Model_the_decision_of clients_and_define_principal_features_for_marketing_strategy.ipynb

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      "name": "Prediction_of_making_a_deposit_term_by_client",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNYvflNzMT5yAk7v4rjVsbm",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/akhsassoualid/Prediction_of_making_a_deposit_by_clients/blob/master/Model_the_decision_of%20clients_and_define_principal_features_for_marketing_strategy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MeMo-0984wJe",
        "colab_type": "text"
      },
      "source": [
        "**Introduction**\n",
        "\n",
        "**1.Import important libraries**\n",
        "\n",
        "**2.Define usable functions**\n",
        "\n",
        "**3.Import, Manipulate, explore visually, and scale data**\n",
        " - 3.1.Import data, drop unnecessary variables:\n",
        " - 3.2. Explore categorical data visually\n",
        " - 3.3. Explore correlation of numerical data\n",
        "\n",
        "**4.Filtering out low variance and highly correlated features**\n",
        " - 4.1. Filtering out highly correlated features\n",
        " - 4.2. Filtering out low variance features\n",
        "\n",
        "**5.Fit multiple classifiers:**\n",
        " - 5.1. Label encoder for categorical variables\n",
        " - 5.2. Fit multiple classifier using the built functions \"grid_model\"\n",
        "    \n",
        "     - 5.2.1. Fit a KNN Classifier and DecisionTress Classifier with a GridSearch\n",
        "    \n",
        "     - 5.2.2. Fit a Bagged classifier with a base learner 'Decision Tree'\n",
        "    \n",
        "     - 5.2.3. Fit a Random Forest classifier with a GridSearch\n",
        "    \n",
        "     - 5.2.4. Fit a Logistic Regression with a GridSearch\n",
        "    \n",
        "     - 5.2.5. Feature importance based on Decision Tree model , Random Forest, and Logistic Regression\n",
        "\n",
        "**6.To sum up:**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lsv0Qa-DEZkD",
        "colab_type": "text"
      },
      "source": [
        "**Introduction**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "In the following python project, the data concerns a bank's marketing strategy, **The purpose is to figure out how possible this strategy could attract more clients to term deposit subscription**. A term deposit is a fixed-term investment where the customer deposits funds in an account at a financial institution, and carry short-term maturities ranging from one month to a few years.\n",
        "\n",
        "The data we got is mixed data, it contains in total 21 categorical and numerical features. **The project aims to exploit these informations about clients to predict whether they will choose a term deposit, to specify the principal axes on which the bank can rely on to attract more customers for such a product.**\n",
        "\n",
        "We need to sheed light on some variables. The **duration** feature need to be dropped, since it highly affects the output target ( In fact, if duration=0 then y='no', if duration=1 then y=1), so this attribute will be included for benchmark purposes and dropped from data.\n",
        "For **pdays** attribute, the data description confirm that if pdays=999 it means client was not previously contacted so we aim to replace 999 with 0 and not 999.\n",
        "\n",
        "The project aims for **EXPLORATION** and then **EXPLOITATION**. We will import the bank marketing data, manipulate, and perform some descriptive statistics, and graphical exploring, then prepare it for the step of modeling and concluding main results."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8SjzRx2rKzNt",
        "colab_type": "text"
      },
      "source": [
        "***1.   Import important libraries***\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNMxHj4tHyEt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Imported libraries for dataframes, arrays, and plots\n",
        "import pandas as pd \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Libraries to preprocess the data\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import VarianceThreshold, RFE\n",
        "\n",
        "# Libraries of modeling\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
        "\n",
        "# Library for metrics\n",
        "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, roc_auc_score, roc_curve, balanced_accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qr0cj1e8Le3k",
        "colab_type": "text"
      },
      "source": [
        "***2. Define usable functions***\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0-fpauKLagh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define Function of descriptive statistics\n",
        "def explor(data):\n",
        "    print(data.info())\n",
        "    # detect missing values\n",
        "    x = data.isnull().sum()\n",
        "    # detect missing values\n",
        "    if sum(x)==0:\n",
        "        print('There are no missed values')\n",
        "    else:\n",
        "        for i in np.arange(0,len(x)):\n",
        "            if x[i]!=0:\n",
        "                print('There are '+str(x[i])+' missied value in the column '+ x.index[i])\n",
        "\n",
        "# Build a function to run multiple models at once\n",
        "def grid_model(estim, param, CV, X_tr, X_te, y_tr, y_te):\n",
        "    gr_estimator= GridSearchCV(estimator = estim, param_grid = param, cv=CV, scoring = 'f1')\n",
        "    gr = gr_estimator.fit(X_tr, y_tr)\n",
        "    model = gr.best_estimator_\n",
        "    y_pred = model.predict(X_te)\n",
        "    print(\"The BEST PARAMETERS are:{}\".format(gr.best_params_))\n",
        "    print(\"The best model results a BALANCED ACCURACY of {}:\".format(round(gr.best_score_,2)))\n",
        "    print(\"The F1-SCORE based on the model is:{}\".format(round(f1_score(y_test, y_pred),2)))\n",
        "    print(\"The ACCURACY based on the best model's predictions is:{}\".format(round(accuracy_score(y_te, y_pred),3)))\n",
        "    print(\"The AREA UNDER THE CURVE RATE is {}:\".format(round(roc_auc_score(y_test, y_pred),2)))\n",
        "    print(pd.DataFrame(confusion_matrix(y_te, y_pred), \n",
        "                       index = ['true negative', 'true positive'], columns=['predicted negative','predicted positive']))\n",
        "    models.append(model)\n",
        "    # Plot the ROC-Curve\n",
        "    y_pred_prob = model.predict_proba(X_te)[:,1]\n",
        "    fpr, tpr, threshold = roc_curve(y_te, y_pred_prob)\n",
        "    plt.plot([0,1],[0,1], 'g--')\n",
        "    plt.plot(fpr, tpr, label='The ROC-CURVE plot')\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel('True Positive Rate')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    return y_pred\n",
