PySpark and Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Spark = SparkSession.builder.appName(\"ML using pyspark\").getOrCreate()\n",
    "df = Spark.read.csv('F:/bank.csv',header = True,inferSchema = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- age: integer (nullable = true)\n",
      " |-- job: string (nullable = true)\n",
      " |-- marital: string (nullable = true)\n",
      " |-- education: string (nullable = true)\n",
      " |-- default: string (nullable = true)\n",
      " |-- balance: integer (nullable = true)\n",
      " |-- housing: string (nullable = true)\n",
      " |-- loan: string (nullable = true)\n",
      " |-- contact: string (nullable = true)\n",
      " |-- day: integer (nullable = true)\n",
      " |-- month: string (nullable = true)\n",
      " |-- duration: integer (nullable = true)\n",
      " |-- campaign: integer (nullable = true)\n",
      " |-- pdays: integer (nullable = true)\n",
      " |-- previous: integer (nullable = true)\n",
      " |-- poutcome: string (nullable = true)\n",
      " |-- deposit: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>59</td>\n",
       "      <td>56</td>\n",
       "      <td>41</td>\n",
       "      <td>55</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>admin.</td>\n",
       "      <td>admin.</td>\n",
       "      <td>technician</td>\n",
       "      <td>services</td>\n",
       "      <td>admin.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <td>married</td>\n",
       "      <td>married</td>\n",
       "      <td>married</td>\n",
       "      <td>married</td>\n",
       "      <td>married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>secondary</td>\n",
       "      <td>secondary</td>\n",
       "      <td>secondary</td>\n",
       "      <td>secondary</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>2343</td>\n",
       "      <td>45</td>\n",
       "      <td>1270</td>\n",
       "      <td>2476</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact</th>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "      <td>may</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>1042</td>\n",
       "      <td>1467</td>\n",
       "      <td>1389</td>\n",
       "      <td>579</td>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poutcome</th>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deposit</th>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0          1           2          3         4\n",
       "age               59         56          41         55        54\n",
       "job           admin.     admin.  technician   services    admin.\n",
       "marital      married    married     married    married   married\n",
       "education  secondary  secondary   secondary  secondary  tertiary\n",
       "default           no         no          no         no        no\n",
       "balance         2343         45        1270       2476       184\n",
       "housing          yes         no         yes        yes        no\n",
       "loan              no         no          no         no        no\n",
       "contact      unknown    unknown     unknown    unknown   unknown\n",
       "day                5          5           5          5         5\n",
       "month            may        may         may        may       may\n",
       "duration        1042       1467        1389        579       673\n",
       "campaign           1          1           1          1         2\n",
       "pdays             -1         -1          -1         -1        -1\n",
       "previous           0          0           0          0         0\n",
       "poutcome     unknown    unknown     unknown    unknown   unknown\n",
       "deposit          yes        yes         yes        yes       yes"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame(df.take(5),columns = df.columns).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting pyspark df to pandas using toPandas()\n",
    "pdf = df.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2343</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>45</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
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       "      <td>may</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1270</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2476</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>184</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         job  marital  education default  balance housing loan  contact  \\\n",
       "0   59      admin.  married  secondary      no     2343     yes   no  unknown   \n",
