bank-marketing-campaigns-dataset-analysis-815024.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/mayarmohamedswilam/bank-marketing-campaigns-dataset-analysis-815024?scriptVersionId=129838232\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5"},"source":["# Bank marketing campaigns dataset analysis"]},{"cell_type":"markdown","metadata":{},"source":["### Abstract\n","This is dataset that describe Portugal bank marketing campaigns results. \n","Conducted campaigns were based mostly on direct phone calls, offering  bank's clients to place a term deposit. \n","If after all marking afforts client had agreed to place deposit - target variable marked 'yes', otherwise 'no'.\n","\n","Sourse of the data\n","https://archive.ics.uci.edu/ml/datasets/bank+marketing\n","\n","Dataset description\n","https://www.kaggle.com/volodymyrgavrysh/bank-marketing-campaigns-data-set-description\n","\n","**Citation Request:**\n","> This dataset is public available for research. The details are described in 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","### Task\n","\n","* predicting the future results of marketing companies based on available statistics and, accordingly, formulating recommendations for such companies in the future.\n","* building a profile of a consumer of banking services (deposits).\n","\n","### Approach\n","\n","The following steps will be performed to complete the task:\n","1. Loading data and holding a short Expanatory Data Analysis (EDA).\n","2. Formulating hypotheses regarding individual factors (features) for conducting correct data clearining and data preparation for modeling.\n","3. The choice of metrics result.\n","4. Building a pipline for Cross Validation and Grid Search procedures (search for optimal parameters of the model)\n","5. The choice of the most effective model **, build learninig curve rate\n","6. Conclusions and recomendations.\n","\n"," > ****** we intentionally use most basic machine learning models to increase the level of interprebility of the solution\n"]},{"cell_type":"markdown","metadata":{},"source":["### Feature description\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')"]},{"cell_type":"code","execution_count":1,"metadata":{"_cell_guid":"","_uuid":""},"outputs":[{"data":{"text/plain":["' Citing libraries \\nscikit-learn\\nauthors={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}\\n\\n category Encoders\\n author = Will McGinnis\\n \\n matplotlib\\n author = Hunter, J. D.\\n \\n seaborn\\n author = Michael Waskom\\n \\n \\n'"]},"execution_count":1,"metadata":{},"output_type":"execute_result"}],"source":["# Import libraries \n","import pandas as pd\n","import numpy as np\n","import time\n","import gc\n","import warnings\n","warnings.filterwarnings(\"ignore\")\n","\n","from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold\n","from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import roc_curve, roc_auc_score\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.pipeline import Pipeline\n","from sklearn.linear_model import SGDClassifier\n","\n","import category_encoders as ce\n","\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import plotly.graph_objs as go\n","from tqdm import tqdm\n","\n","''' Citing libraries \n","scikit-learn\n","authors={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n","         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n","         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n","         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}\n","\n"," category Encoders\n"," author = Will McGinnis\n"," \n"," matplotlib\n"," author = Hunter, J. D.\n"," \n"," seaborn\n"," author = Michael Waskom\n"," \n"," \n","'''"]},{"cell_type":"markdown","metadata":{},"source":["### 1. Loading data and holding a short Expanatory Data Analysis"]},{"cell_type":"code","execution_count":2,"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>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>56</td>\n","      <td>housemaid</td>\n","      <td>married</td>\n","      <td>basic.4y</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>telephone</td>\n","      <td>may</td>\n","      <td>mon</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>999</td>\n","      <td>0</td>\n","      <td>nonexistent</td>\n","      <td>1.1</td>\n","      <td>93.994</td>\n","      <td>-36.4</td>\n","      <td>4.857</td>\n","      <td>5191.0</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>57</td>\n","      <td>services</td>\n","      <td>married</td>\n","      <td>high.school</td>\n","      <td>unknown</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>telephone</td>\n","      <td>may</td>\n","      <td>mon</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>999</td>\n","      <td>0</td>\n","      <td>nonexistent</td>\n","      <td>1.1</td>\n","      <td>93.994</td>\n","      <td>-36.4</td>\n","      <td>4.857</td>\n","      <td>5191.0</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>37</td>\n","      <td>services</td>\n","      <td>married</td>\n","      <td>high.school</td>\n","      <td>no</td>\n","      <td>yes</td>\n","      <td>no</td>\n","      <td>telephone</td>\n","      <td>may</td>\n","      <td>mon</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>999</td>\n","      <td>0</td>\n","      <td>nonexistent</td>\n","      <td>1.1</td>\n","      <td>93.994</td>\n","      <td>-36.4</td>\n","      <td>4.857</td>\n","      <td>5191.0</td>\n","      <td>no</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>3 rows × 21 columns</p>\n","</div>"],"text/plain":["   age        job  marital    education  default housing loan    contact  \\\n","0   56  housemaid  married     basic.4y       no      no   no  telephone   \n","1   57   services  married  high.school  unknown      no   no  telephone   \n","2   37   services  married  high.school       no     yes   no  telephone   \n","\n","  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n","0   may         mon  ...         1    999         0  nonexistent          1.1   \n","1   may         mon  ...         1    999         0  nonexistent          1.1   \n","2   may         mon  ...         1    999         0  nonexistent          1.1   \n","\n","   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n","0          93.994          -36.4      4.857       5191.0  no  \n","1          93.994          -36.4      4.857       5191.0  no  \n","2          93.994          -36.4      4.857       5191.0  no  \n","\n","[3 rows x 21 columns]"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["'There is 41188 observations with 21 features'"]},"metadata":{},"output_type":"display_data"}],"source":["# Read data\n","data = pd.read_csv('../input/bank-additional-full.csv', sep=';')\n","display(data.head(3))\n","display('There is {} observations with {} features'.format(data.shape[0], data.shape[1]))"]},{"cell_type":"markdown","metadata":{},"source":["### 1. Explore categorical features (EDA)"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["# Build a function to show categorical values disribution\n","def plot_bar(column):\n","    # temp df \n","    temp_1 = pd.DataFrame()\n","    # count categorical values\n","    temp_1['No_deposit'] = data[data['y'] == 'no'][column].value_counts()\n","    temp_1['Yes_deposit'] = data[data['y'] == 'yes'][column].value_counts()\n","    temp_1.plot(kind='bar')\n","    plt.xlabel(f'{column}')\n","    plt.ylabel('Number of clients')\n","    plt.title('Distribution of {} and deposit'.format(column))\n","    plt.show();"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"image/png":"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size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure 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PP5/bbruNDh06MHLkyGrXvfvuuznzzDO5++67Oemkk1aVH3744bz99tvsv//+QHaq8SOPPEKrVq3Yddddeeihh7jooovo1q0bF198MW3atGHkyJGccsoplJeXs88++zBw4EAWL17MCSecwPLly4mIKk8sOPfccxk4cCAbbbQREyZMaLRxFc9S3Mh8nYpZ/bz99tvsuuuueYeRizlz5nDsscfy5ptv5tJ+VZ99sbMUu/vLzMxKxt1fZmYlcvPNN/Ob3/xmjbJTTjmFwYMH12k7Xbp0ye0opaGcVMzMSmTw4MF1TiDrGnd/mZlZyTipmJlZyTRaUpE0QtICSW8WlD0uaUp6zJE0JZV3kfRFwbJhBevsLWmapFmShipdESRpC0njJM1Mz+0ba1/MzKw4jXmk8iCwxmWdEXFaRPSIiB7Ab4EnCxa/W7EsIgYWlN8HDAC6pUfFNq8CXoiIbsAL6b2ZmeWo0QbqI+JlSV2qWpaONk4FDqlpG5K2BdpGxIT0/mHgROCPwAnAQanqQ8CfgJ80PHIzWytd367E21tS7aKI4IADDmDw4MEcddRRAIwePZoRI0bw3HPPlSyEWbNmcfLJJ1d5BXypDR48mL59+3LwwQdz5513cskll9CmTZuSt5PXmMoBwIcRMbOgrKukv0p6SdIBqawjUFZQpyyVAWwTEfMB0vPW1TUmaYCkyZImf/TRR6XbCzNbJ0li2LBhDBo0iOXLl/PZZ58xePBg7rnnnrxDq7ebb76Zgw8+GIA777yzQfehr0leSeUM4LGC9/OB7SNiT2AQ8GtJbYGqZmir8xQAETE8InpGRM8OHTrUK2Aza1l23313jjvuOG699VZuuOEG+vXrx0477cRDDz3EvvvuS48ePbjkkktYuXIl5eXlnHPOOeyxxx7svvvuX5vRuNCkSZPo3r07+++/P8OGrRo+pry8nEGDBrHvvvvSvXt37r//fgDGjx/PwQcfzIknnshuu+3GpZdeumpiyUceeWRVm9dcc82q7VQVy9lnn83TTz/NXXfdxYIFCzjggAPo27dvyT+3Jr9ORdL6wA+AvSvKImIFsCK9fl3Su8A3yY5MOhWs3gmouHXZh5K2jYj5qZtsQVPEb2Ytx5AhQ9hrr73YYIMNmDx5Mm+++SZPPfUUf/nLX1bdwGvUqFHstNNOLFy4kGnTpgHwySefVLvNc889l+HDh9O7d2+uuOKKVeXDhw9n6623ZuLEiaxYsYJevXpx+OGHA/Daa6/x1ltv0blzZw477DCeeeYZevbsybXXXsvkyZNp164dffv25fe//z0dOnSoMZYrrriCO+64gz//+c9svvnmpf7IcjlS6Qu8ExGrurUkdZDUKr3ekWxA/r3UrbVUUq80DtMPeCatNgbon173Lyg3MyuJTTbZhNNOO41zzjmHDTfckPHjxzNp0iR69uxJjx49eOmll3j33XfZeeedmTFjBpdffjnPP/887dpVPf6zcOFCvvjii1X3Sal8b5WRI0fSo0cP9ttvPz755JNV91bp1asXXbp0oVWrVpx++um88sorvPbaaxxyyCFstdVWtG7dmjPPPJOXX3656FgaS2OeUvwYMAH4lqQySRekRaezZtcXwPeAqZL+BjwBDIyIxWnZxcD9wCzgXbJBeoBbgMMkzQQOS+/NzEpqvfXWY731sq/KiOD8889fdf+TGTNmcN1117HlllsydepU+vTpw9ChQ7nooouq3V51912JCO69995V2549ezaHHnpolevUdG+VusTSGBotqUTEGRGxbUS0johOEfFAKj83IoZVqvvbiPh2RHwnIvaKiN8VLJscEbtHxE4RcVmkTzIiFkXEoRHRLT0vxsysEfXt25fRo0ezcOFCABYtWsTcuXP56KOPiAhOOeUUbrjhBt54440q199qq61o06bNqvukVL63yr333kt5eTmQ3U644l7yr776KnPnzuWrr75i9OjR9OnTh169evHiiy+yaNEiysvLGTVqFAceeGBRsTTmvVU895eZNQ81nALcVPbYYw+GDBlC3759WblyJa1bt2bYsGG0atWKCy64gIhAErfeemu12xg5ciQ//OEP2WSTTVaNmQBcdNFFzJ07lx49egCw9dZb88wzWa/+d7/7Xa688kqmT5/OQQcdxPHHH48kbrzxRg466CAiguOOO45jjjmGN954o9ZYBgwYQN++fencuTPjx48v6Wfk+6k0Mt9Pxax+WvL9VAqNHz+eX/ziFzz99NNN1qbvp2JmZmsFd3+ZmTWCgQMH8uqrr65RNmjQIPr161en7fTt27dRridpLE4qZtVw16U1ROGFjS2Ju7/MbK3V0sZ81wYN/cydVMxsrdSmTRsWLVrkxNKEIoJFixY1aKJJd3+Z2VqpU6dOlJWV4Ulgm1abNm3o1KlT7RWr4aRiZmul1q1b07Vr17zDsDpy95eZmZWMk4qZmZWMk4qZmZWMk4qZmZWMk4qZmZWMk4qZmZWMk4qZmZWMk4qZmZWMk4qZmZVMY96jfoSkBZLeLCi7XtIHkqakx9EFy66WNEvSDElHFJQfmcpmSbqqoLyrpNckzZT0uKQNGmtfzMysOI15pPIgcGQV5XdFRI/0eBZA0m7A6cC30zr3SmolqRVwD3AUsBtwRqoLcGvaVjfgY+CCRtwXMzMrQqMllYh4GVhcZPUTgFERsSIiZgOzgH3TY1ZEvBcR/wRGASdIEnAI8ERa/yHgxJLugJmZ1VkeYyqXSZqausfap7KOwN8L6pSlsurKtwQ+iYjySuVmZpajpk4q9wE7AT2A+cAdqVxV1I16lFdJ0gBJkyVN9jTaZmaNp0mTSkR8GBFfRcRK4Jdk3VuQHWl0LqjaCZhXQ/lCYHNJ61cqr67d4RHRMyJ6dujQoTQ7Y2ZmX9OkSUXStgVvvw9UnBk2Bjhd0oaSugLdgInAJKBbOtNrA7LB/DGR3QruReDktH5/4Jmm2AczM6teo92kS9JjwEHAVpLKgCHAQZJ6kHVVzQEuAoiI6ZJGA28B5cClEfFV2s5lwPNAK2BERExPTfwEGCXpZ8BfgQcaa1/MzKw4jZZUIuKMKoqr/eKPiJuBm6sofxZ4tory91jdfWZmZmsBX1FvZmYl46RiZmYl46RiZmYl46RiZmYl46RiZmYlU6ekIqm9pO6NFYyZmTVvtSYVSX+S1FbSFsDfgJGS7mz80MzMrLkp5kilXUR8CvwAGBkRewN9GzcsMzNrjopJKuun6VVOBX7fyPGYmVkzVkxSuYFsmpRZETFJ0o7AzMYNy8zMmqNipmmZHxGrBucj4j2PqZiZWVWKOVL57yLLzMyshav2SEXS/sB3gQ6SBhUsaks2Y7CZmdkaaur+2gDYNNXZrKD8U1bfx8TMzGyVapNKRLwEvCTpwYh4vwljMjOzZqqYgfoNJQ0HuhTWj4hDGisoMzNrnopJKr8BhgH3A181bjhmZtacFZNUyiPivkaPxMzMmr1iTin+naRLJG0raYuKR20rSRohaYGkNwvKbpP0jqSpkp6StHkq7yLpC0lT0mNYwTp7S5omaZakoZKUyreQNE7SzPTcvh77b2ZmJVRMUukP/BvwF+D19JhcxHoPAkdWKhsH7J4upvx/wNUFy96NiB7pMbCg/D5gANAtPSq2eRXwQkR0A15I783MLEe1JpWI6FrFY8ci1nsZWFypbGxElKe3rwKdatpGmnOsbURMiIgAHgZOTItPAB5Krx8qKDczs5wUM/X9xpKuTWeAIambpGNL0Pb5wB8L3neV9FdJL0k6IJV1BMoK6pSlMoBtImI+QHreugQxmZlZAxTT/TUS+CfZ1fWQfbH/rCGNShoMlAOPpqL5wPYRsScwCPi1pLaAqlg96tHeAEmTJU3+6KOP6hu2mZnVopikslNE/Bz4EiAivqDqL/uiSOoPHAuclbq0iIgVEbEovX4deBf4JlkCK+wi6wTMS68/TN1jFd1kC6prMyKGR0TPiOjZoUOH+oZuZma1KCap/FPSRqQjBEk7ASvq05ikI4GfAMdHxOcF5R0ktUqvdyQbkH8vdWstldQrnfXVD3gmrTaG7CQC0nNFuZmZ5aSY61SGAM8BnSU9CvQGzq1tJUmPAQcBW0kqS9u5GtgQGJfODH41nen1PeBGSeVkF1gOjIiKQf6Lyc4k24hsDKZiHOYWYLSkC4C5wClF7IuZmTWiWpNKRIyT9AbQi6zb6/KIWFjEemdUUfxANXV/C/y2mmWTgd2rKF8EHFpbHGZm1nSq7f6StEt63gvYgWwwfR6wfSozMzNbQ01HKlcCFwJ3VLEsAE8oaWZma6hp6vsL0/PBTReOmZk1ZzXd+fEHNa0YEU+WPhwzM2vOaur+Oq6GZQE4qZiZ2Rpq6v46rykDMTOz5q+Yub/+o2KK+vS+vaQGTdNiZmbrpmKuqD8qIj6peBMRHwNHN15IZmbWXBWTVFpJ2rDiTZqyZcMa6puZWQtVzDQtjwAvSBpJNkB/PqvvY2JmZrZKMdO0/FzSVKAv2TQtN0XE840emZmZNTvFHKkQEc+RTSppZmZWrWLGVMzMzIripGJmZiVT0yzFL6TnW5suHDMza85qGlPZVtKBwPGSRlHpFsIR8UajRmZmZs1OTUnlp8BVZPeFv7PSMk99b2ZmX1PT3F9PAE9Iui4ibmrCmMzMrJmqdaA+Im6SdLyk29Pj2GI3LmmEpAWS3iwo20LSOEkz03P7VC5JQyXNkjS18O6Skvqn+jMl9S8o31vStLTOUKUb35uZWT6KmVDyP4HLgbfS4/JUVowHgSMrlV0FvBAR3YAX0nuAo4Bu6TEAuC+1vwUwBNgP2BcYUpGIUp0BBetVbsvMzJpQMacUHwMcFhEjImIE2Rf3McVsPCJeBhZXKj6B1dO8PAScWFD+cGReBTaXtC1wBDAuIhanySzHAUemZW0jYkJEBPBwwbbMzCwHxV6nsnnB63YNbHObiJgPkJ63TuUdgb8X1CtLZTWVl1VRbmZmOSlmmpb/BP4q6UWy04q/B1zdCLFUNR4S9Sj/+oalAWTdZGy//fb1jc/MzGpRzED9Y0AvstsHPwnsHxGjGtDmh6nrivS8IJWXAZ0L6nUC5tVS3qmK8qr2YXhE9IyInh06dGhA6GZmVpOiur8iYn5EjImIZyLiHw1scwxQcQZXf+CZgvJ+6SywXsCS1D32PHB4uuNke+Bw4Pm0bKmkXumsr34F2zIzsxwUNUtxfUl6DDgI2EpSGdlZXLcAoyVdAMwFTknVnyW7o+Qs4HPgPICIWCzpJmBSqndjRFQM/l9MdobZRsAf08PMzHLSqEklIs6oZtGhVdQN4NJqtjMCGFFF+WRg94bEaGZmpVNj95ek9QovXDQzM6tJjUklIlYCf5PkU6bMzKxWxXR/bQtMlzQR+KyiMCKOb7SozMysWSomqdzQ6FGYmdk6odakEhEvSdoB6BYR4yVtDLRq/NDMzKy5KWZCyQuBJ4D/SUUdgacbMygzM2ueirn48VKgN/ApQETMZPV8XWZmZqsUk1RWRMQ/K95IWp9q5tgyM7OWrZik8pKka4CNJB0G/Ab4XeOGZWZmzVExSeUq4CNgGnAR2XQq1zZmUGZm1jwVc/bXSkkPAa+RdXvNSFOqmJmZraHWpCLpGGAY8C7ZPUy6SrooIjx5o5mZraGYix/vAA6OiFkAknYC/oBnBDYzs0qKGVNZUJFQkvdYfWMtMzOzVao9UpH0g/RyuqRngdFkYyqnsPreJmZmZqvU1P11XMHrD4ED0+uPgPaNFpGZmTVb1SaViDivKQMxM7Pmr5izv7oC/wJ0Kazvqe/NzKyyYs7+ehp4gOwq+pUNbVDSt4DHC4p2BH4KbA5cSNa9BnBNRDyb1rkauAD4CvhRRDyfyo8E7iabNfn+iLilofGZmVn9FZNUlkfE0FI1GBEzgB4AkloBHwBPAecBd0XE7YX1Je0GnA58G9gOGC/pm2nxPcBhQBkwSdKYiHirVLGamVndFJNU7pY0BBgLrKgojIg3StD+ocC7EfG+pOrqnACMiogVwGxJs4B907JZEfEegKRRqa6TiplZTopJKnsA5wCHsLr7K9L7hjodeKzg/WWS+gGTgSsj4mOy+7e8WlCnLJUB/L1S+X4liMnMzOqpmIsfvw/sGBEHRsTB6dHghCJpA+B4slmPAe4DdiLrGptPdiU/ZFPDVBY1lFfV1gBJkyVN/uijj6qqYmZmJVBMUvkb2SB6qR0FvBERHwJExIcR8VXeWkokAAAN0klEQVRErAR+yeourjKgc8F6nYB5NZR/TUQMj4ieEdGzQ4cOJd4NMzOrUEz31zbAO5ImseaYSkNPKT6Dgq4vSdtGxPz09vvAm+n1GODXku4kG6jvBkwkO1Lplk55/oCsK+3MBsZkZmYNUExSGVLqRiVtTHbW1kUFxT+X1IOsC2tOxbKImC5pNNkAfDlwaUR8lbZzGfA82SnFIyJieqljNTOz4hVzP5WXSt1oRHwObFmp7Jwa6t8M3FxF+bNkNw0zM7O1QDFX1C9l9QD4BkBr4LOIaNuYgZmZWfNTzJHKZoXvJZ3I6kF0MzOzVYo5+2sNEfE0pblGxczM1jHFdH/9oODtekBPqrkexMzMWrZizv4qvK9KOdmZWSc0SjRmZtasFTOm4vuqmJlZUWq6nfBPa1gvIuKmRojHzMyasZqOVD6romwTsvuabAk4qZiZ2Rpqup1wxYSOSNoMuJzsniejWD3Zo5mZ2So1jqlI2gIYBJwFPATslaajNzMz+5qaxlRuA34ADAf2iIhlTRaVmZk1SzVd/Hgl2azA1wLzJH2aHkslfdo04ZmZWXNS05hKna+2NzOzls2Jw8zMSsZJxczMSsZJxczMSsZJxczMSsZJxczMSia3pCJpjqRpkqZImpzKtpA0TtLM9Nw+lUvSUEmzJE2VtFfBdvqn+jMl9c9rf8zMLP8jlYMjokdE9EzvrwJeiIhuwAvpPcBRQLf0GADcB6uu+B8C7Ed2N8ohFYnIzMyaXt5JpbITyKaDIT2fWFD+cGReBTaXtC1wBDAuIhan6WPGAUc2ddBmZpbJM6kEMFbS65IGpLJtImI+QHreOpV3BP5esG5ZKquu3MzMclDMnR8bS++ImCdpa2CcpHdqqKsqyqKG8jVXzpLWAIDtt9++PrGamVkRcjtSiYh56XkB8BTZmMiHqVuL9LwgVS8DOhes3gmYV0N55baGR0TPiOjZoUOHUu+KmZkluSQVSZuke7QgaRPgcOBNYAxQcQZXf+CZ9HoM0C+dBdYLWJK6x54HDpfUPg3QH57KzMwsB3l1f20DPCWpIoZfR8RzkiYBoyVdAMwFTkn1nwWOBmYBn5PdLIyIWCzpJmBSqndjRCxuut0wM7NCuSSViHgP+E4V5YuAQ6soD+DSarY1AhhR6hjNzKzu1rZTis3MrBlzUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5Jp8qQiqbOkFyW9LWm6pMtT+fWSPpA0JT2OLljnakmzJM2QdERB+ZGpbJakq5p6X8zMbE153KO+HLgyIt6QtBnwuqRxadldEXF7YWVJuwGnA98GtgPGS/pmWnwPcBhQBkySNCYi3mqSvTAzs69p8qQSEfOB+en1UklvAx1rWOUEYFRErABmS5oF7JuWzYqI9wAkjUp1nVTMzHKS65iKpC7AnsBrqegySVMljZDUPpV1BP5esFpZKquu3MzMcpJH9xcAkjYFfgv8OCI+lXQfcBMQ6fkO4HxAVaweVJ0Qo5q2BgADALbffvuGB782u75dE7e3pGnbM7O1Wi5HKpJakyWURyPiSYCI+DAivoqIlcAvWd3FVQZ0Lli9EzCvhvKviYjhEdEzInp26NChtDtjZmar5HH2l4AHgLcj4s6C8m0Lqn0feDO9HgOcLmlDSV2BbsBEYBLQTVJXSRuQDeaPaYp9MDOzquXR/dUbOAeYJmlKKrsGOENSD7IurDnARQARMV3SaLIB+HLg0oj4CkDSZcDzQCtgRERMb8odMTOzNeVx9tcrVD1O8mwN69wM3FxF+bM1rWdmZk0rt4F6M6vEJ1nYOsDTtJiZWck4qZiZWck4qZiZWck4qZiZWck4qZiZWck4qZiZWck4qZiZWck4qZiZWck4qZiZWck4qZiZWck4qZiZWck4qZiZWcl4QkkzWyd1ueoPTdrenFuOadL21lY+UjEzs5JxUjEzs5JxUjEzs5JxUjEzs5JxUjEzs5Jp9klF0pGSZkiaJemqvOMxM2vJmnVSkdQKuAc4CtgNOEPSbvlGZWbWcjX361T2BWZFxHsAkkYBJwBv5RqVmbU817dr4vaWNG17RWruSaUj8PeC92XAfpUrSRoADEhvl0ma0QSx5UKwFbCwyRq8QU3W1LrOf7vmrQX8/XYoplJzTypVfarxtYKI4cDwxg8nf5ImR0TPvOOwuvPfrnnz3y/TrMdUyI5MOhe87wTMyykWM7MWr7knlUlAN0ldJW0AnA6MyTkmM7MWq1l3f0VEuaTLgOeBVsCIiJiec1h5axHdfOso/+2aN//9AEV8bQjCzMysXpp795eZma1FnFTMzKxknFTMzKxknFTMzKxkmvXZXwaSTgGei4ilkq4F9gJ+FhFv5ByaFUFSO+B64IBU9BJwY0SsnXNw2BokbQicBHSh4Ps0Im7MK6a8+Uil+bsuJZQ+wBHAQ8B9OcdkxRsBfAqcmh6fAiNzjcjq4hmy+QbLgc8KHi2WTylu5iT9NSL2lPSfwLSI+HVFWd6xWe0kTYmIHrWV2dpJ0psRsXvecaxNfKTS/H0g6X/IfuU+mw7H/XdtPr5IR5kASOoNfJFjPFY3f5G0R95BrE18pNLMSdoYOJLsKGWmpG2BPSJibM6hWREk9SDrsqyYN/1joH9ETM0vKiuWpLeAnYHZwAqySW4jIrrnGliOPFDfzEXE55IWAH2AmWR9uzPzjcrq4G3g58BOwObAEuBEwEmleTgq7wDWNk4qzZykIUBP4FtkA7ytgUeA3nnGZUV7BvgEeAP4IOdYrO4uAP4M/CUiWvQAfQUnlebv+8CeZF9KRMQ8SZvlG5LVQaeIODLvIKze5gBnAEMlLSVLMC9HxDO5RpUjD+g2f/+MbGAsACRtknM8Vjce6G3GImJERJwPHEzWQ3BKem6xfKTS/I1OZ39tLulC4HzglznHZMXrA5wryQO9zZCk+4HdgA/JjlJOJvUatFROKs3fCmA82UVz3wJ+GhHj8g3J6sADvc3blmT3cvoEWAwsjIjyfEPKl5NK87cNcDnZr6MRZAnGmomIeD/vGKz+IuL7AJJ2JZvR4kVJrSKiU76R5cfXqawDJAk4HDiP7Eyw0cADEfFuroGZreMkHUs2b9v3gPbABODPETEi18By5COVdUBEhKR/AP8gu06lPfCEpHER8e/5Rme2TjsKeBm4OyLm5R3M2sBHKs2cpB8B/YGFwP3A0xHxpaT1gJkRsVOuAZqt4yRtA+yT3k6MiAV5xpM3H6k0f1sBP6jcNx8RK9OhuZk1knTriduBP5Gdufffkv4tIp7INbAc+UjFzKyeJP0NOKzi6ERSB2B8RHwn38jy44sfzczqb71K3V2LaOHfq+7+MjOrv+ckPQ88lt6fBjybYzy5c/eXmVkDSDqJbAJXkc379VTOIeXKScXMzEqmRff9mZk1hKQfSJopaYmkTyUtlfRp3nHlyUcqZmb1JGkWcFxEvJ13LGsLH6mYmdXfh04oa/KRiplZPUm6G/gG8DTZjOEARMSTuQWVM59SbGZWf22Bz8kmdK0QQItNKj5SMTOrJ0lbRMTiSmVdI2J2XjHlzWMqZmb19ztJbSvepPuq/C7HeHLnpGJmVn//QZZYNpW0N/AEcHbOMeXKYypmZvUUEX+Q1BoYC2wGnBgRM3MOK1ceUzEzqyNJ/002IF/hEOA9YA5ARPwoh7DWCj5SMTO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size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"data":{"text/plain":["(None, None, None, None, None, None)"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["plot_bar('job'), plot_bar('marital'), plot_bar('education'), plot_bar('contact'), plot_bar('loan'), plot_bar('housing')"]},{"cell_type":"markdown","metadata":{},"source":["**Primary analysis of several categorical features reveals:**\n","1. Administrative staff and technical specialists opened the deposit most of all. In relative terms, a high proportion of pensioners and students might be mentioned as well.\n","2. Although in absolute terms married consumers more often agreed to the service, in relative terms the single was responded better.\n","3. Best communication channel is secullar.\n","4. The difference is evident between consumers who already use the services of banks and received a loan.\n","5. Home ownership does not greatly affect marketing company performance."]},{"cell_type":"markdown","metadata":{},"source":["### Explore numerical features (EDA)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["# Convert target variable into numeric\n","data.y = data.y.map({'no':0, 'yes':1}).astype('uint8')"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"data":{"text/html":["<style  type=\"text/css\" >\n","    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col0 {\n","            background-color:  #023858;\n","            color:  #f1f1f1;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col1 {\n","            background-color:  #f5eff6;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col2 {\n","            background-color:  #f4edf6;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col3 {\n","            background-color:  #afc1dd;\n","  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{\n","            background-color:  #e0deed;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col8 {\n","            background-color:  #034267;\n","            color:  #f1f1f1;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col9 {\n","            background-color:  #023858;\n","            color:  #f1f1f1;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col10 {\n","            background-color:  #fff7fb;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col0 {\n","            background-color:  #f5eff6;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col1 {\n","            background-color:  #8bb2d4;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col2 {\n","            background-color:  #fdf5fa;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col3 {\n","            background-color:  #e3e0ee;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col4 {\n","            background-color:  #6da6cd;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col5 {\n","            background-color:  #f2ecf5;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col6 {\n","            background-color:  #f7f0f7;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col7 {\n","            background-color:  #eae6f1;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col8 {\n","            background-color:  #f0eaf4;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col9 {\n","            background-color:  #f0eaf4;\n","            color:  #000000;\n","        }    #T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col10 {\n","            background-color:  #023858;\n","            color:  #f1f1f1;\n","        }</style><table id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >age</th>        <th class=\"col_heading level0 col1\" >duration</th>        <th class=\"col_heading level0 col2\" >campaign</th>        <th class=\"col_heading level0 col3\" >pdays</th>        <th class=\"col_heading level0 col4\" >previous</th>        <th class=\"col_heading level0 col5\" >emp.var.rate</th>        <th class=\"col_heading level0 col6\" >cons.price.idx</th>        <th class=\"col_heading level0 col7\" >cons.conf.idx</th>        <th class=\"col_heading level0 col8\" >euribor3m</th>        <th class=\"col_heading level0 col9\" >nr.employed</th>        <th class=\"col_heading level0 col10\" >y</th>    </tr></thead><tbody>\n","                <tr>\n","                        <th id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637level0_row0\" class=\"row_heading level0 row0\" >age</th>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col0\" class=\"data row0 col0\" >1</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col1\" class=\"data row0 col1\" >-0.000865705</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col2\" class=\"data row0 col2\" >0.00459358</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col3\" class=\"data row0 col3\" >-0.034369</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col4\" class=\"data row0 col4\" >0.0243647</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col5\" class=\"data row0 col5\" >-0.000370685</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col6\" class=\"data row0 col6\" >0.000856715</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col7\" class=\"data row0 col7\" >0.129372</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col8\" class=\"data row0 col8\" >0.0107674</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col9\" class=\"data row0 col9\" >-0.0177251</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row0_col10\" class=\"data row0 col10\" >0.0303988</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637level0_row1\" class=\"row_heading level0 row1\" >duration</th>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row1_col0\" class=\"data row1 col0\" >-0.000865705</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row1_col1\" class=\"data row1 col1\" >1</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row1_col2\" class=\"data row1 