        "\n",
        "# Define Function to plot feature importance\n",
        "def feat_importance(model_name, estimator, variables):\n",
        "  estimator.fit(X_train, y_train)\n",
        "  if model_name == 'Decision Tree':\n",
        "    var_impo = estimator.tree_.compute_feature_importances(normalize=False)\n",
        "  elif model_name == 'Random Forest':\n",
        "    var_impo = estimator.feature_importances_\n",
        "  elif model_name == 'Logistic Regression':\n",
        "    var_impo = estimator.coef_[0]\n",
        "  dat = pd.DataFrame({'Variable':variables.tolist(),'Importance':var_impo.tolist()})\n",
        "  sns.barplot(x=\"Importance\",y=\"Variable\", data=dat)\n",
        "  plt.title('Features importance according to the model of '+model_name)\n",
        "  plt.show()\n",
        "\n",
        "  # Define Function for RFE with multiple selector\n",
        "def rfe_select(X, y, estim, n_feat, stepp):\n",
        "  rfe = RFE(estimator=estim, n_features_to_select=n_feat, step=stepp, verbose=1)\n",
        "  rfe.fit(X,y)\n",
        "  return rfe.support_\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMJYg007Lpei",
        "colab_type": "text"
      },
      "source": [
        "***3. Import, Manipulate, explore visually, and scale data***\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IYAUAlJOKqmQ",
        "colab_type": "text"
      },
      "source": [
        "***3.1. Import data, drop unnecessary variables:***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sj7mAYHaLugD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('/content/bank-additional-full.csv', sep=';', comment='#', na_values='NA')\n",
        "# Drop duration attribute\n",
        "df = df.drop('duration', axis=1)\n",
        "# Make the change on observation that have pdays=999\n",
        "for i in range(0,len(df)):\n",
        "    if df.loc[i,'pdays']==999:\n",
        "        df.loc[i,'pdays']=0\n",
        "df_original = df\n",
        "# Select categorical and continuous features\n",
        "categorical_mask = (df.dtypes == 'object')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sb_QqsRINN_u",
        "colab_type": "code",
        "outputId": "d93ed479-1c65-4ae6-924f-7c88436f9331",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        }
      },
      "source": [
        "# Descriptive Statistics of data \n",
        "explor(df)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 41188 entries, 0 to 41187\n",
            "Data columns (total 20 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  campaign        41188 non-null  int64  \n",
            " 11  pdays           41188 non-null  int64  \n",
            " 12  previous        41188 non-null  int64  \n",
            " 13  poutcome        41188 non-null  object \n",
            " 14  emp.var.rate    41188 non-null  float64\n",
            " 15  cons.price.idx  41188 non-null  float64\n",
            " 16  cons.conf.idx   41188 non-null  float64\n",
            " 17  euribor3m       41188 non-null  float64\n",
            " 18  nr.employed     41188 non-null  float64\n",
            " 19  y               41188 non-null  object \n",
            "dtypes: float64(5), int64(4), object(11)\n",
            "memory usage: 6.3+ MB\n",
            "None\n",
            "There are no missed values\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JdqFbQF3NXIp",
        "colab_type": "code",
        "outputId": "1796fc9c-3c6d-4706-cad6-509cbbb08852",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>41188.00000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.02406</td>\n",
              "      <td>2.567593</td>\n",
              "      <td>0.221229</td>\n",
              "      <td>0.172963</td>\n",
              "      <td>0.081886</td>\n",
              "      <td>93.575664</td>\n",
              "      <td>-40.502600</td>\n",
              "      <td>3.621291</td>\n",
              "      <td>5167.035911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.42125</td>\n",
              "      <td>2.770014</td>\n",
              "      <td>1.348874</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",
              "      <td>72.251528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.00000</td>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>32.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>38.00000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>47.00000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>93.994000</td>\n",
              "      <td>-36.400000</td>\n",
              "      <td>4.961000</td>\n",
              "      <td>5228.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>98.00000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>27.000000</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               age      campaign  ...     euribor3m   nr.employed\n",
              "count  41188.00000  41188.000000  ...  41188.000000  41188.000000\n",
              "mean      40.02406      2.567593  ...      3.621291   5167.035911\n",
              "std       10.42125      2.770014  ...      1.734447     72.251528\n",
              "min       17.00000      1.000000  ...      0.634000   4963.600000\n",
              "25%       32.00000      1.000000  ...      1.344000   5099.100000\n",
              "50%       38.00000      2.000000  ...      4.857000   5191.000000\n",
              "75%       47.00000      3.000000  ...      4.961000   5228.100000\n",
              "max       98.00000     56.000000  ...      5.045000   5228.100000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRgWhFTBN1pX",
        "colab_type": "code",
        "outputId": "77611dc1-5e90-4e2f-8ac3-83e8fa95e7eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('The size of data is {} observations and {} features:'.format(df.shape[0], df.shape[1]))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The size of data is 41188 observations and 20 features:\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fHQEY3c4OBqb",
        "colab_type": "text"
      },
      "source": [
        "* Conclusions:\n",
        "\n",
        " - The data is composed of 41188 observations and 20 features, after dropping unnecessary variable \"duration\" since it affects directly the target variable.\n",
        " - The mean age of clients is equal to 40 years old\n",
        " - The scale of measures between numerical variables is quit different, so the data must be scaled before modeling.\n",
        "  - there are 11 categorical and 9 numerical features including the target variables, and other 10 numerical features\n",
        "\n",
        "***3.2. Explore categorical data visually***\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DjxjtKIkQf_r",
        "colab_type": "code",
