Machine Learning 4th Data Science Project.ipynb

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    "# Machine Learning: 4th Data Science Project\n",
    "\n",
    "### Overview\n",
    "In this project I will be applying my knowledge of supervised machine learning to build a classifier. \n",
    "Specifically, I’ll be attempting to classify whether potential customers will be persuaded to become customers of a bank.\n",
    "\n",
    "### Concepts covered:\n",
    "* Cleaning and preparing data\n",
    "* Exploring and visualizing data\n",
    "* Model selection\n",
    "* Improving machine learning model performance\n",
    "\n",
    "### Description of the data set\n",
    "The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.\n",
    "\n",
    "There are four datasets:\n",
    "1. bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014]\n",
    "2. bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs.\n",
    "3. bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs).\n",
    "4. bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs).\n",
    "The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM).\n",
    "\n",
    "The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).\n",
    "\n",
    "### Attribute information:\n",
    "\n",
    "Input variables:\n",
    "##### bank client data:\n",
    "1. age (numeric)\n",
    "2. job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')\n",
    "3. marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)\n",
    "4. education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')\n",
    "5. default: has credit in default? (categorical: 'no','yes','unknown')\n",
    "6. housing: has housing loan? (categorical: 'no','yes','unknown')\n",
    "7. loan: has personal loan? (categorical: 'no','yes','unknown')\n",
    "\n",
    "##### related with the last contact of the current campaign:\n",
    "8. contact: contact communication type (categorical: 'cellular','telephone')\n",
    "9. month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
    "10. day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')\n",
    "11. 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",
    "12. campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "13. 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",
    "14. previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "15. poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')\n",
    "\n",
    "##### social and economic context attributes\n",
    "16. emp.var.rate: employment variation rate - quarterly indicator (numeric)\n",
    "17. cons.price.idx: consumer price index - monthly indicator (numeric)\n",
    "18. cons.conf.idx: consumer confidence index - monthly indicator (numeric)\n",
    "19. euribor3m: euribor 3 month rate - daily indicator (numeric)\n",
    "20. nr.employed: number of employees - quarterly indicator (numeric)\n",
    "\n",
    "Output variable (desired target):\n",
    "21. y - has the client subscribed a term deposit? (binary: 'yes','no')\n",
    "\n",
    "\n",
    "## Import libraries"
   ]
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   "cell_type": "code",
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "#loading the data\n",
    "df = pd.read_csv('bank-full.csv',sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f5e282a",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "id": "d7c5f945",
   "metadata": {
    "scrolled": true
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       "<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",
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      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
       "4   33       unknown   single    unknown      no        1      no   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
       "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
       "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
       "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
       "4  unknown    5   may       198         1     -1         0  unknown  no  "
      ]
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this helps to see a few sample rows of the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb6ba177",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45211 entries, 0 to 45210\n",
      "Data columns (total 17 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   age        45211 non-null  int64 \n",