col2\" >-0.0716992</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row1_col3\" class=\"data row1 col3\" >-0.047577</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row1_col4\" class=\"data row1 col4\" >0.0206404</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row1_col5\" class=\"data row1 col5\" >-0.0279679</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row1_col6\" class=\"data row1 col6\" >0.00531227</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row1_col7\" class=\"data row1 col7\" >-0.00817287</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row1_col8\" class=\"data row1 col8\" >-0.0328967</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row1_col9\" class=\"data row1 col9\" >-0.0447032</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row1_col10\" class=\"data row1 col10\" >0.405274</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637level0_row2\" class=\"row_heading level0 row2\" >campaign</th>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row2_col0\" class=\"data row2 col0\" >0.00459358</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row2_col1\" class=\"data row2 col1\" >-0.0716992</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row2_col2\" class=\"data row2 col2\" >1</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row2_col3\" class=\"data row2 col3\" >0.0525836</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row2_col4\" class=\"data row2 col4\" >-0.0791415</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row2_col5\" class=\"data row2 col5\" >0.150754</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row2_col6\" class=\"data row2 col6\" >0.127836</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row2_col7\" class=\"data row2 col7\" >-0.0137331</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row2_col8\" class=\"data row2 col8\" >0.135133</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row2_col9\" class=\"data row2 col9\" >0.144095</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row2_col10\" class=\"data row2 col10\" >-0.0663574</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637level0_row3\" class=\"row_heading level0 row3\" >pdays</th>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row3_col0\" class=\"data row3 col0\" >-0.034369</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row3_col1\" class=\"data row3 col1\" >-0.047577</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row3_col2\" class=\"data row3 col2\" >0.0525836</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row3_col3\" class=\"data row3 col3\" >1</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row3_col4\" class=\"data row3 col4\" >-0.587514</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row3_col5\" class=\"data row3 col5\" >0.271004</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row3_col6\" class=\"data row3 col6\" >0.0788891</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row3_col7\" class=\"data row3 col7\" >-0.0913424</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row3_col8\" class=\"data row3 col8\" >0.296899</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row3_col9\" class=\"data row3 col9\" >0.372605</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row3_col10\" class=\"data row3 col10\" >-0.324914</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637level0_row4\" class=\"row_heading level0 row4\" >previous</th>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row4_col0\" class=\"data row4 col0\" >0.0243647</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row4_col1\" class=\"data row4 col1\" >0.0206404</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row4_col2\" class=\"data row4 col2\" >-0.0791415</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row4_col3\" class=\"data row4 col3\" >-0.587514</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row4_col4\" class=\"data row4 col4\" >1</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row4_col5\" class=\"data row4 col5\" >-0.420489</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row4_col6\" class=\"data row4 col6\" >-0.20313</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row4_col7\" class=\"data row4 col7\" >-0.0509364</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row4_col8\" class=\"data row4 col8\" >-0.454494</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row4_col9\" class=\"data row4 col9\" >-0.501333</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row4_col10\" class=\"data row4 col10\" >0.230181</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637level0_row5\" class=\"row_heading level0 row5\" >emp.var.rate</th>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row5_col0\" class=\"data row5 col0\" >-0.000370685</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row5_col1\" class=\"data row5 col1\" >-0.0279679</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row5_col2\" class=\"data row5 col2\" >0.150754</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row5_col3\" class=\"data row5 col3\" >0.271004</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row5_col4\" class=\"data row5 col4\" >-0.420489</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row5_col5\" class=\"data row5 col5\" >1</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row5_col6\" class=\"data row5 col6\" >0.775334</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row5_col7\" class=\"data row5 col7\" >0.196041</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row5_col8\" class=\"data row5 col8\" >0.972245</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row5_col9\" class=\"data row5 col9\" >0.90697</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row5_col10\" class=\"data row5 col10\" >-0.298334</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637level0_row6\" class=\"row_heading level0 row6\" >cons.price.idx</th>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row6_col0\" class=\"data row6 col0\" >0.000856715</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row6_col1\" class=\"data row6 col1\" >0.00531227</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row6_col2\" class=\"data row6 col2\" >0.127836</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row6_col3\" class=\"data row6 col3\" >0.0788891</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row6_col4\" class=\"data row6 col4\" >-0.20313</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row6_col5\" class=\"data row6 col5\" >0.775334</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row6_col6\" class=\"data row6 col6\" >1</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row6_col7\" class=\"data row6 col7\" >0.0589862</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row6_col8\" class=\"data row6 col8\" >0.68823</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row6_col9\" class=\"data row6 col9\" >0.522034</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row6_col10\" class=\"data row6 col10\" >-0.136211</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637level0_row7\" class=\"row_heading level0 row7\" >cons.conf.idx</th>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row7_col0\" class=\"data row7 col0\" >0.129372</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row7_col1\" class=\"data row7 col1\" >-0.00817287</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row7_col2\" class=\"data row7 col2\" >-0.0137331</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row7_col3\" class=\"data row7 col3\" >-0.0913424</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row7_col4\" class=\"data row7 col4\" >-0.0509364</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row7_col5\" class=\"data row7 col5\" >0.196041</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row7_col6\" class=\"data row7 