCAILjQLK33DE8Y+nkmAcwEpN76eTkfyAo/7XnU9MSiwSZf/DOXLni5TKHaTK+SRMLBzEmDrm0yLRTkOB3Lin6LNougcuWKfCLKyVg7OnGGnq0c3oHjoVENk8sHCwGfeoGf+p0HtIh6ZQqInE4/ELAkaBM+w7yX4ebal6sI0mVWIfc01gHEJi484SpPTExibbV9ism6lDa9d5si8Sh8B7muduJHXGRhAmsI52sPV/oqnXl+554Pcw6rGKEljskIRVuBWvh4HMkyO6tP/MuCIRLQ6afcK2MNuQFLzL9pQiFfy5CEN8rn4IHv1wahej02+D0W6VNSfRJZ26ff2qySffasvC7e8v9w1U9mLlfgn+cRbAySe8O528W3C//I3HxW/2B/zOWit3z7V6xudq3L9VNLTurfXsIhkVfRqfAm/9RPozwivMlOWGC8rusLK799ttr4eCFmOxA6b7MmoeZfwts94myOejtgmXQspWdwxsYx8LlPau470sHYApZ923LAXAy0kGio1XqOdom98WY0u+W6TI6IMBrj4qdPOcUiLTBIV+EhyqGdn7wPMzpP2JKMMSdi/YnmU4zbVoHgwOb+efP700g0oqTHuTf1gwUp8Ail8JSwETiVR3rtQOsUkKmj+SIC32dpIzYVblUuZxaR+I4NYYdjz22hLPPvJ/F977MyrfW88u/OZqv/fTlqmkl7rvg8JpTkkxG/1kZPeIhw4n7TIXsBswLd8Hcc6WT2qoemHMKwc69pYPK4kdkqH3vhS4/Tl5iGCtqyPhpN0vHuOK2BXkxrGehDOVfLy4cjMo0hl5HrvWvy9v502cTyAwClh2ntrD4sCmQ2YyJdMq+F7nn9ZdjVY+8mGaQDn/JPqZP3QUnM8A9K9/mO4//kXm7zyib1tBxZNqCy5b9vmx011ZS3LjgQL5+3E48/MqmYadCdBxLOl/AcdyROTLSfmxpu5HMFcpGmF351nou71nFd0/9JD/89zfGdEQBRVG2Dak6z/mPFx9Kuyb5mxeNPY0IbcFGgONY8oU8cZOFbEECDWVBifPEADvwTJg2Swy3o6+UQITHrHli4J14vSSbjr5Cku9esMXruXzAAnj2RyVDbMoO0hPfC5wMfCBvS3TsKr+dggSHsoOw7+fEia8Mwpx2M0zdWYI/P/taafnpP5KE2LKF5R0WrDu8kxdYOvF6+OT88sDRaTdLedx5/oi2SfDpoqegcx/MwIfEg3kIx+WtkreeFIPTLZfxzhWfAZ/6sqxLrq/uoe2dw50jc++/f2rkCbNcotoxfPA8CSRt7cgAzYKTl4CPv1dw91IITBUHQWk6ktkCV64oD15cueJlMWJaJpcREwgYnPQgJhiWYc9NQBzrXLJcl3TfAe8/J4Gizn3K9ajX4WlwHTYzyJ8SIV77YCOf3bMd29JWHPYVKM7hK8tiBJJ9NTsPJXNOmVOrDqRCIStvdlTq4lAEAjXf+lYaHROE/ED1MLOhaeNdsm2D2hMTk8yg3MuKuVXJDNbrJd/cZBO1r7d3tbx57iW1QXwsf2I8W1FXB5wJDywu9y9evFuC4d65/G/Rtc2UzoGpPrErskmZAuOBxb5OAPeUl9efOD/6CvjTKnkb3usUsP5N6NxTkmJrn6te17W3JHmD0eoOB/NvhdgMSdJD7aS8MeIXZgfLA/9tM8Xe8ttA82+RZNyrK8Sv9dfVft3w2e/JW4d+/fG1NdA6HVIbpPNDJF7dYcArW+c+8rtlWvnbi/4kxN9tKJuKYCISiwZ5/p0NMkVCtE3kyNPBLVOr/eludz7LdL/42y1T4K9+CJ37wuA6iRF4HQFq1fuUHTD/cQMtR1xES0cHpDYz5f2nYKeD4InvEZyxd2kKrE+cAnNOK28XXdvYwWgHWKWECUBifbVNYR3RLaEonHoTfP528evq6YS1Kwm1tnHfeXNlxJJonPvP/SQbsmH+/ok/FKeVSGYKfGr29LI3+T81e/qwSUZFqYdTyGOyAxgDxDrhsPOl7X3lwdrx2NeegEPPFV0bDJU6CJsgYGTb/bqlre/cRzoFTN0Zsimwefes7sgt+35O2rp69puvExzdSyVZMtgL1srUr/mUdEo4dLHYLYcvkW0rO+i5L6aZnrPlGfS95R+YNY/F3UtZNPdYCoEomYLFcZxivKTNpNl+iiRjTj1we35w2l6EjUxxtvOUMJceuQO5AAym8zUT+I5jGUjnyDkOg+kCu0RiFJwCZFPFqXlywVbCofqdxuLRUM3OPXvObCv7r519FKV5qfecx7Vtb2409jQidLh+l6GGrin2Ng+CySdljnuAq7tE4DwCIQmQpPtLw+KZoARE4p3u8Ptu0CIUlUB/tF163T/rG0p6/q2wapn0AC3kywMx3Utln3AL9P9ZEgNer2ZviEZv6H+v1/47/w/u/0JpWgBvCCbPcNz4rhhrTr40VUCiT3rzh1tkSMfjrpKOC/1/Bhxo37E0ROMRF0EhA8/fKUZtIVf+AM6/RYaYmrG39NKOtFUNE2gX3C/z1+azUr7K4aa86Qm6l2LjnVJ3TqFqioO68y86jvQ6rXfPzLBJ0YaPOgw5FF6mH7OyxhDR8y7CRCdg8HYS4FjLHz/YxL4zQsWE8x/X59l3x2n1hjtreBmGrRuu33EsUMAUchgTkDf6A4Hy4O9+3aUh7hJ98nZTNlE+ROz8WyDSjg1FKQQiBNPrMb4AqT1jKYRimERvaWjYeGfdIY5zwRhfX/EKj6z6oLhqLIak2ya409MUdW4kLr0ox56Gr7ihdbE7t2KVLr5466ehUcYVm+7H9K6WJFq0XZyQ3texXfs07TDnMMRw/WpPTEhsLo2xjvSS9+Q4EMaaACZcObsq0ARyPOwQ0ZjS9RZyklDy2rhQi+unef8jkkBvmSrJ7s3vlQL33+4VX+mor8ozsfk9eYPfsx8q12cTcgwcOV5mQEYBqEyE++2TM+8Sn7Jlirs/4Nkig73SJtu8XEuir5RcPfpKSSZ4flm9qQP808b5bZr9uqWTd2ZAzmVMuS91yUrZv+p47lu51kL/B6Wh37/6B3jwr8ttpq/+UcoficvocWWJ6dvhl98qjej2hXtkeEavrO89C1N3KnXcDMWkY0Fx+OOpOKFWApFYLTFoeBmG+nLsDQ994n7by5v8gZDcl1xKNqicUmL2UdJ5pGWK3E8nBz/9siTfI23wxq9gxZdL9vC/f1fq3bOfO3YV2YvEJen02uOSGFr7HOz1meKw1BJ/CMjxuvYsbyf+4ivF7TKJARJ5S8eUKazfuJH2dnlLNWpTxeNkTCuRkL7lPwwNXznDTmNVK/az4D4Zwn/ulyVuFonLMx1tlxdlPJ3gn/4jM4i1TmlIdHeO8ESog+8+/geZVuLcue6bxau0k0nj0BQVX9cuziYwuZTEbaNuW+a3E6rimv/ktpMXS7vdMlXaLROS+ERyvSTjk76h/Kfs4M5LXLF9KAwv/Cvsc2J5Z4LupdJOhlohvUnaB78Pv/k9edHskEVyvLBMx0MwIrZPy5TSFC/+qWmzSXfqn7S0N8VYbADCMazPXjK+8udbOkjZMG2RgHRM89qKTAJapmB9x7KBMGknSEskRMCdFsBJD7KpEOHS+1dx0n4zWbR/nMCLd8kINJ17YzOD/HGDQ2d7C53RvBw/l8Y6BYmT5dIMpjK0t0+lkB4g0NLG4GCCWBgC0TY2bdpIKBSmvb2tfL9MgmyglUh4i0adaXg51uH6lWFoeBmGoe3iJXe/WDUdj7wEV3M6W6UZyPRDjdgT8y6SEeGqaQo53lZM2Nc9jTEnGmNWG2PWGGO+sbXHcRwLTo54IIdJrZd5CHtXl94AAXF+L3GHb8wmkSHGjDixTkGCGVd3SdAk2SeBpGxSAjpXd0mC//Alsv9pN8sbDMdcIQGm/g/EELxqnXy/eBcMfCj7PXyhGF6xGeLcXNUrx3nzt1LGcEyMwVnz4FsfwV9cKuVa/Kj03H/tCbhmpvTwzA5ImbIJCUQ9+NfyMGUScPw1YjR657RWvn/2demdGonJdR5xsRiQ3hBT/mH8T7xOAgE958g5f/Z16QG6X7e86crhOAAAIABJREFU0R9tg1xGjlVruKmufeDcxyAUxbz5a0xyPabnbMzVXfKd7MM8tIRAz0JMopdCPo/jFLDpfqx1JJjhv2ces+Zhswmc9ID0OG0wRkuOCcfFiP/Z10v1f8giWa40JYV8gU9sFyeAxQABLJ/YLk4hXxjvopUxajKM2+Eqm2cwnZcpOzJ50tkcJjeIcQoYExT9FHR79J37mASPT7xe9M3jl4kee/CvpUPR7++v1lW5JCYzQDBgMIGQ9JC/6ClY/Cgm2IKxeTmOqztNnSGOTbSNcCjAP5++J69/5xheueoY3v77o3nj2hMx2UFS2RzprFzHQDpHwXFIZvKksjlsZgBrHdFLhULZ9edy+aJes+l+nHy+WCcFx2EgnSsds+DgOJbBjJwnnc0PuX4wk3c7TFRWfEGSCT1nl9qyRK8s38r76D9noeAMX4ZxZvR0cayOLq6ZgFCagUgMtvtk+bLtPinLG4hRtScOPqdchg8+R+2JZicYkiR3Yr20o4n18j/YWD3kR02OA2Hxc3rOgQ9fkXlxvTaud3X5/56zZY7cvjXyP5uQxPqC+ySBn0vBKTfBlJ3Ez2rtgEh7aUqzXFpsEO+ZCUZkuP3lX5TjRSrsiM/8ndgnnv93+q1yL5Yvku2XLZTRyV55UI4Xisp5khvFl/NGmnPy0vl609pSWTt2rd0GeSMVBYKS9J19lPh0x/+DdOT27B5vxAePzn3EF/Qf78Az5ZpAkgWtHaXzxzvLr/Wyl2RbWyiNvuS3y1ZcIB29AyFJ8O92lHRisFa+d5oLvWvceoiVplXw/NtcquEiPaMlw5GA4fhPzBRf3ymUhnouuEkgr569OMXiR0qdM5MbJEG07+ekzgtZCZ59/W34wp0yIsLx/wCXvyQj04RjEpPoOdu9z1+T+/zi3dLpAiMxg4eWwMpbpQxzPgfRqfD8HRBsgWOuwDh5zMpbMVd30fLgOcygn8Dzt9PFZqLhAFEnUVxvli0kmlmPwRG7OJvAZpNi/2YGsNkkTi4tvx2xjXFt43zetSnTYiM7TrntbP3/MwOks3mSGbGjB9O17dCi7eqIfS7xhUGJxYwyW2SbjzOjpos9P+rE60X+vrNR9EWkDf7iEsApyd3jl8kzfvw/iE6YfZTI53N3yPrl58gUHr7pJKOPXEBHOMeNp+/B/efPpdUm6YyHuH/xHN645njuXzyHzrYwJjuA47h+ku8ei7wUcNIDpLLl6yR+VGAgnSNfqPTBChXbOeL3+ZYV8oWiD+jf1ztWPl+9fDCdI5kp+Y/eNv5jbIlvVagobzpbvU09OdwS+ZxUMow79UwkJm1pZlB08bRZteOanfvA4RdIO+7ZAcu/KP+x8Pv7pM332vhXHpR9a23vOLDf6bD6F2KHfLsXzviJ2Bn3nwUPL5EXzvz2jJfgP2ihjFI08FHJ9klvlmH9N60VvZ/oE73ec7b8fuY2+Z/sq7CRNsFDSzDP3IbJZzDe1AIPXwiPX0bI5mkP5DDJPrFhPFvGAPlMKa77zG0ECllaoyF5llfegrm6i+CztzE9mOb+8w/ji4d0SoL/wDOlLbpmJmb5Oew7Jcu0QAqzbCHmoSWYZJ/Ehh9aQiDZx5SHv4i5povQcz8ikE8xJZAitPxsAtd0Mf2xc5li+zFrfoUpZAlEYpje1QSeuRWT7OV/Lv89F9z1AusT2YaT5dGMtSnKeDBaMhwOGG5ceBDzdp9BKGCYt/sMblx4EGHtwNfcaC5rREzIJL8xJgj8C3ASMAdYaIyZs1UHcwqY1AZMIYvxGxO5pPRyP/ZbpcDNwxfKMIPL3eR9zzlifO37uVKw4sHzxHjyHJA5p8kxly8SgX30UnCyYjw98yOYtkt