        "outputId": "a8d55b84-ab60-4a82-f496-c04fb0d620eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for var in df.columns[categorical_mask]:\n",
        "  chart = sns.countplot(x=var, hue='y', data = df, palette=\"RdBu\")\n",
        "  chart.set_xticklabels(chart.get_xticklabels(), rotation=45, horizontalalignment='right')\n",
        "  plt.title('The Bar plot of variable '+var)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Bu4CNgb4VXNuSwBvAQbm+MPANohW+S8mylgdWABYCzieUxhJVlGMujwL65/Km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\nIkI5cOXg1BoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fzumnn/6Z5TbaaCOGDh3aqvv26Cwzsxo2Y8YMNt54YwYPHky/fv3afP+uiZiZ1bD+/fsz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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Er371qxx22GFst912bLLJJp0Sg5OImVkL22+//Tj77LN5/PHHmTRpEldddRWHH344Bx54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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wDvhxkTW2FA6RFirtyDsYGA2MAvYE7gWuSf+IjwFOdoC0HCUBciiwHPgt8HlJ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KJxFJe0j6k6R5kuZKuijFr5K0WNKc9DquZJ/LJdVJelrSMSXxoSlWJ+mySl+L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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8XwHWAR+R9OlK1q09cYi0cwW/ZAYDX4qIqyPiK8DvgIsl7Vi52llrCs7dfsDf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\nM8vt/wMs3NBgFMHnqgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aWksjaWvgFbKWxlBgZET8UtI7wDmAyH4mW5F90PV3laqrrZm7s9ZDaWDyH8B1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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J/SWNkrR+tWMxs9rlJGItGQJcCOyfr3yrGkkq/VuF+tdrbrojqtZ70BJJW9dA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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VMG4zM6ujYskiIv4KqIHFg+qpH8BJDWzrWuDaxovOzMzWhq/gNjOzQk4WZmZW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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JEjp16sTq1avp2bMnRx55JN27d+faa6/d4GM7iZiZtXGdO3dm//33p3v37nTo0IHhw4fz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\nC1KDFLTcTQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mZpZbqw8RSUMlPS+pTtKVle6PmVlb0qpDRFIV8FPgFKAfcLakfpXtlZlZ29Gq\nQwQ4BqiLiMURsQGYCgyrcJ/MzNqM1h4iPYGlBa/rU83MzJpB+0p3oDlIGg2MTi/XSXq+kv3ZdXxp\nP+C1SvfCrBH++WxaBxUrtvYQWQb0LnjdK9U+ICImABOaq1NthaQ5ETGw0v0wK8Y/n82jtQ9nzQZq\nJPWR1BE4C5he4T6ZmbUZrfpMJCI2SroYeBCoAiZGxIIKd8vMrM1o1SECEBEzgBmV7kcb5SFCa8n8\n89kMFBGV7oOZmbVSrf2aiJmZVZBDxMzMcnOImJlZbg4RK5kk/7xYiyFpf0m7V7ofbZ1/KVijJH1J\n0jcljZV0YERsdpBYSyDp34Hx+DFHFedfCFaUpDHA/wLeJHvcwb2S+kbE5sr2zNo6SccBNwDXRsQi\nSZ0l7ZOWqbK9a3ta/ftErGlJUmT3fR8OfC0inkz1K4BrJF0YEe9UtJPWJhX8bB4K/BnYJOkiYDCw\nQdLlEfFSRTvZBvlMxBqqkdSB7DlkJxTU/wBscIBYBe2Vvs4GdgPuAQK4DXgB6FKhfrVpPhOx96RH\nyFwC/AZ4BviapNciYiLZmcnBkvaJiNcr2U9reyR9huxD5xYDc4ErgXYRsUrSAOB04FeV7GNb5RAx\n4L0LlUcAQ8iGB/YG/gjcmP6Tngic6QCx5ibp48B3yT5w7nagGnggW6R/A+4Avh4RtRXrZBvmx54Y\nknoCfwP+GBEjJXUC/ifZY/a7kj2D6PWIWFXBblobJOlAsrOMFcBLwC3AFyLipfRzux/QISLmVLCb\nbZqviRgRsYxsGGuopLMiYj3ZRw2vBDYDqx0g1twk9SC7Q/A1sg+VmwAMTwHyeeAi4DkHSGV5OMsA\niIj7JK0H/lMSETFV0iRgj4h4s8Lds7bpNaAG6AM8DzwE7C3pAOAa4JsRsaGC/TM8nGUNSDqF7C++\nr0fEryvdH2t70jDVnhHxfBrOugz4b6Ab2bW5dcBtEXF/wW2/ViEOEduKpE8DL0TE4kr3xdoWSXsA\nNwJHkg2p/o1s2OrOiPirpL3IroGsdoC0DA4RM2tRJHUG+gFXAPPIrtctAT4XEUsr2DUrwtdEzKxF\niYh3gackjQY6kd0A1J/sDbBLfQbSsvhMxMxaPElXAwdFxOhK98U+yLf4mlmLVfBAxReAgyTtVsn+\n2NYcImbWYkVEpCB5C7jUz25reTycZWZmuflMxMzMcnOImJlZbg4RMzPLzSFiZma5OUTMzCw3h4hZ\nBUkaJ+mSgtc3SRpbyT6Z7Qjf4mtWQZKqgfsi4ihJ7YBFwDH+/BZrLfzsLLMKioglkrZ8TngP4GkH\niLUmDhGzyvs58BXgw8DEyupwpSUAAAB6SURBVHbFbMd4OMuswiR1BJ4FOgA1EbGpwl0yK5nPRMwq\nLCI2SHoEWOsAsdbGIWJWYemC+iDgC5Xui9mO8i2+ZhUkqR9QBzwcEYsq3R+zHeVrImZmlpvPRMzM\nLDeHiJmZ5eYQMTOz3BwiZmaWm0PEzMxyc4iYmVlu/x/sYbHNebtakAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PaDKj3YjW8Vd",
        "colab_type": "text"
      },
      "source": [
        "*   Conclusions:\n",
        " - First look on the barplot of the target variable **y**, we notice a problem of unbalancing between its class, we have more observation with y=0 than those with y=1.\n",
        "\n",
        " - We noticed there is unknow data on some variables, we talk about: **\"martial, default, housing, loan\"**, we can assume that the number of unknown values for these variables is quite weak, so we consider them as a class of the concerned variables. However, we can impute these variables using **\"fancyimpute\"** module, to impute with KNN, but this will certainly cause a problem of multicollinearity between categorical variables, which allow the risk of overfitting.\n",
        "\n",
        " - The problem of unbalancing data is quite clear, it seems that the number of clients that refuse a deposit term is very important on each categorical feature.\n",
        "\n",
        " - The number of clients having these variable-class \"job-admin.\", \"martial-married, \"education-university degree\" is so important. The majority of clients with these class refuse the bank's product\n",
        "\n",
        " - **First impression is that the new strategy must be focused on these class of these 3 variables, and look to make the majority of clients with these class accept the product.**\n",
        "\n",
        "***3.3. Explore correlation of numerical data***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIk5Nt0cbyE2",
        "colab_type": "code",
        "outputId": "d7cbe8cf-84c4-4906-8e0e-5c54b8bc2ab9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "my_corr = df[df.columns[categorical_mask==False]].corr()\n",
        "cmap = sns.diverging_palette(h_neg=12,h_pos=240, as_cmap=True)\n",
        "sns.heatmap(round(my_corr,2),cmap=cmap,linewidths=1,annot = True,fmt=\".2f\", )\n",
        "plt.xticks(rotation=45)\n",
        "plt.yticks(rotation=0)\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FUCGl1ANP3d67VFFRUbGNO9KqlJH/lBG6Ae8JIdoBzihTcLe3S6SioqLyL7gXpuNUIwRI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\nwRNCHjx48OAhbPh/3MVlxOXax1oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CdJQ6LZycPbu",
        "colab_type": "text"
      },
      "source": [
        "*   Conclusions:\n",
        " - The variables **\"emp.var.rate, nr.employed, cons.conf.idx, euribor3m\"** are the most correlated features with a correlation higher than 0.9, Therefore we should take into consideration this problem, before modeling.\n",
        " - We should shed light that the rest of the features aren't correlated, In that case we can be sure about the absence of any linear relation between them. It's a plus for a better model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OGoDAtoofNdS",
        "colab_type": "text"
      },
      "source": [
        "***4. Filtering out low variance and highly correlated features***\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        " \n",
        "***4.1. Filtering out highly correlated features***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0f3XuNKfP4o",