       "1   56      admin.  married  secondary      no       45      no   no  unknown   \n",
       "2   41  technician  married  secondary      no     1270     yes   no  unknown   \n",
       "3   55    services  married  secondary      no     2476     yes   no  unknown   \n",
       "4   54      admin.  married   tertiary      no      184      no   no  unknown   \n",
       "\n",
       "   day month  duration  campaign  pdays  previous poutcome deposit  \n",
       "0    5   may      1042         1     -1         0  unknown     yes  \n",
       "1    5   may      1467         1     -1         0  unknown     yes  \n",
       "2    5   may      1389         1     -1         0  unknown     yes  \n",
       "3    5   may       579         1     -1         0  unknown     yes  \n",
       "4    5   may       673         2     -1         0  unknown     yes  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>summary</th>\n",
       "      <td>count</td>\n",
       "      <td>mean</td>\n",
       "      <td>stddev</td>\n",
       "      <td>min</td>\n",
       "      <td>max</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>11162</td>\n",
       "      <td>41.231947679627304</td>\n",
       "      <td>11.913369192215518</td>\n",
       "      <td>18</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>11162</td>\n",
       "      <td>1528.5385235620856</td>\n",
       "      <td>3225.413325946149</td>\n",
       "      <td>-6847</td>\n",
       "      <td>81204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>11162</td>\n",
       "      <td>15.658036194230425</td>\n",
       "      <td>8.420739541006462</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>11162</td>\n",
       "      <td>371.99381831213043</td>\n",
       "      <td>347.12838571630687</td>\n",
       "      <td>2</td>\n",
       "      <td>3881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>11162</td>\n",
       "      <td>2.508421429851281</td>\n",
       "      <td>2.7220771816614824</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>11162</td>\n",
       "      <td>51.33040673714388</td>\n",
       "      <td>108.75828197197717</td>\n",
       "      <td>-1</td>\n",
       "      <td>854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>11162</td>\n",
       "      <td>0.8325568894463358</td>\n",
       "      <td>2.292007218670508</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0                   1                   2      3      4\n",
       "summary   count                mean              stddev    min    max\n",
       "age       11162  41.231947679627304  11.913369192215518     18     95\n",
       "balance   11162  1528.5385235620856   3225.413325946149  -6847  81204\n",
       "day       11162  15.658036194230425   8.420739541006462      1     31\n",
       "duration  11162  371.99381831213043  347.12838571630687      2   3881\n",
       "campaign  11162   2.508421429851281  2.7220771816614824      1     63\n",
       "pdays     11162   51.33040673714388  108.75828197197717     -1    854\n",
       "previous  11162  0.8325568894463358   2.292007218670508      0     58"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting continous values and describing the values\n",
    "numeric_features = [t[0] for t in df.dtypes if t[1] == 'int']\n",
    "df.select(numeric_features).describe().toPandas().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 49 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_data = df.select(numeric_features).toPandas()\n",
    "axs = pd.plotting.scatter_matrix(numeric_data, figsize=(8, 8));\n",
    "n = len(numeric_data.columns)\n",
    "for i in range(n):\n",
    "    v = axs[i, 0]\n",
    "    v.yaxis.label.set_rotation(0)\n",
    "    v.yaxis.label.set_ha('right')\n",
    "    v.set_yticks(())\n",
    "    h = axs[n-1, i]\n",
    "    h.xaxis.label.set_rotation(90)\n",
    "    h.set_xticks(())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop day and month columns are they are not required\n",
    "#df = df.drop('day','month')\n",
    "df = df.select('age', 'job', 'marital', 'education', 'default', 'balance', 'housing', 'loan', 'contact', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'deposit')\n",
    "cols = df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- age: integer (nullable = true)\n",
      " |-- job: string (nullable = true)\n",
      " |-- marital: string (nullable = true)\n",
      " |-- education: string (nullable = true)\n",
      " |-- default: string (nullable = true)\n",
      " |-- balance: integer (nullable = true)\n",
      " |-- housing: string (nullable = true)\n",
      " |-- loan: string (nullable = true)\n",