14KRjli/4cZ4Ykg8shmu65DizDpPe9dfMlP+5hHQ4yPSLMeUtP/BMOb9XrlxSHKe//E5pTkSc8mCRlwg76qulOaLS/bKNN1xgzbkXI9XHefRSGU3AGx5z+TkSVDz6yvL9Z82DDe+4Bu0i2H5/GaayTpnMivMI5JOYRF/RYGT5OYCRe+YFrmYfBfNvwaQ3FTsHNFKif1TlOOcGXvx19tAS15lQmpGQcTCpDWUOlkltIGQmpgx7Q7VtSGS54O4X+OryVaRyeengs+Y3GCcPWAm2J9eXHMiHLoADF5R60vt155xTyk+ydqXoqxXnYfpehwfOlSlKvA5Ry8+RDkNe8KhtZt3OQ2x4pxikjGQ3iWO7bGFxWSi1nky+wD0r32HJ3S+ybiBD3ikQSPYVtwv0LMQk+3AKBUnw53OE0n3lHZxSfVjH4SdPv8WfNqZZcveL7P2tn7Pk7hdZn8wykM5xwV0vcM/Kd+hP58vWD2TyrE9kuOCuF9j7Wz+v77xmE9X6e8X5snwr7uP6RLZ4zp889VbZ/0Z0oFUXK0Pi5EvJQs/2y/SXjxo0zoyqDPuHHvdk+OELt0ofKA1EPiO+iT9Bmh2Q5Q3CqMpxPl1q1zp2K2/juvap7rC84jxZ7uQlkD9tl9IzHwjV1gGn/Yucy8mXpj5z8pJQ9Z/PG2rXY+ou5YlzAtXtxorzZRsnL2/Nefeu0g9rmVJe1qHaoKu75O3rQESSBVetk7cSH76otH0wIkk1z5fKJqrtq0cvleVXd7mjAA2Wzl9pM7VvV0pcROK1EyKtHVKWvT5TW0b3PK5UD/6yvvOU/LeN0/l1NGU4FIAADia1UZI0ToHiyyvpfqnn/bolTvHaE7D5/fJkaXYADl4kdm90SilZ4xRg3sUQMKVO/rmkjMZQeZ/nnFIagSEQlA4ZBy0sxUIevlBiDa88WHo2/PGHRy6BA86STrNXd2Eq1hvXHjfLFkoH/2xCkjfLFor/nkuJD+D6/Li2cTDVx3++/hEX3P0CxjplMQHTc7b8f/O3RZvcJHtJ5fJsGMzyk6ffYn0iU2aHerbrnU+9RXrTh2KfX90FyxZAsndUE/2VdvKEt4szg9B9p/hbDywuycnm96RzVWXHn0cuEb101TqZVuXFu2VqTP/6o68oHX/tSkwkXrrvvatL97+GXAQplN1j03M2ZvN7BJ65lVBqPbnExuK6QM9CSPTx5rp+NiSyRR/rX59+m4CXYHS3y/d/RDDbX7YskJJ9K/23DYksvQNpNiSzVcufeqOXDcksX12+iqfd33c+/XbZMTyZ+clTb9WUoULBYX2i/Nj9xQ4xpUR+LTn09h1KPiedDAdCEgMuS3pvkBcKasUI+lbLyDy1fOtCTjpIrThfvh23w1QhV3t7Jy8drPY6Xt6m/48bpJ1+wB3J5aivltsfRd39OdHpc06Rzn/HXwude8sLZd4UA1d3SVtx3FUS8/DKVOuYD18En71a9vXae/++mQHpPFm534rzZfk7T/ni4ueUtwfHXytTBSxfhLmmCxOJS7krbA+z4jzCmU3V1+3/7Z2jlm55+CLY5bBSXN7tzBZddQ//89M7F6ddTOYmpk2hKOPBqNrFwQCRYIDrPr8/q685ies+vz+RYIBQcEKmPScPGj8dERNV2g8D1lhr37LWZoEe4LStOZDJJzErzpehwfzGRLxLhqA/7PzSsloGz4rzZa5Gj7UrxZDyHJCjr6gOjngJa8/AqzTK0v3lx4u2V59zzueqDcB6SfbKcrVMKz92vR6o3m/vbQFvCMlKY/bYv61+S8V/nNNuhie/XwqiHb6kKhHPb64pv57djhqyTCbahqkM0K2UobiLgatTbpK3X/7tO0XDsMGC1KMmx3Xr33vTRmk+/AFq/7ORT493yfyMmgwncwU2JnN87acvs/Kt9Vx87J44mQTRRy6A3Y4szd1Yy4Gsq8f2Ll/mOd7eulr61B88OvoKcaxrdB4q01kPX1RyYN1l4YfPJ5sa5LSDdmLlW+v56vKXiJGR66lwWMklSeYKRJy0tEdl68+nxaY4Yb8duHKF1E3esax8az2XL1vFxmSOlW+t57SDduLynlVl6zclc1y2rHxZTee1zmgFREeuP5K5Apct+33xnCfst0NVuRrNgUZ1sTIU+WwdXZwd75L5GT0ZHkV9oDQQ1qluOx+5RJY3DttGjitlejgZr3xbPp+pH6zfkuM/eYP4QvUS51N2qF2eabPkt//epTaV+2GVZR2qDXLyYk898EX43wfD96ZXJ96dnCTVvFEGhqorr6OT//wmUG4z+RMX9TpNZhNSlnoy6s1NXK+TQKSh3vQYNRkO5JOYfFp8545dpX5sQZIpz/5YZOrTV5USOrXiDVjx0/tWl8uuNxWgt33HrvXt6L7VcqzMgCyv7AxQaUdXxh8q4xiV6z173DuH96LBQ0vEtt7tyKrnz6w4nyN3jbHyrfW02GrbWWIJR5a97e1kEiSyBU7YbwcuW7aqzA71bNfT959G7LGKQKP3osQoUWknT3i7OBCEPY6pHaeqp/sicUnCRdtkusvK9V5MCEpzk3v3vWvv0v2vITetZKvvsfsMhR8+n2h2c9m6wIrz2H9mpMyXOX3/aVVxqMgjF5QSkD4fb/+ZkWr/rWcVXe0tVf7R5T2rmLdHJ1/76ctcfOyezNujk8t7VtX2Ad3ltWQomSvUPHbenaLU26aeHA4nn5NOhuvFZArZ8rZ99lEyUs5rTwzddnbuUy7HnfsMvX3HrjId4a++Jx1e/HEP71iV+xXP4erwA86UY9VInhd1srdfvWO2b1d/345d67cjHbvK71px8UcvlbL5l/etlnIPdSx/Gf2/vXPU0y212qM5p7BjVycg83vHIsFqGRg/Rk+OFWV8GDUZTuUKXHzv7zj2ht+yx9/+jGNv+C0X3/s7Uo3V9igjReOnI2KiJvl3At7z/X/fXVaGMWaJMeYFY8wLvb29tY/kGVTZRLkxkU3IsPn+4fDqGTyt00r/y5JIQxhJQ63zhj8qHu/12vv7/9czDL3t/OXygiGz5kkvx3o9UP2/PQfKBKuN2emzpYy1jpPuF4PUm1fNM6684NGC+8vXF+t0avWx/GXKJqqv98nroaUdCm4SNN4Jv/hm2blNtKECQcPK8RbJMAwdOFOak+ZItIyaLo5FguwyPcbz72wAYM+Zbczo6Cjp2Ei85OhW1ks9/ZMZKNdVp90swXa/PqmlT/1O979dJZ2FzrpHdNbCZbV1lud0+pbN6OhgSqvMD/X8OxsIttQb+j8uDmWdex5oaWfPmW3FuvF4/p0N7DJdhgyf0hquWu+vT/8+Vc5r5fC8xfobZKTEIsGyc9Yrd4M50KqLlfpMMl2sMjxBaY4E6ejpYn+7VtnGDSfjI+kUEAhVH7/y/6srZOjcBffXTpwPVx7/vXv5gfI37Ud6rNZpQ28fiYs/dcs8SbyPtK5CLWIzFUcK8K03wdqdJk2w+jr99ezJaHPoptGNUXj117da6iESF3vzyevFFp0+uzyh48eru+mzxfb1L6/sHFs52gSU7OgnbyjZuf64iP949ezo4eIYlfZ4x67lLxp07Fots8VraCvV03DxGdcm32V6rGiX+u1Qz3bdsauzTj2O3vQ8lXYyTHC7ONxavzN2vWd609qSH1Jr/cZ3y307T0f4ZXsouRkqKVrDnwtE42X3rK6cbMG+IPc7Hg3VXO75c3vObCv7XWvbPWe2lf33ZGioY3vb1JPDevvWel6G2qYBGP14sR9Pln71vVJc85Sb5IWjg88Z2rf29K2n+/pWD739xneh94+l2IM/7lFPdxfjt64Ob50m+9VLnnfuU9qv3jFrxV+9fTe+K596zysMHUuv7BiZGRj6WP4y+n9759hSe8HtCPFBbx8An5o9nWS2oRKGo6eLFWV8GDVdXK99ikcba+o5ZYQ0h3/XMEzUJP8WYa39sbV2rrV2bldXV+2NigaVKTcm0psk8OA3VuoZPOnNtZNIG9+tn3wayoDyOy7dS2Vo/lr7l5Whv855Xq8u16a1pTnO3n6qOmk//xZ46oel/V57QrYNhKTn/0sPyHB9V62TOdUyg1LGyuN0L4Xnbi9Phnll+tnXZZg2kM4UleWuTM75yzT/ltJ2Vdf7hhy7/0/ydkvFuW2muRTFFskwVL85UwycTWoV0NyMYuJ1vNkSOU5mC7y3IcmnZk8HYM26QdZv3Oh2RtokOsFzdCvr5bXHRd/45f/0H8FbT8JZ7jyxp9wEv74GBteJPvMCnrX0qaeDN74r+ukX35ThaJN9Mk9vLZ3lOZ2+Zes3bqQ/JW/6fWr2dArp2vfUZhLiUNa55056gDXrBot14/Gp2dN5b4O8XdSfylWt99enf58q5zUSL08aeO3DViR/ktlC2TnrlbvBHOhhUV08iZlkulhleIIyQeR4i3VxMFxq1/y/AyEZrryWjDuFLUvaQ6nurlpXffxAqLpNPXSxvNH6veliT/iP5/mcleXJJOR/oq+0/S+uhP96RDoefru3umzDPb+VIwFUnnukx6v0P7MJeOGuUodr//HyaQi1Voy41loaoapeUD8zIOeeQLppi2MUXv09eYP8Tm0s2cGvrpDEjz+h42fWPOj/M6QHqn3xyrp+8obquu2+A17qkX09Ozc9zMsBxf+vl45RM45REZ/wlm98t7xT/8Z3q2W2eA2DpXqqtT61qez/+o0beW9DsmiX+u1Qz3b9oLev9rGyo/gmf4WdDBPcLs4M1pebmrrvVok1zb9FdGbl+tNvg1BYdMjJ/yTbZgdLx/Rkeyi5GSopWsOfczKJsntWV062YF+Q+53I5Gsu9/y5NesGy37X2nbNusGy/54MDXVsb5t6clhv31rPy1DbNAsjixf78GRpcB3cdhTcfZpMKQMit6GW2r51KFqKrb72hKx77YlqO8LbPhCSjiv+jlr+uMdTP6x+Rorx26XSiW5wnbzY9drjQyfPvTLVOub8W2RQnXr7RtvlU7nf538s02/MPqp+XLzypbNXV8BbT2IrYju2eym56LTq6/b/9s4xlF1VcX6bGeAHv3mfebvP4KaFBxMLN1RnlWHZYl2sKA3MlshxvfYpkWmcKRSVrWAC+XdjgbG2ceZGGi2MMfOA71prT3D/fxPAWntdvX3mzp1rX3jhharlTj6PSfVh1j4He3xa5n1Ycb7MX3fCdW7Ax8hQbe3by7x3j1wivf5mzSsZay1TZV753/6DJIC6l4K10NYpwZkV55f2mX+L9PqcsbcEffzrPv9jGYqvfUe3p+fr0NYlQwn5z/ni3dKLf9Y8SWa9/yLMOqz8WN1L5W32je+WlyveKUEYpwBOVoIxc06Rnp3ZQXmwwq1iBEbaZN5J4/WOsnI9HbvKcVs75O35RJ+UyTtOZlCOkdoowwzWK9O+p9Yo9x3SozPdD7EZpREVwq1FI9KGWiCXlKHS/PvFO2HjWgqtHRgg8MAXi+tt91JsvItAoKayMB9TLEfMSOW4ngyDK8fZAZm30b03trUDG2knENKebc1IUTf5ng3bfQe2tbPePW14GYYhdLFjGUjnGMjk+dpPX2a7KVG+/blP0JbfSPRPz8Oen4ZgVHRXdqBcD5+xFP78MkzfQ95YSm2C5+6AQxZJADkQktqJxEWvvfkfsOLLtfXp/FshEIb2mZDcWJqndO1KOPpKmHcJNpfAPOjTPaffBuG4DIHrLsudfgfJcAePrPqAX7z6ET8860DikQDRzAYZst+vl2KdYALk8jkimQ1V9zwZns7Sp99h/sE7c+WKl3n+nQ18avZ0blx4ENFggIvu/R0n7rcdJ7lD43vrb110CLmCw2XLSstuWngwM+IRAoEKcXEKUrfRNtHfkbgEKUaIN0/jZct+z/PvbOCy4/ZkwWGzyspVtwyuWI34pB8T1cXKUEw6XawyPCEROV5fZjfb7qXY1hlNK8fD6uJ8CmPzEnjOpsDmxZ/IJrCFfLmMxzowwYj4GoU8NuVri//2z+X/XR1gwjH4p93gijewgbCcq2Uq5NJYp1D6n96MNSHMm78R2+PLv8BOm1W6F0dfiT3sgqpnzoTc8uRS2Mxghc+zFOIzwHHKy/Y3r2JDLdXHyqfhf+0H3XdiZx1W2r7y3Nkk1u9fXfIsNjaj+njJ9XDL4dXH674TO+vw0v7+9e3bw0nXS0Dff6znbhcbrHJfV0ZNyxTxtQs5bDY5Et3U8DIMw+hiHLneFedJxw6QRItnB3uxiVU9pXmW/TGFUCsEQ7BsYbm/HJ0i9rBfps66RxJC0TbpGPDsj3y28S1yrHCL2IqVfrvfjvZkc/P7EJ0qsYYHK/31rtK0A5Xn+OU3xff3yv/2U1WxAtt9B09/ALf8xzv85NxDaMmWP5903wFrnyva+pn5tzMY6sBxYNlza1l4+CxmxKNFO9SzXXuefZfzDmmT4dz9PkasS+bDHgUq7eRJYRfnU5hchdycdrMk6OeeCwV32hLP/xj4EMIxufe7HSX+SLRNOgtYyvwtuu+AD1+B/7xRfm96D6btIve/htykwtMguaH8HrtlyR28GCcQJrri3OI6p3spL28MseO0eNGXufy4Pbn08Klluio7/3ZMKEr4wcVl+uuljSGmx1vL/bcFBzGYydEWDZf5RzcuOIgX393A/jtP44ZfruaET27HobtOp+e5tdU+4IKD6HluLTf9ek2VDBUKDusT2apjxyJBYpEQgYCpK4fTY2E2JHNDyudElGHYgnhxVbxyOhQyIrPZBBgjOtTa0stRhWzJtw5GRB+nByR+mk2W1oVaZGogn92ACUnHgVwSln+xvP1vnSbbR9tgYJ10rJu6i5TDK08kVrJ9om68tsZzwRlLIRSDSGupTLm0TLcTbauI+a4vbzfOWAqOlZEPZ+wN82SaGOuVoZCXNn7OKdC5Fzaxvip+a+OdkKhhn8ZmQC6FicaxmQQ2FCPnWKI2JdeYS0v8IhrHFOtgZynji3eJbskkyuwFDJjl5fHhXMsMQqEgyWyBWDhYT4ahCeR4KF3sZ/Y3/s8Wl+Gdf/yrLd5WaXgaXoahvhzn8w4bktVt2/RYhFBIE8JNi5OXdtHn3xXbnEBj+HeNxERN8oeA14HPAH8CngfOttb+V719hnc+kmII5TNikEXikEvJ73CslPzIpUUIi8ZaWAIP3raeQePNQxhtk3VOQX5n3SHvw62uIxMrN/ACvnXBsJzLf/7KY3vOUC7pvp2SdxNZ7vJ8pry83vlCEZlP1iuvd2wTAIys98rlLQ9F3fopuMl/3/XnM+VlCobl46/PjNuBoPLYwbCUxV/GfLZUbu/84dbi70JAHHPjlt14xw63YLMJMqaVSMhANlk0DInE6yX4YXwavBHJ8XBGW1GO3Xq0oZgG5JucEd7ThpdhGEYXO5Z0voDjQCwaJJ0tEDCWqE0XA++YgMwZW6Zvw65eGYRAxP3t6o1AWN4SKzrREdFVfl1dqTezidJ2lfrXBLChKGTF6ZRlwVK53P3TpgWDIRIOksjkiUWCZHIOxlhabBoirl4KxwgEg8XrLxQKhArl9zztgONAayRAMlsgHg3JMcNBjDEkcwVikSDZXIGcY+uu3wLndVRw3DkfvXO2hgKk8s6WlqHh5Vh18eRj0ulileEJyUST4xHp4kKuFBB3r72yLprmv98vGu+yjPS/3ycepWM3swzDFuhinJIdW8iLzQquPx4vxSZq+fTexyf7BGUaKYLh8hiE59NX2saenSsnlWSU/zzBSPnxQy2lxFdlrKNYrqB7/hoxCK8jgQliTQDj5NwYQBITbcNmBimExHZOZgoEAtASAuO/lopzZkwrjoVoOEAqK/ZopR1atF3DAcgmMNG4HDMcG7UEf9W5JpNdbHPV8auqhGSyxv86336/Lhgpv+91trWRGImsJR4JFu+xVxabTZIJtBANldZJ/ChGIuvQGg6Syvl9sAAmVx5nyuYdIk55UjLp83+8fb1jtYaCpPLly9O5AgFjaImI/+ht4z/GlvhWhYJD0lfecMAQCZVvU08Ot0Q+J5oMw8jsYkItEret1GHhVtGHns4si0G0VHQKcLfPpV0dEyjfvhi3jUpngVrx1MrnoeoZ8LULXrzWv8wXQy0+l4WsxFn8MdzK57KWrvVtZyNxElmHeCRQjMeKPm3F5krlsiZIzkQIB0cUty2/N45T9syaYFhiMhUxnLQpf763Qr83vBxrkl8ZhoaXYRhajvN5p7zdDAU1wT8RcPLVbUvtBD9okn/iJfkBjDEnA/8LCAI/sdZeO9T2W9rgKZOWcVEUI5FjlWFlGBpehkHlWBmWhpdjlWFlGBpehkHlWBmWhpdjlWFlGBpehkHlWBmWhpdjlWFlGBpehkHlWBmWhpdjTfIrw9DwMgyqi5VhmdRJ/gn72o219mfAz8a7HIrycVA5VpodlWFlIqByrDQ7KsPKREDlWGl2VIaViYDKsdLsqAwrEwGVY6XZURlWlNFDx61QFEVRFEVRFEVRFEVRFEVRFEVRFEVRlCZBk/yKoiiKoiiKoiiKoiiKoiiKoiiKoiiK0iRokl9RFEVRFEVRFEVRFEVRFEVRFEVRFEVRmgRN8iuKoiiKoiiKoiiKoiiKoiiKoiiKoihKk2CsteNdhobAGNMLvDuKh+wE+kbxeKOFlmtkeOXqs9aeON6FGYoRyHCj1vVoM1muE7bsWhtehmFUdfFEvP96TU0gx6qLq5gs1wmTTxfrvZ2YTAg5HkKGG/leatm2jq0pW8PLMEwaXazl33oaXo63QIYb9f5ruUbG1par4WUYJo0uHm0mU300vBxPwBhFs5QTmqOsDS/DoLq4Bnqt5TSFHG8rNMm/jTDGvGCtnTve5ahEyzUyGrVcH4eJeE21mCzXCZPrWreUiVgnek0Ti8ly7ZPlOmFyXStMruvVa504NPL1adm2jkYu21jQ7Nev5Z/cNGr9ablGRqOWayzROihH66M5aZb71izlhOYq60RgMtW3XqviR4frVxRFURRFURRFURRFURRFURRFURRFUZQmQZP8iqIoiqIoiqIoiqIoiqIoiqIoiqIoitIkaJJ/2/Hj8S5AHbRcI6NRy/VxmIjXVIvJcp0wua51S5mIdaLXNLGYLNc+Wa4TJte1wuRERi3FAAAgAElEQVS6Xr3WiUMjX5+Wbeto5LKNBc1+/Vr+yU2j1p+Wa2Q0arnGEq2DcrQ+mpNmuW/NUk5orrJOBCZTfeu1KkWMtXa8y6AoiqIoiqIoiqIoiqIoiqIoiqIoiqIoyhagb/IriqIoiqIoiqIoiqIoiqIoiqIoiqIoSpOgSf6PiTFmF2PMb4wxrxlj/ssYc7m7/LvGmD8ZY1a5n5PHoWzvGGNecc//grtsujHm34wxb7jfHWNcpn18dbLKGNNvjPmb8aovY8xPjDHrjDGv+pbVrCMj3GSMWWOMedkYc8hYlHFrMMacaIxZ7Zb1GzXWR40xy931zxpjZo99KUeHLbjWLxljen2ydf54lPPjUktWK9Y3jXxuS4aTh0ZgJLp5qPtqjDnX3f4NY8y5vuWHusdf4+5rttF1jIr+HOl1jHc7NhJUF5etV13cpKgcl61XOW5SGs0+GEkbOsblqufbjnvZ3HK0GGOeM8a85Jbv793lu7n6Z42rjyLjUb6xptHkGkYuQ1tjI47RdQSNMb83xjzh/q8pY0O1gcaYb7rLVxtjThjL8jc6jSK7TaDztkgOx7hM04wxDxpj/miM+YMxZl6j1Nd40SjyPJaYUYppKI1Bo8mwaZJY/WjaPMrIGE5mh7LPmo0tuFaNUShgrdXPx/gAOwCHuL/bgdeBOcB3gSvGuWzvAJ0Vy/4J+Ib7+xvA9eNYviDwIbDreNUXcDRwCPDqcHUEnAz8HDDAEcCz43l/h6nXN4HdgQjwEjCnYptLgNvc3wuA5eNd7m14rV8Cbh7vso7CtVbJasX6ppDP8ZaHRviMRDfXu6/AdOAt97vD/d3hrnvO3da4+560ja7jY+vPrbmORmrHPq48qi5uvs9k08UqxyrHk0WOG+EeNEL7Rn3fdtzL5p7bAG3u7zDwrCujDwAL3OW3ARePt9yNQV00nFxvjQzV0zcMYSOO0XV8FbgfeML9X1PG6rWB7jW/BESB3dx7FRzv+9MIn0aS3SbQeVskh2NcpruA893fEWBao9TXON2jhpHnMb7ud/iYMQ39NManEWWYJonVj7QN0Wdh1OpdYxTl23wJjVFM+o++yf8xsdb+2Vr7O/f3APAHYKfxLdWQnIYY5bjf88exLJ8B3rTWvjteBbDWPglsqFhcr45OA+62wjPANGPMDmNT0hFxGLDGWvuWtTYL9CBl9+O/xgeBzxizbd723cZsybVOCOrIqp9mkc9tSTPLw0j1zgnAv1lrN1hrNwL/BpzorptirX3GihV0N9tIz4+S/tya62ikdmwoVBdPQCahLlY5noCoHI//vR1hGzpmDOHbjnvZ3DJZa+2g+zfsfixwHKJ/oLFtg9Gk4eQatkqGRmQjjsU1GGN2Bv4KuMP9b6gvY/XawNOAHmttxlr7NrAGuWdKA8luI+u8EcrhWJVpKhKEXwpgrc1aazfRAPU1jjSMPDcAzR5Lnaw0nAw3S6x+FG0eZWRojGICMgljFKOKJvlHEXfoj4ORNwoALnWHj/jJOA1XZYH/a4x50RizxF22nbX2z+7vD4HtxqFcHguAZb7/411fHvXqaCfgPd9279OYHTq2pJzFbay1eWAzMGNMSje6bOk96XZl60FjzC5jU7Qxp1nkc1vSLHUwEt1c75qGWv5+jeVjxVhcRyO1Y0Ohurga1cXNh8pxNSrHzUezXE9DtW8Vvm3DlM3I8NWrgHVI4vdNYJOrf6Bx7+9o0/ByvYUyNFIbcSz4X8DXAcf9P4P6MlavDWz4+zOONGTdNKDOG4kcjhW7Ab3AnUamEbjDGBOnMeprvGhIeR4DRiOmoTQGzXJ/Glq+PqbNo4wMjVFUozGKSY4m+UcJY0wbsAL4G2ttP3ArsAdwEPBn4AfjUKwjrbWHACcBXzHGHO1f6b4dacehXBiZO+xU4KfuokaoryrGs46UUeNxYLa19gAkEHjXMNsryramYXXzaDIW1zFR6mqSoLpYmQioHCtjwni3bzV82yLjXTZrbcFaexCwM/J2y77jVRalPo0sQ0NhjPkcsM5a++J4l0UZOxpNXhtYDkPIULq3WmsPBhLIUNRFGvn5VkaVSRHTUBqTRpOvRmtDlEmHxigUTfKPBsaYMKLM77PWPgRgrf3IDUA4wO2Mw9Bs1to/ud/rgIfdMnzkDWXhfq8b63K5nAT8zlr7kVvGca8vH/Xq6E+AvzfUzu6yRmNLylncxhgTAqYC68ekdKPLsNdqrV1vrc24f+8ADh2jso01zSKf25KmqIMR6uZ61zTU8p1rLB8rxuI6GqUdGw7VxT5UFzctKsc+VI6blma5noZo32r5to1SNj/uENG/AeYhwzWG3FWNen9Hm4aV6xHK0EhtxG3NfwNONca8gwyJehxwI/VlrF4b2LD3pwFoqLppUJ03UjkcK94H3rfWeqOXPogk/ce7vsaThpLnsWKUYhpKY9As96ch5WuUbB5lZGiMwofGKBTQJP/Hxp3PYynwB2vtD33L/XNCnA68Osblihtj2r3fwPFuGR4DznU3Oxd4dCzL5WMhvqH6x7u+KqhXR48Bi41wBLDZN/xOI/E8sJcxZjd3xIQFSNn9+K/xDODXbu/CZmPYa62QrVOROZImIs0in9uSLZH9cWUrdHO9+/pL4HhjTIc7vcnxwC/ddf3GmCPc9mkxY6vnx+I6GqUdGw7VxT5UFzctKsc+VI6bloa3D1zGvX2r59s2QtkAjDFdxphp7u9W4LPIc/gbRP+Ma/nGmIaU662QoRHZiNu6/Nbab1prd7bWzkbq9NfW2nOoL2P12sDHgAXGmKgxZjdgL+C5bV3+JqFhZLdRdd5WyOFYletD4D1jzD7uos8Ar9EgbcQ40TDyPFaMYkxDaQyaRYYbTr5G0eZRRobGKHxojEIBwFqrn4/xAY5Ehl15GVjlfk4G7gFecZc/BuwwxuXaHXjJ/fwX8C13+QzgV8AbwL8D08ehzuJI76mpvmXjUl9IR4M/AzmkV/J59eoIMMC/IPM+vgLMHW/5G+K6TgZed8vq3fvvAae6v1uQqRLWIMGG3ce7zNvwWq9zn4GXEKd43/Eu81ZeZy1ZvQi4yF3fNPI51vLQSJ+R6uah7ivw1+4zvAb4sm/5XMTJfhO4GTBjKJPb/DrqnaMRP6qLVRdPBF2scqxyPFHluAHvwbi3b9T3bce9bG75DgB+75bvVeDv3OW7u/pnjauPouN9j8eoPhpKrrdGhobSN9SxEcfwWo4FnhhKxoZqA4Fvude1GjhpvO9NI30aRXYbXee5ZRxWDse4PAcBL7h19gjQ0Uj1NU73qCHkeQyvd9RiGvppjE+jyTBNEqsfTZtHPyOue41RaIxCnx/fxwuYK4qiKIqiKIqiKIqiKIqiKIqiKIqiKIrS4Ohw/YqiKIqiKIqiKIqiKIqiKIqiKIqiKIrSJGiSX1EURVEURVEURVEURVEURVEURVEURVGaBE3yK4qiKIqiKIqiKIqiKIqiKIqiKIqiKEqToEl+RVEURVEURVEURVEURVEURVEURVEURWkSNMmvKIqiKIqiKIqiKIqiKIqiKIqiKIqiKE2CJvkVRVEURVEURVEURVEURVEURVEURVEUpUnQJL+iKIqiKIqiKIqiKIqiKIqiKIqiKIqiNAma5FcURVEURVEURVEURVEURVEURVEURVGUJkGT/IqiKIqiKIqiKIqiKIqiKIqiKIqiKIrSJGiSX1EURVEURVEURVEURVEURVEURVEURVGaBE3yK4qiKIqiKIqiKIqiKIqiKIqiKIqiKEqToEl+RVEURVEURVEURVEURVEURVEURVEURWkSNMmvKIqiKIqiKIqiKIqiKIqiKIqiKIqiKE2CJvkVRVEURVEURVEURVEURVEURVEURVEUpUnQJL+iKIqiKIqiKIqiKIqiKIqiKIqiKIqiNAma5FcURVEURVEURVEURVEURVEURVEURVGUJkGT/C4nnniiBfSjn3qfhkdlWD/DfJoClWP9DPNpeFSG9TPMpylQOdbPMJ+GR2VYP8N8mgKVY/0M82l4VIb1M8ynKVA51s8wn4ZHZVg/w3yaApVj/QzzmdRokt+lr69vvIugKB8LlWFlIqByrDQ7KsPKREDlWGl2VIaViYDKsdLsqAwrEwGVY6XZURlWJgIqx4pSH03yK4qiKIqiKIqiKIqiKIqiKIqiKIqiKEqToEl+RVEURVEURVEURVEURVEURVEURVEURWkSNMmvKIqiKIqiKIqiKIqiKIqiKIqiKIqiKE2CJvkVRVEURVEURVEURVEURVEURVEURVEUpUlouiS/MeYnxph1xphX66w3xpibjDFrjDEvG2MO+dgndfKQ7pfvbAIyA2AdyKfd5QX5tg5kk6Xf6X7Zxtvev20+69snUb5PNuHbPl9xvEz58bLJ8vOn+yGXqjhnXv7n0hXnSVaXK5eWZZkBKUflNdcrXz4tv/OZ8u0zA6UyVh7HO6dXjox77MrryWfK/3tlqldnuZRsU3kc73rK6sq73kFwnI8tKlvKuMqxvx6V5mYc7+m4yHAtHO/5zYtezWdr6xq/bvb0kl9XesuyCZ9OGqitTyvrPJeqXlZ1rnT5uerpG+966umlSr3mFLZJtVaXa5zOu41RXayMCpNNF6sMT0wmsxz7/bJ6bX2z/M+nG6csIy77NrgPY8T4yHBB6izTX/rt2cHZpGtvFqrjFZ4PXNM3TtWxpSttW9dn99vPlfZ2rXvit7Er/X5vWb3fZfv6bOvh7vtQcjLSGMBwdnqTM266uMqXKoyODshnqtdVflfKjf8ee2Xx7vWW3v9a201w2WkUxt2eSPeLDs2lasckvPhCZXuXz/pixTXa8VrbF/VuxXn88dN6Zcgmyo/plddrO4Z6Fiv1fC5VO67t37fy2F49+Jf59/e3AWX7DZbamsrYiv8Zqzxu5e9aceGq9ig5bs/puMlxWSzfkxdXJnPpUqy9yg5IDB0fcpwaOQLVgROZhokXK8okoumS/MC/AicOsf4kYC/3swS49WOdzclDog8G10F6AJLrYdlCWPMrSG2CZ26Dze9Bz9nw0BJI9snvq7vkO7UJ1vym9H/ze7JPaoN8P7REjunfJ7lelj9zm5y77Hgby4+X3lR9zvRmWHlr6X9iPQx8JNuWnadPzuMvV3oTPHe7XGN6M2RTpWuuLF/P2VK+N38r15nPgrXl2y9bKA1+ckP1cfo/kHN65Vjmlr2qDjfKdsXr6YNCvnadedefz1bXXSFfvax4vQsg2TuWhsa/Mh5y7L/2RJ8G5puZ8b+n/8pYynAtHEee2zW/lucbXH1ToWtSm0TnenW1bKE4K7l0ua5K9onDkU+5OmlhDX3aV9JHz9wmOjK9ufo+eNssWyjnzqUqzlVD33jXs2yBu12FXnIKkOitOFfvtk+4j9d5x4b/z96dx1tS1ffe//7O1BMIjXS8PEDbgKhBxAZbrySakOgNOFxQEem+ea6aoH01MWKIRPKYxxjzJI9DouJ1CkFEvAkgOHEdQoxDpE2jNNLMAk3TCgTthh7oPtOe1v2jqs6uXbtqD6fP2VWr9uf9ep3X2VW7atWqqu9ee9Wqffa5UrTFOBj5n9MrRYZxsPI/r1cqrxxXJlv7CAd2ph+LAzuD6bGlrc8np6PlR0bTn08e6+T13q77Wqcf/H56+Q9+v72+0fXpXN2WtK47Mtq5rsltJ6f7La8y2fr86HjrtNSc3vvz1vMQlVWZ7O28xMsaigzXg5/pPdLmz4R9Mte8kTL1eNB/ja7xo/GK+DVwvRaMb8Svs2f2hTc/9jX7rcmMXr0h6GtH24j6z7NPpp/DePlz1/0bgrpPpZQbPZ98XJmM9c/3Bn3rbd/rfN6z2rbo9dPPGEC3fno5XKlBt8Vp11L7Hu7e9qXlKzk9vUf6xZ3N56I2LavsRq31HEd12fypYP7svu7nPysnvayLhXCl8u4XT++WbDR9/HPzpyVZe1s5vTtox2/+THOcOWp/G4305W00GGetTCXGPva0jjXHn9v3cFCH6T1Bm3rzZ4Lx0OpM870juXz0HjBXZsr487bvtI9rz40x7w9uEsfLvnpD+MGvyeBxclw8avd//A+t623+VLBesqz4ayyqY7zc+OO0942ZvcEHBFq2/3hQZj6v0ys16BzH73f81apgfP7AL4M+xvS+4AZ9PWyzd/40WHZ6b9gPeKL5wY62sqM2cUNRji0G40rlPV4MDBnvbvI7534gaXeHRc6RdJUL3CzpcDM7at4brExJX3qzdMivBB2hr/6BtOMm6dgXBvNPepX0tbcH815yUfP5Ri34/aU3S8e9uDn9tbcH60Trpq3z1T8I5kfLdSrvyxul2cn2ZU56VWz6gqD+ybKi7STr9dzzmmW7Wnb94vX50pulRlWqV9qXn90f/LSV87Zgm/HyetqfN0tqdK5Toxrsd3wZNdKPZ7S/118gVafmHZV+5Jbj5L5XBrO/WAQ5n9OBZzhNdSp43R734qDtqc20ttPRcZnZJ33lre3HanpPezsyuz+4iIm3J8n2J5o+6VVBG5l2HuLrfOWt7dtKa2+i/clarjKZcc4nF/SwtslruwNAW4yDNmxtMRkup2HOsXOt+552zRRdCzZqQV8j/nxyeu5apJ7+/MhY63Tyem/VM1uno+usrOvBeH2j69O5us22Tjfqneua3HZyut/yJla0Pl+vtk5LzellR6SXNbGit/MSL2soMjwZZuuCIEO1meD8TO9pXndH4xTx8Yrk9fT0nvZ+bqMaXJNnZXSuzzzZvJaf3R/UK62/HS8/fs3+lbe2X/fHn08+nt3fOmYwvSf79RGd96y2LT6e0usYQLd+egnk0hanXUt97e3d275u12nR9Mrjmo+jNq1TbpLnOHoNXX+BNHUQ13O9rIuDVph+cW0me/yzXsleJz5WHLW/Wf2MeqU51prW7qaNNUd5jsYnTnpVMB4af+9IW75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      "text/plain": "<matplotlib.figure.Figure at 0x10c0c84e0>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Pairplot allows us to intuitively \"see\" the relations in our data, by plotting the variables against one another.\nHere each column and each row correspond to a variable.\nEach cell in the grid shows the relation between the variable in that row, with the variable in that column.\n\nYou'll notice there are two types of plots.\nThe plots along the diagonal, where the row and column variables are the same, show a histogram of the variable.\nThis helps you get a sense of the variable's distribution.\nEvery other plot shows the relationship between the row and column variables, with the row variable being on the vertical axis, and the column variable on the horizontal axis.\nThis lets you see how one variable changes as the other changes.\nFor example, look at cell (7,3) to see the relation between a customer's balance (on the horizontal) and the duration (on the vertical).\nThere is a clear relationship between the two.\nAs the customer's balance goes up, the duration goes down; but at low balances, the duration goes really.\nMore specifically, it looks like the relationship is approximately:\n$$ \\text{duration} = \\text{balance}^{-2} = \\frac{1}{\\text{balance}^{2}}$$\nCompare this to cell (1,6), the relation between day, and age.\nThe values spread out uniformly suggest that there is no correlation between the two.\n\nThe human visual cortex is the most complex pattern recognition software in the universe (_for now..._).\nActually looking at your data is a great way to begin to understand the relationships that exist within your data."
