Success_Telemarketing.ipynb

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    "# Predicting Success of Telemarketing  - Summer 2020\n",
    "\n",
    "\n",
    "Presented by- **Anushri More**"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Description of Bank-Marketing data\n",
    "Source: S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. _Decision Support Systems,_ Elsevier, 62:22-31, June 2014\n",
    "\n",
    "\n",
    "### Data Set Information:\n",
    "\n",
    "The data is shows 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 determine if the product (bank term deposit) would be purchased/subscribed ('yes') or not ('no') subscribed. \n",
    "\n",
    "The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).\n",
    "\n",
    "\n",
    "### Attribute Information:\n",
    "\n",
    "**Input variables:**<br/><br/>\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')<br/><br/>\n",
    "_(variables related with the last contact of the current campaign:)_<br/><br/>\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.<br/><br/>\n",
    "_(other attributes:)_<br/><br/>\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')<br/><br/>\n",
    "_(social and economic context attributes)_<br/><br/>\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",
    "### Relevant Papers:\n",
    "\n",
    "S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. _Decision Support Systems,_ Elsevier, 62:22-31, June 2014\n",
    "\n",
    "S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimaraes, Portugal, October, 2011. EUROSIS. [bank.zip]\n",
    "\n",
    "\n",
    "\n",
    "### Citation Request:\n",
    "\n",
    "This dataset is publicly available for research. The details are described in [Moro et al., 2014]. \n",
    "Please include this citation if you plan to use this database: \n",
    "\n",
    "S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. _Decision Support Systems,_ Elsevier, 62:22-31, June 2014"
   ]
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    "id": "IxYox14EsI45"
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   "source": [
    "####**Project Summary**\n",
    "\n",
    "\n",
    "\n",
    "*   Problem Statement \n",
    "*   Data Preprocessing \n",
    "*   Exploratory Data Analysis\n",
    "*   Model\n",
    "*   Model Perfromance \n",
    "*  Conclusion\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
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   "cell_type": "markdown",
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    "id": "tJ8oSxj-sezI"
   },
   "source": [
    "#### **1. Problem Statement**\n",
    "\n",
    "We are given the data of direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (target variable y).This is a binary classification problem. Our two classes are “yes” denoting that the customer subscribed to a term deposit, and “no” denoting that the customer did not subscribe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2TExiIAVRwXj"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from google.colab import files"
   ]
  },
  {
   "cell_type": "code",
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       "headers": [
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         "content-type",
         "application/javascript"
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       "ok": true,
       "status": 200,
       "status_text": ""
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     }
    },
    "id": "Mu5ilwRZR4GS",
    "outputId": "21a22df0-018c-43f7-9a37-aea52795c86e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-f18e5dea-807a-478a-8f44-9faa42875fce\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-f18e5dea-807a-478a-8f44-9faa42875fce\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving banking.csv to banking (1).csv\n"
     ]
    }
   ],
   "source": [
    "uploaded=files.upload()  # When this cell is executed, a \"Choose File\" button will appear. Click on this button and load the banking.csv file from your local directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DzrKooziR67z",
    "outputId": "70d956ad-7baf-4524-c563-aaa21117774e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User uploaded file \"banking.csv\" with length 4882918 bytes\n"
     ]
    }
   ],
   "source": [
    "for fn in uploaded.keys():\n",
    "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
    "      name=fn, length=len(uploaded[fn])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gh7GkMLeR9tK"
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   "outputs": [],
   "source": [
    "# Importing all the required  python modules\n",
    "import io\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_theme(style=\"ticks\", color_codes=True)\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from sklearn import preprocessing\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV  \n",
    "from sklearn.linear_model import SGDClassifier \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VOVMX5QPSAVS"
   },
   "outputs": [],
   "source": [
    "#read in data\n",
    "data = pd.read_csv(io.StringIO(uploaded['banking.csv'].decode('utf-8')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3WuBN_0oT7N2"
   },
   "outputs": [],
   "source": [
    "# Since there are missing values in this dataset, we will first clean the data.\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SpoCqRzlUIrN",
    "outputId": "efdc57dc-8e2b-407e-af25-717f350eda25"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "job               0\n",
       "marital           0\n",
       "education         0\n",
       "default           0\n",
       "housing           0\n",
       "loan              0\n",
       "contact           0\n",
       "month             0\n",
       "day_of_week       0\n",
       "duration          0\n",
       "campaign          0\n",
       "pdays             0\n",
       "previous          0\n",
       "poutcome          0\n",
       "emp_var_rate      0\n",