      " 1   job        45211 non-null  object\n",
      " 2   marital    45211 non-null  object\n",
      " 3   education  45211 non-null  object\n",
      " 4   default    45211 non-null  object\n",
      " 5   balance    45211 non-null  int64 \n",
      " 6   housing    45211 non-null  object\n",
      " 7   loan       45211 non-null  object\n",
      " 8   contact    45211 non-null  object\n",
      " 9   day        45211 non-null  int64 \n",
      " 10  month      45211 non-null  object\n",
      " 11  duration   45211 non-null  int64 \n",
      " 12  campaign   45211 non-null  int64 \n",
      " 13  pdays      45211 non-null  int64 \n",
      " 14  previous   45211 non-null  int64 \n",
      " 15  poutcome   45211 non-null  object\n",
      " 16  y          45211 non-null  object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "#this provides the summarized information of the data\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "65f71db3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            77\n",
       "job            12\n",
       "marital         3\n",
       "education       4\n",
       "default         2\n",
       "balance      7168\n",
       "housing         2\n",
       "loan            2\n",
       "contact         3\n",
       "day            31\n",
       "month          12\n",
       "duration     1573\n",
       "campaign       48\n",
       "pdays         559\n",
       "previous       41\n",
       "poutcome        4\n",
       "y               2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this helps us to identify if a column is categorical or continuous (numerical)\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df1d642f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "default      0\n",
       "balance      0\n",
       "housing      0\n",
       "loan         0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "previous     0\n",
       "poutcome     0\n",
       "y            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86de02a6",
   "metadata": {},
   "source": [
    "## Visual Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5ac986",
   "metadata": {},
   "source": [
    "### Categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fa4b8ef6",
   "metadata": {
    "scrolled": false
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#looking at our desired target\n",
    "plt.figure(figsize=(8,6))\n",
    "Y = df[\"y\"]\n",
    "total = len(Y)*1.\n",
    "ax=sns.countplot(x=\"y\", data=df)\n",
    "for p in ax.patches:\n",
    "  ax.annotate('{:.1f}%'.format(100*p.get_height()/total), (p.get_x()+0.1, p.get_height()+5))\n",
    "\n",
    "  #put 11 ticks (therefore 10 steps), from 0 to the total number of rows in the dataframe\n",
    "  ax.yaxis.set_ticks(np.linspace(0, total, 11))\n",
    "  #adjust the ticklabel to the desired format, without changing the position of the ticks.\n",
    "  ax.set_yticklabels(map('{:.1f}%'.format, 100*ax.yaxis.get_majorticklocs()/total))\n",
    "  ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "  # ax.legend(labels=[\"no\",\"yes\"])\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95407d29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_columns = ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'poutcome']\n",
    "\n",
    "fig, axs = plt.subplots(3, 3, sharex=False, sharey=False, figsize=(20,15))\n",
    "fig.subplots_adjust(left=None, bottom=None, right=None, top=1, wspace=None, hspace=.5)\n",
    "\n",
    "counter = 0\n",
    "for cat_column in cat_columns:\n",
    "    value_counts = df[cat_column].value_counts()\n",
    "    \n",
    "    trace_x = counter // 3\n",
    "    trace_y = counter % 3\n",
    "    x_pos = np.arange(0, len(value_counts))\n",
    "    \n",
    "    axs[trace_x, trace_y].bar(x_pos, value_counts.values, tick_label = value_counts.index)\n",
    "    \n",
    "    axs[trace_x, trace_y].set_title(cat_column)\n",
    "    \n",
    "    for tick in axs[trace_x, trace_y].get_xticklabels():\n",
    "        tick.set_rotation(90)\n",
    "        \n",
    "    counter += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6afa430",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "\n",
    "- Top four professions are: blue-collar, management, technician, and administration\n",
    "- Most customers are married\n",
    "- Majority of customers have secondary education\n",