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Step 4: Age differences between subscribers"
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "subscribed = df[df['y'] == 1]  # get subset of observations where the customer subscribed\nnot_subscribed = df[df['y'] == 0]  # get seubset of observations where the customer did not subscribe",
   "execution_count": 16,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "subscribed.head();  # glance at subscribers",
   "execution_count": 17,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "not_subscribed.head();  # glance at the non_subscribed",
   "execution_count": 18,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "median_age_subscribed = sp.median(subscribed['age'])  # get median age of subscribers\nmean_age_subscribed = np.mean(subscribed['age'])  # get mean age of subscribers\n\nprint(f\"Median age of those who subscribed: {median_age_subscribed:.2f}\")\nprint(f\"Mean age of those who subscribed: {mean_age_subscribed:.2f}\")",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Median age of those who subscribed: 38.00\nMean age of those who subscribed: 41.67\n"
    }
   ]
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "median_age_not_subscribed = sp.median(not_subscribed['age'])  # get median age of non-subscribers\nmean_age_not_subscribed = np.mean(not_subscribed['age'])  # get mean age of non-subscribers\n\nprint(f\"Median age of those who did not subscribe: {median_age_not_subscribed:.2f}\")\nprint(f\"Mean age of those who did not subscribe: {mean_age_not_subscribed:.2f}\")",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Median age of those who did not subscribe: 39.00\nMean age of those who did not subscribe: 40.84\n"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Step 4b: Analysis\n\nLooking at the data, there is a difference in the median and mean age for both the customers who subscribed, and did not subscribe.\nFor those that subscribed the mean age was approximatley 41.67 years old, while the median age was 38 years.\nFor thos that did not subscribe the mean age was approximatley 40.84 years old, while the median age was 39 years.\nTo find these values, I first subsetted the dataframe, using the values of the response variable `y`.\nThen, using these subsets, I calulated the mean using the function `mean()` from the `numpy` package, and the median using the `median()` function from the `scipy` package (there is no median function in `numpy`).\nJust seeing that there is a difference isn't enough though.\n\nOne measure that will help to test if there is a difference between the mean and median in the distribution of a variable is the __skew__.\nThe skew tells us if there is greater mass in one tail of the distribution than the other.\nIf the skew value is greater than 0, it means that there is more mass in the right tail, and thus that the mean is greater than the median.\nIf the skew is less than 0, the mean is less than the median."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "skew_subscribed = sp.stats.skew(subscribed['age'])  # get the skew score for the age of those subscribed\nprint(f\"Skew for subscribed age: {skew_subscribed:.4f}\")  # ",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Skew for subscribed age: 0.8668\n"
    }
   ]
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "skew_not_subscribed = sp.stats.skew(not_subscribed['age'])  # get the skew score for the age of those subscribed\nprint(f\"Skew for not subscribed age: {skew_not_subscribed:.4f}\")",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Skew for not subscribed age: 0.5920\n"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "The greater the absolute value of the skew, the greater the difference between the mean and median.\nShowing that the distribution of the variable isn't incredibly important though.\nAs data scientists, the question we need to ask is the difference is _significant_.\nThis question doesn't make sense for the median and mean though, as the mean and median are two parameters that describe the same distribution.\nWhat we should be interested in is the difference in means _between_ the two groups.\nTo do this, we should use a $t$-test.\n\nFirst we set our hypotheses:\n$$H_{0}: \\mu_{\\textit{subscribed}} = \\mu_{\\textit{not subscribed}}$$\n$$H_{1}: \\mu_{\\textit{subscribed}} \\neq \\mu_{\\textit{not subscribed}}$$\n\nThen we conduct our $t$-test using the `scipy` function, `stats.ttest_ind()`, passing in the two arrays of the ages."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "t = sp.stats.ttest_ind(not_subscribed['age'], subscribed['age'])  # conduct t-test on the ages of the two groups\nprint(f\"t: {t[0]:.4f}, p: {t[1]:.4f}\")",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "t: -5.3503, p: 0.0000\n"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "With a $t$ statistic of 5.35, we can reject the null-hypothesis $H_{0}$, that the mean age of those who subscribed, and those who did not subscribe were the same.\nThere is a statistically significant difference in the ages of those who subscribed, and those who did not. "
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Bootstrap Function"
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "import numpy as np\nimport pandas as pd\n\n\ndef bootstap(df, n=100, to_df=True):  # TODO (@messiest) add balanced=True argument\n    \"\"\"\n    generate n bootstrap samples from a DataFrame\n    \n    param:df: pandas DataFrame\n    param:n=100: integer number of samples\n    \n    return:result: DataFrame of bootstrapped samples\n    \"\"\"\n    assert isinstance(df, type(pd.DataFrame())),\\\n        f\"Expected pandas.DataFrame, got type: {type(df)}\"\n    \n    sample = {column: np.random.choice(df[column], size=n) for column in df.columns}  # column: bootstrap sample\n    \n    if to_df: sample = pd.DataFrame.from_dict(sample)   # convert to DataFrame\n    \n    return sample\n\n\nboot = bootstap(df, to_df=True)",
   "execution_count": 24,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "boot.head()",
   "execution_count": 25,
   "outputs": [
    {
     "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>age</th>\n      <th>balance</th>\n      <th>campaign</th>\n      <th>contact</th>\n      <th>day</th>\n      <th>default</th>\n      <th>duration</th>\n      <th>education</th>\n      <th>housing</th>\n      <th>job</th>\n      <th>loan</th>\n      <th>marital</th>\n      <th>month</th>\n      <th>pdays</th>\n      <th>poutcome</th>\n      <th>previous</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>33</td>\n      <td>616</td>\n      <td>2</td>\n      <td>unknown</td>\n      <td>12</td>\n      <td>0</td>\n      <td>150</td>\n      <td>secondary</td>\n      <td>0</td>\n      <td>management</td>\n      <td>1</td>\n      <td>divorced</td>\n      <td>jun</td>\n      <td>-1</td>\n      <td>unknown</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>29</td>\n      <td>1832</td>\n      <td>4</td>\n      <td>cellular</td>\n      <td>7</td>\n      <td>0</td>\n      <td>613</td>\n      <td>secondary</td>\n      <td>0</td>\n      <td>management</td>\n      <td>0</td>\n      <td>married</td>\n      <td>may</td>\n      <td>-1</td>\n      <td>failure</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>57</td>\n      <td>1093</td>\n      <td>1</td>\n      <td>cellular</td>\n      <td>29</td>\n      <td>0</td>\n      <td>59</td>\n      <td>secondary</td>\n      <td>1</td>\n      <td>technician</td>\n      <td>0</td>\n      <td>single</td>\n      <td>jun</td>\n      <td>93</td>\n      <td>unknown</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>50</td>\n      <td>0</td>\n      <td>1</td>\n      <td>unknown</td>\n      <td>13</td>\n      <td>0</td>\n      <td>107</td>\n      <td>tertiary</td>\n      <td>0</td>\n      <td>admin.</td>\n      <td>0</td>\n      <td>married</td>\n      <td>jul</td>\n      <td>-1</td>\n      <td>unknown</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>59</td>\n      <td>152</td>\n      <td>2</td>\n      <td>cellular</td>\n      <td>17</td>\n      <td>0</td>\n      <td>171</td>\n      <td>primary</td>\n      <td>1</td>\n      <td>blue-collar</td>\n      <td>0</td>\n      <td>married</td>\n      <td>nov</td>\n      <td>-1</td>\n      <td>unknown</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   age  balance  campaign   contact  day  default  duration  education  \\\n0   33      616         2   unknown   12        0       150  secondary   \n1   29     1832         4  cellular    7        0       613  secondary   \n2   57     1093         1  cellular   29        0        59  secondary   \n3   50        0         1   unknown   13        0       107   tertiary   \n4   59      152         2  cellular   17        0       171    primary   \n\n   housing          job  loan   marital month  pdays poutcome  previous  y  \n0        0   management     1  divorced   jun     -1  unknown         0  0  \n1        0   management     0   married   may     -1  failure         0  0  \n2        1   technician     0    single   jun     93  unknown         0  0  \n3        0       admin.     0   married   jul     -1  unknown         3  0  \n4        1  blue-collar     0   married   nov     -1  unknown         0  0  "
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {
    "scrolled": false,
    "trusted": false
   },
   "cell_type": "code",
   "source": "# sns.pairplot(boot, hue='y')",
   "execution_count": 26,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Step 5: Variables of Interest\n\nWhich of the different _predictor_ variables $X$ affects our _response_ variable $y$?\nHow do we go about choosing our predictors?\n\nIn order to do choose, let's look at our pairplots.\n(plotted again for convenience)"
  },
  {
   "metadata": {
    "scrolled": true,
    "trusted": false
   },
   "cell_type": "code",
   "source": "# sns.pairplot(df, hue='y')\n",
   "execution_count": 27,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Focusing on the bottom row, we are able to see the how subscriptions interact with the various predictor variables.\nTo locate potential variables of interest, look for variables that show more on one side of the plot than the other.\nThere seem to be two variables that could be of interest here. The first is the `campaign` variable, and the second is the `balance` variable.\nIn both of these, the orange points seem to be clumped more toward the left of the plots, meaning that those who have lower balances, and weren't in the campaign as long.\nLet's take a closer look at them."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "y_sub = subscribed['y'].apply(np.int64) \n\nplt.scatter(subscribed['balance'], y_sub, alpha=0.25);\nplt.scatter(not_subscribed['balance'], not_subscribed['y'], alpha=0.25);",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<matplotlib.figure.Figure at 0x1121e1588>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "We can see that the subscribed values, on top, seem to carry lower balances than those who didn't.\nIt's still not abundently clear what the distributions are though.\nWe can better see this using a __violin plot__."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "sns.violinplot(x='y', y='balance', data=df, whis=.9)  # plot violin plot for balance\n",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x10ced7400>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<matplotlib.figure.Figure at 0x1096a19b0>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "There are some serious outliers though, so let's ignore them, and zoom in on the bulk of our distribution, in the $(-2500 , 10000)$ range."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "sns.violinplot(x='y', y='balance', data=df, whis=.9)  # plot violin plot for balance\nplt.ylim(-2500, 10000)  # ignore outliers for plot",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "(-2500, 10000)"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<matplotlib.figure.Figure at 0x10d11e400>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "From a quick glance, it appears that those who subscribed, the plot on the right, had higher account balances than those who did not subscribe.\n\nNow let's take a look at the `campaign` predictor."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "sns.violinplot(x='y', y='campaign', data=df, whis=.9)  # plot violin plot for campaign\nplt.ylim(0, 10)  # ignore outliers for plot",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "(0, 10)"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<matplotlib.figure.Figure at 0x111393a58>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "It looks like the customers who subscribed tended to be in the campaign a shorter amount of time.\nThis makes sense though, as the customers who subscribe, are most likely no longer going to be advertised to after they've subscribed.\nSo while the length of the campaign may be shorter for those who subscribe, then those who don't, the cause and effect is most likely in the other direction. "