       "cons_price_idx    0\n",
       "cons_conf_idx     0\n",
       "euribor3m         0\n",
       "nr_employed       0\n",
       "y                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see if it worked:\n",
    "data.isnull().sum()\n",
    "\n",
    "# In the output below, we should see '0' missing values for each of the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0nsCQ59HUY99",
    "outputId": "fcda544b-c881-4807-83c2-953e5ae7b980"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 21)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check on the dimensions of the data\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zO4oH0adUgrO",
    "outputId": "19c10c24-6c8e-4be3-faff-a849ed1eac2d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'job',\n",
       " 'marital',\n",
       " 'education',\n",
       " 'default',\n",
       " 'housing',\n",
       " 'loan',\n",
       " 'contact',\n",
       " 'month',\n",
       " 'day_of_week',\n",
       " 'duration',\n",
       " 'campaign',\n",
       " 'pdays',\n",
       " 'previous',\n",
       " 'poutcome',\n",
       " 'emp_var_rate',\n",
       " 'cons_price_idx',\n",
       " 'cons_conf_idx',\n",
       " 'euribor3m',\n",
       " 'nr_employed',\n",
       " 'y']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What are the variables called?\n",
    "list(data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D7vfUBMXWdJr"
   },
   "source": [
    "##Instructions\n",
    "Here are your instructions:\n",
    "1.  Explore alternative machine-learning algorithms (logistics, SVM, Deep learning) that will allow you to successfully predict which customer would be most susceptible to the marketing campaign.\n",
    "2.  Compare the relevative predictive power of the various models. (i.e., which is better)\n",
    "\n",
    "As was the case with the first collaborative assignment, feel free to explore the data and analyze the data in as much detail as you can.  Creativity in your exploration will be rewarded.\n",
    "\n",
    "Include an __Executive Summary__ at the beginning of your notebook highlighting what you found.\n",
    "\n",
    "Include the names of your group members in your notebook.\n",
    "\n",
    "Submit your results to me with an uploaded notebook and  GIST code. As insurance, mail the notebook to me."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l80OiOwLUWuf",
    "outputId": "7dfe04b5-be4d-4df2-91de-0fb104736cfa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 int64\n",
       "job                object\n",
       "marital            object\n",
       "education          object\n",
       "default            object\n",
       "housing            object\n",
       "loan               object\n",
       "contact            object\n",
       "month              object\n",
       "day_of_week        object\n",
       "duration            int64\n",
       "campaign            int64\n",
       "pdays               int64\n",
       "previous            int64\n",
       "poutcome           object\n",
       "emp_var_rate      float64\n",
       "cons_price_idx    float64\n",
       "cons_conf_idx     float64\n",
       "euribor3m         float64\n",
       "nr_employed       float64\n",
       "y                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "id": "QEmyKJ0GuKYC",
    "outputId": "85828980-e75f-4802-b53b-88a6b25cef91"
   },
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
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       "  <tbody>\n",
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       "      <th>count</th>\n",
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       "      <td>41188.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>40.02406</td>\n",
       "      <td>258.285010</td>\n",
       "      <td>2.567593</td>\n",
       "      <td>962.475454</td>\n",
       "      <td>0.172963</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>93.575664</td>\n",
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       "      <td>3.621291</td>\n",
       "      <td>5167.035911</td>\n",
       "      <td>0.112654</td>\n",
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       "      <th>std</th>\n",
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       "      <td>259.279249</td>\n",
       "      <td>2.770014</td>\n",
       "      <td>186.910907</td>\n",
       "      <td>0.494901</td>\n",
       "      <td>1.570960</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>4.628198</td>\n",
       "      <td>1.734447</td>\n",
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       "      <td>0.316173</td>\n",
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       "      <th>min</th>\n",
       "      <td>17.00000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>92.201000</td>\n",
       "      <td>-50.800000</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>4963.600000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.00000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>999.000000</td>\n",
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       "      <td>-1.800000</td>\n",
       "      <td>93.075000</td>\n",
       "      <td>-42.700000</td>\n",
       "      <td>1.344000</td>\n",
       "      <td>5099.100000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.00000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.749000</td>\n",
       "      <td>-41.800000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>5191.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.00000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>999.000000</td>\n",
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       "      <td>93.994000</td>\n",
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       "      <td>4.961000</td>\n",
       "      <td>5228.100000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
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       "      <td>56.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>94.767000</td>\n",