col6\" >0.0589862</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row7_col7\" class=\"data row7 col7\" >1</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row7_col8\" class=\"data row7 col8\" >0.277686</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row7_col9\" class=\"data row7 col9\" >0.100513</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row7_col10\" class=\"data row7 col10\" >0.0548779</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637level0_row8\" class=\"row_heading level0 row8\" >euribor3m</th>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row8_col0\" class=\"data row8 col0\" >0.0107674</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row8_col1\" class=\"data row8 col1\" >-0.0328967</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row8_col2\" class=\"data row8 col2\" >0.135133</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row8_col3\" class=\"data row8 col3\" >0.296899</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row8_col4\" class=\"data row8 col4\" >-0.454494</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row8_col5\" class=\"data row8 col5\" >0.972245</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row8_col6\" class=\"data row8 col6\" >0.68823</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row8_col7\" class=\"data row8 col7\" >0.277686</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row8_col8\" class=\"data row8 col8\" >1</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row8_col9\" class=\"data row8 col9\" >0.945154</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row8_col10\" class=\"data row8 col10\" >-0.307771</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637level0_row9\" class=\"row_heading level0 row9\" >nr.employed</th>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col0\" class=\"data row9 col0\" >-0.0177251</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col1\" class=\"data row9 col1\" >-0.0447032</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col2\" class=\"data row9 col2\" >0.144095</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col3\" class=\"data row9 col3\" >0.372605</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col4\" class=\"data row9 col4\" >-0.501333</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col5\" class=\"data row9 col5\" >0.90697</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col6\" class=\"data row9 col6\" >0.522034</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col7\" class=\"data row9 col7\" >0.100513</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col8\" class=\"data row9 col8\" >0.945154</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col9\" class=\"data row9 col9\" >1</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row9_col10\" class=\"data row9 col10\" >-0.354678</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637level0_row10\" class=\"row_heading level0 row10\" >y</th>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col0\" class=\"data row10 col0\" >0.0303988</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col1\" class=\"data row10 col1\" >0.405274</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col2\" class=\"data row10 col2\" >-0.0663574</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col3\" class=\"data row10 col3\" >-0.324914</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col4\" class=\"data row10 col4\" >0.230181</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col5\" class=\"data row10 col5\" >-0.298334</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col6\" class=\"data row10 col6\" >-0.136211</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col7\" class=\"data row10 col7\" >0.0548779</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col8\" class=\"data row10 col8\" >-0.307771</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col9\" class=\"data row10 col9\" >-0.354678</td>\n","                        <td id=\"T_2a48e49a_f42f_11ed_b8b9_43b7f0d3e637row10_col10\" class=\"data row10 col10\" >1</td>\n","            </tr>\n","    </tbody></table>"],"text/plain":["<pandas.io.formats.style.Styler at 0x78dd627d0780>"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["# Build correlation matrix\n","corr = data.corr()\n","corr.style.background_gradient(cmap='PuBu')"]},{"cell_type":"markdown","metadata":{},"source":["** From correlation matrix we observe next:** \n","* most correlated with target feature is call duration. So we need to transform it to reduce the influence\n","* higly correlated features (employment rate, consumer confidence index, consumer price index) may describe clients state from different social-economic angles. Their variance might support model capacity for generalization."]},{"cell_type":"markdown","metadata":{},"source":["### 2. Formulating hypotheses regarding individual factors (features) for conducting correct data clearining and data preparation for modeling\n","\n","** Data cleaning stategy **\n","\n","Since categorical variables dominate in dataset and the number of weakly correlated numeric variables is not more than 4, we need to transform categorical variables to increase the model's ability to generalize data. (we can not drop them)\n","\n","Particular attention should be paid to the Duration Feature and categories that can be treated as binary. It suggests using binning and simple transformation accordingly (0 and 1)\n","\n","For categories of more than 3 types of possible option (marital and education) it is proposed to use the encode targeting - it will allow correctly relate the values to the target variable and use indicated categories in numerical form.\n","\n","In some cases, rescaling is proposed to normalize the data.\n"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["# Replacing values with binary ()\n","data.contact = data.contact.map({'cellular': 1, 'telephone': 0}).astype('uint8') \n","data.loan = data.loan.map({'yes': 1, 'unknown': 0, 'no' : 0}).astype('uint8')\n","data.housing = data.housing.map({'yes': 1, 'unknown': 0, 'no' : 0}).astype('uint8')\n","data.default = data.default.map({'no': 1, 'unknown': 0, 'yes': 0}).astype('uint8')\n","data.pdays = data.pdays.replace(999, 0) # replace with 0 if not contact \n","data.previous = data.previous.apply(lambda x: 1 if x > 0 else 0).astype('uint8') # binary has contact or not\n","\n","# binary if were was an outcome of marketing campane\n","data.poutcome = data.poutcome.map({'nonexistent':0, 'failure':0, 'success':1}).astype('uint8') \n","\n","# change the range of Var Rate\n","data['emp.var.rate'] = data['emp.var.rate'].apply(lambda x: x*-0.0001 if x > 0 else x*1)\n","data['emp.var.rate'] = data['emp.var.rate'] * -1\n","data['emp.var.rate'] = data['emp.var.rate'].apply(lambda x: -np.log(x) if x < 1 else np.log(x)).astype('uint8')\n","\n","# Multiply consumer index \n","data['cons.price.idx'] = (data['cons.price.idx'] * 10).astype('uint8')\n","\n","# change the sign (we want all be positive values)\n","data['cons.conf.idx'] = data['cons.conf.idx'] * -1\n","\n","# re-scale variables\n","data['nr.employed'] = np.log2(data['nr.employed']).astype('uint8')\n","data['cons.price.idx'] = np.log2(data['cons.price.idx']).astype('uint8')\n","data['cons.conf.idx'] = np.log2(data['cons.conf.idx']).astype('uint8')\n","data.age = np.log(data.age)\n","\n","# less space\n","data.euribor3m = data.euribor3m.astype('uint8')\n","data.campaign = data.campaign.astype('uint8')\n","data.pdays = data.pdays.astype('uint8')\n","\n","# fucntion to One Hot Encoding\n","def encode(data, col):\n","    return pd.concat([data, pd.get_dummies(col, prefix=col.name)], axis=1)\n","\n","# One Hot encoding of 3 variable \n","data = encode(data, data.job)\n","data = encode(data, data.month)\n","data = encode(data, data.day_of_week)\n","\n","# Drop tranfromed features\n","data.drop(['job', 'month', 