    "- Cell phones are the most favored method of reaching out to customers\n",
    "- Most customers have been contacted in the month of May"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e47100",
   "metadata": {},
   "source": [
    "### Numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5fd404f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df['age'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3700817c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_columns = ['balance', 'day', 'duration', 'campaign', 'pdays', 'previous']\n",
    "\n",
    "fig, axs = plt.subplots(2, 3, sharex=False, sharey=False, figsize=(20,15))\n",
    "\n",
    "counter = 0\n",
    "for num_column in num_columns:\n",
    "    \n",
    "    trace_x = counter // 3\n",
    "    trace_y = counter % 3\n",
    "    \n",
    "    axs[trace_x, trace_y].hist(df[num_column])\n",
    "    \n",
    "    axs[trace_x, trace_y].set_title(num_column)\n",
    "        \n",
    "    counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df77d07b",
   "metadata": {
    "scrolled": true
   },
   "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>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.936210</td>\n",
       "      <td>1362.272058</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.618762</td>\n",
       "      <td>3044.765829</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>72.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1428.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        balance           day      duration      campaign  \\\n",
       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  45211.000000  45211.000000  \n",
       "mean      40.197828      0.580323  \n",
       "std      100.128746      2.303441  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      871.000000    275.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11e9e4cc",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "\n",
    "- Most numerical columns have outliers. Probably incorrectly inserted and could just be noisy data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c8e065",
   "metadata": {},
   "source": [
    "### Univariate analysis of Categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dfadd521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Job and Subscribed for Term'}>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#comparing target with jobs\n",
    "jobs_df = pd.DataFrame()\n",
    "\n",
    "jobs_df['yes'] = df[df['y'] == 'yes']['job'].value_counts()\n",
    "jobs_df['no'] = df[df['y'] == 'no']['job'].value_counts()\n",
    "\n",
    "jobs_df.plot.bar(title = 'Job and Subscribed for Term')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6538a9ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Marital Status and Subscribed for Term'}>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#comparing target with marital status\n",
    "ms_df = pd.DataFrame()\n",
    "\n",
    "ms_df['yes'] = df[df['y'] == 'yes']['marital'].value_counts()\n",
    "ms_df['no'] = df[df['y'] == 'no']['marital'].value_counts()\n",
    "\n",
    "ms_df.plot.bar(title = 'Marital Status and Subscribed for Term')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19ad1b06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Education and Subscribed for Term'}>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wtMg+wMaIGCGpDlgOPJ3mzY+I89I6I/nT09LuBC6KiJDUE7gRGAmsB86KiFW79CnMzNqpR48eDBkypOg0CtWWLqOZwLh8ICLOiogRETECuA34aW72ysZ5jcUgmQ5MIXvG8tDcNicDGyLiEOAa4OpyPoiZmbVPqwUhIuaRPed4J5IEnAnc0tI2JO0H9I6IhyLroLsRGJ9mnwbMStM/Acak7ZqZWQdq76Dyh4AXI2JFLjZE0mOSfiXpQyk2EGjILdOQYo3zngeIiG3AJqBfqZ1JmiJpoaSFXaWfz8yss2hvQTibHVsHa4ADIuJo4O+AH0nqDZQ6428cym9p3o7BiBkRMSoiRg0YMKAdaZuZWVNlX2UkqTvwKbLBYAAiYiuwNU0vkrQSOJSsRZAf7h4ErE7TDcBgoCFtsw/NdFGZmVn1tKeF8FHgqYh4uytI0gBJ3dL0QWSDx89ExBpgs6TRaXzgHOCOtNpcYFKaPh24L7rChcBmZjWm1YIg6RbgIeAwSQ2SJqdZE9l5MPnDwBOSHicbID4vIhrP9s8HfgDUAyuBu1L8OqCfpHqybqZL2/F5zMysTK12GUXE2c3EP1cidhvZZailll8IDC8R3wKc0VoeZmZWXb51hZmZAS4IZmaWuCCYmRnggmBmZokLgpmZAS4IZmaWuCCYmRnggmBmZokLgpmZAS4IZmaWuCCYmRnggmBmZokLgpmZAS4IZmaWuCCYmRnggmBmZklbnph2vaS1kpbmYtMkvSBpcXp9PDdvqqR6SU9LGpuLj5S0JM27Nj1KE0k9Jf04xR+WVFfhz2hmZm3QlhbCTGBcifg1ETEive4EkDSM7NGaR6R1vtP4jGVgOjCF7DnLQ3PbnAxsiIhDgGuAq8v8LGZm1g6tFoSImAe83NpyyWnA7IjYGhHPkj0/+VhJ+wG9I+KhiAjgRmB8bp1ZafonwJjG1oOZmXWc9owhXCjpidSl1DfFBgLP55ZpSLGBabppfId1ImIbsAnoV2qHkqZIWihp4bp169qRupmZNVVuQZgOHAyMANYA/5bipc7so4V4S+vsHIyYERGjImLUgAEDdilhMzNrWVkFISJejIjtEfEW8H3g2DSrARicW3QQsDrFB5WI77COpO5AH9reRWVmZhVSVkFIYwKNJgCNVyDNBSamK4eGkA0eL4iINcBmSaPT+MA5wB25dSal6dOB+9I4g5mZdaDurS0g6RbgeKC/pAbg68DxkkaQde2sAr4IEBHLJM0BngS2ARdExPa0qfPJrljaA7grvQCuA26SVE/WMphYgc9lZma7qNWCEBFnlwhf18LyVwJXlogvBIaXiG8BzmgtDzMzqy5/U9nMzAAXBDMzS1wQzMwMcEEwM7PEBcHMzAAXBDMzS1wQzMwMcEEwM7PEBcHMzAAXBDMzS1wQzMwMcEEwM7PEBcHMzAAXBDMzS1wQzMwMcEEwM7Ok1YIg6XpJayUtzcX+VdJTkp6QdLukfVK8TtIbkhan13dz64yUtERSvaRr06M0SY/b/HGKPyyprvIf08zMWtOWFsJMYFyT2L3A8Ig4CvgdMDU3b2VEjEiv83Lx6cAUsucsD81tczKwISIOAa4Brt7lT2FmZu3WakGIiHlkzzrOx+6JiG3p7XxgUEvbkLQf0DsiHoqIAG4ExqfZpwGz0vRPgDGNrQczM+s4lRhDOBe4K/d+iKTHJP1K0odSbCDQkFumIcUa5z0PkIrMJqBfqR1JmiJpoaSF69atq0DqZmbWqF0FQdLXgG3AzSm0BjggIo4G/g74kaTeQKkz/mjcTAvzdgxGzIiIURExasCAAe1J3czMmuhe7oqSJgGnAGNSNxARsRXYmqYXSVoJHErWIsh3Kw0CVqfpBmAw0CCpO9CHJl1UZmZWfWW1ECSNA74CnBoRr+fiAyR1S9MHkQ0ePxMRa4DNkkan8YFzgDvSanOBSWn6dOC+xgJjZmYdp9UWgqRbgOOB/pIagK+TXVXUE7g3jf/OT1cUfRi4QtI2YDtwXkQ0nu2fT3bF0h5kYw6N4w7XATdJqidrGUysyCczM7Nd0mpBiIizS4Sva2bZ24Dbmpm3EBheIr4FOKO1PMzMrLr8TWUzMwNcEMzMLHFBMDMzwAXBzMwSFwQzMwNcEMzMLCn7m8pmhZjWpwrb3FT5bZrVILcQzMwMcEEwM7PEBcHMzAAXBDMzS1wQzMwMcEEwM7PEBcHMzAAXBDMzS1wQzMwMaENBkHS9pLWSluZi75R0r6QV6Wff3LypkuolPS1pbC4+UtKSNO/a9ChNJPWU9OMUf1hSXYU/o5mZtUFbWggzgXFNYpcCv4yIocAv03skDSN7BOYRaZ3vND5jGZgOTCF7zvLQ3DYnAxsi4hDgGuDqcj+MmZmVr9WCEBHzyJ51nHcaMCtNzwLG5+KzI2JrRDwL1APHStoP6B0RD0VEADc2WadxWz8BxjS2HszMrOOUO4awb0SsAUg/35XiA4Hnc8s1pNjANN00vsM6EbEN2AT0K7VTSVMkLZS0cN26dWWmbmZmpVR6ULnUmX20EG9pnZ2DETMiYlREjBowYECZKZqZWSnlFoQXUzcQ6efaFG8ABueWGwSsTvFBJeI7rCOpO9CHnbuozMysysotCHOBSWl6EnBHLj4xXTk0hGzweEHqVtosaXQaHzinyTqN2zoduC+NM5iZWQdq9QE5km4Bjgf6S2oAvg5cBcyRNBn4PXAGQEQskzQHeBLYBlwQEdvTps4nu2JpD+Cu9AK4DrhJUj1Zy2BiRT6ZmZntklYLQkSc3cysMc0sfyVwZYn4QmB4ifgWUkExM7Pi+JvKZmYGuCCYmVnigmBmZoALgpmZJS4IZmYGuCCYmVnigmBmZoALgpmZJS4IZmYGuCCYmVnigmBmZoALgpmZJS4IZmYGuCCYmVnigmBmZoALgpmZJWUXBEmHSVqce70i6WJJ0yS9kIt/PLfOVEn1kp6WNDYXHylpSZp3bXrMppmZdaCyC0JEPB0RIyJiBDASeB24Pc2+pnFeRNwJIGkY2eMxjwDGAd+R1C0tPx2YQvYM5qFpvpmZdaBKdRmNAVZGxHMtLHMaMDsitkbEs0A9cKyk/YDeEfFQRARwIzC+QnmZmVkbVaogTARuyb2/UNITkq6X1DfFBgLP55ZpSLGBabppfCeSpkhaKGnhunXrKpS6mZlBBQqCpN2BU4FbU2g6cDAwAlgD/FvjoiVWjxbiOwcjZkTEqIgYNWDAgPakbWZmTVSihXAy8GhEvAgQES9GxPaIeAv4PnBsWq4BGJxbbxCwOsUHlYibmVkHqkRBOJtcd1EaE2g0AViapucCEyX1lDSEbPB4QUSsATZLGp2uLjoHuKMCeZmZ2S7o3p6VJe0JfAz4Yi78L5JGkHX7rGqcFxHLJM0BngS2ARdExPa0zvnATGAP4K70MjOzDtSughARrwP9msQ+28LyVwJXlogvBIa3JxczM2sff1PZzMwAFwQzM0tcEMzMDHBBMDOzxAXBzMwAFwQzM0tcEMzMDGjn9xDMrIZN61OFbW6q/Datw7iFYGZmgAuCmZklLghmZga4IJiZWeKCYGZmgAuCmZklLghmZga4IJiZWdKugiBplaQlkhZLWphi75R0r6QV6Wff3PJTJdVLelrS2Fx8ZNpOvaRr06M0zcysA1WihXBCRIyIiFHp/aXALyNiKPDL9B5Jw4CJwBHAOOA7krqldaYDU8ieszw0zTczsw5UjS6j04BZaXoWMD4Xnx0RWyPiWaAeOFbSfkDviHgoIgK4MbeOmZl1kPYWhADukbRI0pQU2zci1gCkn+9K8YHA87l1G1JsYJpuGt+JpCmSFkpauG7dunambmZmee29ud0HImK1pHcB90p6qoVlS40LRAvxnYMRM4AZAKNGjSq5jJmZladdLYSIWJ1+rgVuB44FXkzdQKSfa9PiDcDg3OqDgNUpPqhE3MzMOlDZBUHSOyTt3TgNnAQsBeYCk9Jik4A70vRcYKKknpKGkA0eL0jdSpsljU5XF52TW8fMzDpIe7qM9gVuT1eIdgd+FBF3S3oEmCNpMvB74AyAiFgmaQ7wJLANuCAitqdtnQ/MBPYA7kovMzPrQGUXhIh4BnhPifh6YEwz61wJXFkivhAYXm4uZmbWfv6mspmZAS4IZmaWuCCYmRnggmBmZokLgpmZAS4IZmaWuCCYmRnggmBmZokLgpmZAS4IZmaWuCCYmRnggmBmZokLgpmZAS4IZmaWuCCYmRnggmBmZkl7HqE5WNL9kpZLWibpohSfJukFSYvT6+O5daZKqpf0tKSxufhISUvSvGvTozTNzKwDtecRmtuAv4+IR9OzlRdJujfNuyYivplfWNIwYCJwBLA/8AtJh6bHaE4HpgDzgTuBcfgxmmZmHarsFkJErImIR9P0ZmA5MLCFVU4DZkfE1oh4FqgHjpW0H9A7Ih6KiABuBMaXm5eZmZWnImMIkuqAo4GHU+hCSU9Iul5S3xQbCDyfW60hxQam6abxUvuZImmhpIXr1q2rROpmZpa0p8sIAEl7AbcBF0fEK5KmA98AIv38N+BcoNS4QLQQ3zkYMQOYATBq1KiSy7RH3aX/W+lNsqpXxTdpZlYV7WohSOpBVgxujoifAkTEixGxPSLeAr4PHJsWbwAG51YfBKxO8UEl4mZm1oHac5WRgOuA5RHxrVx8v9xiE4ClaXouMFFST0lDgKHAgohYA2yWNDpt8xzgjnLzMjOz8rSny+gDwGeBJZIWp9hXgbMljSDr9lkFfBEgIpZJmgM8SXaF0gXpCiOA84GZwB5kVxf5CiMzsw5WdkGIiF9Tuv//zhbWuRK4skR8ITC83Fysc/KYjFlt8TeVzcwMcEEwM7PEBcHMzIAKfA/BzMyAaX2qsM1Nld9mC9xCMDMzwAXBzMwSFwQzMwNcEMzMLHFBMDMzwAXBzMwSFwQzMwNcEMzMLHFBMDMzwN9UNqsJvnOsdQS3EMzMDHBBMDOzpNMUBEnjJD0tqV7SpUXnY2b256ZTjCFI6gb8F/AxoAF4RNLciHiy2MzMrCvymExpnaWFcCxQHxHPRMQfgdnAaQXnZGb2Z0URUXQOSDodGBcRn0/vPwscFxEXNlluCjAlvT0MeLpDEy1Pf+ClopPoQnw8K8fHsrJq5XgeGBEDSs3oFF1GgErEdqpUETEDmFH9dCpH0sKIGFV0Hl2Fj2fl+FhWVlc4np2ly6gBGJx7PwhYXVAuZmZ/ljpLQXgEGCppiKTdgYnA3IJzMjP7s9IpuowiYpukC4GfA92A6yNiWcFpVUpNdXHVAB/PyvGxrKyaP56dYlDZzMyK11m6jMzMrGAuCGZmBrggmJlZ4oJQBZJOkeRja2Y1xYPKVSDph8D7gNuAGyJiecEp1SxJ3yQ7hl3lqrNC+XhWlqSewF8CdeSu2oyIK4rKqT18FlsFEfEZ4GhgJXCDpIckTZG0d8Gp1aKngBmSHpZ0nqQ+RSdU