       "      <td>-26.900000</td>\n",
       "      <td>5.045000</td>\n",
       "      <td>5228.100000</td>\n",
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      "text/plain": [
       "               age      duration      campaign         pdays      previous  \\\n",
       "count  41188.00000  41188.000000  41188.000000  41188.000000  41188.000000   \n",
       "mean      40.02406    258.285010      2.567593    962.475454      0.172963   \n",
       "std       10.42125    259.279249      2.770014    186.910907      0.494901   \n",
       "min       17.00000      0.000000      1.000000      0.000000      0.000000   \n",
       "25%       32.00000    102.000000      1.000000    999.000000      0.000000   \n",
       "50%       38.00000    180.000000      2.000000    999.000000      0.000000   \n",
       "75%       47.00000    319.000000      3.000000    999.000000      0.000000   \n",
       "max       98.00000   4918.000000     56.000000    999.000000      7.000000   \n",
       "\n",
       "       emp_var_rate  cons_price_idx  cons_conf_idx     euribor3m  \\\n",
       "count  41188.000000    41188.000000   41188.000000  41188.000000   \n",
       "mean       0.081886       93.575664     -40.502600      3.621291   \n",
       "std        1.570960        0.578840       4.628198      1.734447   \n",
       "min       -3.400000       92.201000     -50.800000      0.634000   \n",
       "25%       -1.800000       93.075000     -42.700000      1.344000   \n",
       "50%        1.100000       93.749000     -41.800000      4.857000   \n",
       "75%        1.400000       93.994000     -36.400000      4.961000   \n",
       "max        1.400000       94.767000     -26.900000      5.045000   \n",
       "\n",
       "        nr_employed             y  \n",
       "count  41188.000000  41188.000000  \n",
       "mean    5167.035911      0.112654  \n",
       "std       72.251528      0.316173  \n",
       "min     4963.600000      0.000000  \n",
       "25%     5099.100000      0.000000  \n",
       "50%     5191.000000      0.000000  \n",
       "75%     5228.100000      0.000000  \n",
       "max     5228.100000      1.000000  "
      ]
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data.describe()"
   ]
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  {
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    {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp_var_rate    41188 non-null  float64\n",
      " 16  cons_price_idx  41188 non-null  float64\n",
      " 17  cons_conf_idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr_employed     41188 non-null  float64\n",
      " 20  y               41188 non-null  int64  \n",
      "dtypes: float64(5), int64(6), object(10)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
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  {
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       "    <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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jun</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>apr</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>\n",
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      ],
      "text/plain": [
       "   age          job  marital          education  default housing loan  \\\n",
       "0   44  blue-collar  married           basic.4y  unknown     yes   no   \n",
       "1   53   technician  married            unknown       no      no   no   \n",
       "2   28   management   single  university.degree       no     yes   no   \n",
       "3   39     services  married        high.school       no      no   no   \n",
       "4   55      retired  married           basic.4y       no     yes   no   \n",
       "\n",
       "    contact month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
       "0  cellular   aug         thu  ...         1    999         0  nonexistent   \n",
       "1  cellular   nov         fri  ...         1    999         0  nonexistent   \n",
       "2  cellular   jun         thu  ...         3      6         2      success   \n",
       "3  cellular   apr         fri  ...         2    999         0  nonexistent   \n",
       "4  cellular   aug         fri  ...         1      3         1      success   \n",
       "\n",
       "  emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  nr_employed  y  \n",
       "0          1.4          93.444          -36.1      4.963       5228.1  0  \n",
       "1         -0.1          93.200          -42.0      4.021       5195.8  0  \n",
       "2         -1.7          94.055          -39.8      0.729       4991.6  1  \n",
       "3         -1.8          93.075          -47.1      1.405       5099.1  0  \n",
       "4         -2.9          92.201          -31.4      0.869       5076.2  1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "prTTtMjtvR2L"
   },
   "source": [
    "#### **Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5JhvItypwJov"
   },
   "source": [
    "**Plot1-Distribution of class variable:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 400
    },
    "id": "I9UGDWixwPlq",
    "outputId": "32821094-cd71-45d1-ac35-6ba971db2f10"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "Y = data[\"y\"]\n",
    "total = len(Y)*1.\n",
    "ax=sns.countplot(x=\"y\", data=data)\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",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BF4mX6jSgXr6"
   },
   "source": [
    "Observation :- The dataset we are working with is an imbalanced dataset with the class “no” being the majority class. If we use accuracy as our metric, any random model can give us a very good accuracy.\n",
    "\n",
    "While deciding the performance metrics we should not use accuray as a performance measure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZQutYEAqyGXh",
    "outputId": "3aa7679f-5b29-441a-88ee-edc4cd9081e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    36548\n",