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Step 6: Get Dum\n\n_Wait, there's more!_\n\nNow that we have found one numerical variable of interest, we need to return to our categorical variables.\nWhat we need to do is convert our categorical variables into __dummy variables__.\n\nDummy variables are a sets of binary observations, that are used to represent categorical variables.\nFor example, we could represent `yes` and `no` votes as a series of `1`s and `0`s for each vote, respectively."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "df.dtypes",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "age           int64\njob          object\nmarital      object\neducation    object\ndefault       int64\nbalance       int64\nhousing       int64\nloan          int64\ncontact      object\nday           int64\nmonth        object\nduration      int64\ncampaign      int64\npdays         int64\nprevious      int64\npoutcome     object\ny             int64\ndtype: object"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "dummies = df.select_dtypes(object)\ndummies.dtypes",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "job          object\nmarital      object\neducation    object\ncontact      object\nmonth        object\npoutcome     object\ndtype: object"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Think about `yes` and `no` again.\nNotice how a `1` or `0` represents both `yes` _and_ `no`.\nA vote can either by `yes` or a `no`, but not both.\nThis is refered to as mutual exclusivity, and all dummy variables have it.\nGiven $k$ categories, you will use dummy variables to represent $k-1$ of them.\n\nTo account for this, we drop one of the variables when we construct the dummies.\nWe'll do this by passing `drop_firt=True` to the `pandas.get_dummies()` function.\nThe dummy that was dropped becomes the __baseline comparison__."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "dummies = pd.get_dummies(dummies, drop_first=True)  # get dummy variables, droping first of each",
   "execution_count": 34,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "The final step is concatanating it back onto the original data frame."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "features = pd.concat([df, dummies], axis=1)  # concatenate the dummie variables onto the original data frame",
   "execution_count": 35,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "features.head()  # glance at our new DataFrame",
   "execution_count": 36,
   "outputs": [
    {
     "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>age</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>balance</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>day</th>\n      <th>...</th>\n      <th>month_jul</th>\n      <th>month_jun</th>\n      <th>month_mar</th>\n      <th>month_may</th>\n      <th>month_nov</th>\n      <th>month_oct</th>\n      <th>month_sep</th>\n      <th>poutcome_other</th>\n      <th>poutcome_success</th>\n      <th>poutcome_unknown</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>58</td>\n      <td>management</td>\n      <td>married</td>\n      <td>tertiary</td>\n      <td>0</td>\n      <td>2143</td>\n      <td>1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>...</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>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44</td>\n      <td>technician</td>\n      <td>single</td>\n      <td>secondary</td>\n      <td>0</td>\n      <td>29</td>\n      <td>1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>...</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>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>entrepreneur</td>\n      <td>married</td>\n      <td>secondary</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>...</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>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>47</td>\n      <td>blue-collar</td>\n      <td>married</td>\n      <td>unknown</td>\n      <td>0</td>\n      <td>1506</td>\n      <td>1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>...</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>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>33</td>\n      <td>unknown</td>\n      <td>single</td>\n      <td>unknown</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>...</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>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 49 columns</p>\n</div>",
      "text/plain": "   age           job  marital  education  default  balance  housing  loan  \\\n0   58    management  married   tertiary        0     2143        1     0   \n1   44    technician   single  secondary        0       29        1     0   \n2   33  entrepreneur  married  secondary        0        2        1     1   \n3   47   blue-collar  married    unknown        0     1506        1     0   \n4   33       unknown   single    unknown        0        1        0     0   \n\n   contact  day        ...        month_jul  month_jun  month_mar  month_may  \\\n0  unknown    5        ...                0          0          0          1   \n1  unknown    5        ...                0          0          0          1   \n2  unknown    5        ...                0          0          0          1   \n3  unknown    5        ...                0          0          0          1   \n4  unknown    5        ...                0          0          0          1   \n\n   month_nov month_oct  month_sep  poutcome_other  poutcome_success  \\\n0          0         0          0               0                 0   \n1          0         0          0               0                 0   \n2          0         0          0               0                 0   \n3          0         0          0               0                 0   \n4          0         0          0               0                 0   \n\n   poutcome_unknown  \n0                 1  \n1                 1  \n2                 1  \n3                 1  \n4                 1  \n\n[5 rows x 49 columns]"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Step 7: Data Prep\n\nBefore we can run any models, we need to prepare our data to get it in the proper form.\nWe've already transformed our categorical variables into dummy variables, giving us numeric representations of these variables.\nTo get our data ready, first we need to we need to select only the numeric features, and separate the response from our predictors."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "X = features.select_dtypes(np.number)  # only use the numeric features \ny = X.pop('y')  # remove the tagged response variable from the features",
   "execution_count": 37,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Now that we have our $X$ (our predictors) and $y$ (our response) variables, we need to divide the data into __train__ and __test__ sets.\nThis is important, because we need to ensure that we aren't testing our model on the same data that we're using to train it.\nTo do this, we'll use the `train_test_split` function from `sklearn.model_selection`."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "from sklearn.model_selection import train_test_split\n\nx_trian, x_test, y_train, y_test = train_test_split(X, y, random_state=42)  # divide the data in to training and test sets",
   "execution_count": 38,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "/usr/local/Cellar/python3/3.6.3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n  return f(*args, **kwds)\n/usr/local/Cellar/python3/3.6.3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n  return f(*args, **kwds)\n"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Step 8: Build Models\n\nOur data is cleaned and we've separated our training and test sets, let's get to modelling!\n\n_Slow down..._\n\nBefore we can build a model, we need to think about the question we're attempting to answer.\nWe're tasked with identifying whether a customer will _subscribe_ or _not subscribe_.\nThis is what's known as a __binary classification__ problem.\nGiven our $X$ values, we want to determine whether a customer subscribes $y=1$, or does not subscribe $y=0$.\nThis informs the type of models that we use.\n\nThere are additional considerations that we need to account for when we select our model.\nIs this an __inference__ or a __prediction__ task?\nThe difference is that for inference, the goal is to determine the impact of each $X$ variable on the classification of $y$.\nThis means that we need a model that has _interpretable_ results.\nFor prediction we don't need to be able to interpret the role of each $X$ variable, we simply need the most accurate predictions possible.\nIn order to work in both of context, we're going to use two models to analyze our data: a __Logistic Regrssion__, and a __Random Forest Classifier__.\n"
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "from sklearn.metrics import classification_report, confusion_matrix",
   "execution_count": 39,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Logistic Regression\n\nLogistic Regression is an example of a _generalized linear model_.\nThis class of models uses the simplicity of a linear model $y = \\beta_{0} + \\beta_{1}x_{1} + \\cdots + \\beta{p}x_{p}$, but transforms them in order to apply it to tasks like classification.\n\nWhile you could concievably use a linear regression to classify binary data, there are problems with this application.\nAmong these are that your errors won't be normally distributed, but will follow a Bernoulli distributed; also, the estimates that a line will give, interpretted as the probabilities of an observation belonging to each class,  violate probability theory, as there will be predictions great than $1$, and less than $0$.\nThe transformation that is used in logistic regression corrects for this, bounding the predictions between 0 and one, and handling the errors appropriately.\nThere are still assumptions that must be made, however; our model must be linear in the logit, meaning that the model that gets transformed is linear, and that the transformation is affine, and strictly monotonic, meaning that the transformation preserves the ordering of $p(y)$ as $X$ increases.\nWhile that may be quite technical, the Logistic Model is, once again, built off of a linear model, which means that it is _interpretable_: an essential part of any tool used in an inference task.\n\nLet's look at how we can use Logistic Regression to determine what effects a customer signing up for a term deposit account.\nWe'll do this by using the `LogisticRegression` class from the `sklearn.linear_model` module.\nTo measure it's performance, we'll import the `classification_report` and `confusion_matrix` functions from the `sklearn.metrics` module.\n\nThe following cell will build, fit, and evaluate the performance of a logistic regression model."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "from sklearn.linear_model import LogisticRegression\n\nlogistic = LogisticRegression()  # instantiate the model\n\nlogistic.fit(x_trian, y_train)  # fit the model on the training data\nlogistic_predict = logistic.predict(x_test)  # get predictions for the y values, using the test data\n\nlogistic_matrix = confusion_matrix(y_test, logistic_predict)  # get a confusion matrix for the predictions\nlogistic_report = classification_report(y_test, logistic_predict)  # get a report of the model's performance\n\nprint(f\"Logistic Regression Report:\\n {logistic_matrix}\\n {logistic_report}\")  # print the report",