43w8K+sOsvuubQNey71qklsIVSSpP/AZ4GJgOXAIcG1E/EeRedUiSYcBfw2cDfwG+H5E3F9sVrXLx7MyJC2NiOFF51EpbiFUgaRTJd0O3Af0AI6NiJOB9wCXFJpcDUq3R393er0EPA78naTZhSZWo3w8K+q3ko4sOolKcQuhCiTNAq6LiHkl5o2JiF8WkFZNkvQt4JNkxfW6iFiQm/d0RBxWWHI1yMezsiQ9SdbyfxbYSnajzoiIowpNrEyd4tYVXUk6+xpYqhgAuBi0nSQBG4D3RMTrJRY5toNTqmk+nlVxctEJVJK7jCosIrYDr3uwrv0ia76Ob+Y/LyJiUwenVNN8PKtiMnAo8FJEPNf4KjqpcrmFUB1bgCWS7iV3xUFE/G1xKdWs+ZKOiYhHik6ki/DxrKxVZAPz10raDDwIzIuIOwrNqkweQ6gCSZNKxSNiVkfnUutSH+2hwHNkxbWm+2iL5uNZHZL+AjiT7KKRvhFRk5eYuyBYpybpwFLxWm6WF8nHs7Ik/QAYBrxI1jr4NfBoRGwrNLEyucuoCiQNBf6Z7A+lV2M8Ig4qLKka1fgflaR3kTuWVh4fz4rrR/YMl43Ay2RjCTVZDMCDytVyAzCd7NuLJwA3AjcVmlGNSt/pWEF2Wd+vyPps7yo0qRrm41lZETEhIo4D/gXYB7hfUkOxWZXPBaE69kiXlypddTANOLHgnGrVN4DRwO8iYggwhuybtVYeH88KSjeyvBq4HjiP7Psd/1hsVuVzl1F1bEl3O12RHg36AvCugnOqVW9GxHpJu0naLSLuT/8ArTw+npV1MjAP+HZErC46mfZyQaiOi4E9gb8lOyM7ESh55ZG1aqOkvcj+0d0saS1ZV5yVx8ezgiLiAkn7AsdIei+wICLWFp1XuXyVkXVqkt5B9r0OAZ8G+gA3R8T6QhOrUT6elSXpDOCbwANkx/RDwJci4idF5lUuF4QKkvQzoNkDGhGndmA6Zs2S1Jsd79//coHp1CxJjwMfa2wVSBoA/CIi3lNsZuVxl1FlfTP9/BTwF8AP0/uzya7msDaS9OuI+GD69me+yDZ+kap3QanVNElfBK4A3gDeIh1PwJdEl2e3Jl1E66nhi3XcQqgCSfMi4sOtxcw6Wrrk9H0R8VLRuXQFkv4VOAq4JYXOAp6IiK8Ul1X5araSdXIDJL19xiVpCDCgwHxqlqSdvr9RKmZtthIoeXM723UR8SVgBllReA8wo1aLAbiFUBWSxpH9kTyTQnXAFyPi54UlVaMkPRoR78297052BjaswLRqlqSjyb44+TDZ/fsB33jRMh5DqIKIuDvdvuLdKfRURGxtaR3bkaSpwFeBPSS90hgG/khWbK083yP78tQSsjEEawdJnwKuJvuekajxMS63EKpE0vvJWgb5KzluLCyhGpS+3PeDiDi36Fy6Ckm/jYj3F51HVyGpHvhkRCwvOpdKcAuhClIf98HAYmB7CgfZPY2sjSLiLUk1efleJ3a/pCnAz9ixy8iXnZbnxa5SDMAthKqQtBwYFj647Sbpv4CZfqBLZUh6tkQ4fCfe8kj6Ntkl5v/NjgX2p0Xl1B5uIVTHUrI/kjVFJ9IFnACcJ2kVfqBLu6Ub2lnl9Ca7auukXCyAmiwIbiFUgaT7gRHAAnY8a/A3lXeRH+hSGZJOjIj70iDoTmr1jLZokt7ZtLtN0pCIKNUS6/TcQqiOaUUn0FVExHOSPggMjYgb0q0B9io6rxr0EbKriz5ZYl7NntF2Aj+TdHJEvAIg6XDgVmB4sWmVxy2EKmm8A2J6W9N3QCySpK8Do4DDIuJQSfsDt0bEBwpOreakq7ZOj4g5RefSVUj6BPBl4BPAYWQXjnw6IhYXmVe5/E3lKpB0Jll30RlkD95+WNLpxWZVsyYAp5KNH5DuOV+TDzAvWkS8BVxYdB5dSUT8L3ANcA8wExhfq8UA3GVULV8Djml6B0SgJm+JW7A/RkRICnj79s1WvnslXQL8mFRkwZed7ipJ/8GON13sTXZngv8jqWa/+e2CUB1d6g6IBZsj6XvAPpK+AJwLfL/gnGrZuWT/kf1Nk7gvO901C5u8X1RIFhXmglAdd0v6OTveAdEPMi/PALKW1StkfbT/CHy00Ixq2zCyYvBBssLwIPDdQjOqQRExq+gcqsGDylWSLu/7INl18/Mi4vaCU6pJTW9ul2JP+HsI5ZE0h6y43pxCZwP7RMSZxWVVuyR9gOyqwgPJTrAbvydTky0uF4QqSLe7XhMRW9L7PYB9I2JVoYnVEEnnk53JHkR2y+ZGewO/iYjPFJJYjZP0eNOneZWKWdtIegr4v2RdRo23qaFWH0nqLqPquBXI30Bse4odU3pxK+FHZN1s/wxcmotv9gBouzwmaXREzAeQdBzwm4JzqmWbIqLLdAe7hVAFkhZHxIgmMZ+FWeHSfbYOA36fQgcAy8luhe1bguwiSVcB3ci+2Je/K8GjhSXVDm4hVMc6SadGxFwASacBfmShdQbjik6gizku/RyZfjY+o/rEYtJpHxeE6jgPuDndqTOABuCcYlMy8z2gquCBErGa7XZxQaiCiFgJjJa0F1m33OaiczKzqng1N90LOIWsC64meQyhCtJ9jP4J2D8iTpY0DHhfRFxXcGpmVkWSegJzI2Js0bmUw9+erY6ZwM+B/dP73wEXF5WMmXWYPanhb327y6g6+kfEnPSgeCJim6Ttra1kZrVF0hL+NGbQjeyb9VcUl1H7uCBUx2uS+pH+UCSNBjYVm5KZVcEpueltZM9Y3lZUMu3lMYQqkPRe4D/IHpKxlOys4fSIeKLQxMzMWuAxhOo4GDiZ7NvKPwdW4NaYmXVyLgjVcVl6pF5fsjtzzgCmF5uSmVnLXBCqo3EA+RPAdyPiDmD3AvMxM2uVC0J1vJAe6nImcGe6NtnH2sw6NQ8qV4GkPcnuGbMkIlZI2g84MiLuKTg1M7NmuSCYmRngbgwzM0tcEMzMDHBBMDOzxAXBzMwAFwQzM0v+P/GmzOFkQ8T2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparing target with education\n",
    "ed_df = pd.DataFrame()\n",
    "\n",
    "ed_df['yes'] = df[df['y'] == 'yes']['education'].value_counts()\n",
    "ed_df['no'] = df[df['y'] == 'no']['education'].value_counts()\n",
    "\n",
    "ed_df.plot.bar(title = 'Education and Subscribed for Term')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "de19e1c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Contact Method and Subscribed for Term'}>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#comparing target with contact method\n",
    "ct_df = pd.DataFrame()\n",
    "\n",
    "ct_df['yes'] = df[df['y'] == 'yes']['contact'].value_counts()\n",
    "ct_df['no'] = df[df['y'] == 'no']['contact'].value_counts()\n",
    "\n",
    "ct_df.plot.bar(title = 'Contact Method and Subscribed for Term')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "054fa376",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "\n",
    "- Customers with \"blue-collar\" jobs are less likely to join the Bank\n",
    "- Married customers tend to agree more to join the Bank though single people still had a good response of yes\n",
    "- Cellular is the best method to get people to join"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc651f8",
   "metadata": {},
   "source": [