      "1     4640\n",
      "Name: y, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data[\"y\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_YmH7SGKxQh5"
   },
   "source": [
    "Interpretation :- We can see from the above plot that the dataset in imbalanced, where the number of negative class is close to 8 times the number of positive class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o0nO5kzPxorf"
   },
   "source": [
    "**Univariate analysis of Categorical variables:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cZozExNAVelT"
   },
   "outputs": [],
   "source": [
    "def countplot(label, dataset):\n",
    "  plt.figure(figsize=(15,10))\n",
    "  Y = data[label]\n",
    "  total = len(Y)*1.\n",
    "  ax=sns.countplot(x=label, data=dataset)\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",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rYrqg_vfyXOb"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "def countplot_withY(label, dataset):\n",
    "  plt.figure(figsize=(20,10))\n",
    "  Y = data[label]\n",
    "  total = len(Y)*1.\n",
    "  ax=sns.countplot(x=label, data=dataset, hue=\"y\")\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",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TE6DirKyz6du"
   },
   "source": [
    "**Plot-2**\n",
    "\n",
    "Job: The type of job the customers have. Let’s call the count plot function defined earlier to plot the count plot of the job feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 663
    },
    "id": "NdL2TRPzycRy",
    "outputId": "fdf53cb2-2121-4b04-af09-f49caa6d1eb2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countplot(\"job\", data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xkJg40SSz1dc"
   },
   "source": [
    "Interpretation: In the given dataset, 25 % of cadidates are admin are 22 % cadidates are blue collared."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sRUEhcyJ1KWU"
   },
   "source": [
    "Plot-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 565
    },
    "id": "06SgxQuJysxM",
    "outputId": "46ec9462-77a8-45a7-9936-1228a54819a8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countplot_withY(\"job\", data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xrS3X2Vkzz_S"
   },
   "source": [
    "Interpretation :-As the data set contained maximum admins and blue-collared,almost all of them subscribed to a term deposit.Hence if the candidate is admin or blue-collared then there is high chance that he/she might subscribed to a term deposit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bTw0_GK01U-I"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y-AWT-4Z9b3U"
   },
   "source": [
    "**Plot-4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 645
    },
    "id": "P1e9iNrAzBIi",
    "outputId": "e2ab718b-d73c-461c-a1e3-a511f23c1fa4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countplot(\"marital\", data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 550
    },
    "id": "DETHTKtazHrG",
    "outputId": "6df479d4-8f99-4f90-9703-8868c5293259"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countplot_withY(\"marital\", data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2EHgDcJ42Dt0"
   },
   "source": [
    "Interpretation:- If the cadidate is married, there is high chance that he/she might subscribed to a term deposit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cokvbdO69gm8"
   },
   "source": [
    "**Plot-5**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 645
    },
    "id": "Pcwb1nJv4bUx",
    "outputId": "dc20a696-e8a1-4de9-cc79-2580625e9128"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countplot(\"default\", data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 550
    },
    "id": "r2poe3kz4kI6",
    "outputId": "eec8b345-9020-43b5-b0a8-210229952cb4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countplot_withY(\"default\", data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_SejDehH4zsr"
   },
   "source": [
    "Interpretation :- There is no customer with who has credit in default. Majority of the customers don't have, and the for the rest of the customers this field is unknown."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FCjCr0SW9m8F"
   },
   "source": [
    "**Plot-6**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "id": "ScC4sH_E5Nz1",
    "outputId": "9e00dc6a-5af8-4dc0-aecb-1570c9c0b956"
   },
   "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": [
    "%matplotlib inline\n",
    "sns.boxplot(data=data, x=\"y\", y=\"age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4TL4DNW_5RaI"
   },
   "source": [
    "Interpretation :- The agegroup of candidates are in 30 to 50 years and there age is not an affecting factor for the decision of subscription to a term deposit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xCattQ3S9suP"
   },
   "source": [
    "Plot-7- Correlation Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 705
    },
    "id": "R7qHSQmn9S_j",
    "outputId": "0c88cc7e-69ab-4871-c080-4347181ffaf8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'Pearson correlation of Features')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Idea of correlation matrix of numerical feature: https://medium.com/datadriveninvestor/introduction-to-exploratory-data-analysis-682eb64063ff\n",
    "%matplotlib inline\n",
    "corr = data.corr()\n",
    "\n",
    "f, ax = plt.subplots(figsize=(10,12))\n",
    "\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "_ = sns.heatmap(corr, cmap=\"YlGn\", square=True, ax=ax, annot=True, linewidth=0.1)\n",
    "\n",
    "plt.title(\"Pearson correlation of Features\", y=1.05, size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OPufiWPm-In6"
   },
   "source": [
    "From the above heatmap we can see that there are some numerical features which share a high correlation between them, e.g nr.employed and euribor3m these features share a correlation value of 0.95, and euribor3m and emp.var.rate share a correlation of 0.97, which is very high compared to the other features that we see in the heatmap.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IXVWLQZb-iC0"