      " |-- contact: string (nullable = true)\n",
      " |-- duration: integer (nullable = true)\n",
      " |-- campaign: integer (nullable = true)\n",
      " |-- pdays: integer (nullable = true)\n",
      " |-- previous: integer (nullable = true)\n",
      " |-- poutcome: string (nullable = true)\n",
      " |-- deposit: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "categoricalColumns = ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'poutcome']\n",
    "stages = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in categoricalColumns:\n",
    "    stringIndexer = StringIndexer(inputCol = c, outputCol = c + 'Index')\n",
    "    encoder = OneHotEncoder(inputCols=[stringIndexer.getOutputCol()], outputCols=[c + \"classVec\"])\n",
    "    stages += [stringIndexer, encoder]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_stringIdx = StringIndexer(inputCol = 'deposit', outputCol = 'label')\n",
    "stages += [label_stringIdx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "numericCols = ['age', 'balance', 'duration', 'campaign', 'pdays', 'previous']\n",
    "assemblerInputs = [i + \"classVec\" for i in categoricalColumns] + numericCols\n",
    "assembler = VectorAssembler(inputCols=assemblerInputs, outputCol=\"features\")\n",
    "stages += [assembler]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- label: double (nullable = false)\n",
      " |-- features: vector (nullable = true)\n",
      " |-- age: integer (nullable = true)\n",
      " |-- job: string (nullable = true)\n",
      " |-- marital: string (nullable = true)\n",
      " |-- education: string (nullable = true)\n",
      " |-- default: string (nullable = true)\n",
      " |-- balance: integer (nullable = true)\n",
      " |-- housing: string (nullable = true)\n",
      " |-- loan: string (nullable = true)\n",
      " |-- contact: string (nullable = true)\n",
      " |-- duration: integer (nullable = true)\n",
      " |-- campaign: integer (nullable = true)\n",
      " |-- pdays: integer (nullable = true)\n",
      " |-- previous: integer (nullable = true)\n",
      " |-- poutcome: string (nullable = true)\n",
      " |-- deposit: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Pipeline to chain multiple Transformers and Estimators together to specify our machine learning workflow. \n",
    "from pyspark.ml import Pipeline\n",
    "pipeline = Pipeline(stages = stages)\n",
    "pipelineModel = pipeline.fit(df)\n",
    "df = pipelineModel.transform(df)\n",
    "selectedCols = ['label', 'features'] + cols\n",
    "df = df.select(selectedCols)\n",
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>features</th>\n",
       "      <td>(0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>59</td>\n",
       "      <td>56</td>\n",
       "      <td>41</td>\n",
       "      <td>55</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>admin.</td>\n",
       "      <td>admin.</td>\n",
       "      <td>technician</td>\n",
       "      <td>services</td>\n",
       "      <td>admin.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <td>married</td>\n",
       "      <td>married</td>\n",
       "      <td>married</td>\n",
       "      <td>married</td>\n",
       "      <td>married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>secondary</td>\n",
       "      <td>secondary</td>\n",
       "      <td>secondary</td>\n",
       "      <td>secondary</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>2343</td>\n",
       "      <td>45</td>\n",
       "      <td>1270</td>\n",
       "      <td>2476</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact</th>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>1042</td>\n",
       "      <td>1467</td>\n",
       "      <td>1389</td>\n",
       "      <td>579</td>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poutcome</th>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deposit</th>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                           0  \\\n",
       "label                                                      1   \n",
       "features   (0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "age                                                       59   \n",
       "job                                                   admin.   \n",
       "marital                                              married   \n",
       "education                                          secondary   \n",
       "default                                                   no   \n",
       "balance                                                 2343   \n",
       "housing                                                  yes   \n",