    "### Univariate analysis on Numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cfb046a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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45STt27W/j2UFX7/kwd2XgBsyc39EvAp4JCL+CvgpYEtm3hMR64B1wAcL5pB6lmcVveL7Y2MAvPoH/DdZRtnPRrHiz8wE9jfDVzU/CdwCXN+sH6FzL16LX1XyjLNXeMZZ9xTdxx8RfRHxOLAX2JyZXwMWZ+Y4QLNcdJJtV0fEaESMTkxMlIwpSVUpWvyZeSgzrwJeB1wTEW8+hW03ZOZQZg719/cXyyhJtenKWT2Z+R06u3RuBPZExBKAZrm3GxkkSR3Fij8i+iPi4ubxecBKYDvwEDDcPG0YeLBUBknSiUqe1bMEGImIPjp/YO7PzD+PiK8C90fEHcAu4PaCGSRJxyl5Vs/fA2+dZv0ksKLU+0qSZuY3dyWpMha/JFXG4pekylj8klQZi1+SKmPxS1JlLH5JqozFL0mVsfglqTIWvyRVxuKXpMpY/JJUGYtfkipj8UtSZSx+SaqMxS9JlbH4JakyJe+5e1lEfCEitkXE0xGxtlm/ICI2R8SOZnlJqQySpBOVnPEfBP5zZr4JeAfw/oj4QWAdsCUzlwFbmrEkqUuKFX9mjmfmo83jF4FtwFLgFmCkedoIcGupDJKkE3VlH39EDNC58frXgMWZOQ6dPw7AopNsszoiRiNidGJiohsxJakKxYs/Ii4E/gT4QGbum+12mbkhM4cyc6i/v79cQEmqTNHij4hX0Sn9P8rMzzWr90TEkub3S4C9JTNIkqYqeVZPAPcB2zLzt4751UPAcPN4GHiwVAZJ0okiM8u8cMS/Br4EPAkcblZ/iM5+/vuBy4FdwO2Z+dxMrzU0NJSjo6NFcnbL+vXrGRsbazvG0QyDg4Ot5hgcHGTNmjWtZtAreuHz2SufzSMZ5sLnMyK2ZubQ8evnl3rDzHwEiJP8ekWp99XMzjvvvLYjSNPys9k9xWb8Z9JcmPFLUredbMbvJRskqTIWvyRVxuKXpMpY/JJUGYtfkipj8UtSZSx+SaqMxS9JlTkrvsAVERPAM23nmENeC3y77RDSNPxsnlk/kJknXN74rCh+nVkRMTrdt/mktvnZ7A539UhSZSx+SaqMxV+nDW0HkE7Cz2YXuI9fkirjjF+SKmPxS1JlLH5JqozFL0mVsfjnsIgYiIhtEfHJiHg6IjZFxHkRcVVE/G1E/H1EPBARl7SdVXWIiP8aEWuPGf+3iLgzIn4pIv6u+Uz+SvO7CyLiLyLiiYh4KiL+bXvJ5xaLf+5bBvxuZv4Q8B3g3cCngQ9m5g8DTwJ3tRdPlbkPGAaIiHnAe4A9dD6n1wBXAW+LiOuAG4FnM3N5Zr4Z2NhK4jnI4p/7/iEzH28ebwVeD1ycmV9s1o0A17URTPXJzJ3AZES8Ffgx4DHg7cc8fhR4I50/BE8CKyPi1yPi2sx8oZ3Uc8/8tgOouJeOeXwIuLilHNIRnwJ+HviXwP8AVgC/lpn3Hv/EiHgbcBPwaxGxKTN/tZtB5ypn/PV5AXg+Iq5txj8HfHGG50tn2gN0duO8Hfjr5ucXI+JCgIhYGhGLIuJS4EBm/iHwm8DVbQWea5zx12kY+IOIOB/4BvALLedRRTLz+xHxBeA7mXkI2BQRbwK+GhEA+4H3AoPAb0TEYeBl4D+2lXmu8ZINkrqqOaj7KHB7Zu5oO0+N3NUjqWsi4geBMWCLpd8eZ/ySVBln/JJUGYtfkipj8UtSZSx+SaqMxS9JlbH4pdNwsqtMtplJmi1P55ROQ0QMAJ/LzKubLyTtAK7JzMl2k0n/PC/ZIJ2GzNwZEUeuMrkYeMzS19nC4pdO3/FXmZTOCu7qkU5TRJxD55rxrwKWNRcck3qeM37pNE1zlUnprGDxS6epOaj7DuD2trNIp8LTOaXT4FUmdTZzH78kVcYZvyRVxuKXpMpY/JJUGYtfkipj8UtSZf4fBIyqX62HceEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparing target with age\n",
    "sns.boxplot(data=df, x=\"y\", y=\"age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2a192eb",
   "metadata": {},
   "source": [
    "From the above boxplot we know that for both the customers that subscibed or didn’t subscribe a term deposit, has a median age of around 38–40. And the boxplot for both the classes overlap quite a lot, which means that age isn’t necessarily a good indicator for which customer will subscribe and which customer will not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9d10ee1e",
   "metadata": {
    "scrolled": true
   },
   "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": [
    "#comparing target with duration of last contact\n",
    "sns.boxplot(data=df, x=\"y\", y=\"duration\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51124c9a",
   "metadata": {},
   "source": [
    "## Cleaning the data\n",
    "\n",
    "Before applying machine learning, we need to prepare the dataset for processing.\n",
    "\n",
    "- Converting columns with \"yes\" and \"no\" values to boolean\n",
    "- Converting categorical columns into dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b944dd30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting column 'y' (the target), 'housing', and 'loan' to a numeric value\n",
    "from sklearn import preprocessing\n",
    "le=preprocessing.LabelEncoder()\n",
    "#here is a simple example to how this work\n",
    "le.fit_transform(['yes','yes','no'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "05eb7a6a",
   "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>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>1</td>\n",
       "      <td>0</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>0</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>1</td>\n",
       "      <td>0</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>0</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>1</td>\n",
       "      <td>1</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>0</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>1</td>\n",
       "      <td>0</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>0</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>0</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance  housing  loan  \\\n",