   },
   "source": [
    "#### **Data Preprocessing**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vLc1Ogn8ZsVW",
    "outputId": "3edea238-e63d-413b-b4b1-a562f293a579"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp_var_rate    41188 non-null  float64\n",
      " 16  cons_price_idx  41188 non-null  float64\n",
      " 17  cons_conf_idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr_employed     41188 non-null  float64\n",
      " 20  y               41188 non-null  int64  \n",
      "dtypes: float64(5), int64(6), object(10)\n",
      "memory usage: 7.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "U1UmheDmWj3N",
    "outputId": "0b2e5388-ffbd-4a80-c929-d11c8043c72b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-d5fd1cec-225c-4309-817d-ddac97f30987\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jun</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>apr</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>\n",
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      ],
      "text/plain": [
       "   age          job  marital          education  default housing loan  \\\n",
       "0   44  blue-collar  married           basic.4y  unknown     yes   no   \n",
       "1   53   technician  married            unknown       no      no   no   \n",
       "2   28   management   single  university.degree       no     yes   no   \n",
       "3   39     services  married        high.school       no      no   no   \n",
       "4   55      retired  married           basic.4y       no     yes   no   \n",
       "\n",
       "    contact month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
       "0  cellular   aug         thu  ...         1    999         0  nonexistent   \n",
       "1  cellular   nov         fri  ...         1    999         0  nonexistent   \n",
       "2  cellular   jun         thu  ...         3      6         2      success   \n",
       "3  cellular   apr         fri  ...         2    999         0  nonexistent   \n",
       "4  cellular   aug         fri  ...         1      3         1      success   \n",
       "\n",
       "  emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  nr_employed  y  \n",
       "0          1.4          93.444          -36.1      4.963       5228.1  0  \n",
       "1         -0.1          93.200          -42.0      4.021       5195.8  0  \n",
       "2         -1.7          94.055          -39.8      0.729       4991.6  1  \n",
       "3         -1.8          93.075          -47.1      1.405       5099.1  0  \n",
       "4         -2.9          92.201          -31.4      0.869       5076.2  1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Gwa4B6s_WmgB",
    "outputId": "a9460ec4-ff9f-4612-a247-acad1c9ed016"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique value in job:12\n",
      "Unique value in marital:4\n",
      "Unique value in education:8\n",
      "Unique value in default:3\n",
      "Unique value in housing:3\n",
      "Unique value in loan:3\n",
      "Unique value in contact:2\n",
      "Unique value in month:10\n",
      "Unique value in day_of_week:5\n",
      "Unique value in poutcome:3\n"
     ]
    }
   ],
   "source": [
    "## Getttign list of columns basd o data type\n",
    "cat_columns=[]\n",
    "num_columns=[]\n",
    "for col in data.columns:\n",
    "    if data[col].dtype=='object':\n",
    "      print(\"Unique value in {}:{}\".format(col,data[col].nunique()))\n",
    "      #print(data[col].dtype,col,data[col].unique())\n",
    "      cat_columns.append(col)\n",
    "\n",
    "    else:\n",
    "      num_columns.append(col)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aHpMemmPO6hk"
   },
   "source": [
    "**List of categorical columns**\n",
    "\n",
    "['job',\n",
    " 'marital',\n",
    " 'education',\n",
    " 'default',\n",
    " 'housing',\n",
    " 'loan',\n",
    " 'contact',\n",
    " 'month',\n",
    " 'day_of_week',\n",
    " 'poutcome']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Swyks6ACPfa0"
   },
   "source": [
    "**List of numerical columns**\n",
    "\n",
    "['age',\n",
    " 'duration',\n",
    " 'campaign',\n",
    " 'pdays',\n",
    " 'previous',\n",
    " 'emp_var_rate',\n",
    " 'cons_price_idx',\n",
    " 'cons_conf_idx',\n",
    " 'euribor3m',\n",
    " 'nr_employed',\n",
    " 'y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4HRwBoUp_f5t"
   },
   "source": [
    "##### **One Hot Encoding Categorical features**\n",
    "\n",
    "This is one of the most popular encoding schemes to deal with the categorical features. For each unique category it creates a separate column with binary values (0 or 1). In each of these new columns there is a value of 1 if the corresponding category is present in this row and 0 otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CVXLrgoHdds5"
   },
   "outputs": [],
   "source": [
    "#import Label Encoder\n",
    "le = LabelEncoder()\n",
    "dummy_columns = [] #array for multiple value columns\n",
    "\n",
    "for column in data.columns:\n",
    "    if data[column].dtype == object:\n",
    "        if data[column].nunique() == 2:\n",
    "            #apply Label Encoder for binary ones\n",
    "            data[column] = le.fit_transform(data[column]) \n",
    "        else:\n",
    "            dummy_columns.append(column)\n",
    "\n",
    "#apply get dummies for selected columns\n",
    "df_data = pd.get_dummies(data = data,columns = dummy_columns) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KJATaUVKSWfo"
   },
   "source": [
    "#### **Model Development**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oyWlvnBcfaXi"
   },
   "outputs": [],
   "source": [
    "## Traaining features and target values \n",
    "\n",
    "X = df_data.drop(['y'],axis=1)\n",
    "y = df_data.y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_OfMjL3mBAUh",