   "execution_count": 40,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "/usr/local/Cellar/python3/3.6.3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n  return f(*args, **kwds)\n"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Logistic Regression Report:\n [[9688  262]\n [ 881  472]]\n              precision    recall  f1-score   support\n\n          0       0.92      0.97      0.94      9950\n          1       0.64      0.35      0.45      1353\n\navg / total       0.88      0.90      0.89     11303\n\n"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Logistic Regression: Performance\n\nWhen we look at the output, there are a lot of numbers, but what does it all mean?\n\nFirst, right under where it says \"Logistic Regression Report:\", you'll see a $2\\times2$ matrix of values.\nThis is the __confusion matrix__.\nThe confusion matrix tells us how our predictions for our test data compared with their _true_ values.\nThe columns are our predictions, while the rows are the _actual_ values.\nFor both the column and row space, the first value is the negative, or when $y=0$, and the second is the positive, where $y=1$.\n\nIt is read as:\n$$\\begin{bmatrix}\\text{True Negative} & \\text{False Positive} \\\\ \\text{False Negative} & \\text{True Positive} \\end{bmatrix}$$\n\nWhere $\\text{True Negative}$ is the count of observations that we predicted as negative, $\\hat{y}=0$, where it actually was negative, $y=0$.\nThen the same for the postitive values.\nThe confusion matrix helps us to interpret the next section, the classification report.\n\nThe ouput of the `classification_report()` function gives us several import metrics to help us determine the performance of our model.\nThese include the __precision__, the __recall__, and the __f1-score__.\n\n__Precision__ is the ratio of true positive, to all positive observations:\n\n$$\\text{precision} = \\frac{\\text{True Positive}}{\\text{True Positive} + \\text{False Positive}}$$\n\nIt can be intuitively seen as a measure of how well our model classifies the positive values that are observed. More information can be found [here](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html).\n\nThe second metric that we see is the __recall__. Recall is the ratio of the true positives, to the sum of the true positives and false negatives.\n\n$$\\text{recall} = \\frac{\\text{True Positive}}{\\text{True Positive} + \\text{False Negative}}$$.\n\nThe recall score can be seen as the ability of the model to identifify all of the positive values in our test data.\nFor more information, read [this](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html).\n\nLastly, we have the __f1-score__. The f1-score can be seen as the weighted average of the precision and recall score, in the form of\n\n$$\\text{f1} = \\frac{2 \\times \\text{precision} \\times \\text{recall}}{\\text{precision} + \\text{recall}}$$\n\nHigher values for the f1 score, which is in $(0, 1)$, indicate better balance between precision and recall.\n\nFor each of theses scores, you'll see score for both of the classes, so with the `0` class, switch 'positive' and 'negative' in the explanations above. \n\nNow that we know all that, how well did our model do?"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "For our logistic regression our model had an average precision of $88\\%$, an average recall of $90\\%$, and an average f1-score of $88\\%$.\n\nThat's pretty good, right?\n\nBefore we declare our model a success, consider the scores that we got for the positive class, where $y=1$.\nHere are scores are quite low, $65\\%$, $34\\%$, and $45\\%$ respectively.\nThis means our model performed quite poorly when it comes to identifying the customers who did sign up.\n\nWhy is this?\n\nThe reason for this can be seen by looking at the last column of the classification report, the __support__.\nThis is the number of observations that were actually in each class.\nWe'll notice is that the number of negative observations is _much_ larger than the number of positive observations.\nWhen we have more of one class than the other, we call this having __unbalanced__ classes.\nWhat's happening, is that the model is getting a higher score by predicting $\\hat{y}=0$ more often.\nLuckily we can account for this by _balancing_ our classes.\nThis is done by passing the `class_weight='balanced'` argument when we instantiate our model.\n\nLet's try running a logistic regression again, this time while balancing the classes."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "logistic_balanced = LogisticRegression(class_weight='balanced')  # instantiate the model\n\nlogistic_balanced.fit(x_trian, y_train)  # fit the model on the training data\nlogistic_balanced_predict = logistic_balanced.predict(x_test)  # get predictions for the y values, using the test data\n\nlogistic_balanced_matrix = confusion_matrix(y_test, logistic_balanced_predict)  # get a confusion matrix for the predictions\nlogistic_balanced_report = classification_report(y_test, logistic_balanced_predict)  # get a report of the model's performance\n\nprint(f\"Logistic Regression Report:\\n {logistic_balanced_matrix}\\n {logistic_balanced_report}\")  # print the report",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Logistic Regression Report:\n [[8437 1513]\n [ 241 1112]]\n              precision    recall  f1-score   support\n\n          0       0.97      0.85      0.91      9950\n          1       0.42      0.82      0.56      1353\n\navg / total       0.91      0.84      0.86     11303\n\n"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Balancing the classes gives us a different results.\nOur average precision score increased, while our recall and f1-scores have gone down.\nEven with this, we still have a better model.\n\nWhy is this?\n\nLooking at the recall score for the positive class we see that it has increased dramatically.\nAccounting for the imbalanced classes has lead our model to identify the positve classes far better.\nThis is important, because what we're interested in is identifying what makes someone sign up for an account.\nBiasing our model toward predicting the negative class makes this task much harder.\n\nTo further improve our model, we'd want to look at some additional __hyper parameters__.\nThese are values that you pass to the model when you instantiate it, such as the `class_weight`.\nIn order to obtain the best possible model, you will want to perform a __grid search__ over a number of different hyper parameters.\nThis is especially important when it comes to the fit of our model.\nThe hyper parameter for the model, `C` controls the effect of the regularization.\nIt's important to note that's it's the inverse though.\nThis means that lower values of `C` prevent over fitting by increasing the effect of the model's regularization\n\nNow that we've fit a better model, lets look closer at what our model says.\n\nAs we stated above, one of the most valuable parts of a logistic regression is that it provides interpretable results.\nThese interpretations can be found by analyzing the coefficient values, found in the `.coef_` attribute of our model's class.\n\nThe following line code will give us a dictionary that maps the coefficient value to the associated $X$ variable."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "coef = logistic.coef_.flatten()  # coefficients of the model, flattened to 1d array\n\ncoef_dict = {col: coef[i] for i, col in enumerate(X.columns)}  # create a dictionary for feature: coefficient value",
   "execution_count": 42,
   "outputs": []
  },
  {
   "metadata": {
    "scrolled": false,
    "trusted": false
   },
   "cell_type": "code",
   "source": "coef_dict",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "{'age': -0.0023427715220143575,\n 'balance': 9.6923174513005368e-06,\n 'campaign': -0.092719040604929845,\n 'contact_telephone': -0.085197485823242325,\n 'contact_unknown': -1.6068369883376235,\n 'day': 0.0089191382327491898,\n 'default': -0.018187012265079686,\n 'duration': 0.004198257088486099,\n 'education_secondary': 0.12793184498375706,\n 'education_tertiary': 0.29549751542853792,\n 'education_unknown': 0.15658361623238343,\n 'housing': -0.73059146465519875,\n 'job_blue-collar': -0.32964574197108981,\n 'job_entrepreneur': -0.32987298278511823,\n 'job_housemaid': -0.50353289840011506,\n 'job_management': -0.20675209544971468,\n 'job_retired': 0.22005772015802119,\n 'job_self-employed': -0.25219530905610749,\n 'job_services': -0.26168795188741167,\n 'job_student': 0.22190170522714395,\n 'job_technician': -0.26891424414713022,\n 'job_unemployed': -0.16870275949390648,\n 'job_unknown': -0.20867825859298386,\n 'loan': -0.40946542041119927,\n 'marital_married': -0.25780918759127625,\n 'marital_single': -0.0094544029866762153,\n 'month_aug': -0.84324447813121062,\n 'month_dec': 0.39000795278899708,\n 'month_feb': -0.39699036423347484,\n 'month_jan': -1.4371110453695259,\n 'month_jul': -1.0101474562202779,\n 'month_jun': 0.25207895979274886,\n 'month_mar': 1.4341580247926367,\n 'month_may': -0.57999614674166522,\n 'month_nov': -1.0274833753611483,\n 'month_oct': 0.72106350168033595,\n 'month_sep': 0.66638974688196939,\n 'pdays': -0.00051028383739962235,\n 'poutcome_other': 0.17164935316016861,\n 'poutcome_success': 2.211153788073581,\n 'poutcome_unknown': -0.27501268229449627,\n 'previous': 0.0054021724809710757}"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "_Cool! Numbers!_\n\nWhat do they mean?\n\nInterpretting the coefficient values of a logistic regression takes a bit of math.\n\nIf we have some coefficient $\\beta_{i}$ for some variable $x_{i}$, the interpretation is that a one unit increase in $x_{i}$ means that the response is $e^{\\beta_{i}}$ _as_ likely to occur.\nThis value is the __odds ratio__ for a $\\beta_{i}$ increase in $x_{i}$.\n\nNow, the coefficient itself isn't super interpretable, so let's convert these coefficient values to the odds ratio for each feature."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "for x in coef_dict.keys():  # iterate over the dictionary keys\n    coef_dict[x] = np.e**coef_dict[x]  # find e^beta",
   "execution_count": 44,
   "outputs": []
  },
  {
   "metadata": {
    "scrolled": true,
    "trusted": false
   },
   "cell_type": "code",
   "source": "coef_dict  # view the coefficients",
   "execution_count": 54,
   "outputs": [
    {
     "data": {
      "text/plain": "{'age': 0.9976599706253616,\n 'balance': 1.0000096923644219,\n 'campaign': 0.911449544638123,\n 'contact_telephone': 0.91833090915692339,\n 'contact_unknown': 0.20052086188678409,\n 'day': 1.0089590322647437,\n 'default': 0.98197737337274316,\n 'duration': 1.0042070821153652,\n 'education_secondary': 1.1364755435483453,\n 'education_tertiary': 1.343794751026163,\n 'education_unknown': 1.1695085481292802,\n 'housing': 0.48162404223214184,\n 'job_blue-collar': 0.71917846305401145,\n 'job_entrepreneur': 0.71901505492183704,\n 'job_housemaid': 0.6043916292288366,\n 'job_management': 0.81322122624307747,\n 'job_retired': 1.2461486564089417,\n 'job_self-employed': 0.77709294995606848,\n 'job_services': 0.76975118563922962,\n 'job_student': 1.2484486558558665,\n 'job_technician': 0.7642087882146622,\n 'job_unemployed': 0.84475996295056488,\n 'job_unknown': 0.81165633708949447,\n 'loan': 0.66400511885848146,\n 'marital_married': 0.77274266692988436,\n 'marital_single': 0.99059014936539325,\n 'month_aug': 0.43031211785166673,\n 'month_dec': 1.4769925400459563,\n 'month_feb': 0.67234050411756774,\n 'month_jan': 0.23761322188696118,\n 'month_jul': 0.3641652771769156,\n 'month_jun': 1.2866976306521389,\n 'month_mar': 4.1961105000039876,\n 'month_may': 0.55990052400260626,\n 'month_nov': 0.35790654456519649,\n 'month_oct': 2.0566192660463001,\n 'month_sep': 1.9471947496370572,\n 'pdays': 0.99948984633525517,\n 'poutcome_other': 1.187261450740817,\n 'poutcome_success': 9.126240067774491,\n 'poutcome_unknown': 0.75956249016869537,\n 'previous': 1.0054167905259406}"
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Now, the values in the dictionary are the _odds ratios_.\nTo go into the interpretation a bit more.\nThe odds ratio will always be positive.\nIf the value of the odds ratio is $1$, the $y=1$ is equally as likely no matter how $x_{i}$ changes. \nValues less than $1$ mean that $y=1$ is _less_ likely as $x_{i}$ increases.\nValues greater than $1$ mean that $y=1$ is _more_ likely as $x_{i}$ increases.\nThe problem we have now is that we have a large number predictors, so we want to get only the best features.\n\nThe function below will help us get the predictors with 10 highest odds ratios."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "def see_best_features(features, n=10):\n    \"\"\"\n    see the best features used in a logistic regression\n    \n    :param: features: dictionary of feature: coefficient pairs\n    :param: n=10: number of features to return\n    :return: output: dictionary of n best features: coefficient\n    \n    \"\"\"\n    results = [(x, features[x]) for x in sorted(features, key=features.get, reverse=True)]  # get list of feature:odds tuples\n    print(\"Best {} features:\".format(n))\n    output = {}  # instantiate empty dict for output\n    for i, feature in enumerate(results):  # iterate over the results list\n        x, coef = feature  # unpack the feature tuple\n        print(\"  \", x, coef)  # print the feature and coefficient value\n        output[x] = coef  # add the feature: coefficient to the output dictionary\n        if i+1 == n:  # exit if i+1 equals n, adding 1 to account for 0 indexing\n            break\n\n    return output\n",
   "execution_count": 46,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Let's say we need to find the 3 features that make a person most likely to sign-up.\nWe'll use the function above to retrieve this list."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "see_best_features(coef_dict, n=3);  # see the top three features",
   "execution_count": 47,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Best 3 features:\n   poutcome_success 9.12624006777\n   month_mar 4.1961105\n   month_oct 2.05661926605\n"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "From this we can see that those customers who the previous marketing campaign was successful are approximately $9.6$ as likely to sign up for a term deposit account.\nCustomers who were last contacted in March are $4.8$ times as likely, and those last contacted in October are $2.0$ times as likely. \n\nMany times people are interested in _why_ something is happening.\nBeing able to provide these inferences can be invaluable in many contexts.\nIf you're just looking for prediction though, there are other approaches that may provide better insights.\nTo show this, we will look at our data again using a __Random Forest Classifier__."
