Module_1g_Exercise_LogisticRegression-Completed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise - Logistic Regression\n",
    "\n",
    "The client bank *XYZ* is running a direct marketing (phone calls) campaign. The classification goal is to predict if the client will subscribe a term deposit or not.\n",
    "\n",
    "The data is obtained from [UCI Machine Learning repository]( http://mlr.cs.umass.edu/ml/datasets/Bank+Marketing)\n",
    "\n",
    "### Attribute Information:\n",
    "\n",
    "#### bank client data:\n",
    "- **age**: (numeric)\n",
    "- **job** : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')\n",
    "- **marital** : marital status (categorical: 'divorced', 'married', 'single', 'unknown'; note: 'divorced' means divorced or widowed)\n",
    "- **education** (categorical: 'basic.4y', 'basic.6y', 'basic.9y','high.school', 'illiterate' ,'professional.course', 'university.degree', 'unknown')\n",
    "- **default**: has credit in default? (categorical: 'no','yes','unknown')\n",
    "- **housing: has housing loan? (categorical: 'no','yes','unknown')\n",
    "- **loan**: has personal loan? (categorical: 'no','yes','unknown')\n",
    "\n",
    "#### related with the last contact of the current campaign:\n",
    "- **contact**: contact communication type (categorical: 'cellular', 'telephone') \n",
    "- **month**: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
    "- **day**: last contact day of the month (numerical: 1, 2, 3, 4, ...)\n",
    "- **duration**: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.\n",
    "\n",
    "#### other attributes:\n",
    "- **campaign**: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "- **pdays**: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)\n",
    "- **previous**: number of contacts performed before this campaign and for this client (numeric)\n",
    "- **poutcome**: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')\n",
    "\n",
    "#### Output variable (desired target):\n",
    "- **deposit** - has the client subscribed a term deposit? (binary: 'yes','no')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#1. import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.rcParams['figure.figsize'] = (10, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#2. import data\n",
    "bank = pd.read_csv(\"data/bank.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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",
       "      <td>5</td>\n",
       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3. Display first few records\n",
    "bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
       "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'deposit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4. check column names\n",