       "loan                                                      no   \n",
       "contact                                              unknown   \n",
       "duration                                                1042   \n",
       "campaign                                                   1   \n",
       "pdays                                                     -1   \n",
       "previous                                                   0   \n",
       "poutcome                                             unknown   \n",
       "deposit                                                  yes   \n",
       "\n",
       "                                                           1  \\\n",
       "label                                                      1   \n",
       "features   (0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "age                                                       56   \n",
       "job                                                   admin.   \n",
       "marital                                              married   \n",
       "education                                          secondary   \n",
       "default                                                   no   \n",
       "balance                                                   45   \n",
       "housing                                                   no   \n",
       "loan                                                      no   \n",
       "contact                                              unknown   \n",
       "duration                                                1467   \n",
       "campaign                                                   1   \n",
       "pdays                                                     -1   \n",
       "previous                                                   0   \n",
       "poutcome                                             unknown   \n",
       "deposit                                                  yes   \n",
       "\n",
       "                                                           2  \\\n",
       "label                                                      1   \n",
       "features   (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "age                                                       41   \n",
       "job                                               technician   \n",
       "marital                                              married   \n",
       "education                                          secondary   \n",
       "default                                                   no   \n",
       "balance                                                 1270   \n",
       "housing                                                  yes   \n",
       "loan                                                      no   \n",
       "contact                                              unknown   \n",
       "duration                                                1389   \n",
       "campaign                                                   1   \n",
       "pdays                                                     -1   \n",
       "previous                                                   0   \n",
       "poutcome                                             unknown   \n",
       "deposit                                                  yes   \n",
       "\n",
       "                                                           3  \\\n",
       "label                                                      1   \n",
       "features   (0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "age                                                       55   \n",
       "job                                                 services   \n",
       "marital                                              married   \n",
       "education                                          secondary   \n",
       "default                                                   no   \n",
       "balance                                                 2476   \n",
       "housing                                                  yes   \n",
       "loan                                                      no   \n",
       "contact                                              unknown   \n",
       "duration                                                 579   \n",
       "campaign                                                   1   \n",
       "pdays                                                     -1   \n",
       "previous                                                   0   \n",
       "poutcome                                             unknown   \n",