'day_of_week'], axis=1, inplace=True)"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["'''Drop the dublicates'''\n","data.drop_duplicates(inplace=True) "]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["'''Convert Duration Call into 5 category'''\n","def duration(data):\n","    data.loc[data['duration'] <= 102, 'duration'] = 1\n","    data.loc[(data['duration'] > 102) & (data['duration'] <= 180)  , 'duration'] = 2\n","    data.loc[(data['duration'] > 180) & (data['duration'] <= 319)  , 'duration'] = 3\n","    data.loc[(data['duration'] > 319) & (data['duration'] <= 645), 'duration'] = 4\n","    data.loc[data['duration']  > 645, 'duration'] = 5\n","    return data\n","duration(data);"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[],"source":["''' Target encoding for two categorical feature '''\n","# save target variable before transformation\n","y = data.y\n","# Create target encoder object and transoform two value\n","target_encode = ce.target_encoder.TargetEncoder(cols=['marital', 'education']).fit(data, y)\n","numeric_dataset = target_encode.transform(data)\n","# drop target variable\n","numeric_dataset.drop('y', axis=1, inplace=True)"]},{"cell_type":"code","execution_count":11,"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>marital</th>\n","      <th>education</th>\n","      <th>default</th>\n","      <th>housing</th>\n","      <th>loan</th>\n","      <th>contact</th>\n","      <th>duration</th>\n","      <th>campaign</th>\n","      <th>pdays</th>\n","      <th>...</th>\n","      <th>month_mar</th>\n","      <th>month_may</th>\n","      <th>month_nov</th>\n","      <th>month_oct</th>\n","      <th>month_sep</th>\n","      <th>day_of_week_fri</th>\n","      <th>day_of_week_mon</th>\n","      <th>day_of_week_thu</th>\n","      <th>day_of_week_tue</th>\n","      <th>day_of_week_wed</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>4.025352</td>\n","      <td>0.101565</td>\n","      <td>0.102490</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.043051</td>\n","      <td>0.101565</td>\n","      <td>0.108389</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.610918</td>\n","      <td>0.101565</td>\n","      <td>0.108389</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>3 rows × 44 columns</p>\n","</div>"],"text/plain":["        age   marital  education  default  housing  loan  contact  duration  \\\n","0  4.025352  0.101565   0.102490        1        0     0        0         3   \n","1  4.043051  0.101565   0.108389        0        0     0        0         2   \n","2  3.610918  0.101565   0.108389        1        1     0        0         3   \n","\n","   campaign  pdays  ...  month_mar  month_may  month_nov  month_oct  \\\n","0         1      0  ...          0          1          0          0   \n","1         1      0  ...          0          1          0          0   \n","2         1      0  ...          0          1          0          0   \n","\n","   month_sep  day_of_week_fri  day_of_week_mon  day_of_week_thu  \\\n","0          0                0                1                0   \n","1          0                0                1                0   \n","2          0                0                1                0   \n","\n","   day_of_week_tue  day_of_week_wed  \n","0                0                0  \n","1                0                0  \n","2                0                0  \n","\n","[3 rows x 44 columns]"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["(41175, 44)"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["(41175,)"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["'We observe 41175 rows and 44 numerical features after transformation. Target variable shape is (41175, 0 ) as expected'"]},"metadata":{},"output_type":"display_data"}],"source":["'''Check numerical data set'''\n","display(numeric_dataset.head(3), numeric_dataset.shape, y.shape)\n","display('We observe 41175 rows and 44 numerical features after transformation. Target variable shape is (41175, 0 ) as expected')"]},{"cell_type":"markdown","metadata":{},"source":["### 3. The choice of metrics result\n","\n","It is proposed to use ROC_AUC metrics for evaluating different models with additional monitoring of the accuracy metric dynamic.\n","\n","This approach will allow us to explore models from different angles."]},{"cell_type":"markdown","metadata":{},"source":["### 4. Building a pipline for Cross Validation and Grid Search procedures (search for optimal parameters of the model)"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"text/plain":["13491"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["''' Split data on train and test'''\n","# set global random state\n","random_state = 11\n","# split data\n","X_train, X_test, y_train, y_test = train_test_split(numeric_dataset, y, test_size=0.2, random_state=random_state)\n","# collect excess data\n","gc.collect()"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"text/plain":["'check the shape of splitted train and test sets'"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["(32940, 44)"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["(32940,)"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["(8235, 44)"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["(8235,)"]},"metadata":{},"output_type":"display_data"}],"source":["display('check the shape of splitted train and test sets', X_train.shape, y_train.shape, X_test.shape, y_test.shape)"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[],"source":["'''Build pipline of classifiers'''\n","# set all CPU\n","n_jobs = -1\n","# LogisticRegression\n","pipe_lr = Pipeline([('lr', LogisticRegression(random_state=random_state, n_jobs=n_jobs, max_iter=500))])\n","# RandomForestClassifier\n","pipe_rf = Pipeline([('rf', RandomForestClassifier(random_state=random_state, oob_score=True, n_jobs=n_jobs))])\n","# KNeighborsClassifier\n","pipe_knn = Pipeline([('knn', KNeighborsClassifier(n_jobs=n_jobs))])\n","# DecisionTreeClassifier\n","pipe_dt = Pipeline([('dt', DecisionTreeClassifier(random_state=random_state, max_features='auto'))])\n","# BaggingClassifier\n","# note we use SGDClassifier as classier inside BaggingClassifier\n","pipe_bag = Pipeline([('bag',BaggingClassifier(base_estimator=SGDClassifier(random_state=random_state, n_jobs=n_jobs, max_iter=1500),\\\n","                                              random_state=random_state,oob_score=True,n_jobs=n_jobs))])\n","# SGDClassifier\n","pipe_sgd = Pipeline([('sgd', SGDClassifier(random_state=random_state, n_jobs=n_jobs, max_iter=1500))])"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["'''Set parameters for Grid Search '''\n","# set number \n","cv = StratifiedKFold(shuffle=True, n_splits=5, random_state=random_state)\n","# set for LogisticRegression\n","grid_params_lr = [{\n","                'lr__penalty': ['l2'],\n","                'lr__C': [0.3, 0.6, 0.7],\n","                'lr__solver': ['sag']\n","                }]\n","# set for RandomForestClassifier\n","grid_params_rf = [{\n","                'rf__criterion': ['entropy'],\n","                'rf__min_samples_leaf': [80, 100],\n","                'rf__max_depth': [25, 27],\n","                'rf__min_samples_split': [3, 5],\n","                'rf__n_estimators' : [60, 70]\n","                }]\n","# set for KNeighborsClassifier\n","grid_params_knn = [{'knn__n_neighbors': [16,17,18]}]\n","\n","# set for DecisionTreeClassifier\n","grid_params_dt = [{\n","                'dt__max_depth': [8, 10],\n","                'dt__min_samples_leaf': [1, 3, 5, 7]\n","                  }]\n","# set for BaggingClassifier\n","grid_params_bag = [{'bag__n_estimators': [10, 15, 20]}]\n","\n","# set for SGDClassifier\n","grid_params_sgd = [{\n","                    'sgd__loss': ['log', 'huber'],\n","                    'sgd__learning_rate': ['adaptive'],\n","                    'sgd__eta0': [0.001, 