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Random Forest Classifier\n\nWhile the logistic regression was really powerful, and performed quite well, it does have downsides.\nOne of the biggest comes from the assumptions that are relied on to get it to work, specifically, that the model be \"linear in the logit\".\nThis is what's known as a __parametric__ assumption.\nWe have to assume that there is some functional relation between our $X$ and $y$ variables.\nWhile this might be beneficial when the underlying relation is known, parametric assumptions can be quite restrictive when we don't have a good understanding of this relationship.\nA more flexible approach in this case is to use a __non-parametric__ model.\n\nThe __Random Forest Classifier__ is an example of a non-parametric classifier.\nIt is a variation on an even simple model, known as a decision tree. \nDecision trees don't assume that there is a relation in the data, instead it simply chooses the predictor that divides the data best at each step.\nIt judges the _best_ division to be the one that results in the most purity at each split.\nIf we were to use our data for example, it would choose the predictor that would put the most observations where $y=1$ on one side, and the most where $y=0$ on the other.\nIt continues to do this until the final groupings are _pure_, and contain only observations where $y=1$ or $y=0$.\nThis can lead to problems.\n\nContinuing to divide the data until you get sets with $100\\%$ purity means that you will have a model that has incredibly high __variance__, meaning that it wil not perform well on new data.\nThis is what's known as __overfitting__.\nWe've come up with a model that explains the training data too well, but can't explain new observations.\nThis is a constant problem with non-parametric models.\nLuckily the __Random Forest__ helps us avoid some of this overfitting.\n\nInstead of considering all of the predictors at each step, and using the one that leads to the best purity, the Random Forest considers a random subset of predictors at each split.\nWith this, you won't have a model that simply fits the training data super well, but instead the randomization ensures that there is more flexibility.\nAlong with this, the Random Forest \"grows\" multiple trees, and then aggregates the results to arrive at the final model.\nThis is known as __bagging__ or bootstrap aggregation.\nThe result is that we have a flexible, robust model, that works quite well.\n\nLet's take a look at this in the code below.\n"
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "from sklearn.ensemble import RandomForestClassifier\n\n\nforest = RandomForestClassifier()  # instantiate the random forest classifier\n\nforest.fit(x_trian, y_train)  # fit the model on the training data\nforest_predict = forest.predict(x_test)  # get the model predictions\n\nforest_matrix = confusion_matrix(y_test, forest_predict)  # get confusion matrix\nforest_report = classification_report(y_test, forest_predict)  # get the classification report\nprint(f\"Random Forest Report:\\n {forest_matrix}\\n {forest_report}\")",
   "execution_count": 48,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "/usr/local/Cellar/python3/3.6.3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n  return f(*args, **kwds)\n"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Random Forest Report:\n [[9690  260]\n [ 875  478]]\n              precision    recall  f1-score   support\n\n          0       0.92      0.97      0.94      9950\n          1       0.65      0.35      0.46      1353\n\navg / total       0.88      0.90      0.89     11303\n\n"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Ouch.\nThe positive class scores aren't looking so good.\n\n_What happened?! I thought you said this would be good???_\n\nJust like with the __Logistic Regression__, we have a problem with __unbalanced class__.\nWe'll fix it the same way, by passing our model the `class_weight='balanced'` argument."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "balanced_forest = RandomForestClassifier(class_weight='balanced')  # account for the unbalanced classes\n\nbalanced_forest.fit(x_trian, y_train)  # fit the model on the training data\nbalanced_forest_predict = balanced_forest.predict(x_test)  # get model predictions\n\nbalanced_forest_matrix = confusion_matrix(y_test, balanced_forest_predict)  # get confusion matrix\nbalanced_forest_report = classification_report(y_test, balanced_forest_predict)  # get classification report\nprint(f\"Balanced Random Forest Report:\\n {balanced_forest_matrix}\\n {balanced_forest_report}\")\n",
   "execution_count": 49,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Balanced Random Forest Report:\n [[9755  195]\n [ 951  402]]\n              precision    recall  f1-score   support\n\n          0       0.91      0.98      0.94      9950\n          1       0.67      0.30      0.41      1353\n\navg / total       0.88      0.90      0.88     11303\n\n"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Comparing the score of our Random Forest Classifier with that of our Logistic Regression, we see that our average precision score has fallen slightly, from $91\\%$ to $88\\%$, however our average recall and f1-scores have both gone up.\n\nSo which is better?\n\nDeciding which model is better is largely determined by the problem you're trying to solve, and what the application is.\nConsidering the application, determining who is likely to sign up for a term deposit account, we'll want a model that best identifies the customers who will sign up.\nWith this in mind, the Random Forest is the better choice.\nLooking at the precision for the positive class, $y=1$, the Random Forest performs better than the Logistic regression.\nThis matters because we want to make sure we identify all the customers who will sign up the the account.\nIt's okay if we happen to market to people who do not sign up, but this model allows us to identify more of the customers who will sign up.\n\nThere is one caveat though: it's hard to know _why_ those customers are likely to sign up.\n\nOnce again, let's look at the features that were most important.\n\nWe'll build a feature dictionary and use our `see_top_features()` function to return the top 3 features."
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "forest_coef = balanced_forest.feature_importances_.flatten()  # feature importances of the model, flattened to 1d array\n\nforest_coef = {col: coef[i] for i, col in enumerate(X.columns)}  # create a dictionary for feature: importance value",
   "execution_count": 50,
   "outputs": []
  },
  {
   "metadata": {
    "scrolled": true,
    "trusted": false
   },
   "cell_type": "code",
   "source": "forest_coef  # view coefficients",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "{'age': -0.0023427715220143575,\n 'balance': 9.6923174513005368e-06,\n 'campaign': -0.092719040604929845,\n 'contact_telephone': -0.085197485823242325,\n 'contact_unknown': -1.6068369883376235,\n 'day': 0.0089191382327491898,\n 'default': -0.018187012265079686,\n 'duration': 0.004198257088486099,\n 'education_secondary': 0.12793184498375706,\n 'education_tertiary': 0.29549751542853792,\n 'education_unknown': 0.15658361623238343,\n 'housing': -0.73059146465519875,\n 'job_blue-collar': -0.32964574197108981,\n 'job_entrepreneur': -0.32987298278511823,\n 'job_housemaid': -0.50353289840011506,\n 'job_management': -0.20675209544971468,\n 'job_retired': 0.22005772015802119,\n 'job_self-employed': -0.25219530905610749,\n 'job_services': -0.26168795188741167,\n 'job_student': 0.22190170522714395,\n 'job_technician': -0.26891424414713022,\n 'job_unemployed': -0.16870275949390648,\n 'job_unknown': -0.20867825859298386,\n 'loan': -0.40946542041119927,\n 'marital_married': -0.25780918759127625,\n 'marital_single': -0.0094544029866762153,\n 'month_aug': -0.84324447813121062,\n 'month_dec': 0.39000795278899708,\n 'month_feb': -0.39699036423347484,\n 'month_jan': -1.4371110453695259,\n 'month_jul': -1.0101474562202779,\n 'month_jun': 0.25207895979274886,\n 'month_mar': 1.4341580247926367,\n 'month_may': -0.57999614674166522,\n 'month_nov': -1.0274833753611483,\n 'month_oct': 0.72106350168033595,\n 'month_sep': 0.66638974688196939,\n 'pdays': -0.00051028383739962235,\n 'poutcome_other': 0.17164935316016861,\n 'poutcome_success': 2.211153788073581,\n 'poutcome_unknown': -0.27501268229449627,\n 'previous': 0.0054021724809710757}"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "metadata": {
    "trusted": false
   },
   "cell_type": "code",
   "source": "see_best_features(forest_coef, n=3);  # view top 3 features",
   "execution_count": 53,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Best 3 features:\n   poutcome_success 2.21115378807\n   month_mar 1.43415802479\n   month_oct 0.72106350168\n"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Once again, we see that the best features for predicting whether someone will sign up for a term deposit account are the previous campaign being successful, them being contacted last in March, and the customer being contacted last in October.\nWhat are the numbers though?\n\nA fundamental problem with the Random Forrest is that we don't know _why_ the features are important.\nIt's a better model, in that it performs better, but we aren't able to see how each of those features plays a role in  determining whether a customer signs up."
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Section 8: Conclusion\n\n_\"Explain the limitations of your analysis and identify possible further steps you could take.\"_ (To a technical audience)\n\nWhile there were a few technical shortcomings with this analysis, the largest short coming was a lack of preparation.\nThis was a model that was constructed in a (necessarily) short period of time, meaning I was not able to do the real heavy lifting that is required to understand the topic that is being studied.\nIf allowed more time, I would do more background research into the Portugese banking industry, into term deposit accounts, and the marketing of them, collecting more data on the way in which the accounts were marketed.\nPerhaps most importantly, I would spend time feature engineering, to build out more features that may help make better predictions.\nThis may not be technical, at least not in a sexy data science way, but much of a data scientist's job is simply developing an understanding of what we're analyzing.\nThat being said, there were a few technical points that can be improved.\n\nFor this analysis, I simply used \"out-of-the-box\" models.\nTaking time to find a better model could have been quite beneficial.\nWhile I had mentioned this breifly, something that could lead to finding a much better model would be conducting a grid search over the hyper parameters, so as to discover the best \"settings\" of the model.\nAt an even higher level, considering additional models would have been helpful as well.\nA few candidates that I considered were an extreme gradient boosting classifier, an ADA boost classifier, and a support vector clssifier.\nIn a professional setting I would have conducted my due dilligence, and tested variations of all of these; but there are only so many models that can be run in 8 hours."
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "_\"Write a short summary of your analysis, explaining how your model works and how it performs. Briefly explain the factors that contributed to identifying subscription versus non-subscription.\"_ (To a nontechnical audience)\n\nI was presented with data pertaining to a Portugese bank's marketing campaign for term deposit accounts.\nWith this I was tasked to predict the customers who were likely to sign up for the account.\nAfter cleaning up the data, which entailed getting everything into the proper format, I divided my data into two parts.\nThis allows me to train my model on one part, and test it on the other.\nThe model I decided to use was a Random Forest clssifier, which is a really flexible model that is also very accurate. It performed extremely well on this data, predicting the customers who were most likely to sign up for a term deposit account.\n\nWhat we were able to learn from this model is that the most important times of the year to run the marketing campaigns were in October and March, as we found that customers who were last contacted then were more likely to sign up for the account.\nThe most important finding though, was that the best predictor of a customer signing up was the success of the previous marketing campaign; meaning that if you want to target customers, start with the ones who you successfully marketed to in the past."
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Part 2"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Student 1:"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import pandas as pd\nimport numpy as np\nfrom sklearn import LinearRegression\nfrom sklearn.cross_validation import cross_val_score\n\n# Load data\nd = pd.read_csv('../data/train.csv')\n\n\n# Setup data for prediction\nx1 = data.SalaryNormalized\nx2 = pd.get_dummies(data.ContractType)\n\n# Setup model\nmodel = LinearRegression()\n\n# Evaluate model\nfrom sklearn.cross_validation import cross_val_score\nfrom sklearn.cross_validation import train_test_split\nscores = cross_val_score(model, x2, x1, cv=1, scoring='mean_absolute_error')\nprint(scores.mean())",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "First thing, I really like that you commented the different sections of your code to indicate what you are doing in each part.\nThis is a _really_ good habit to develop, and makes your code more readable.\nThat being said, try to name variables something meaningful.\nThis makes your code more readable as well, and helps you keep track of the values assigned to it.\n\nThe bad news is that your code doesn't execute.\nThe good news is it's nothing that can't be easily fixed.\nI know this can be frustrating after writing all this code.\nThat's why it's a good practice to try running your code as you build it.\nIt's better to catch errors early.\n\nI'm going to point out the errors, and give you some pointers as  well.\n\n- `from sklearn import LinearRegression` should be `from sklearn.linear_model import LinearRegression`, you want to import LinearRegression from the `linear_model` library, which is a part of `sklearn`\n- When you lead your data, assign it to a variable name that's dscriptive such as `data`, not `d`\n- Along with that, you have two variables `x1` and `x2`, but you appear to use `x2` as your `y` variable. It would be helpful to name it that. Also, when using dummy variables, make sure you drop one.\n- You assigned your data to the variable `d`, but then referred to it as `data`. I assume this was a type.\n- Make sure you put all of the import statements on at the top of your file\n- When using `cross_val_score`, make sure you have your arguments in the right positions, it seems that you switched your x and y values. Also, setting `cv=1` doesn't make sense. This is the number of splits that you want to run.\n\n\nIf you have any questions, let me know!\n\n-- Chris\n"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Student 2"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.cross_validation import cross_val_score\n\n# Load data\ndata = pd.read_csv('../data/train.csv')\n\n\n# Setup data for prediction\ny = data.SalaryNormalized\nX = pd.get_dummies(data.ContractType)\n\n# Setup model\nmodel = LinearRegression()\n\n# Evaluate model\nscores = cross_val_score(model, X, y, cv=5, scoring='mean_absolute_error')\nprint(scores.mean())",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Your code looks great!\nI really liked that you separated t