    "outputId": "3383053e-bec3-41b3-de21-ad8d340044de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the train dataset:  (32950, 62)\n",
      "The shape of the test dataset:  (8238, 62)\n"
     ]
    }
   ],
   "source": [
    "## With duration column\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2,random_state=42)\n",
    "\n",
    "\n",
    "## Without duration column\n",
    "X_train_nd = X_train.drop(\"duration\", axis=1)\n",
    "print(\"The shape of the train dataset: \", X_train.shape)\n",
    "X_test_nd = X_test.drop(\"duration\", axis=1)\n",
    "print(\"The shape of the test dataset: \", X_test.shape)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TQLNUiePAIlP"
   },
   "source": [
    "### **Model Pipeline**\n",
    "\n",
    "For every model that we are going to create from hereafter, we will be performning following steps. \n",
    "\n",
    "\n",
    "We will create 2 models for each machine learning algorithm.\n",
    "\n",
    "\n",
    "1.**Benchmark model** (with duration column)\n",
    "\n",
    "2.**Realistic predictive model**(without duration column)\n",
    "\n",
    "\n",
    "As mentioned the problem statement \n",
    "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",
    "\n",
    "\n",
    "**Step-1**\n",
    "In the Benchmark model we will use \"Duration\" feature to see how the model performs with this feature. It will probably give very high ROC/AUC as the duration feature is very correlated with the target variable. But obviously we can't use the Duration feature for actual modelling.\n",
    "\n",
    "\n",
    "**Step-2**\n",
    "In the next step for Realistic predictive model we will remove the \"Duration\" feature, and apply the same model to check how the model performs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sGhmmRMBkVuW"
   },
   "source": [
    "**Performance Metric Used:**\n",
    "As we know, the given dataset is an imbalanced data and we should use accuracy as a perfronce mentric, thus we decided to use something that gives impoartance to true positive rate.\n",
    "The performance metric used for this case study is AUC ROC score also known as AUROC (Area Under the Receiver Operating Characteristics).\n",
    "\n",
    "Why use AUC?\n",
    "\n",
    "The reason we are choosing AUC over accuracy is because,\n",
    "as we will see in Exploratory data analysis, the dataset we are working with is an imbalanced dataset with the class “no” being the majority class. \n",
    "AUC gets over this problem by looking into both the True positive rate (TPR) and False positive rate (FPR). Only if both the TPR and FPR are well above the random line in the ROC curve, we will get a good AUC. Accuracy does not guarantee that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DnqPg5FOR6Gp"
   },
   "source": [
    "**Model-1 : Applying Logistic Regression with \n",
    "“Duration” feature**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F3bAbDY6Q3K7",
    "outputId": "b30e84d1-c685-4ac0-be9f-30a4fb336a3c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The AUC is: 0.9240943512930674\n"
     ]
    }
   ],
   "source": [
    "### Logistics Regression  with duration column\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "model1=LogisticRegression(penalty='l2',C=0.01,class_weight='balanced',max_iter=100,solver='lbfgs')\n",
    "model1.fit(X_train,y_train)\n",
    "y_pred_proba = model1.predict_proba(X_test)[::,1]\n",
    "print (\"The AUC is:\",roc_auc_score(y_test,y_pred_proba)) #[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cDUF2or2YCxY"
   },
   "source": [
    "Model-1 ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "5Rw7_QaDXwkO",
    "outputId": "f63745be-0c92-4cd2-f328-cb1529ebfebc"
   },
   "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": [
    "from sklearn.metrics import roc_curve, auc\n",
    "y_test_pred = model1.predict_proba(X_test)[::,1]\n",
    "y_train_pred = model1.predict_proba(X_train)[::,1]\n",
    "\n",
    "train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)\n",
    "test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.plot(train_fpr, train_tpr, label=\" AUC TRAIN =\"+str(auc(train_fpr, train_tpr)))\n",
    "plt.plot(test_fpr, test_tpr, label=\" AUC TEST =\"+str(auc(test_fpr, test_tpr)))\n",
    "plt.plot([0,1],[0,1],'g--')\n",
    "plt.legend()\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"AUC(ROC curve)\")\n",
    "plt.grid(color='black', linestyle='-', linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7g5VlaP_YPNV"
   },
   "source": [
    "**Model-2 : Applying Logistic Regression without “Duration” feature**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uXlcbp0PnUSO",
    "outputId": "91768348-a45c-4242-9042-76061cd7cb78"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The AUC is: 0.7779476057120137\n"
     ]
    }
   ],
   "source": [
    "### Logistics Regression  with duration column\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "model2=LogisticRegression(penalty='l2',C=0.01,class_weight='balanced')\n",
    "model2.fit(X_train_nd,y_train)\n",
    "\n",
    "predict_y = model2.predict(X_test_nd)\n",
    "y_pred_proba = model2.predict_proba(X_test_nd)[::,1]\n",
    "print (\"The AUC is:\",roc_auc_score(y_test,y_pred_proba)) #[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GqlrIdJEYWiF"
   },
   "source": [
    "Model-2 ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "c8lRGN9FM9P7",
    "outputId": "a6adc73e-c66a-41b1-ed8b-35328fc66220"
   },
   "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": [
    "from sklearn.metrics import roc_curve, auc\n",
    "y_test_pred = model2.predict_proba(X_test_nd)[::,1]\n",
    "y_train_pred = model2.predict_proba(X_train_nd)[::,1]\n",
    "\n",
    "train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)\n",
    "test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.plot(train_fpr, train_tpr, label=\" AUC TRAIN =\"+str(auc(train_fpr, train_tpr)))\n",