    "bank.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#5. do label encoding\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bank_e = bank.apply(preprocessing.LabelEncoder().fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2288</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1021</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>469</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1283</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1618</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1246</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2356</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>577</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>608</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>671</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0   41    0        1          1        0     2288        1     0        2   \n",
       "1   38    0        1          1        0      469        0     0        2   \n",
       "2   23    9        1          1        0     1618        1     0        2   \n",
       "3   37    7        1          1        0     2356        1     0        2   \n",
       "4   36    0        1          2        0      608        0     0        2   \n",
       "\n",
       "   day  month  duration  campaign  pdays  previous  poutcome  deposit  \n",
       "0    4      8      1021         0      0         0         3        1  \n",
       "1    4      8      1283         0      0         0         3        1  \n",
       "2    4      8      1246         0      0         0         3        1  \n",
       "3    4      8       577         0      0         0         3        1  \n",
       "4    4      8       671         1      0         0         3        1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#6. do some exploratory data analysis\n",
    "bank_e.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10e5d1eb8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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kFStWaNKkSerRo0fM9bRv356vVvhKTU1VQYGlvDz7a/jwxu9G2LHDHfV4PC68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\nLV+eEjYnhwyx52BNjVRVZSg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EqmyxnTt3SpIKCgoaHevSpYvS0tKC50TzySefSLIT+Pvv\nv19er1eSZFmW8vLy9NJLL2ns2LFJbDkAAAAA4FyV0E/Kly9fHkxSm7Nx40YtX748keoSUlVVJUnK\nzMyMeDw9PT14TjTHjh2TZH+TYfbs2dq8ebN27typxx57TFVVVbrxxht15MiR5DUcAAAAAHDOOmNv\nX/f7/efE71sHPsjtiiuu0H/9138pPz9f2dnZ+u53v6vvfe97qqqq0quvvupwKwEAAAAAZ4Mz8unr\nlmVp7969Sk9PPxPVRZSRkSFJqqysjHi8urpaWVlZMZVTXl6uK664otGxK6+8Ur/73e/0r3/9K6Y2\n1dbWxnQekivwKweB/+LMov+dxxg4jzFwFv3vPMbAeYyBs+h/Z7Vv397pJoSJKynftGmTNm7cGPba\nsWPHon5q+alTp7Rs2TIdPHjQ0d+17tevnyRp165dGjZsWNixI0eOqKamRhdddFGz5QwYMECrV6+O\n+Db4wGuxJtsHDhyQ3++P6Vwk3+HDh51uQptG/zuPMXAeY+As+t95jIHzGANn0f9nntvtjvg5Y06K\nKymfP3++HnvssbDXdu7cqdmzZ0e9zrIsuVyuZs9rTcXFxXriiSf0wQcf6Jprrgk7tnjxYkmK6ZsG\n48aNU2lpqbZv365p06aFHdu2bZskqVevXjG1qXv37jGdh+Tyer06fPiwunbtqtTUVKeb0+bQ/85j\nDJzHGDiL/nceY+A8xsBZ9D9CxZWU9+rVS2PGjAn+/4oVK5Senq6hQ4dGPN8wDHXs2FF9+/bVN77x\nDX31q19NrLUJGD9+vPr06aM333xTt912W7DNlZWVeuKJJ9SuXTtdd911wfPLy8t1/Phx5ebmKicn\nJ/j6DTfcoCeffFLPPfecZs6cqfz8fEn2299/85vfyDAM/cd//EdMbfp3e9tEW5OamsoYOIj+dx5j\n4DzGwFn0v/MYA+cxBs6i/yHFmZTPnDlTM2fODP5/dna2hgwZovnz5ye9Ycnmdrv15JNP6tprr9XU\nqVP1f/7P/1FaWprmzZunffv26eGHH1bPnj2D5z/77LN67LHHNGfOHN17773B13v37q0HH3xQc+bM\n0dixYzVt2jSlpqZq4cKF+vzzz/Wd73xHl1xyiRO3CAAAAAA4yyT0QW/vvvtu8APUzgbjxo1TSUmJ\nfvWrX2nu3Lny+XwaMmSIHnroIU2fPj3sXMMwgl8N3Xbbberdu7eefPLJYDmDBw/WT37yE914441n\n6nYAAAAAAGe5hJJyJz+4raWGDx+uN954o9nz5syZozlz5jR5/PLLL9fll1+ezKYBAAAAANqYs/8P\nhwMAAAAAcJaK+SflgT8jVlBQoLlz54a9FivDMLR+/fq4rgEAAAAA4FwVc1K+d+9eSeGfGB54LVaR\nfj8bAAAAAIC2Kuak/N1335UkdezYsdFrAAAAAAAgfjEn5ZE+1O1s/KA3AAAAAAD+XfBBbwAAAAAA\nOCShP4kWjWVZ2rBhgw4ePKgLLrhA3bt3b62qAAAAAAA4KyX0k/Jly5bp9ttv1zvvvBP2enl5uaZM\nmaJLL71UM2fO1LBhw/T0008n1FAAAAAAAM41CSXlf/3rX/W3v/1NXbp0CXv9F7/4hUpLSyVJGRkZ\n8vl8uv/++4OvAQAAAACABJPydevWqVOnTho9enTwtS+++EL/+7//q06dOmnFihXavXu3fvnLX8qy\nLP3hD39IuMEAAAAAAJwrEkrKjx49qvz8/LDXSktLderUKU2bNk2FhYWSpO9///vKzMzkJ+UAAAAA\nAIRIKCmvqqpSenp62Gtr1qyRYRiaMGFC8DWPx6PevXvr6NGjiVQHAAAAAMA5JaGkPC0tTYcOHQp7\nbeXKlZKkUaNGhb1uGIZSUlISqQ4AAAAAgHNKQkl5YWGhDh48GEzEd+/erVWrVql79+7q06dP2Lmf\nf/658vLyEqkOAAAAAIBzSkJJ+Te/+U1ZlqXrr79eN910k6688kqZpqnrr78+7LyysjKVl5dr0KBB\nCTUWAAAAAIBzSUJJ+U033aQZM2aoqqpK8+fP16FDhzRmzBj953/+Z9h5f/vb3yRJ48aNS6Q6AAAA\nAADOKZ5ELjYMQ88995zuvPNOlZWVqWfPnhoxYkSj8/r3769HH31U06dPT6Q6AAAAAADOKQkl5QFD\nhw7V0KFDmzz+jW98IxnVAAAAAABwTkno7esAAAAAAKDlkvKT8urqar3yyitauHChysrKVFNTo7S0\nNA0cOFCXX365brzxxkZ/zxwAAAAAgLYu4aT8448/1re+9S0dOHBAlmUFX6+urtbBgwe1dOlSPfXU\nU3r11Vc1fPjwRKsDAAAAAOCckVBSfvjwYc2YMUPl5eVKT0/XTTfdpCFDhqhbt246dOiQtmzZoj/9\n6U/av3+/ZsyYoRUrVqhr167JajsAAAAAAGe1hJLyJ598UuXl5Ro/frxefvllZWVlNTrnnnvu0be/\n/e5CJgEAACAASURBVG0tWbJEv//97/Xwww8nUiUAAAAAAOeMhD7obdGiRUpNTdULL7wQMSGXpMzM\nTD333HPyeDxauHBhItUBAAAAAHBOSSgp37dvnwoLC5WXlxf1vM6dO6uwsFD79u1LpDoAAAAAAM4p\nCSXlHo9HdXV1MZ3r9Xrl8STlw94BAAAAADgnJJSU9+vXTzt27ND27dujnrdt2zZt375d/fr1S6Q6\nAAAAAADOKQkl5VdffbVM09RNN92k9evXRzxn/fr1uvHGGyVJ06dPT6Q6AAAAAADOKQm9n/z222/X\nX//6V23fvl0TJ07UqFGjNGTIEHXp0kVHjhzRli1bVFpaKsuyVFhYqNtvvz1Z7QYAAAAA4KyXUFLe\nsWNHvf3227r11lu1YsUKrVq1SqWlpcHjlmVJksaOHavnn39eHTp0SKy1AAAAAACcQxL+5LVu3bpp\n/vz5WrVqlRYuXKiysjLV1NQoLS1NAwcO1Ne+9jWNGjUqGW0FAAAAAOCckrSPQx89erRGjx6drOIA\nAAAAADjnJfRBbwAAAAAAoOWS9pPyDRs2aOHChdqxY4eqq6uVnp6ugQMHavLkyRo2bFiyqgEAAAAA\n4JyRcFJ+9OhR/eAHP9CiRYsknf5wN0kyDEOPPvqoJk+erCeffFJdu3ZNtDoAAAAAAM4ZCSXllZWV\nmjJlinbu3CnLsvTVr35VgwcPDv5JtG3btunjjz/WokWLNG3aNC1evFiZmZnJajsAAAAAAGe1hJLy\nxx9/XJ9++qn69u2rZ555RiNHjmx0zpo1a/S9731PO3fu1OOPP66HHnookSoBAAAAADhnJPRBb/Pm\nzZPL5dLrr78eMSGXpKKiIv35z38Ong8AAAAAAGwJJeWHDh3SoEGDNGjQoKjnDRo0SIMHD9bhw4cT\nqQ4AAAAAgHNKQkl5Xl6ePJ7Y3gHv8XiUm5ubSHUAAAAAAJxTEkrKJ0yYoK1bt+rgwYNRzztw4IC2\nbt2qiRMnJlIdAAAAAADnlISS8p/+9KfKzMzUzTffrP3790c8Z//+/brllluUlZWl++67L5HqgP/P\n3p2HR1Xe/R//nJlJCCGQBAIh7IQlBBSqYMIqIloUcYcHtyoWt0pr61at1j61tpViS7Fa+2ir1v1n\na4ulgLFQF2QJi4giylKQRXYSsgHJZGbO74/TCZNkZjJbcpS8X9eVC51zn/t87/WcbzKZAAAAAMAp\nJa5PX1+2bJlmzpyp3/72tzrjjDM0ZcoU5efn1/uTaAsXLpQk3XnnnXr//feD1nP11VfHEwYAAAAA\nAF9LcSXlt99+uwzDkGmakqQ333xTb775Zr0y/mNz5swJWQ9JOQAAAACgNYorKR89erQMw0hULAAA\nAAAAtCpxJeWLFi1KVBwAAAAAALQ6cX3QGwAAAAAAiB1JOQAAAAAANonr7et+ZWVleumll/T+++9r\n7969OnHihDZs2FB3/O2331ZpaamuvPJKJScnJ+KSAAAAAAB87cWdlK9YsUIzZsxQSUlJ3SetN/zw\nt3Xr1uk3v/mNOnbsqEmTJsV7SQAAAAAATglxvX19165duvrqq3XkyBFNmjRJTz31lPLz8xuVu+KK\nK2SaphYvXhzP5QAAAAAAOKXElZTPmzdPlZWVuvPOO/Xaa6/p6quvVnp6eqNy+fn5Sk9P1+rVq+O5\nHAAAAAAAp5S4kvJ33nlH7dq1049+9KMmy/bq1Ut79+6N53IAAAAAAJxS4krKDxw4oNzcXCUlJTVZ\ntk2bNqqpqYnncgAAAAAAnFLiSsrbtm2rsrKyiMru379fGRkZ8VwOAAAAAIBTSlxJeV5envbt26cv\nv/wybLlNmzZp7969Gjp0aDyXAwAAAADglBJXUn7ppZfK6/XqRz/6kbxeb9Ay1dXVuueee2QYhi6/\n/PJ4LgcAAAAAwCklrqT829/+tvLy8rRo0SJNmjRJf/7zn1VRUSHJ+vvlTz/9tMaNG6fi4mINHTpU\n06dPT0jQ8Vi/fr2mTZum3r17q3v37jr//PP15ptvxlxfbW2txo4dq8zMTBUWFiYwUgAAAADAqc4V\nz8kpKSl64403dNVVV+nDDz/U+vXr645dfPHFkiTTNDV48GC9+uqrcrniulzcli1bpqlTpyolJUVX\nXnml0tLStGDBAt14443au3evZs2aFXWds2fP1q5du2QYRjNEDAAAAAA4lcX1k3JJ6tGjh9599109\n/vjjGj9+vDp16iSn06kOHTpo5MiRmjNnjt59911169YtEfHGzOv16vvf/76cTqfeeust/fa3v9Uj\njzyi5cuXq3///nrkkUea/N34hj788EM9/vjj+ulPfyrTNJspcgAAAADAqSrupFySkpKSdP3112v+\n/Pnatm2bDh8+rJ07d2rx4sW6+eablZycnIjLxGX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tybiHD/eoVy9TtbVSRoZX7dpJPp+h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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e5f6208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot balance vs deposit\n",
    "plt.scatter(bank_e.balance, \n",
    "bank_e.deposit,\n",
    "           s=150)\n",
    "plt.xlabel('balance')\n",
    "plt.ylabel('deposit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1111bc828>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X7LGWC1c2cFv9/dHW3blz43O4OfokEXP1/PO9WrAgJaExxnP+BdYTyfh37mwq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\n49OmTWf6d/x4jwYP9urIEat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Thw45K5xn3br5lJRkjefWrWXXwzZt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\n79m2rgTPU7uvN3V9E2Y41x///10uq5312eZw7hfKl1XffV5THU6cMHT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vqJB/crKpSy91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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e6a9160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot duration vs deposit\n",
    "plt.scatter(bank_e.duration, \n",
    "bank_e.deposit,\n",
    "           s=150)\n",
    "plt.xlabel('balance')\n",
    "plt.ylabel('deposit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#7. Build logistic regression model\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#8. Build L2 logistic regression model\n",
    "model_logistic_l2 = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logistic_l2.fit(bank_e.iloc[:,:16], bank_e.iloc[:,16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.39131423e-03,   1.04630926e-02,   1.82253065e-01,\n",
       "          2.05163223e-01,  -2.83499942e-01,   2.25858269e-04,\n",
       "         -1.06173681e+00,  -7.03062975e-01,  -6.74726785e-01,\n",
       "         -9.53047484e-03,   3.30559565e-02,   5.22986138e-03,\n",
       "         -1.31484677e-01,   3.32804702e-03,   1.27949904e-01,\n",
       "          1.69222679e-01]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logistic_l2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#9. Build L1 logistic regression model\n",
    "\n",
    "model_logistic_l1 = LogisticRegression(penalty=\"l1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.62204522e-03,   1.08722084e-02,   1.85516456e-01,\n",
       "          2.07293217e-01,  -2.58272079e-01,   2.28094656e-04,\n",
       "         -1.06078174e+00,  -6.86212414e-01,  -6.75458792e-01,\n",