        "colab_type": "code",
        "outputId": "5c6e4072-7bf0-4df7-9561-af55a4f1a2e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "my_corr = my_corr.abs()\n",
        "corr_mask = np.triu(np.ones_like(my_corr, dtype=bool))\n",
        "tri = my_corr.mask(corr_mask)\n",
        "to_drop = [c for c in tri.columns if any(tri[c] > 0.9)] ## we select variables wuth hight correlation to drop\n",
        "print(' The feature that must be dropped are:'+str(to_drop))\n",
        "df = df.drop(to_drop,axis=1)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " The feature that must be dropped are:['emp.var.rate', 'euribor3m']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BR5jfTVigEpz",
        "colab_type": "text"
      },
      "source": [
        "We build a mask that select variables that cause a risk of multicollinearity, for that we drop from data **df** the variables: **\"emp.var.rate, euribor3m\".**\n",
        "\n",
        "***4.2. Filtering out low variance features***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0uG1uwnmeuj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dfs =df\n",
        "categorical_mask = (df.dtypes == 'object')\n",
        "cont_var = df.columns[categorical_mask==False].tolist()\n",
        "scaler = MinMaxScaler()\n",
        "dfs[cont_var]=scaler.fit_transform(dfs[cont_var])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dt4xoXEJfd2w",
        "colab_type": "code",
        "outputId": "af2ac9db-aeb9-40dc-ef7a-8eb804c85563",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Put the selector\n",
        "selector = VarianceThreshold(0.001)\n",
        "selector.fit(dfs[cont_var])\n",
        "mask = selector.get_support().tolist()\n",
        "if all(mask):\n",
        "    print('No Features can be elimiated')\n",
        "else:\n",
        "    s=df.shape[1]\n",
        "    df = df.drop([cont_var[mask==False]], axis=1)\n",
        "    print('Dimensionality of reduced from {} to {}.'.format(s, df.shape[1]))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No Features can be elimiated\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-OuLgs-iAB7",
        "colab_type": "text"
      },
      "source": [
        "It seems that all variable have important variance, so no features can be elimnated."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6SQy3k3ujGfD",
        "colab_type": "text"
      },
      "source": [
        "***5. Fit multiple classifiers:***\n",
        "\n",
        "---\n",
        "\n",
        "***5.1. Label encoder for categorical variables***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28igpmDIiK12",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "le = LabelEncoder()\n",
        "for var in df.columns[categorical_mask==True].tolist():\n",
        "    df[var]=le.fit_transform(df[var])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h9GCyWtdjt6-",
        "colab_type": "text"
      },
      "source": [
        "***5.2. Fit multiple classifier using the built functions \"grid_model\"***\n",
        "\n",
        "To note that we haven't ignored the problem of unbalanced data, in fact we can see it throw the following tables:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mb-oSHpyjwVz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Spiliting original data \n",
        "X = df.drop('y', axis=1)\n",
        "y = df['y']\n",
        "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.25,random_state=100)\n",
        "# Spiliting scaled data \n",
        "Xs = dfs.drop('y', axis=1)\n",
        "ys = dfs['y']\n",
        "Xs_train, Xs_test, ys_train, ys_test=train_test_split(Xs,ys,test_size=0.25,random_state=100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2LlqpNcykg-U",
        "colab_type": "code",
        "outputId": "3ae67e1a-6555-4f8b-de2b-f0246fd37121",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "print(round((df['y'].value_counts()/len(df))*100,2))\n",
        "print(round((y_train.value_counts()/len(y_train))*100,2))\n",
        "print(round((y_test.value_counts()/len(y_test))*100,2))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    88.73\n",
            "1    11.27\n",
            "Name: y, dtype: float64\n",
            "0    88.7\n",
            "1    11.3\n",
            "Name: y, dtype: float64\n",
            "0    88.85\n",
            "1    11.15\n",
            "Name: y, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7XS8ZaRSmFZf",
        "colab_type": "text"
      },
      "source": [
        "We have approximately the same percentage of each class in train and test sets, but we notice the problem of unbalanced data we talk about.\n",
        "\n",
        "**To note that the capacities of our machine are limited, which consume more time to get the results, so we tried as possible to get the best models.**\n",
        "\n",
        "***5.2.1. Fit a KNN Classifier and DecisionTress Classifier with a GridSearch***\n",
        "\n",
        "Right now, we are sure that the data is unbalanced, so it seems logical to wait for a high level of accuracy but a low rate of precision and sensitivity (we measure both of them with the F1-Score). In that case, the built function **grid_model**, which is a function that allows us to run multiple classifiers and get the necessary metrics rapidly, with a grid search to tune hyperparameters, we will select the best one in function to a scoring parameter called 'balanced accuracy'.\n",
        "In fact, we used before other scoring parameters such as the F1 score and area under the curve score, but the results show the impact of unbalanced data.\n",
        "\n",
        "**KNN need to be fitted on scaled data.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GkMqkGwWlBKp",
        "colab_type": "code",
        "outputId": "e326c7fe-d362-4788-eb7b-cc64fc03f9f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 813
        }
      },
      "source": [
        "# Fit a KNN Classifier with a GridSearch\n",
        "knn_param = {'n_neighbors':[10,20,45,65], \n",
        "             'weights':['uniform','distance'],'metric':['manhattan'],\n",
        "             }\n",
        "knn_pred = grid_model(KNeighborsClassifier(), knn_param,10, Xs_train, Xs_test, ys_train, ys_test)\n",
        "# Fit a Decision Tree Classifier with a GridSearch\n",
        "dt_param={\"max_depth\": [10,17,25],\n",
        "          \"max_leaf_nodes\": [70,90,110], ## max leaves in a tree\n",
        "          \"min_samples_leaf\": [30,40,45,50], ## minimum number of samples required to be at a leaf node\n",
        "          \"min_samples_split\": [2,3,5,10], 'class_weight':['balanced']}\n",
        "dt_pred = grid_model(DecisionTreeClassifier(), dt_param, 10, X_train, X_test, y_train, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The BEST PARAMETERS are:{'metric': 'manhattan', 'n_neighbors': 10, 'weights': 'distance'}\n",