    "plt.plot(test_fpr, test_tpr, label=\" AUC TEST =\"+str(auc(test_fpr, test_tpr)))\n",
    "plt.plot([0,1],[0,1],'g--')\n",
    "plt.legend()\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"AUC(ROC curve)\")\n",
    "plt.grid(color='black', linestyle='-', linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0sZeVV7DYpLs"
   },
   "source": [
    "**Model-3 : Applying Neural Network with “Duration” feature**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VVcGjiY7eADQ",
    "outputId": "2440ad37-be1a-499d-e45a-d2a8234f70ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "2636/2636 [==============================] - 8s 3ms/step - loss: 3.6806 - accuracy: 0.8633 - val_loss: 0.7543 - val_accuracy: 0.8697\n",
      "Epoch 2/10\n",
      "2636/2636 [==============================] - 6s 2ms/step - loss: 0.9821 - accuracy: 0.8720 - val_loss: 0.8737 - val_accuracy: 0.9042\n",
      "Epoch 3/10\n",
      "2636/2636 [==============================] - 7s 3ms/step - loss: 0.5196 - accuracy: 0.8827 - val_loss: 0.4813 - val_accuracy: 0.8929\n",
      "Epoch 4/10\n",
      "2636/2636 [==============================] - 6s 2ms/step - loss: 0.3034 - accuracy: 0.8934 - val_loss: 0.3963 - val_accuracy: 0.8918\n",
      "Epoch 5/10\n",
      "2636/2636 [==============================] - 7s 2ms/step - loss: 0.2416 - accuracy: 0.9019 - val_loss: 0.2248 - val_accuracy: 0.9076\n",
      "Epoch 6/10\n",
      "2636/2636 [==============================] - 7s 3ms/step - loss: 0.2245 - accuracy: 0.9028 - val_loss: 0.2413 - val_accuracy: 0.8945\n",
      "Epoch 7/10\n",
      "2636/2636 [==============================] - 6s 2ms/step - loss: 0.2174 - accuracy: 0.9037 - val_loss: 0.2097 - val_accuracy: 0.9079\n",
      "Epoch 8/10\n",
      "2636/2636 [==============================] - 6s 2ms/step - loss: 0.2100 - accuracy: 0.9058 - val_loss: 0.2245 - val_accuracy: 0.9102\n",
      "Epoch 9/10\n",
      "2636/2636 [==============================] - 9s 3ms/step - loss: 0.2083 - accuracy: 0.9049 - val_loss: 0.1953 - val_accuracy: 0.9135\n",
      "Epoch 10/10\n",
      "2636/2636 [==============================] - 7s 3ms/step - loss: 0.2053 - accuracy: 0.9069 - val_loss: 0.1978 - val_accuracy: 0.9120\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Activation, Dense\n",
    "model3 = Sequential()\n",
    "model3.add(Dense(128, activation='relu', input_dim=62))\n",
    "model3.add(Dense(128, activation='relu'))\n",
    "model3.add(Dense(1, activation='sigmoid'))\n",
    "model3.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "hist3 = model3.fit(X_train,y_train, epochs=10, batch_size=10, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1EQ1rgW7eBiC",
    "outputId": "5cc1317d-7d5e-4541-eb7f-0eeabb2df076"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 128)               8064      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               16512     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 129       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 24,705\n",
      "Trainable params: 24,705\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model3.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BDAkM3qvaMz-"
   },
   "source": [
    "Model-3 ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "NJdRtAfwNaxR",
    "outputId": "3290289f-f742-4247-bb06-9a2a12051d06"
   },
   "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": [
    "from sklearn.metrics import roc_curve, auc\n",
    "y_test_pred = model3.predict(X_test)\n",
    "y_train_pred = model3.predict(X_train)\n",
    "\n",
    "train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)\n",
    "test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.plot(train_fpr, train_tpr, label=\" AUC TRAIN =\"+str(auc(train_fpr, train_tpr)))\n",
    "plt.plot(test_fpr, test_tpr, label=\" AUC TEST =\"+str(auc(test_fpr, test_tpr)))\n",
    "plt.plot([0,1],[0,1],'g--')\n",
    "plt.legend()\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"AUC(ROC curve)\")\n",
    "plt.grid(color='black', linestyle='-', linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 319
    },
    "id": "F6juMrZXg4Lv",
    "outputId": "bf82321f-156e-4a1c-d80d-bb663dccf041"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    " \n",
    "acc = hist3.history['accuracy']\n",
    "val = hist3.history['val_accuracy']\n",
    "epochs = range(1, len(acc) + 1)\n",
    " \n",
    "plt.plot(epochs, acc, '-', label='Training accuracy')\n",
    "plt.plot(epochs, val, ':', label='Validation accuracy')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZeN56Vf5q0XU"
   },
   "source": [
    "**Model-4 : Applying Neural Network without “Duration” feature**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CuErnsfZhdZy",
    "outputId": "90984d65-445a-4cbb-f348-25d7b3f27d40"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "2636/2636 [==============================] - 7s 3ms/step - loss: 4.7565 - accuracy: 0.8265 - val_loss: 1.9579 - val_accuracy: 0.8991\n",
      "Epoch 2/10\n",
      "2636/2636 [==============================] - 6s 2ms/step - loss: 1.2238 - accuracy: 0.8428 - val_loss: 1.4869 - val_accuracy: 0.3669\n",
      "Epoch 3/10\n",
      "2636/2636 [==============================] - 6s 2ms/step - loss: 0.6829 - accuracy: 0.8580 - val_loss: 0.3045 - val_accuracy: 0.8945\n",
      "Epoch 4/10\n",
      "2636/2636 [==============================] - 6s 2ms/step - loss: 0.4459 - accuracy: 0.8764 - val_loss: 0.3401 - val_accuracy: 0.8874\n",
      "Epoch 5/10\n",
      "2636/2636 [==============================] - 7s 3ms/step - loss: 0.3477 - accuracy: 0.8922 - val_loss: 0.2932 - val_accuracy: 0.8991\n",
      "Epoch 6/10\n",
      "2636/2636 [==============================] - 6s 2ms/step - loss: 0.3170 - accuracy: 0.8936 - val_loss: 0.2964 - val_accuracy: 0.8991\n",
      "Epoch 7/10\n",
      "2636/2636 [==============================] - 6s 2ms/step - loss: 0.3059 - accuracy: 0.8953 - val_loss: 0.3341 - val_accuracy: 0.8958\n",
      "Epoch 8/10\n",