       "0   58    management  married   tertiary      no     2143        1     0   \n",
       "1   44    technician   single  secondary      no       29        1     0   \n",
       "2   33  entrepreneur  married  secondary      no        2        1     1   \n",
       "3   47   blue-collar  married    unknown      no     1506        1     0   \n",
       "4   33       unknown   single    unknown      no        1        0     0   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome  y  \n",
       "0  unknown    5   may       261         1     -1         0  unknown  0  \n",
       "1  unknown    5   may       151         1     -1         0  unknown  0  \n",
       "2  unknown    5   may        76         1     -1         0  unknown  0  \n",
       "3  unknown    5   may        92         1     -1         0  unknown  0  \n",
       "4  unknown    5   may       198         1     -1         0  unknown  0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['y']=le.fit_transform(df['y'].astype(str))\n",
    "df['housing']=le.fit_transform(df['housing'].astype(str))\n",
    "df['loan']=le.fit_transform(df['loan'].astype(str))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "42a00e02",
   "metadata": {
    "scrolled": true
   },
   "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>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",
       "      <th>...</th>\n",
       "      <th>month_dec</th>\n",
       "      <th>month_feb</th>\n",
       "      <th>month_jan</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>2143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>1506</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  balance  housing  loan  day  duration  campaign  pdays  previous  y  \\\n",
       "0   58     2143        1     0    5       261         1     -1         0  0   \n",
       "1   44       29        1     0    5       151         1     -1         0  0   \n",
       "2   33        2        1     1    5        76         1     -1         0  0   \n",
       "3   47     1506        1     0    5        92         1     -1         0  0   \n",
       "4   33        1        0     0    5       198         1     -1         0  0   \n",
       "\n",
       "   ...  month_dec  month_feb  month_jan  month_jul  month_jun  month_mar  \\\n",
       "0  ...          0          0          0          0          0          0   \n",
       "1  ...          0          0          0          0          0          0   \n",
       "2  ...          0          0          0          0          0          0   \n",
       "3  ...          0          0          0          0          0          0   \n",
       "4  ...          0          0          0          0          0          0   \n",
       "\n",
       "   month_may  month_nov  month_oct  month_sep  \n",
       "0          1          0          0          0  \n",
       "1          1          0          0          0  \n",
       "2          1          0          0          0  \n",
       "3          1          0          0          0  \n",
       "4          1          0          0          0  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting categorical columns into dummy variables\n",
    "df=pd.get_dummies(df,columns=['job','marital','education','default','contact','poutcome','month'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e41c62ea",
   "metadata": {},
   "source": [
    "## Data split and model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2d5169e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x=df.drop('y',axis=1)\n",
    "y=df['y']\n",
    "\n",
    "#split into training and test sets\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8b120c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#cross validation score\n",
    "#standard method for estimating the performance of a machine learning algorithm or configuration on a dataset\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d236aa",
   "metadata": {},
   "source": [
    "### Logistic Regression\n",
    "\n",
    "A fundamental classification technique. It belongs to the group of linear classifiers and is somewhat similar to polynomial and linear regression. Logistic regression is fast and relatively uncomplicated, and it’s convenient to interpret the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "07f6a995",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8822186034758408"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logr=LogisticRegression() #default parameters (penalty='l2', c='1.0', solver='liblinear')\n",