       "         -9.38798206e-03,   3.34503745e-02,   5.23595218e-03,\n",
       "         -1.31234717e-01,   3.37434296e-03,   1.28626865e-01,\n",
       "          1.74566923e-01]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logistic_l1.fit(bank_e.iloc[:,:16], bank_e.iloc[:,16])\n",
    "model_logistic_l1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.01659869,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.00012844,  0.        ,  0.        ,  0.        , -0.03488104,\n",
       "        -0.07538053,  0.00362568, -0.06279656,  0.00249069,  0.        ,\n",
       "        -0.03541971]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#10. Build L1 logistic regression model with different values of C\n",
    "model_logistic_l1 = LogisticRegression(penalty=\"l1\", C=0.001)\n",
    "model_logistic_l1.fit(bank_e.iloc[:,:16], bank_e.iloc[:,16])\n",
    "model_logistic_l1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#10. Find generalization error. Use 80/20 split\n",
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(bank_e.iloc[:,:16],\n",
    "bank_e.iloc[:,16],test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_test_split(bank_e.iloc[:,:16], bank_e.iloc[:,16],test_size=0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((11162, 17), (8929, 16), (8929,), (2233, 16), (2233,))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_e.shape, x_train.shape, y_train.shape, x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.54151087e-03,   1.36789433e-02,   2.01141115e-01,\n",
       "          2.28293670e-01,  -2.42196203e-01,   2.05421093e-04,\n",
       "         -1.06519532e+00,  -7.07532716e-01,  -6.62971576e-01,\n",
       "         -1.23241517e-02,   3.08375020e-02,   5.37293247e-03,\n",
       "         -1.32053481e-01,   3.61355118e-03,   1.34697585e-01,\n",
       "          1.82823457e-01]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using L2 Logistic Regression\n",
    "\n",
    "model_logistic_l2 = LogisticRegression()\n",
    "model_logistic_l2.fit(x_train, y_train)\n",
    "model_logistic_l2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#11. report mis-classification rate\n",
    "l2_predict = model_logistic_l2.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logistic_l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Prediction  Actual\n",
       "0           0       0\n",
       "1           1       0\n",
       "2           1       1\n",
       "3           1       1\n",
       "4           1       0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = pd.DataFrame([l2_predict, y_test]).T\n",
    "report.columns = [\"Prediction\", \"Actual\"]\n",
    "report.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "report.match = (report.Prediction == report.Actual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "misclassification_rate = 100 - report.match.sum()/report.shape[0]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.6000895656\n"
     ]
    }
   ],
   "source": [
    "print(misclassification_rate)"
   ]
  }
 ],
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