0.01, 0.1],\n","                    'sgd__penalty': ['l1', 'l2', 'elasticnet'], \n","                    'sgd__alpha':[0.1, 1, 5, 10]\n","                    }]"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[],"source":["'''Grid search objects'''\n","# for LogisticRegression\n","gs_lr = GridSearchCV(pipe_lr, param_grid=grid_params_lr,\n","                     scoring='accuracy', cv=cv) \n","# for RandomForestClassifier\n","gs_rf = GridSearchCV(pipe_rf, param_grid=grid_params_rf,\n","                     scoring='accuracy', cv=cv)\n","# for KNeighborsClassifier\n","gs_knn = GridSearchCV(pipe_knn, param_grid=grid_params_knn,\n","                     scoring='accuracy', cv=cv)\n","# for DecisionTreeClassifier\n","gs_dt = GridSearchCV(pipe_dt, param_grid=grid_params_dt,\n","                     scoring='accuracy', cv=cv)\n","# for BaggingClassifier\n","gs_bag = GridSearchCV(pipe_bag, param_grid=grid_params_bag,\n","                     scoring='accuracy', cv=cv)\n","# for SGDClassifier\n","gs_sgd = GridSearchCV(pipe_sgd, param_grid=grid_params_sgd,\n","                     scoring='accuracy', cv=cv)"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[],"source":["# models that we iterate over\n","look_for = [gs_lr, gs_rf, gs_knn, gs_dt, gs_bag, gs_sgd]\n","# dict for later use \n","model_dict = {0:'Logistic_reg', 1:'RandomForest', 2:'Knn', 3:'DesionTree', 4:'Bagging with SGDClassifier', 5:'SGD Class'}"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","+++++++ Start New Model ++++++++++++++++++++++\n","Estimator is Logistic_reg\n","---------------------------------------------\n","best params {'lr__C': 0.6, 'lr__penalty': 'l2', 'lr__solver': 'sag'}\n","best score is 0.9095931997571342\n","---------------------------------------------\n","ROC_AUC is 0.9216519675583464 and accuracy rate is 0.905525197328476\n","It lasted for 219.533 sec\n","++++++++ End Model +++++++++++++++++++++++++++\n","\n","\n","\n","+++++++ Start New Model ++++++++++++++++++++++\n","Estimator is RandomForest\n","---------------------------------------------\n","best params {'rf__criterion': 'entropy', 'rf__max_depth': 25, 'rf__min_samples_leaf': 80, 'rf__min_samples_split': 3, 'rf__n_estimators': 70}\n","best score is 0.9031268973891925\n","---------------------------------------------\n","ROC_AUC is 0.926898425815701 and accuracy rate is 0.9038251366120219\n","It lasted for 256.801 sec\n","++++++++ End Model +++++++++++++++++++++++++++\n","\n","\n","\n","+++++++ Start New Model ++++++++++++++++++++++\n","Estimator is Knn\n","---------------------------------------------\n","best params {'knn__n_neighbors': 16}\n","best score is 0.9051001821493625\n","---------------------------------------------\n","ROC_AUC is 0.9019898470991563 and accuracy rate is 0.9024893746205221\n","It lasted for 73.302 sec\n","++++++++ End Model +++++++++++++++++++++++++++\n","\n","\n","\n","+++++++ Start New Model ++++++++++++++++++++++\n","Estimator is DesionTree\n","---------------------------------------------\n","best params {'dt__max_depth': 8, 'dt__min_samples_leaf': 1}\n","best score is 0.9037644201578627\n","---------------------------------------------\n","ROC_AUC is 0.7808247466786017 and accuracy rate is 0.9029751062537947\n","It lasted for 1.58 sec\n","++++++++ End Model +++++++++++++++++++++++++++\n","\n","\n","\n","+++++++ Start New Model ++++++++++++++++++++++\n","Estimator is Bagging with SGDClassifier\n","---------------------------------------------\n","best params {'bag__n_estimators': 20}\n","best score is 0.9086520947176685\n","---------------------------------------------\n","ROC_AUC is 0.9049315136935216 and accuracy rate is 0.9064966605950212\n","It lasted for 150.23 sec\n","++++++++ End Model +++++++++++++++++++++++++++\n","\n","\n","\n","+++++++ Start New Model ++++++++++++++++++++++\n","Estimator is SGD Class\n","---------------------------------------------\n","best params {'sgd__alpha': 0.1, 'sgd__eta0': 0.001, 'sgd__learning_rate': 'adaptive', 'sgd__loss': 'log', 'sgd__penalty': 'l2'}\n","best score is 0.8969034608378871\n","---------------------------------------------\n","ROC_AUC is 0.8994453391525427 and accuracy rate is 0.8964177292046145\n","It lasted for 250.53 sec\n","++++++++ End Model +++++++++++++++++++++++++++\n","\n","\n"]}],"source":["''' Function to iterate over models and obtain results'''\n","# set empty dicts and list\n","result_acc = {}\n","result_auc = {}\n","models = []\n","\n","for index, model in enumerate(look_for):\n","        start = time.time()\n","        print()\n","        print('+++++++ Start New Model ++++++++++++++++++++++')\n","        print('Estimator is {}'.format(model_dict[index]))\n","        model.fit(X_train, y_train)\n","        print('---------------------------------------------')\n","        print('best params {}'.format(model.best_params_))\n","        print('best score is {}'.format(model.best_score_))\n","        auc = roc_auc_score(y_test, model.predict_proba(X_test)[:,1])\n","        print('---------------------------------------------')\n","        print('ROC_AUC is {} and accuracy rate is {}'.format(auc, model.score(X_test, y_test)))\n","        end = time.time()\n","        print('It lasted for {} sec'.format(round(end - start, 3)))\n","        print('++++++++ End Model +++++++++++++++++++++++++++')\n","        print()\n","        print()\n","        models.append(model.best_estimator_)\n","        result_acc[index] = model.best_score_\n","        result_auc[index] = auc"]},{"cell_type":"markdown","metadata":{},"source":["### 5. The choice of the most effective model, build learninig curve rate\n"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.plot(model_dict.values(), result_acc.values(), c='r')\n","plt.plot(model_dict.values(), result_auc.values(), c='b')\n","plt.xlabel('Models')\n","plt.xticks(rotation=45)\n","plt.ylabel('Accouracy and ROC_AUC')\n","plt.title('Result of Grid Search')\n","plt.legend(['Accuracy', 'ROC_AUC'])\n","plt.show();"]},{"cell_type":"code","execution_count":20,"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>Model</th>\n","      <th>Accuracy_rate</th>\n","      <th>Roc_auc_rate</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Logistic_reg</td>\n","      <td>0.909593</td>\n","      <td>0.921652</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>RandomForest</td>\n","      <td>0.903127</td>\n","      <td>0.926898</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Knn</td>\n","      <td>0.905100</td>\n","      <td>0.901990</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>DesionTree</td>\n","      <td>0.903764</td>\n","      <td>0.780825</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Bagging with SGDClassifier</td>\n","      <td>0.908652</td>\n","      <td>0.904932</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>SGD Class</td>\n","      <td>0.896903</td>\n","      <td>0.899445</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                        Model  Accuracy_rate  Roc_auc_rate\n","0                Logistic_reg       0.909593      0.921652\n","1                RandomForest       0.903127      0.926898\n","2                         Knn       0.905100      0.901990\n","3                  DesionTree       0.903764      0.780825\n","4  Bagging with SGDClassifier       0.908652      0.904932\n","5                   SGD Class       0.896903      0.899445"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["\"\"\" Model performance during Grid Search \"\"\"\n","pd.DataFrame(list(zip(model_dict.values(), result_acc.values(), result_auc.values())), \\\n","                  columns=['Model', 'Accuracy_rate','Roc_auc_rate'])"]},{"cell_type":"markdown","metadata":{},"source":["#### Best model \n","Our best performed model with ROC_AUC (0.9269) metric is ** Random forest **. \n","This classifier could achive accuracy rate 0.903 that is average accuracy among all classifiers (0.904)."]