      "2636/2636 [==============================] - 7s 2ms/step - loss: 0.3019 - accuracy: 0.8956 - val_loss: 0.2915 - val_accuracy: 0.8991\n",
      "Epoch 9/10\n",
      "2636/2636 [==============================] - 7s 3ms/step - loss: 0.2975 - accuracy: 0.8971 - val_loss: 0.2891 - val_accuracy: 0.8992\n",
      "Epoch 10/10\n",
      "2636/2636 [==============================] - 6s 2ms/step - loss: 0.2962 - accuracy: 0.8973 - val_loss: 0.2919 - val_accuracy: 0.8991\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Activation, Dense\n",
    "model4 = Sequential()\n",
    "model4.add(Dense(128, activation='relu', input_dim=61))\n",
    "model4.add(Dense(128, activation='relu'))\n",
    "model4.add(Dense(1, activation='sigmoid'))\n",
    "model4.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "hist4 = model4.fit(X_train_nd,y_train, epochs=10, batch_size=10, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rSOCWsrgaPpn"
   },
   "source": [
    "Model-4 ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "WEqIIzTlOAIn",
    "outputId": "e908cdde-5da4-4b06-ba7b-6ade240ef646"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "y_test_pred = model4.predict(X_test_nd)\n",
    "y_train_pred = model4.predict(X_train_nd)\n",
    "\n",
    "train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)\n",
    "test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.plot(train_fpr, train_tpr, label=\" AUC TRAIN =\"+str(auc(train_fpr, train_tpr)))\n",
    "plt.plot(test_fpr, test_tpr, label=\" AUC TEST =\"+str(auc(test_fpr, test_tpr)))\n",
    "plt.plot([0,1],[0,1],'g--')\n",
    "plt.legend()\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"AUC(ROC curve)\")\n",
    "plt.grid(color='black', linestyle='-', linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 319
    },
    "id": "0LnWKPvvr55q",
    "outputId": "e8a213b7-674c-4aeb-e3e1-1b1d2ae83c9f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    " \n",
    "acc = hist4.history['accuracy']\n",
    "val = hist4.history['val_accuracy']\n",
    "epochs = range(1, len(acc) + 1)\n",
    " \n",
    "plt.plot(epochs, acc, '-', label='Training accuracy')\n",
    "plt.plot(epochs, val, ':', label='Validation accuracy')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qjA086vpsqpU"
   },
   "source": [
    "**Model-5 : Applying Linear SVM with “Duration” feature**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "r8CkrY1VGf-v",
    "outputId": "437e4e2c-a4da-4542-c74e-a073a4b724b1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model5 = SGDClassifier(penalty='l2', alpha=0.1, class_weight='balanced')\n",
    "model5.fit(X_train,y_train)\n",
    "sig_clf = CalibratedClassifierCV(model5, method=\"sigmoid\")\n",
    "sig_clf.fit(X_train, y_train)\n",
    "predict_test = sig_clf.predict_proba(X_test)\n",
    "predict_train = sig_clf.predict_proba(X_train)\n",
    "\n",
    "\n",
    "y_test_pred=predict_test[:,1]\n",
    "y_train_pred = predict_train[:,1]\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)\n",
    "test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.plot(train_fpr, train_tpr, label=\" AUC TRAIN =\"+str(auc(train_fpr, train_tpr)))\n",
    "plt.plot(test_fpr, test_tpr, label=\" AUC TEST =\"+str(auc(test_fpr, test_tpr)))\n",
    "plt.plot([0,1],[0,1],'g--')\n",
    "plt.legend()\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"AUC(ROC curve)\")\n",
    "plt.grid(color='black', linestyle='-', linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m9JOfeJ4p9xk"
   },
   "source": [
    "**Model-6 : Applying Linear SVM without “Duration” feature**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "xMUmUzZCLD4G",
    "outputId": "26d200ba-322c-485d-e0ad-a8a20cc4fdac"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model6 = SGDClassifier(penalty='l2', alpha=0.1, class_weight='balanced')\n",
    "model6.fit(X_train,y_train)\n",
    "sig_clf = CalibratedClassifierCV(model5, method=\"sigmoid\")\n",
    "sig_clf.fit(X_train_nd, y_train)\n",
    "predict_test = sig_clf.predict_proba(X_test_nd)\n",
    "predict_train = sig_clf.predict_proba(X_train_nd)\n",
    "\n",
    "\n",
    "y_test_pred=predict_test[:,1]\n",
    "y_train_pred = predict_train[:,1]\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)\n",
    "test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.plot(train_fpr, train_tpr, label=\" AUC TRAIN =\"+str(auc(train_fpr, train_tpr)))\n",
    "plt.plot(test_fpr, test_tpr, label=\" AUC TEST =\"+str(auc(test_fpr, test_tpr)))\n",
    "plt.plot([0,1],[0,1],'g--')\n",
    "plt.legend()\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"AUC(ROC curve)\")\n",
    "plt.grid(color='black', linestyle='-', linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JNHevvCfPv7A"
   },
   "source": [
    "### **Comparing Model Perfromance**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OmnG2swXV-XX"
   },
   "source": [
    "![Screenshot 2022-07-29 at 7.43.20 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F2uIFOqlXmDL"
   },
   "source": [
    "### **Conclusion**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hvXzKmyqXxIn"
   },
   "source": [
    "**Best performing model** \n",
    "\n",
    "The realistic predictive model(without duration feature) - Neural Network with 2 hidden layer,sigmoid output activation function having adam optimiser\n",
    " has AUC value close to **0.80**.The model stable is and not overfitted because after 10th epoch validation and train accuracy is equaly constant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ETjXB7byZP4R"
   },
   "source": [
    "**Key features with most importance:**\n",
    "The top 3 key features or attributes that helped in the prediction of the class variable were:\n",
    "1. nr.employed\n",
    "2. euribor3m\n",
    "3. emp.var.rate\n",
    "\n",
    "Other than these 3 cons.price.idx and the categorical feature month and poutcome were also held a lot of importance in our case study"
   ]
  }
 ],
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