            "The best model results a BALANCED ACCURACY of 0.58:\n",
            "The F1-SCORE based on the model is:0.26\n",
            "The ACCURACY based on the best model's predictions is:0.875\n",
            "The AREA UNDER THE CURVE RATE is 0.58:\n",
            "               predicted negative  predicted positive\n",
            "true negative                8790                 359\n",
            "true positive                 927                 221\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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xqYW8R3mApGNp3DDu1/PW3dayNkM61NckUAyczjzN8wuf590V71KrYi1+uuMn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\nKQ+niUAppTycJgKllPJw/w8LB/XUWuQj5gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The BEST PARAMETERS are:{'class_weight': 'balanced', 'max_depth': 10, 'max_leaf_nodes': 70, 'min_samples_leaf': 40, 'min_samples_split': 2}\n",
            "The best model results a BALANCED ACCURACY of 0.75:\n",
            "The F1-SCORE based on the model is:0.44\n",
            "The ACCURACY based on the best model's predictions is:0.822\n",
            "The AREA UNDER THE CURVE RATE is 0.74:\n",
            "               predicted negative  predicted positive\n",
            "true negative                7745                1404\n",
            "true positive                 426                 722\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hKYs4kgiK7N+TRWQ4kATUc15I6kJSsvKZ+cMB/rX2CCLw0o0dGBDVQPv8lUP2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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tzAs8sUYOnCH",
        "colab_type": "text"
      },
      "source": [
        "***5.2.2. Fit a Bagged classifier with a base learner 'Decision Tree'***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWIScr3RsY6I",
        "colab_type": "code",
        "outputId": "71ba5e98-25e4-42fc-eb58-b386e93d5858",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        }
      },
      "source": [
        "## 5.2.3 Fit a Bagged classifier with a base learner 'Decision Tree' and a GridSearch\n",
        "dt = DecisionTreeClassifier(max_depth=10, max_leaf_nodes=70, min_samples_leaf=40, min_samples_split=2, class_weight='balanced')\n",
        "bg = BaggingClassifier(base_estimator=dt, n_estimators=600, oob_score=True,n_jobs=-1,random_state=100)\n",
        "bg_model = bg.fit(X_train, y_train)\n",
        "bg_pred = bg.predict(X_test)\n",
        "print('The OOB-score is equal to:{}'.format(round(bg.oob_score_,2)))\n",
        "print(\"The F1-SCORE based on the model is:{}\".format(round(f1_score(y_test, bg_pred),2)))\n",
        "print(\"The ACCURACY based on best model's predictions is:{}\".format(round(accuracy_score(y_test, bg_pred),2)))\n",
        "print('The AREA UNDER THE CURVE RATE is equal to:{}'.format(round(roc_auc_score(y_test, bg_pred),2)))\n",
        "print(pd.DataFrame(confusion_matrix(y_test, bg_pred), \n",
        "                       index = ['true negative', 'true positive'], columns=['predicted negative','predicted positive']))\n",
        "# Plot the ROC-Curve\n",
        "bg_pred_prob = bg_model.predict_proba(X_test)[:,1]\n",
        "fpr, tpr, threshold = roc_curve(y_test, bg_pred_prob)\n",
        "plt.plot([0,1],[0,1], 'g--')\n",
        "plt.plot(fpr, tpr, label='The ROC-CURVE plot')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The OOB-score is equal to:0.85\n",
            "The F1-SCORE based on the model is:0.47\n",
            "The ACCURACY based on best model's predictions is:0.85\n",
            "The AREA UNDER THE CURVE RATE is equal to:0.74\n",
            "               predicted negative  predicted positive\n",
            "true negative                8075                1074\n",
            "true positive                 467                 681\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PXLcNaVeLoR1q4e/jOd/y87P31F4env8wi3cupkVEC9pUbWN1SFdNE4FSHsYYw+q9Jxkz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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x_eZNF2dru8q",
        "colab_type": "text"
      },
      "source": [
        "*   Conclusions:\n",
        " - We fitted three models, the first two are KNN and Decision Tree classifiers, and a bagging model with decision tree as a based learner. it seems that with the chosen scoring parameters, grid search performed well.\n",
        "\n",
        " - If we think according to accuracy, we have 0.875 for KNN, 0.822 for decision tree and 0.85 for bagging, so we would say the KNN is the best choice to model this data. **However :**\n",
        "\n",
        " - If we take a look at the f1- score, we have {0.26 - KNN, 0.44 - Decision tree, 0.47 - Bagging}. In that case we would say it's bagging with decision tree as a based learner. \n",
        " \n",
        "  - If we take a look on the area under the curve, we will notice that KNN performs the lowest one {0.58}, while Decision Tree and bagging have the same ROC-curve area {0.74}, which is quite acceptable, and show good performance of both classifier, so we would say that best model is bagged model or the Decision tree model.\n",
        " \n",
        " - To sum up, the metrics show that bagging with decision tree as a based learner is the best choice we can make. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYUOtLuIUKvx",
        "colab_type": "text"
      },
      "source": [
        "***5.2.3. Fit a Random Forest classifier with a GridSearch***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3SQendywsRR",
        "colab_type": "code",
        "outputId": "7ffa1a35-8eaf-474f-8b35-0edf336b7688",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        }
      },
      "source": [
        "### 5.2.4 Fit a Random Forest classifier with a GridSearch\n",
        "rf_param = {'n_estimators':[300],\n",
        "            \"max_depth\": [10,17,25],\n",
        "            \"max_leaf_nodes\": [70,90,110], ## max leaves in a tree\n",
        "            \"min_samples_leaf\": [30,40,45,50], ## minimum number of samples required to be at a leaf node\n",
        "            \"min_samples_split\": [2,3,5,10], 'class_weight':['balanced']}\n",
        "rf_pred = grid_model(estim=RandomForestClassifier(), param=rf_param, CV=10, X_tr=X_train, X_te=X_test, y_tr=y_train, y_te=y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The BEST PARAMETERS are:{'class_weight': 'balanced', 'max_depth': 17, 'max_leaf_nodes': 110, 'min_samples_leaf': 30, 'min_samples_split': 5, 'n_estimators': 300}\n",
            "The best model results a BALANCED ACCURACY of 0.75:\n",
            "The F1-SCORE based on the model is:0.47\n",
            "The ACCURACY based on the best model's predictions is:0.848\n",
            "The AREA UNDER THE CURVE RATE is 0.74:\n",