       "deposit                                                  yes   \n",
       "\n",
       "                                                           4  \n",
       "label                                                      1  \n",
       "features   (0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "age                                                       54  \n",
       "job                                                   admin.  \n",
       "marital                                              married  \n",
       "education                                           tertiary  \n",
       "default                                                   no  \n",
       "balance                                                  184  \n",
       "housing                                                   no  \n",
       "loan                                                      no  \n",
       "contact                                              unknown  \n",
       "duration                                                 673  \n",
       "campaign                                                   2  \n",
       "pdays                                                     -1  \n",
       "previous                                                   0  \n",
       "poutcome                                             unknown  \n",
       "deposit                                                  yes  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df.take(5), columns=df.columns).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Dataset Count: 7855\n",
      "Test Dataset Count: 3307\n"
     ]
    }
   ],
   "source": [
    "train, test = df.randomSplit([0.7, 0.3], seed = 2018)\n",
    "print(\"Training Dataset Count: \" + str(train.count()))\n",
    "print(\"Test Dataset Count: \" + str(test.count()))\n",
    "#print(\"There are %d training examples and %d test examples.\" % (train.count(), test.count()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import LogisticRegression\n",
    "lr = LogisticRegression(featuresCol = 'features', labelCol = 'label', maxIter=10)\n",
    "lrModel = lr.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "beta = np.sort(lrModel.coefficients)\n",
    "plt.plot(beta)\n",
    "plt.ylabel('Beta Coefficients')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set areaUnderROC: 0.8865478305561797\n"
     ]
    }
   ],
   "source": [
    "trainingSummary = lrModel.summary\n",
    "roc = trainingSummary.roc.toPandas()\n",
    "plt.plot(roc['FPR'],roc['TPR'])\n",
    "plt.ylabel('False Positive Rate')\n",
    "plt.xlabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()\n",
    "print('Training set areaUnderROC: ' + str(trainingSummary.areaUnderROC))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IViJCRVkuL9xxOieVZHHXMxu45L43WVF1wO+yhiQvjwjmAJXOuSrnXCewBLi0zzYOyLDANWTpQAPQ5WFNIhJFCjOH8ZvF87jn6pkcbOnk6odX8He/Ws2eQ21+lzakeBkExUB1r9c1wWW93QdMBvYAG4CvOed6+n6QmS02s9Vmtrqurs6rekUkApkZl84s5rVvLOSb503k9Q/rOe8nr/PbNTVEWx9npPIyCPq77qvvb+08YC0wEpgJ3GdmmZ94k3MPO+cqnHMVBQUFg1+piES8YYnxfOWscbz09QVMHp7JN55ax62/XE3t4Xa/S4t6XgZBDVDa63UJgb/8e7sJeMYFVAI7gEke1iQiUW50XhpLFs/jWxdN5vVt9Vx4zxu8ua3e77KimpdBsAoYb2Zjgh3AVwPP99lmN3A2gJkVAROBKg9rEpEhIC7O+NKCsfzhjtPJSU3ihkff5cevbKVbVxZ9Kp4FgXOuC7gdeBnYDPyXc26jmd1mZrcFN/s+cKqZbQCWAnc65xTtIhKSCUUZPHf7aVwxq4R7X6vkhp+/S2PbEb/Lijq6oUxEhoSnVlfzz7/bwJj8NH5x0xyNctqHbzeUiYiEy5UVpTx+0xz2HmrnCw+8xcY9jX6XFDUUBCIyZJw6Lp/ffvlU4sy4/IG3uX9ZJUe6P3FFuvShIBCRIWXi8EC/wTmTC/nhy1u55Kdvsq76kN9lRTQFgYgMOYUZw3jgutk8dMNsDrZ2cvkDb/HjVz+kS0cH/VIQiMiQdd7U4bz6D2dy+ckl3Lt0G1c+9A67DrT4XVbEURCIyJCWOSyRH101g59eczKVtc1ceM8b/GF933tbY5uCQERiwiUzRvLS189g4vAMbn/iff71xc06VRSkIBCRmFGcncKSxfO5Yd5oHn69iht+vpKmdt2ApiAQkZiSlBDH9y+bxn9cOYNVOxv42pPvx/zQFCEFgZmdZmavmtmHZlZlZjvMTGMCiUjUumJ2Cd+9dCrLttbxg5e3+F2OrxJC3O7nwN8Da4Bu78oREQmf6+aOZsvewzy0vIrygnSuqigd+E1DUKhB0Oic+6OnlYiI+ODbl0xhR30Ldz69HhxcdUrshUGofQTLzOyHZjbfzGZ9/PC0MhGRMEiMj+NnN1awYHwB//T0eh5/e6ffJYVdqEcEc4M/e49c54BFg1uOiEj4pSTF87MbZ3P7E+/znec3cqS7hy8tGOt3WWETUhA4587yuhARET8lJ8TzwHWz+PqStfzLC5vpcY7FZ5T7XVZYhBQEZpYFfAc4I7hoOfA955zGeRWRISMxPo57rp6JGfzri1swjFvPGPpHBqH2ETwKHAauCj6agF94VZSIiF8S4uP4yd/M5MKThvNvf9wcEyOXhhoE5c657zjnqoKP7wJDPyZFJCYlxMdx9xenk5+ezJ1Prx/ycxqEGgRtZnb6xy/M7DSgzZuSRET8lzkske9fNo0t+w5z/7JKv8vxVKhXDX0ZeDzYV2BAA/DfvCpKRCQSnDd1OJfMGMlP/rSN5vYu7rpgEgnxQ29knlCvGloLzDCzzODrJk+rEhGJED++aga5qYk88uYONu1t4v5rZ5GTluR3WYPquEFgZtc7535tZv/QZzkAzrkfe1ibiIjvEuPj+O6l05hWnMX/fPYD7njyfX51y5yj34NDwUDHOGnBnxnHeIiIxIQrK0r5XxdN5s3Kep557yO/yxlUxz0icM49FPz53fCUIyISua6bO5pn1+7h+y9s4syJBeSnJ/td0qAIdRjqH5hZppklmtlSM6s3s+u9Lk5EJJLExRn//sWTaO3o5vYn3qOja2gMxhxq9/fngh3EFwM1wATgm55VJSISocYVZvCDK6azoqqBbz61np4hMKlNqJePJgZ/Xgg86ZxrGEodJSIiJ+Kyk4vZ09jGD17ayrjCdL569ni/S/pMQj0i+L2ZbSEw+uhSMysA2r0rS0Qksn35zHIuOmkEDy7fTmNrdM97HFIQOOfuAuYDFc65I0ALcKmXhYmIRDIz446zx9Ha2c2vVuz0u5zPZKD7CBY5514zsy/0WtZ7k2e8KkxEJNJNGp7JwokF/OKtnfztqWVkDEsc+E0RaKAjgjODPy/p53Gxh3WJiESFOxaNp7HtCH/76Eqa2qPzFJE5512Pt5mdD9wDxAOPOOfu7rP+m8B1wZcJwGSgwDnXcKzPrKiocKtXr/aoYhGRE/fSB/u4/Yn3mFqcxS9vnkNWSuQdGZjZGudcRX/rQr2P4F/NLLvX6xwz+5cB3hMP3A9cAEwBrjGzKb23cc790Dk30zk3E/gfwPLjhYCISCQ6f9pw/vP62Wza08gtj62i/Uh03V8Q6lVDFzjnjs7O4Jw7SOBS0uOZA1QG5y/oBJZw/A7ma4AnQ6xHRCSinDuliJ/8zcms2X2Q2594j64omsMg1CCIN7Oj91KbWQow0L3VxUB1r9c1wWWfYGapwPnA08dYv9jMVpvZ6rq6uhBLFhEJr4umj+B7n5/KnzbX8h+vfOh3OSELNQh+TeD+gVvM7GbgVeDxAd7T3x1nx+qQuAR461inhZxzDzvnKpxzFQUFBSGWLCISfjfML+PzM0by6xW7aO7o8ruckIR6H8EPgH8h0Jk7Ffh+cNnx1AClvV6XAHuOse3V6LSQiAwRN58+huaOLp5eU+N3KSE5kal2NgMvOef+EXjDzAYahnoVMN7MxphZEoEv++f7bhSc9exM4LkTqEVEJGLNLM1mRmk2j7+zMyrGIgr1qqFbgd8CDwUXFQPPHu89zrku4HbgZQIh8l/OuY1mdpuZ3dZr08uBV5xzLSdavIhIpLr5tDKq6lpY/mHk92uGekTwFeA0oAnAObcNKBzoTc65F51zE5xz5c65/xNc9qBz7sFe2zzmnLv6xEsXEYlcF0wbQVFmMo++tcPvUgYUahB0BC8BBcDMEjh2x6+ISMxLSojjxvllvLGtnq37DvtdznGFGgTLzeyfgRQzOxd4Cvi9d2WJiES/a+eMIikhLuIHpQs1CO4E6oANwN8BLwLf8qooEZGhICctiUumj+R3733E4Qgeh2jAIDCzOGCDc+5nzrkrnXNXBJ/r1JCIyABumD+als5unloduZeSDhgEzrkeYJ2ZjQpDPSIiQ8qMkizmj83jnqXbONDc4Xc5/Qr11NAIYGNw4vrnP354WZiIyFBgZnzv0qm0dHRx9x+3+F1Ov0Kds/i7nlYhIjKEjS/K4JYFY3hoeRXXzB3FrFE5fpf0V457RGBmw8zs68CVwCQC4wEt//gRlgpFRIaAOxaNpyAjme/9fhOR1sU60KmhxwlMWL+BwLwCP/K8IhGRISg9OYF/Om8ia6sP8dzaYw275o+BgmCKc+5659xDwBXAgjDUJCIyJH1xVglTRmTyf//0YUTNVzBQEBy98DU4dpCIiHxKcXHG184Zz64DrRF1VDBQEMwws6bg4zAw/ePnZtYUjgJFRIaSz00pYvKITO5fVhkxI5MeNwicc/HOuczgI8M5l9DreWa4ihQRGSrMjNvOHEtVfQtvVNb7XQ5wYvMRiIjIILhg2gjy05P55ds7/S4FUBCIiIRdUkIcV59Symtba2lo6Rz4DR5TEIiI+OC0cfk4B+tqDvldioJARMQP00uyiDNYu1tBICISk9KSE5hQlMErm/b7fk+BgkBExCe3LxrH5r1Nvk9nqSAQEfHJRSeN4NwpRfzolQ/Z19juWx0KAhERn5gZd54/iY6uHl7ZtM+3OhQEIiI+GleYTlleKks31/pWg4JARMRniyYV8U7VAdqPdPuyfwWBiIjP5pfn0dnVw/qaRl/2ryAQEfHZ7NGBGctW72rwZf8KAhERn+WmJTGuMJ0/b63zZf8KAhGRCHDl7BJW7mhg457wnx5SEIiIRICrTxlFalI8j721M+z7VhCIiESArNREFozPZ83ug2Hft4JARCRCjMlPp7qhle4wz1ymIBARiRBleakc6XbUHGwN6349DQIzO9/MtppZpZnddYxtFprZWjPbaGbLvaxHRCSSzSjNBuDdqvBeRupZEJhZPHA/cAEwBbjGzKb02SYbeAD4vHNuKnClV/WIiES6ScMzGJ45jGVbwzvchJdHBHOASudclXOuE1gCXNpnm2uBZ5xzuwGcc/4NtiEi4jMzY+HEAt7YVk9nV/jmKPAyCIqB6l6va4LLepsA5JjZn81sjZnd2N8HmdliM1ttZqvr6vy54UJEJBwWTSqkuaOL1TvDd3rIyyCwfpb17QpPAGYDFwHnAf/LzCZ84k3OPeycq3DOVRQUFAx+pSIiEeK0cfkkJcSxdEv4TpAkePjZNUBpr9clwJ5+tql3zrUALWb2OjAD+NDDukREIlZgCst0quqaw7ZPL48IVgHjzWyMmSUBVwPP99nmOWCBmSWYWSowF9jsYU0iIhEvPz2Z+ubOsO3PsyMC51yXmd0OvAzEA4865zaa2W3B9Q865zab2UvAeqAHeMQ594FXNYmIRIO8tGS27D0ctv15eWoI59yLwIt9lj3Y5/UPgR96WYeISDQZW5DG0+/V0Nh2hKyURM/3pzuLRUQizNSRmQBs2tMUlv0pCEREIsyUYBBs3qsgEBGJSQXpyWSlJFIZpiuHFAQiIhHGzBhXmE5lrYJARCRmjcpNZW9jW1j2pSAQEYlABRnJ1B3uwDnv5yZQEIiIRKCC9GTaj/RwuKPL830pCEREIlBBRjIAtU0dnu9LQSAiEoEKg0FQd1hBICISk0ZmpwCwu6HF830pCEREIlBpbirDEuPYus/7S0gVBCIiESg+LngvQRhuKlMQiIhEqJFZKexvbPd8PwoCEZEIVZQ5jH1NCgIRkZg1Oi+VxrYjNLR4O0mNgkBEJEJNGh6eUUgVBCIiEWp8UToA2z3uMFYQiIhEqPz0ZOLjjH0edxgrCEREIlR8nFGQnkytx3cXKwhERCJYWnI8bZ3dnu5DQSAiEsGSE+JpP6IgEBGJWcOzhlFz0NsJahQEIiIRbNrITLbVHvb09JCCQEQkgk0YnkGPg10ejkKqIBARiWAjsgLDUe895N0lpAoCEZEIVpLz8bwErZ7tQ0EgIhLBCjOSSU9OoMrDu4sVBCIiEczMKMtPZecBHRGIiMSsvLRkDrZ6NwKpgkBEJMLlpSV5Oom9gkBEJMKNLUhjb2M7LR1dnny+p0FgZueb2VYzqzSzu/pZv9DMGs1sbfDxbS/rERGJRuMKA8NR76j35l6CBE8+FTCzeOB+4FygBlhlZs875zb12fQN59zFXtUhIhLtCjKSAahv9ub0kJdHBHOASudclXOuE1gCXOrh/kREhqTs1CQADrUe8eTzvQyCYqC61+ua4LK+5pvZOjP7o5lN9bAeEZGolBgX+Kru7nGefL5np4YA62dZ31a8B4x2zjWb2YXAs8D4T3yQ2WJgMcCoUaMGu04RkajgTQx4e0RQA5T2el0C7Om9gXOuyTnXHHz+IpBoZvl9P8g597BzrsI5V1FQUOBhySIikSdjWOBv9qa26Ds1tAoYb2ZjzCwJuBp4vvcGZjbczCz4fE6wngMe1iQiEnWyUhKJM2ho8eamMs9ODTnnuszsduBlIB541Dm30cxuC65/ELgC+LKZdQFtwNXOOa+OfkREolJcnJEQH0dXFPYRfHy658U+yx7s9fw+4D4vaxARkePTncUiIjFOQSAiEuMUBCIiMU5BICIS4xQEIiIxTkEgIhLjFAQiIjFOQSAiEgXOnzqcScMzPPlsT28oExGRwXHvNSd79tk6IhARiXEKAhGRGKcgEBGJcQoCEZEYpyAQEYlxCgIRkRinIBARiXEKAhGRGGfRNjOkmdUBuz7l2/OB+kEsJxqozbFBbY4Nn6XNo51zBf2tiLog+CzMbLVzrsLvOsJJbY4NanNs8KrNOjUkIhLjFAQiIjEu1oLgYb8L8IHaHBvU5tjgSZtjqo9AREQ+KdaOCEREpA8FgYhIjBuSQWBm55vZVjOrNLO7+llvZnZvcP16M5vlR52DKYQ2Xxds63oze9vMZvhR52AaqM29tjvFzLrN7Ipw1ueFUNpsZgvNbK2ZbTSz5eGucbCF8G87y8x+b2brgm2+yY86B4uZPWpmtWb2wTHWD/73l3NuSD2AeGA7MBZIAtYBU/pscyHwR8CAecC7fjzc85YAAARISURBVNcdhjafCuQEn18QC23utd1rwIvAFX7XHYbfczawCRgVfF3od91haPM/A/8efF4ANABJftf+Gdp8BjAL+OAY6wf9+2soHhHMASqdc1XOuU5gCXBpn20uBX7pAlYA2WY2ItyFDqIB2+yce9s5dzD4cgVQEuYaB1sov2eAO4CngdpwFueRUNp8LfCMc243gHMu2tsdSpsdkGFmBqQTCIKu8JY5eJxzrxNow7EM+vfXUAyCYqC61+ua4LIT3SaanGh7biHwF0U0G7DNZlYMXA48GMa6vBTK73kCkGNmfzazNWZ2Y9iq80Yobb4PmAzsATYAX3PO9YSnPF8M+vfXUJy83vpZ1vca2VC2iSYht8fMziIQBKd7WpH3QmnzT4A7nXPdgT8Wo14obU4AZgNnAynAO2a2wjn3odfFeSSUNp8HrAUWAeXAq2b2hnOuyevifDLo319DMQhqgNJer0sI/KVwottEk5DaY2bTgUeAC5xzB8JUm1dCaXMFsCQYAvnAhWbW5Zx7NjwlDrpQ/23XO+dagBYzex2YAURrEITS5puAu13gBHqlme0AJgErw1Ni2A3699dQPDW0ChhvZmPMLAm4Gni+zzbPAzcGe9/nAY3Oub3hLnQQDdhmMxsFPAPcEMV/HfY2YJudc2Occ2XOuTLgt8B/j+IQgND+bT8HLDCzBDNLBeYCm8Nc52AKpc27CRwBYWZFwESgKqxVhtegf38NuSMC51yXmd0OvEzgioNHnXMbzey24PoHCVxBciFQCbQS+IsiaoXY5m8DecADwb+Qu1wUj9wYYpuHlFDa7JzbbGYvAeuBHuAR51y/lyFGgxB/z98HHjOzDQROm9zpnIva4anN7ElgIZBvZjXAd4BE8O77S0NMiIjEuKF4akhERE6AgkBEJMYpCEREYpyCQEQkxikIRERinIJApB/B0UrXmtkHwZEtswf583eaWX7wefNgfrbIiVIQiPSvzTk30zk3jcAAYF/xuyARrygIRAb2DsFBvcys3MxeCg7o9oaZTQouLzKz3wXHxF9nZqcGlz8b3HajmS32sQ0ixzTk7iwWGUxmFk9g+IKfBxc9DNzmnNtmZnOBBwgMdnYvsNw5d3nwPenB7W92zjWYWQqwysyeHgLjPMkQoyAQ6V+Kma0FyoA1BEa0TCcwwc9TvUYzTQ7+XATcCOCc6wYag8u/amaXB5+XAuMBBYFEFAWBSP/anHMzzSwL+AOBPoLHgEPOuZmhfICZLQTOAeY751rN7M/AMG/KFfn01EcgchzOuUbgq8A3gDZgh5ldCUfnjv147uelwJeDy+PNLBPIAg4GQ2ASgWkFRSKOgkBkAM659wnMlXs1cB1wi5mtAzbyl2kTvwacFRwBcw0wFXgJSDCz9QRGyFwR7tpFQqHRR0VEYpyOCEREYpyCQEQkxikIRERinIJARCTGKQhERGKcgkBEJMYpCEREYtz/B3CkROOC7BpUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pr = trainingSummary.pr.toPandas()\n",
    "plt.plot(pr['recall'],pr['precision'])\n",
    "plt.ylabel('Precision')\n",
    "plt.xlabel('Recall')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+----------+-----+--------------------+----------+--------------------+\n",
      "|age|       job|label|       rawPrediction|prediction|         probability|\n",
      "+---+----------+-----+--------------------+----------+--------------------+\n",
      "| 33|management|  0.0|[1.93500470498078...|       0.0|[0.87380233100701...|\n",
      "| 49|management|  0.0|[2.04809361205494...|       0.0|[0.88575484704982...|\n",
      "| 52|management|  0.0|[-0.7002122133357...|       1.0|[0.33176517908283...|\n",
      "| 53|management|  0.0|[1.03431610227355...|       0.0|[0.73775180622517...|\n",
      "| 58|management|  0.0|[2.74273671692568...|       0.0|[0.93950183380104...|\n",
      "| 32|management|  0.0|[1.25033004378243...|       0.0|[0.77735698810450...|\n",
      "| 57|management|  0.0|[1.19540519867322...|       0.0|[0.76770638557283...|\n",
      "| 52|management|  0.0|[3.26456982709638...|       0.0|[0.96319314409492...|\n",
      "| 46|management|  0.0|[2.15036209979034...|       0.0|[0.89570260888977...|\n",
      "| 31|management|  0.0|[1.47751802200863...|       0.0|[0.81419739986183...|\n",
      "+---+----------+-----+--------------------+----------+--------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = lrModel.transform(test)\n",
    "predictions.select('age', 'job', 'label', 'rawPrediction', 'prediction', 'probability').show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Area Under ROC 88.37112925002687\n"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
    "evaluator = BinaryClassificationEvaluator()\n",
    "print('Test Area Under ROC', evaluator.evaluate(predictions)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+----------+-----+--------------+----------+--------------------+\n",
      "|age|       job|label| rawPrediction|prediction|         probability|\n",
      "+---+----------+-----+--------------+----------+--------------------+\n",
      "| 33|management|  0.0|[2498.0,481.0]|       0.0|[0.83853642161799...|\n",
      "| 49|management|  0.0|[2498.0,481.0]|       0.0|[0.83853642161799...|\n",
      "| 52|management|  0.0|[520.0,1931.0]|       1.0|[0.21215830273357...|\n",
      "| 53|management|  0.0|[2498.0,481.0]|       0.0|[0.83853642161799...|\n",
      "| 58|management|  0.0|[2498.0,481.0]|       0.0|[0.83853642161799...|\n",
      "| 32|management|  0.0|[2498.0,481.0]|       0.0|[0.83853642161799...|\n",
      "| 57|management|  0.0|[2498.0,481.0]|       0.0|[0.83853642161799...|\n",
      "| 52|management|  0.0|[2498.0,481.0]|       0.0|[0.83853642161799...|\n",
      "| 46|management|  0.0|[2498.0,481.0]|       0.0|[0.83853642161799...|\n",
      "| 31|management|  0.0|[2498.0,481.0]|       0.0|[0.83853642161799...|\n",
      "+---+----------+-----+--------------+----------+--------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.classification import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier(featuresCol = 'features', labelCol = 'label', maxDepth = 3)\n",
    "dtModel = dt.fit(train)\n",
    "predictions = dtModel.transform(test)\n",
    "predictions.select('age', 'job', 'label', 'rawPrediction', 'prediction', 'probability').show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Area Under ROC: 78.08118726917547\n"
     ]
    }
   ],
   "source": [
    "evaluator = BinaryClassificationEvaluator()\n",