},{"cell_type":"markdown","metadata":{},"source":["We can build graph to check RandomForestClassifier performatce with OOB score to be sure that critical hyperparametr was correctly selected during Grid Search. \n","As you may see it almost the same - 80 estimators with best ROC_AUC score and 90 estimators with maximun of OOB score\n"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 39/39 [01:05<00:00,  1.67s/it]\n"]},{"data":{"text/plain":["'max oob 0.9044323011536126 and number of estimators 90'"]},"metadata":{},"output_type":"display_data"},{"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":["def graph(model, X_train, y_train):\n","    obb = []\n","    est = list(range(5, 200, 5))\n","    for i in tqdm(est):\n","        random_forest = model(n_estimators=i, criterion='entropy', random_state=11, oob_score=True, n_jobs=-1, \\\n","                           max_depth=25, min_samples_leaf=80, min_samples_split=3,)\n","        random_forest.fit(X_train, y_train)\n","        obb.append(random_forest.oob_score_)\n","    display('max oob {} and number of estimators {}'.format(max(obb), est[np.argmax(obb)]))\n","    plt.plot(est, obb)\n","    plt.title('model')\n","    plt.xlabel('number of estimators')\n","    plt.ylabel('oob score')\n","    plt.show();\n","    \n","graph(RandomForestClassifier, X_train, y_train)"]},{"cell_type":"markdown","metadata":{},"source":["Let see the ROC_AUC graph"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"data":{"text/html":["        <script type=\"text/javascript\">\n","        window.PlotlyConfig = {MathJaxConfig: 'local'};\n","        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n","        if (typeof require !== 'undefined') {\n","        require.undef(\"plotly\");\n","        requirejs.config({\n","            paths: {\n","                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n","            }\n","        });\n","        require(['plotly'], function(Plotly) {\n","            window._Plotly = Plotly;\n","        });\n","        }\n","        </script>\n","        "]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<div>\n","        \n","        \n","            <div id=\"179789e8-4b76-4415-94ed-0c01f93584d3\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n","            <script type=\"text/javascript\">\n","                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\"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"ROC Curve\"}, \"xaxis\": {\"title\": {\"text\": \"False Positive Rate\"}}, \"yaxis\": {\"title\": {\"text\": \"True Positive Rate\"}}},\n","                        {\"responsive\": true}\n","                    ).then(function(){\n","                            \n","var gd = document.getElementById('179789e8-4b76-4415-94ed-0c01f93584d3');\n","var x = new MutationObserver(function (mutations, observer) {{\n","        var display = window.getComputedStyle(gd).display;\n","        if (!display || display === 'none') {{\n","            console.log([gd, 'removed!']);\n","            Plotly.purge(gd);\n","            observer.disconnect();\n","        }}\n","}});\n","\n","// Listen for the removal of the full notebook cells\n","var notebookContainer = gd.closest('#notebook-container');\n","if (notebookContainer) {{\n","    x.observe(notebookContainer, {childList: true});\n","}}\n","\n","// Listen for the clearing of the current output cell\n","var outputEl = gd.closest('.output');\n","if (outputEl) {{\n","    x.observe(outputEl, {childList: true});\n","}}\n","\n","                        })\n","                };\n","                });\n","            </script>\n","        </div>"]},"metadata":{},"output_type":"display_data"}],"source":["''' Build graph for ROC_AUC '''\n","\n","fpr, tpr, threshold = roc_curve(y_test, models[1].predict_proba(X_test)[:,1])\n","                                        \n","trace0 = go.Scatter(\n","    x=fpr,\n","    y=tpr,\n","    text=threshold,\n","    fill='tozeroy',\n","    name='ROC Curve')\n","\n","trace1 = go.Scatter(\n","    x=[0,1],\n","    y=[0,1],\n","    line={'color': 'red', 'width': 1, 'dash': 'dash'},\n","    name='Baseline')\n","\n","data = [trace0, trace1]\n","\n","layout = go.Layout(\n","    title='ROC Curve',\n","    xaxis={'title': 'False Positive Rate'},\n","    yaxis={'title': 'True Positive Rate'})\n","\n","fig = go.Figure(data, layout)\n","fig.show();"]},{"cell_type":"markdown","metadata":{},"source":["Curve is well distributed with tendency to False Positive Rate.  The roc auc values of the best model of 0.9269 is quite high level to make later assumptions about the data."]},{"cell_type":"markdown","metadata":{},"source":["#### We can build feature importance of RandomForestClassifier with best ROC_AUC score"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x432 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["''' Build bar plot of feature importance of the best model '''\n","\n","def build_feature_importance(model, X_train, y_train):\n","    \n","    models = RandomForestClassifier(criterion='entropy', random_state=11, oob_score=True, n_jobs=-1, \\\n","                           max_depth=25, min_samples_leaf=80, min_samples_split=3, n_estimators=70)\n","    models.fit(X_train, y_train)\n","    data = pd.DataFrame(models.feature_importances_, X_train.columns, columns=[\"feature\"])\n","    data = data.sort_values(by='feature', ascending=False).reset_index()\n","    plt.figure(figsize=[6,6])\n","    sns.barplot(x='index', y='feature', data=data[:10], palette=\"Blues_d\")\n","    plt.title('Feature inportance of Random Forest after Grid Search')\n","    plt.xticks(rotation=45)\n","    plt.show();\n","    \n","build_feature_importance(RandomForestClassifier, X_train, y_train)"]},{"cell_type":"markdown","metadata":{},"source":["### 6. Conclusions and recomendations"]},{"cell_type":"markdown","metadata":{},"source":["#### What general recommendations can be offered for a successful marketing company in the future?"]},{"cell_type":"markdown","metadata":{},"source":["This analysis can be carried out at the level of individual bank branches as does not require much resources and special knowledge (the model itself can be launched automatically with a certain periodicity). \n","\n","Potentially similar micro-targeting will increase the overall effectiveness of the entire marketing company."]},{"cell_type":"markdown","metadata":{},"source":["1. Take into account the time of the company (May is the most effective)\n","2. Increase the time of contact with customers (perhaps in a different way formulating the goal of the company). It is possible to use other means of communication.\n","3. Focus on specific categories. The model shows that students and senior citizens respond better to proposal.\n","4. It is imperative to form target groups based on socio-economic categories. Age, income level (not always high), profession can accurately determine the marketing profile of a potential client.\n","\n","Given these factors, it is recommended to **concentrate on those consumer groups** that are potentially more promising.\n","\n","The concentration of the bank’s efforts will effectively distribute the company’s resources to the main factor - the bank’s contact time with the client - it affects most of all on conversion.\n","\n","The continuation of such a study may be the **formation of a clear customer profile** - by age, gender, income and other factors, as well as the adaptation of the product itself (deposit) for a specific category of consumer.\n"]},{"cell_type":"markdown","metadata":{},"source":["Thank you for your time! \n","\n","Feel free to conctact https://www.linkedin.com/in/volodymyrgavrish/"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.6"}},"nbformat":4,"nbformat_minor":4}