    "score=cross_val_score(logr,x,y,cv=5)\n",
    "logr_test_acc=score.mean()\n",
    "logr_test_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53a5f3ec",
   "metadata": {},
   "source": [
    "### K-NN Classifier\n",
    "\n",
    "The k-nearest neighbors (K-NN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4ee226cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8722208936472338"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn=KNeighborsClassifier()\n",
    "score=cross_val_score(knn,x,y,cv=5)\n",
    "knn_test_acc=score.mean()\n",
    "knn_test_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fed564b",
   "metadata": {},
   "source": [
    "### Hyperparameter optimization\n",
    "\n",
    "To perform a search in order to discover the set of specific model configuration arguments that result in the best performance of the model on a specific dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c15fc2b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.4, 'penalty': 'l2'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters={'C':[0.1,0.4,0.8,1,2,5]\n",
    "           ,'penalty':['l1','l2']}\n",
    "grid_search=GridSearchCV(logr,parameters,cv=5)\n",
    "grid_search.fit(x_train,y_train)\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aa8b5ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#using the best parameters to test model\n",
    "logistic_model=LogisticRegression(penalty=grid_search.best_params_['penalty'],\\\n",
    "                                  C=grid_search.best_params_['C']).fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d92c3d57",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=logistic_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e15498ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy score computes subset accuracy\n",
    "#precision score is the ability of the classifier not to label as positive a sample that is negative.\n",
    "#recall score is the ability of the classifier to find all the positive samples.\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "def summarize_classification(y_test, y_pred):\n",
    "    \n",
    "    acc = accuracy_score(y_test, y_pred, normalize=True)\n",
    "    num_acc = accuracy_score(y_test, y_pred, normalize=False)\n",
    "\n",
    "    prec = precision_score(y_test, y_pred)\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    \n",
    "    print(\"test data count: \",len(y_test))\n",
    "    print(\"accuracy_count : \" , num_acc)\n",
    "    print(\"accuracy_score : \" , acc)\n",
    "    print(\"precision_score : \" , prec)\n",
    "    print(\"recall_score : \", recall)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2301292a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test data count:  9043\n",
      "accuracy_count :  8041\n",
      "accuracy_score :  0.8891960632533451\n",
      "precision_score :  0.6126582278481013\n",
      "recall_score :  0.22181484876260313\n",
      "\n"
     ]
    }
   ],
   "source": [
    "summarize_classification(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c61f0a9",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "I chose to go with the Logistic Regression as my final model. Which is a fundamental classification technique and I chose it because it is fast and relatively uncomplicated, and it’s convenient to interpret the results.\n",
    "\n",
    "As part of my preprocessing, I made sure to convert the 'objects' data types into numerical values, 'integers'. Some additional preprocessing I could have done was to drop any irrelevant columns and removed any duplicate columns to see if it would help increase the accuracy score of the model.\n",
    "\n",
    "After running a hyperparameter optimization, the final accuracy score was .889, very close to 90, which we can say it's a pretty good score. I also included the precision and recall score which I think should also be looked at when running machine learning as we could have a good accuracy score but theclassifier could be labeling a sample positive when it is in fact negative. Thus, looking at those scores for precision and recall, we can deduce we should look at other ways to preprocess the data to see if we can increase those numbers as well."
   ]
  }
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