    "print(\"Test Area Under ROC: \" + str(evaluator.evaluate(predictions, {evaluator.metricName: \"areaUnderROC\"})*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+----------+-----+--------------------+----------+--------------------+\n",
      "|age|       job|label|       rawPrediction|prediction|         probability|\n",
      "+---+----------+-----+--------------------+----------+--------------------+\n",
      "| 33|management|  0.0|[15.6540933043595...|       0.0|[0.78270466521797...|\n",
      "| 49|management|  0.0|[15.3229105243638...|       0.0|[0.76614552621819...|\n",
      "| 52|management|  0.0|[6.99148109353427...|       1.0|[0.34957405467671...|\n",
      "| 53|management|  0.0|[14.2760622264025...|       0.0|[0.71380311132012...|\n",
      "| 58|management|  0.0|[15.8365151435869...|       0.0|[0.79182575717934...|\n",
      "| 32|management|  0.0|[15.3527668080084...|       0.0|[0.76763834040042...|\n",
      "| 57|management|  0.0|[14.8487362415329...|       0.0|[0.74243681207664...|\n",
      "| 52|management|  0.0|[17.1570366000320...|       0.0|[0.85785183000160...|\n",
      "| 46|management|  0.0|[16.1662856002583...|       0.0|[0.80831428001291...|\n",
      "| 31|management|  0.0|[15.0422225190135...|       0.0|[0.75211112595067...|\n",
      "+---+----------+-----+--------------------+----------+--------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.classification import RandomForestClassifier\n",
    "rf = RandomForestClassifier(featuresCol = 'features', labelCol = 'label')\n",
    "rfModel = rf.fit(train)\n",
    "predictions = rfModel.transform(test)\n",
    "predictions.select('age', 'job', 'label', 'rawPrediction', 'prediction', 'probability').show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Area Under ROC: 87.67880207006243\n"
     ]
    }
   ],
   "source": [
    "evaluator = BinaryClassificationEvaluator()\n",
    "print(\"Test Area Under ROC: \" + str(evaluator.evaluate(predictions, {evaluator.metricName: \"areaUnderROC\"})*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosted Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+----------+-----+--------------------+----------+--------------------+\n",
      "|age|       job|label|       rawPrediction|prediction|         probability|\n",
      "+---+----------+-----+--------------------+----------+--------------------+\n",
      "| 33|management|  0.0|[0.94992370947246...|       0.0|[0.86987425549896...|\n",
      "| 49|management|  0.0|[1.28281748604659...|       0.0|[0.92861688965145...|\n",
      "| 52|management|  0.0|[-0.5021429054421...|       1.0|[0.26809961471379...|\n",
      "| 53|management|  0.0|[0.09646513340517...|       0.0|[0.54808351175538...|\n",
      "| 58|management|  0.0|[1.05831377292163...|       0.0|[0.89250881497055...|\n",
      "| 32|management|  0.0|[0.57268875896254...|       0.0|[0.75866558918304...|\n",
      "| 57|management|  0.0|[0.34932946844840...|       0.0|[0.66789037418062...|\n",
      "| 52|management|  0.0|[1.33264456943866...|       0.0|[0.93494709840561...|\n",
      "| 46|management|  0.0|[1.28262818068783...|       0.0|[0.92859178839697...|\n",
      "| 31|management|  0.0|[1.16094954405484...|       0.0|[0.91067454521219...|\n",
      "+---+----------+-----+--------------------+----------+--------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.classification import GBTClassifier\n",
    "gbt = GBTClassifier(maxIter=10)\n",
    "gbtModel = gbt.fit(train)\n",
    "predictions = gbtModel.transform(test)\n",
    "predictions.select('age', 'job', 'label', 'rawPrediction', 'prediction', 'probability').show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Area Under ROC: 89.35091626908479\n"
     ]
    }
   ],
   "source": [
    "evaluator = BinaryClassificationEvaluator()\n",
    "print(\"Test Area Under ROC: \" + str(evaluator.evaluate(predictions, {evaluator.metricName: \"areaUnderROC\"})*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8954659543871663"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n",
    "paramGrid = (ParamGridBuilder()\n",
    "             .addGrid(gbt.maxDepth, [2, 4, 6])\n",
    "             .addGrid(gbt.maxBins, [20, 60])\n",
    "             .addGrid(gbt.maxIter, [10, 20])\n",
    "             .build())\n",
    "cv = CrossValidator(estimator=gbt, estimatorParamMaps=paramGrid, evaluator=evaluator, numFolds=5)\n",
    "# Run cross validations.  This can take about 6 minutes since it is training over 20 trees!\n",
    "cvModel = cv.fit(train)\n",
    "predictions = cvModel.transform(test)\n",
    "evaluator.evaluate(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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