            "               predicted negative  predicted positive\n",
            "true negative                8028                1121\n",
            "true positive                 447                 701\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ROFWoou4IPgRCgVXAOyJyBIgCnjTGfO+K4DyJMYbRn61nwea4/HX1giowbWgU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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wsqolIkeu9G0",
        "colab_type": "text"
      },
      "source": [
        "*   Conclusions:\n",
        " - We could run several Random Forest models with a multi choice number of estimator, but the capacities of the machine are limited.\n",
        " - For the ROC-curve area, and f1-score, we got 0.47 the same results as a bagged model. However, we got an accuracy of 0.848 approximately equal to bagging's accuracy of 0.85, so with random forest there is no important increasement.\n",
        " - As we haven't yet got a model with an f1-score higher than 0.5, we still not have a good model with good performance. To note that the machine could not help to tune more hyperparameters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t2GcxUhfgpro",
        "colab_type": "text"
      },
      "source": [
        "***5.2.4. Fit a Logistic Regression with a GridSearch***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvd1GHUPZVvU",
        "colab_type": "code",
        "outputId": "d6d850b9-16e0-4468-a9b4-2d0198c03530",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        }
      },
      "source": [
        "lr_param = {\"penalty\":[\"l2\"], \n",
        "            \"C\":[0.1,0.5, 1,5,10], \"class_weight\":['balanced']}\n",
        "lr_pred = grid_model(LogisticRegression(max_iter=700), lr_param, 10, Xs_train, Xs_test, ys_train, ys_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The BEST PARAMETERS are:{'C': 1, 'class_weight': 'balanced', 'penalty': 'l2'}\n",
            "The best model results a BALANCED ACCURACY of 0.73:\n",
            "The F1-SCORE based on the model is:0.4\n",
            "The ACCURACY based on the best model's predictions is:0.775\n",
            "The AREA UNDER THE CURVE RATE is 0.73:\n",
            "               predicted negative  predicted positive\n",
            "true negative                7216                1933\n",
            "true positive                 388                 760\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X5D4tEqcuqaBEEAM0MsZkiUgAcBSoaYyJd01oKrev1sTxxJcbcp5vG9eDQD+t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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pk00a89uxVuD",
        "colab_type": "text"
      },
      "source": [
        "*   Conclusion:\n",
        " - The logistic regression haven't results any important increasement for the evaluation's metrics.\n",
        " - However, we can use the statistical approach of logistic regression to evaluate the significance of features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKq4byGKgl2A",
        "colab_type": "text"
      },
      "source": [
        "***5.2.5. Feature importance based on Decision Tree model , Random Forest, and Logistic Regression***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twLCSxhkLhUD",
        "colab_type": "code",
        "outputId": "9bd9e8e4-82a2-4905-fe73-84f9275ed62b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        }
      },
      "source": [
        "feat_importance(model_name=\"Decision Tree\", estimator=models[1], variables=X.columns)\n",
        "feat_importance(model_name=\"Random Forest\", estimator=models[3], variables=X.columns)\n",
        "feat_importance(model_name='Logistic Regression', estimator=models[-1], variables=X.columns)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cmZl1U63uQUbEyRExJyKuIZ17HBoRP6p/1xpmHHl4r3y49KWIeLWF8k5IbmbW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\nCv4fmI5CirDgvhEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lIs6PiOaIaB46YNkeCsvMzDrLnWbXjAdGSuonaSXgk6S0egBbSfpovpY5Eri7VkGamVl1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\nZhX6/6qh+ffGg9M/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4ALgAoK2tzam3zMyajINsJ0gaTgqc20TE65LGk4p8L3J0mi0BfCQi3uydHpqZWV/ka7Kd\nswLwYg6wQ4GPAMsB/6NUzHxJYJ/C9LdQqKsqaViv9tbMzPoEB9nOuQlYUtIDpMLa95AKr/8YmEwq\nsP04qXA4wFFAm6RZkv4OHNHrPTYzs7pzFZ7FULrOmo9krwMuiYhu1e5sa2sLp1U0M+saSdMioq3e\n/ajGR7KL5wRJM4D7gMeAP9S5P2Zm1of4xqfFEBFH17sPZmbWd/lI1szMrEYcZLtJ0l3tjBsu6Ybe\n7I+ZmfU9DrLdFBHb1rsPZmbWtznIdpOkuUpOk3SfpNmSRhYmWV7SnyU9JOl8Sd7WZmYtxjc+LZ69\ngWHApsCqpJzGd+RxWwEbAP8i/c52b+CaenTSzMzqw0dXi2d7YGxEzIuIZ4AJwJZ53OSIeDQi5gFj\n87QLkXS4pKmSps6ZM6f3em1mZr3CQbZ2yrN8LJL1IyIuiIi2iGgbNGhQL3XLzMx6i4Ps4pkIjJTU\nT9Ig4KOkNIsAW0n6YL4WOxK4s16dNDOz+nCQ7b4gpVKcRaojOw74bkT8J4+fApwDPEDKBtWtdItm\nZta4fONTN0haBXghUuLn7+S/d0XEeNJRrZmZtTAfyXaRpNWBu4Gf17svZmbWt/lItosi4ilgvXr3\nw8zM+j4HWTNretPP36PDaTY74k+90BNrNT5dbGZmViMOsl0gaXVJ1+THwyTt1ol5XCzAzKxFOch2\nkqQlI+KpiNg3DxoGdBhkzcysdTV9kJU0RNKDkkZLeljSGEkjJE2S9IikrfLf3ZKmS7pL0vp53lGS\nrpc0Drgtt3WfpPcAJ5ESUcyQNLJaG2Zm1rpa5candYD9gENISSI+R8ol/Cng+8BBwA4R8Y6kEcCP\ngX3yvJsDm0TEC5KGAETEfyUdD7RFxNcAJC3fThsVSTocOBxg8ODBPbe2ZmbWJ7RKkH0sImYDSLof\nuC0iQtJsYAiwAnCZpHVJmZyWKsx7a0S80IlltNdGRRFxAXABQFtb2yK5jc3MrLE1/eni7K3C4/mF\n5/NJXzROBm6PiI2APYD+helf6+Qy2mvDzMxaUKsE2Y6sAPw7Px7VyXleBQYuZhtmZtbEHGSTnwE/\nkTSdzp9Cvx3YoHTjUzfbMDOzJqaU497qra2tLaZOnVrvbpiZNRRJ0yKird79qMZHsmZmZjXiIGtm\nZlYjDrJm1vRuvHg3brzYCdqs9znImpmZ1UhTBdmcBvGcHm5zL0kbFJ6flDM6mZmZtaupgmyN7AW8\nG2Qj4viI+Gsd+2NmZg2ioYKspM9Lmpx/m/prSf0kHZwT/08GtitMO1rSvoXncwuPj5E0W9JMSafm\nYYdJmpKHXStpWUnbkvIbn5aXuXaxXUk754IAsyVdImnpPPxxSSdKujePG9pLm8jMzPqQhgmykj4M\njAS2i4hhwDzg88CJpOC6PYUjznba2RXYE9g6IjYlJZEA+H1EbJmHPQAcGhF3AdcD34mIYRHxz0I7\n/YHRwMiI2JiUgOLLhUU9FxGbA78Cjq7Sl8MlTZU0dc6cOZ3dFGZm1iAaJsgCOwNbAFMkzcjPvwmM\nj4g5EfFf4MpOtDMCuDQiXgcoJP/fSNLEXDTgAGDDDtpZn1R44OH8/DLgo4Xxv8//p5GKECwiIi6I\niLaIaBs0aFAnum5mZo2kkYKsgMvyEeWwiFgfOKGd6d8hr5+kJYD3dND+aOBr+aj0RBY/wX+pCME8\nnGbRzKwlNVKQvQ3YV9J7ASStDEwH/kfSKpKWItWMLXmcdOQL6bpqqfTcrcDBkpYttAMp2f/TuZ0D\nCu2UFwIoeQgYImmd/PxAYEL3V8/MzJpNwwTZiPg7cBxwi6RZpGC5Gulo9m5gEulaasmFpAA8E9iG\nXLIuIm4iXWedmk87l66X/gD4W27nwUI7vwO+k29wWrvQnzeBg4Gr8ynm+cD5PbnOZmbW2FwgoI9w\ngQAzs65zgQAzM7MW5SBrZmZWI77r1cz6pEsu26XH2jrkC7f0WFtmXeEjWTMzsxppuSAr6QRJFTMw\n5fGDJP0t3028Qzfaf7dIQXlxATMzay0tF2Q7YWdgdkRsFhETF7OthYoLmJlZa2mJICvp2FxE4E5S\nOkRysv+bJE3L6RSHShpGymW8Zy4IsIykX+X8wvdLOrHQ5uOSVs2P2ySNL1vmIsUFemt9zcysb2j6\nG58kbQHsDwwjre+9pHzCFwBHRMQjkrYGzouInSQdD7RFxNfy/MdGxAuS+gG3SdokImZ1tNyIuEvS\n9cANEXFNlb4dDhwOMHjw4MVfWTMz61OaPsgCOwDXlQoC5MDXH9iWlK2pNN3SVeb/TA6GS5IyTG0A\ndBhkOyMiLiAFe9ra2pwVxMysybRCkK1kCeClXDKvKkkfJKVd3DIiXpQ0mgWFA94tQMDiFxMwM7Mm\n1ArXZO8A9srXVwcCewCvA49J2g9AyaYV5l2elPP4ZUnvA3YtjHucBQUI9qmy7GrFBczMrAU0fZCN\niHtJdWZnAn8BpuRRBwCH5gIC95MKuZfPO5NU6edB4Lek4gElJwJnSppKKmdXScXiAmZm1hpcIKCP\ncIEAM7Ouc4EAMzOzFuUga2ZmViOtenexmTW5H1358XcfHzfy5jr2xFqZj2TNzMxqpOmCrKQhku6r\nYft31aptMzNrLk0XZGstIratdx/MzKwxNGuQ7SfpwpzU/5aciGKYpHskzZJ0naSVACSNl9SWH68q\n6fH8eENJk3Ny/1mS1s3D5+b/w/O810h6UNIY5RyNknbLw6ZJOkvSDXXZCmZmVlfNGmTXBc6NiA2B\nl0gZmS4HjomITYDZwA87aOMI4MycerENeLLCNJsB3yDlM/4QsJ2k/sCvgV0jYgtgULUFSDo8V/iZ\nOmfOnC6toJmZ9X3NGmQfi4gZ+fE0YG1gxYiYkIddBny0gzbuBr4v6RhgrYh4o8I0kyPiyYiYD8wA\nhgBDgUcj4rE8zdhqC4iICyKiLSLaBg2qGovNzKxBNWuQfavweB6wYjvTVkz0HxG/JdWDfQO4UdJO\nnViOfxJlZmbvatYgW+5l4EVJO+TnBwKlo9rHWZDof9/SDJI+RDoiPQv4I7BJJ5f1EPAhSUPy85Hd\n7rWZmTW0Vjry+gJwvqRlgUeBg/PwnwNX5Zqxfy5M/xngQElvA/8BftyZhUTEG5K+Atwk6TUWFCQw\nM7MW4wIBNSBpQETMzXcbnws8EhFntDePCwSYmXWdCwS0psMkzSCV0FuBdLexmZm1mFY6Xdxr8lFr\nu0euZmbW/Hwka2ZmViMOslWUMjuZmZl1l4OsmZlZjTjIdkDJaZLukzRb0sg8fICk2yTdm4fvmYcP\nkfRAee7k+q6FmZnVg4Nsx/YGhgGbAiOA0yStBrwJfDoiNgd2BE4vFQigcu7kRTh3sZlZc3OQ7dj2\nwNiImBcRz5AyRW0JCPixpFnAX4E1gPflecpzJw+p1LBzF5uZNTf/hKf7DiBV2NkiIt7OJfJKuY/L\ncxr7dLGZWQvykWzHJgIjJfWTNIhUvWcyKcnEsznA7gisVc9OmplZ3+Mj2Y5dB2wDzAQC+G5E/EfS\nGOBPkmYDU4EH69hHMzPrg5y7uI9w7mIzs65z7mIzM7MW5SBrZmZWI52+Jitp2Yh4vZadMbPmt9t1\nP+31Zd746WN6fZlm0IkjWUnbSvo7+cYeSZtKOq/mPTMzM2twnTldfAbwceB5gIiYSfoZS9PLKRI/\ntxjzj5K0ek/2yczMGkenrslGxBNlg+bVoC990RCg20EWGAU4yJqZtajOBNknJG0LhKSlJB0NPFDj\nfvUISQdJmiVppqQr8pHpuDzsNkmD83SjJZ0l6S5Jj0raNzdxKrCDpBmSvpnnn5iLAtybt0tpWcfk\nQgEzJZ2a22gDxuT5nfXJzKzFdObGpyOAM0m5ef8N3AJ8tZad6gmSNgSOA7aNiOckrQxcBlwWEZdJ\nOgQ4C9grz7IaKU/xUOB64Brge8DREbF7bnNZ4GMR8aakdYGxQJukXYE9ga0j4nVJK0fEC5K+luev\n+ANYSYcDhwMMHjy4FpvBzMzqqMMgGxHPkfL0NpqdgKtz/8lBbxtSVR2AK4CfFab/Q0TMB/4u6X1U\nthRwjqRhpFPm6+XhI4BLS3dfR8QLnelgRFwAXAApGUWn18zMzBpC1SAr6WxSGsGKIuKomvSofopJ\n/VVlmm8Cz5DK3i1BKndnZmZWUXvXZKeSyrRV++vrxgH7SVoFIJ8uvgvYP48/gJT8vz2vAgMLz1cA\nns5HvAcC/fLwW4GD8+nk0rIqzW9mZi2k6pFsRFxWfC5p+TQ4Xq15r3pARNwv6RRggqR5wHTgSOBS\nSd8B5gAHd9DMLGCepJnAaOA84FpJBwE3Aa/lZd2UTyFPlfRf4Ebg+3me8yW9AWwTEW/08GqamVkf\n1mGBAEltwKWkIzIBLwGHREQjHM02DBcIMDPrur5eIKAzdxdfAnwlIiYCSNqeFHQ3qWXHzMzMGl1n\nfic7rxRgASLiTuCd2nXJzMysObR3d/Hm+eEESb8m/SY0gJHA+Np3zcys63a/9uJFht2wz6F16IlZ\n+6eLTy97/sPCY/+m08zMrAPt3V28Y292pFFJWhH4XEScl58Pp5AlyszMWlen6slK+iSwIdC/NCwi\nTqpVpxrMisBXSD/vMTMze1dn6smeT7oOeyTpJzz7AWvVuF81kRP8P5gLAjwsaYykEZImSXpE0laS\nVpb0h1xE4B5Jm+R5T5B0iaTxuYhAKePVqcDauQjAaXnYAEnX5GWNkVQtg5SZmTWxzhzJbhsRm0ia\nFREnSjod+EutO1ZD65C+KBwCTCGVstse+BQpgcQTwPSI2EvSTsDlwLA871BgR9Jvhh+S9CtSEYGN\nImIYvHu6eDPSkf9TwCRgO+DO8o64QICZWXPrzE94SlmKXs8FyN8mVaxpVI9FxOycGvF+4LZIGTlm\nk+rHbk8qHkBEjANWydmuAP4cEW/logPPAtUKCUyOiCfzMmbkdhcRERdERFtEtA0aNKiHVs/MzPqK\nzhzJ3pBv7jkNuJd0Z/FFNe1VbRULAcwvPJ9P2h5vd3LeeVTffp2dzszMmliHR7IRcXJEvBQR15Ku\nxQ6NiB/Uvmt1M5Fc2i+f+n0uIl5pZ3oXATAzs4raS0axU0SMk7R3hXFExO9r27W6OQG4RNIs4HXg\nC+1NHBHP5xun7iNdq/5z7btoZmaNoGqBAEknRsQPJV1aYXRExCG17VprcYEAM7Oua9gCATnALgH8\nJSKu6sU+mZmZNYV2r8nmu2O/20t9MTMzayqd+QnPXyUdLWnNnKhhZUkr17xnZmY9ZI9rrmWPa66t\ndzesBXXmpyUj8/+vFoYF8KGe746ZmVnz6DDIRsQHe6MjZmZmzaazBQI2AjZg4QIBly/uwiWdAMyN\niJ8vblvtLGMo8DvS0fe+EfHPWi2rsMzxpEo8vl3YzKyFdRhkJf0QGE4KsjcCu5Ly8C52kO0lewHX\nRMSP6t0RMzNrLZ258WlfYGfgPxFxMLApsEJ3Fyjp2FwB505g/TzsMElTJM2UdK2kZSUNlPSYpKXy\nNMsXn1dod1iumjNL0nWSVpK0G/AN4MuSbq8y33dKFXUknSFpXH68k6Qx+fEuku6WdK+kqyUNyMO3\nkDRB0jRJN0taraztJXLFn4oBXtLhkqZKmjpnzpxubE0zM+vLOhNk38w/5XknJ8p/FlizOwuTtAWw\nP6mqzW7AlnnU7yNiy4jYFHgAODQiXgXGA5/M0+yfp6uWW/hy4JiI2ISU7P+HEXEjcD5wRjtF6CcC\nO+THbaQydUvlYXdIWhU4DhgREZsDU4Fv5WnOJp2C3gK4BDil0O6SwBjgkYg4rtKCXSDAzKy5tZdW\n8VxgLDA5Fwi4EJgGzAXu7ubydgCui4jX8zKuz8M3ykd7KwIDgJvz8ItIv9P9A3AwcFiVvq4ArBgR\nE/Kgy4CrO9mnacAW+QvEW6QiCG25r0cBHyGdKp+Uy8K+h7T+6wMbAbfm4f2Apwvt/hq4KiKKgdfM\nzFpIe9dkHyZV3lkdeI0UcD8GLB8Rs3q4H6OBvSJipqRRpGvARMSkXGh9ONAvIu7r4eUSEW9LegwY\nBdwFzCLVjF2HdFS9NnBrRHy2OJ+kjYH7I2KbKk3fBewo6fSIeLOn+21mZn1f1dPFEXFmDiAfBZ4n\nnQ69Cfi0pHW7ubw7gL0kLSNpILBHHj4QeDqfgj2gbJ7Lgd8ClXIol/r6MvCipNJp3wOBCdWmr2Ai\ncHTu30TgCFLh9gDuAbaTtGeqva8AABXgSURBVA6ApOUkrQc8BAyStE0evpSkDQttXky6UewqSS51\nZ2bWgjpT6u5fEfHTiNgM+Czpbt0Hu7OwiLgXuBKYSapYMyWP+gHwN2BShbbHACuRjqTb8wXgtFw9\nZxhwUhe6NpFUiP7uiHgGeDMPIyLmkI5yx+a27yaV+/sv6aawn0qaSSrOvm3Z+v4CmA5ckfNAm1kd\n/GnfffjTvvvUuxvWgqpW4Xl3gnQUtivpxqOdSTcjjY2IP9a8d2n5+wJ7RsSBvbG8enEVHjOzrmvY\nKjySPkY6ct0NmExK6HB4RLzWS31D0tmkAL9bby3TzMysp7R3rfB/SddCvx0RL/ZSfxYSEUeWD8t3\nPW9XNvjMiKh6zTbPtwpwW4VRO0fE893vpfW2T19b8SfPZlVdt0+1X/CZ1VZ79WR36s2OdFZEfLXj\nqSrO9zzpWq2ZmVmv8M04XSDpU5K+V+9+mJlZY/BPS7ogIq4Hru9wQjMzMxrsSFbSQTk38UxJV0ja\nQ9LfJE2X9FdJ78vTnSDpMkkTJf1L0t6SfiZptqSbCvmQHy8Mn1z4LWy1dkdJOic/XjvnSp4t6UeS\n5ubhwyWNl3SNpAcljVFOCWVmZq2lYYJsTvRwHLBTznH8dVI1oI/k3/D+jpSCsWRtYCfgU8BvgNsj\nYmPgDRbkQwZ4OQ8/B/hlHtZeuyVnkm642hh4smzcZqTCBBuQituX36hVWicXCDAza2INE2RJAfPq\niHgOICJeAD4A3CxpNvAdoJhx6S+5mMBsUl7hm/Lw2cCQwnRjC/9LKRLba7dkGxbkR/5t2bjJEfFk\nLqwwo2x573KBADOz5tZIQbaSs4Fz8tHklygUlScl+ycHurdjQdaN+Sx8LToqPG6v3c54q/B4Hr72\nbWbWkhopyI4D9su/d0XSyqS6tv/O47/QzXZHFv6Xqgt1pt17gFKetv27uWwzM2tiDXOEFRH3SzoF\nmCBpHikn8AnA1ZJeJAXhD3aj6ZVyTuK3SBmu6GS73wB+I+lY0qnol7uxbDMza2Id5i5uZpIeB9pK\n13m7OO+ywBsREZL2Bz4bEXt2ty/OXWxm1nUNm7vYOrQFcE7+ec5LwCF17o+ZmfUxLR1kI2LIYsw7\nEdi053pjZrUy8tqH690Fq+LKfdardxdqqpFufDIzM2soDrLtyNmbbqh3P8zMrDE5yJqZmdVIywZZ\nSUMKuYUfyLmGl5X0iTz8XmDvwvRbSbo75zO+S9L6efgdkoYVprtT0qaS/kfSjPw3XdLAOqymmZnV\nUcsG2Wx94LyI+DDwCvAt4EJgD9Ldw+8vTPsgsEPOZ3w88OM8/GJgFICk9YD+ETETOBr4akQMA3Yg\n5UxeiHMXm5k1t1YPsk9ExKT8+DdAG/BYRDyS0zD+pjDtCqQEFfcBZ7Agn/HVwO65ss8hwOg8fBLw\nC0lHAStGxDvlC3fuYjOz5tbqQbY8E8cK7Ux7MqmSz0akI93+ABHxOnArsCfwGWBMHn4q8EVgGWCS\npKE923UzM+vrWj3IDpZUqrzzOeCvwBBJa+dhny1MW8xnPKqsnYuAs4ApEfEipHqzETE7In4KTAEc\nZM3MWkyrB9mHgK9KegBYiXQa+HDgz/nGp2cL0/4M+Imk6ZQl8YiIaaRrupcWBn9D0n05L/LbwF9q\ntxpmZtYXtXTGJ+CdiPh82bCbqHDUGRF3A8XUJMeVHkhanfSF5ZbC9Ef2bFfNzKzRtHqQXWySDgJO\nAb6Va9eaWR/T7Kn7rO9q2SAbEY8DG/VAO5cDly92h8zMrOm0bJA1s9Zxwe+f7XiiFnL43u+tdxda\nRqvf+GRmZlYzDrLtkHSjpBXr3Q8zM2tMLXO6WFK/iJjXlXkiYrda9cfMzJpfUxzJtpPs/3FJP82/\ned1P0i45yf+9kq6WNCAXBLi60Na75e3y/Kvmx9/Kv3u9T9I3Csu9rzDv0ZJOyI+PkvR3SbMk/a43\nt4eZmfUNzXQkuz5waERMknQJ8JU8/PmI2DwHy98DIyLiNUnHkAoC/Bi4QNJyEfEaMBJYKChK2gI4\nGNgaEPA3SROAF9vpz/eAD0bEW9VOOUs6nJT8gsGDB3dvrc3MrM9qiiPZrDzZ//b58ZX5/0eADUh5\nhGcAXwDWyon7bwL2kLQk8Engj2Vtbw9cFxGvRcRcUrDeoYP+zALGSPo8sEhxAHCBADOzZtdMR7Ll\nyf5Lz1/L/wXcGhGfZVG/A74GvABMjYhXO7nMd1j4i0r/wuNPAh8lFRM4VtLGlSrxmJlZ82qmI9ny\nZP93lo2/B9hO0joAkpbL9V8BJgCbA4dRdqo4mwjsla/zLgd8Og97BnivpFUkLQ3sntteAlgzIm4H\njiEVFxjQQ+tpZmYNopmCbHmy/18VR0bEHFL1nLE5af/d5BzF+a7jG4Bd83/K5r2XVCd2MvA34KKI\nmB4RbwMn5eG3kgq7A/QDfiNpNjAdOCsiXurJlTUzs75PqTZ5Y5M0BLgh13ptSG1tbTF16tR6d8PM\nrKFImhYRbfXuRzXNdCRrZmbWpzTFjU89lezfzMysJzVFkDUza8+4MXPq3YVu2+kA/7yvkfl0sZmZ\nWY20XJDNaRO3rXc/zMys+bVckAWGAw6yZmZWcw0TZNspArCzpOmSZku6JCeFKE/u3yZpfP6pzxHA\nNyXNkLSDpPdJuk7SzPy3bZ6nWkGAByWNlvRw7ssISZMkPSJpqzzdcrkvk3Pf9qzHNjMzs/pqmCCb\nrQ+cFxEfBl4hJfgfDYyMiI1JN3J9udrM+S7k84EzImJYREwEzgImRMSmpKxP95cVBPgIcJikzXIz\n6wCnkxJZDCVll9oeOBr4fp7mWGBcRGwF7AicljNFLUTS4ZKmSpo6Z07j3phhZmaVNVqQLS8CsDPw\nWEQ8nIddRsoX3BU7kbNDRcS8iHiZ9gsCPBYRsyNiPnA/cFukjB6zgSF5ml2A7+VCBONJOY0XKbPj\nAgFmZs2t0X7CU56e6iVglSrTFpP3968yTXe8VXg8v/B8Pgu2p4B9IuKhHlyumZk1mEY7ki0vAjAV\nGFJK+g8cSEr2D/A4sEV+vE+hjVeBgYXnt5FPMUvqJ2kFqhcE6KybgSMlKbe7WQfTm5lZE2q0IFte\nBOAM0rXTq3My/vmka64AJwJnSpoKzCu08Sfg06Ubn4CvAzvm+acBG1QrCNCFfp4MLAXMknR/fm5m\nZi2mYQoENEMRgPa4QICZWde5QICZmVmLapgbn1wEwMzMGk3DBFkzs+565JxnerS9db/2vh5tz5qX\nTxebmZnVSMsE2Zxa8aweaOcISQdVGD5E0n2L276ZmTWPljhdLGnJiJhK+l3tYomI8zueyszMrMZH\nspIOkjQrJ96/Ih/tjcvDbpM0OE83WtJZku6S9KikffPw1STdkX/Tel/+XWv5MsZLOrMwTSlJ/wl5\nmZOAK3KJuxvyuAGSLs1FBWZJ2icP30XS3ZLulXS1pAEVlneCpKPz4y1KhQWArxam+aakS/LjjXO/\nlu3p7WtmZn1bzYKspA2B44CdcvL9rwNnA5dFxCbAGFJy/pLVSDmDdwdOzcM+B9wcEcOATYEZVRa3\nbJ7mK8AlheEbACMi4rNl0/8AeDkiNs59GZcr9hyXp9+cdNT7rQ5W81LgyLx+RWcC60j6dJ7mSxHx\nevnMLhBgZtbcankkuxNwdUQ8BxARLwDbAL/N468gBdWSP0TE/Ij4O1C6dW8KcLCkE4CNI+LVKssa\nm5dxB7C8pBXz8Osj4o0K048Azi09iYgXSdV2NgAm5cT+XwDWqrZyeRkr5mWW1qfU3nxgVB42oVDU\nYCEuEGBm1tz60o1PxcT7gneD5keBfwOjK91wlJWnrSo9f60Lyxdway6BNywiNoiIQ7swf7l1gbnA\n6ovRhpmZNbBaBtlxwH6SVgGQtDJwF7B/Hn8AHSTdl7QW8ExEXAhcRKr3WsnIPP32pNPAL3fQt1tZ\n+BrqSsA9wHalYgO58Pp61RqIiJeAl/IyS+tTam8F0qnwjwKrlK4xm5lZa6lZkI2I+4FTgAn5xqBf\nAEeSTv/OIlXM+XoHzQwHZkqaTgqkZwJIukhSMVflm3ma84HOHH3+CFgp35A0E9gxIuaQTvGOzf27\nm1SUHUknSfpUhXYOBs7Np5dVGH4GcG6uc3socKqk93aiX2Zm1kQapkBANZLGA0fnn+g0LBcIMDPr\nOhcIMDMza1ENn4wiIobXuw9m1rf95xf3123Z7//WhnVbttWfj2TNzMxqxEG2Ckk7SLo/Z5JapjD8\nJEkjKkz/bkYpMzMzaILTxTV0APCTiPhNcWBEHF+n/piZWYPp1SPZXsplvI6kv+Zl3CtpbSWn5Xlm\nSyr9rnZ4zn18jaQHJY3J034R+AxwsqQxZe2PLvTnE3m+e4G9C9OcKen4/Pjjuc8+a2Bm1mJ67Ui2\nkMt424h4LienuIyUy/gySYeQEjjslWcp5TIeClwPXMOCXManSOoHVEq6PwY4NSKuk9Sf9EVib6CU\n/3hVYIqkUjrEzYANgaeAScB2EXFRTjJxQ0RcU2V9+gMXktJH/gO4sjD6f/MyJuZ12i2nWjQzsxbS\nm0dXNc9lLGkgsEZEXJeX8WZOzL89MDYi5kXEM8AEYMs82+SIeDIHwRnAkE6uz1DgsYh4JNKPjd89\nrZyXeRgps9Q5EfHPSg24QICZWXPry6cwFyeXcXeXM4+eO7rfGHiednIXu0CAmVlz680gW/NcxvnI\n9klJe+Xpl851XCcCIyX1kzSIFKgnL+b6PAgMkbR2fv5uOb3cz2+TTkXvKmnrxVyWmZk1oF4Lsr2Y\ny/hA4Kjc5l3A+4HrgFnATFKw/25E/Kezfa+QK5mIeBM4HPhzvvHp2TytgItJqR6fIuUuvihfwzUz\nsxbS8LmLm4VzF5vVjjM+Na++nrvYv5M1s6bnQGf10pdvfDIzM2toPpJtAs+efVu9u2DWp733yJ3r\n3QVrUT6SNTMzq5GWD7I5teN9NV7G+PK7k83MrPn16SAryaezzcysYdUtyOYjyAckXZhLyt0iaZl8\n1PdLSVMp+92spO9ImpILCpxYaOfBnLj/4Zzkf4SkSZIekbRVnu6EXJTg7jz8sAp96i/p0lxEYLqk\nHfPwOyQNK0x3p6RNJS0n6RJJk/P0e+bxy0j6XV6/64BlypdlZmbNr95HsusC50bEhsBLwD55+Hty\nusHTSxNK2iVPvxUp2f8Wkj6aR68DnE7KJzyUVEhge+Bo4PuF5W1CyqG8DXC8pPKUh18FIiI2JmVw\nuiwnkbgYGJX7sR7QPyJmAscC4yJiK2BH4DRJywFfBl6PiA8DPwS2qLTyzl1sZtbc6h1kH4uIGfnx\nNBYk57+ywrS75L/pwL2kYLpuoZ3ZOcn//cBtOWn/bBZO+P/HiHgjFym4nRSwi7YnJ/qPiAeBfwHr\nAVcDu0taCjgEGF3o0/ckzQDGA/2BwaS0jaV2ZpGyTS3CuYvNzJpbva95lifnL51Wfa3CtCIVUf/1\nQgOlIWXtzC88n8/C61ie3qpT6a4i4nVJtwJ7kurMlo5MBewTEQ+V9akzzZqZWZOr95FsV9wMHCJp\nAICkNSS9t4tt7Jmvu65CyoM8pWz8RFKhgtJp4cFAKYBeRKoNOyUiXiz06cicrxhJm+Xhd5BOWSNp\nI9JpajMzazF9OshKapN0EUBE3EKqPXu3pNmkIu4Du9jkLNJp4nuAk3MC/6LzgCVy+1cCoyLirbz8\nacArwKWF6U8GlgJmSbo/Pwf4FTBA0gPASaRT4WZm1mJapkBALvQ+NyJ+3s35Vydddx2ar/32qMUp\nEOCMT2btc8an5uUCAU0gF4c/BfhWLQLs4vIHiJlZ39QyR7J9naQ5pLuZF8eqwHM90J1G5+2wgLdF\n4u2QNON2WCsi+uzPMxxkm4ikqX35tElv8XZYwNsi8XZIvB16X5++8cnMzKyROciamZnViINsc7mg\n3h3oI7wdFvC2SLwdEm+HXuZrsmZmZjXiI1kzM7MacZA1MzOrEQfZJiPp5Fxvd0au0Vtezq8lSDot\n1xmeJek6SSvWu0/1IGm/XK95vqSW++mGpE9IekjSPyR9r979qZdc9/pZSffVuy+txkG2+ZwWEZtE\nxDDgBuD4eneoTm4FNoqITYCHgf+tc3/q5T5gb1LRipYiqR9wLrArsAHwWUkb1LdXdTMa+ES9O9GK\nHGSbTES8Uni6HJ0s59dsIuKWiHgnP70H+EA9+1MvEfFAeSnGFrIV8I+IeDQi/gv8jlSusuVExB3A\nC/XuRyty7uImJOkU4CDgZWDHOnenLziEVFXJWssawBOF508CW9epL9aiHGQbkKS/Au+vMOrYiPhj\nRBwLHCvpf4GvAT/s1Q72ko62Q57mWOAdYExv9q03dWY7mFl9OMg2oIgY0clJxwA30qRBtqPtIGkU\nsDuwczTxD8K7sD+0mn8DaxaefyAPM+s1vibbZCStW3i6J/BgvfpST5I+AXwX+FREvF7v/lhdTAHW\nlfRBSe8B9geur3OfrMU441OTkXQtsD4wn1Q674iIaLlv75L+ASwNPJ8H3RMRR9SxS3Uh6dPA2cAg\n4CVgRkR8vL696j2SdgN+CfQDLomIU+rcpbqQNBYYTip19wzww4i4uK6dahEOsmZmZjXi08VmZmY1\n4iBrZmZWIw6yZmZmNeIga2ZmViMOsmZmZjXiIGvWgCTN7eXlDZH0ud5cplkzcJA1s3ZJWhIYAjjI\nmnWRg6xZA5M0XNIESX+U9KikUyUdIGmypNmS1s7TjZZ0vqSpkh6WtHse3l/SpXna6ZJ2zMNHSbpe\n0jjgNuBUYIdcp/ib+ch2oqR789+2hf6Ml3RNruc7RpLyuC0l3SVpZu7fQEn9cu3fKbn275fqsiHN\nasS5i80a36bAh0mlzB4FLoqIrSR9HTgS+Eaebgip/NvawO2S1gG+CkREbCxpKHCLpPXy9JsDm0TE\nC5KGA0dHRCk4Lwt8LCLezKk8xwKlovCbARsCTwGTgO0kTSZVQhoZEVMkLQ+8ARwKvBwRW0paGpgk\n6ZaIeKwWG8qstznImjW+KRHxNICkfwK35OGzWbjU4VURMR94RNKjwFBge1LaRSLiQUn/AkpB9taI\nqFaDdCngHEnDgHmFeQAmR8STuT8zSMH9ZeDpiJiSl/VKHr8LsImkffO8KwDrAg6y1hQcZM0a31uF\nx/MLz+ez8Hu8PIdqRzlVX2tn3DdJOXA3JV12erNKf+bR/ueMgCMj4uYO+mLWkHxN1qx17CdpiXyd\n9kPAQ8BE4ACAfJp4cB5e7lVgYOH5CqQj0/nAgaQE/O15CFhN0pZ5WQPzDVU3A1+WtFSpD5KW6+4K\nmvU1PpI1ax3/B0wGlidVZ3pT0nnAryTNJhW3HxURb+V7lYpmAfMkzQRGA+cB10o6CLiJ9o96iYj/\nShoJnC1pGdL12BHARaTTyffmG6TmAHv1xMqa9QWuwmPWAiSNBm6IiGvq3RezVuLTxWZmZjXiI1kz\nM7Ma8ZGsmZlZjTjImpmZ1YiDrJmZWY04yJqZmdWIg6yZmVmN/D+4YsZdyjQfRAAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "684TCOiozrI2",
        "colab_type": "text"
      },
      "source": [
        "*   Conclusion:\n",
        " - Decision Tree and Random Forest prove that **nr.employed** and **cons.conf.idx** variables are the most important variable by far in comparison to the rest of the features.\n",
        " - The decision tree shows that the rest of variables have approximately zero importance, while the random forest sheds light on some features that perform significant importance. \n",
        " - **So if there is any built strategy, it needs to focus on nr.employed and cons.conf.idx variables.**\n",
        " - Logistic Regression insists on the importance of **nr.employed**, but also of **pdays** ad **compaign**. \n",
        " - **\"nr.employed\" and \"compaign\" make the probability that a client accepts the products increases, while if \"pdays\" increases it impact the probability of accepting a product negatively.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZzldWTjym9r",
        "colab_type": "text"
      },
      "source": [
        "***6. To sum up***\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        " - We can say that all the models give approximate results in general.\n",
        " - These results are acceptable since at least the f1-score is approximately equal to 50%.\n",
        " - The bagged model gives the best results according to all metrics. We can say that the decision tree and random forest give good results too.\n",
        " - Any increasement of **nr.employed** and **compain** increase the probability that a client accept a term deposit.\n",
        " - Any increasement of pdays decrease the probability that a client accept a term deposit.\n",
        " - As conclusion, the bank should build its strategy on improving these features, since the models show their importance on the probability of accepting the new product.\n",
        " - To note that we could use other algorithms (AdaBoost, GBMBoost, XgBoost) to fit predictions, but they require a machine with high capacities. \n",
        " - The purpose of this project was to predict if a customer could accept the bank's new product and look for a feature that can impact the modeled probability. For this, we reached the bagged model with a decision tree as the base learner and realized the accuracy of 85%."
      ]
    }
  ]
}