aies_project.ipynb

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      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n"
      ],
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      "source": [
        "df_client=pd.read_csv(\"../content/client_train.csv\")     #client whether fraud or not fraud\n",
        "df_invoice=pd.read_csv(\"../content/invoice_train.csv\")   #consumption info"
      ],
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          "text": [
            "<ipython-input-46-b06a212f775f>:2: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df_invoice=pd.read_csv(\"../content/invoice_train.csv\")   #consumption info\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#%% Data preprocessing\n",
        "\n",
        "# 1. dropping columns\n",
        "df_client.head()\n",
        "#df_client.drop(['disrict','client_catg','creation_date'], axis='columns',inplace=True)\n",
        "df_client.drop(['creation_date','region'], axis='columns',inplace=True)\n",
        "df_invoice.drop(['invoice_date','tarif_type','counter_number','counter_statue','counter_code','reading_remarque','counter_coefficient','consommation_level_2','consommation_level_3','consommation_level_4','old_index','new_index','months_number','counter_type'], axis='columns',inplace=True)\n",
        "\n",
        "# 2. filling NaNs with average of that\n",
        "grouped=df_invoice.groupby('client_id')\n",
        "\n",
        "df_invoice['consommation_level_1'] = grouped['consommation_level_1'].transform(lambda x: x.fillna(x.mean()))\n"
      ],
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      "source": [
        "#%%\n",
        "# 3. Treatment of outliers\n",
        "lower_outliers=grouped.quantile(0.05)\n",
        "upper_outliers=grouped.quantile(0.95)\n",
        "\n",
        "###SKIPPED\n",
        "\n",
        "\n"
      ],
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      "execution_count": 48,
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      "cell_type": "code",
      "source": [
        "df_mean=df_invoice.groupby('client_id').agg({'consommation_level_1':'mean'})\n",
        "df_median=df_invoice.groupby('client_id').agg({'consommation_level_1':'median'})\n",
        "df_std_dev=pd.DataFrame(df_invoice.groupby('client_id')[\"consommation_level_1\"].std())\n",
        "#df_kurtosis=df_invoice.groupby('client_id')[\"consommation_level_1\"].apply(pd.DataFrame.kurt)\n",
        "\n",
        "def compute_kurtosis(group):\n",
        "    return group.kurt()\n",
        "\n",
        "# Apply the custom function within each group\n",
        "df_kurtosis = df_invoice.groupby('client_id')[\"consommation_level_1\"].apply(compute_kurtosis)\n"
      ],
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      "source": [
        "df_new1=pd.merge(df_mean,df_median,\"inner\",on=['client_id'])\n",
        "df_new2=pd.merge(df_std_dev,df_kurtosis,on=['client_id'])\n",
        "df_new3=pd.merge(df_new1,df_new2,'inner',on=['client_id'])\n",
        "df_final=pd.merge(df_new3,df_client,on=['client_id'])\n",
        "\n",
        "\n",
        "df_final.rename(columns = {'consommation_level_1_x_x':'mean','consommation_level_1_y_x':'median','consommation_level_1_x_y':'std_dev','consommation_level_1_y_y':'kurt'}, inplace = True)\n",
        "df_final.dropna(inplace=True)\n",
        "\n",
        "df_final.groupby('target').count()\n",
        "\n",
        "#%% Categorical variable treatment\n",
        "df_final['disrict']=df_final['disrict'].astype('object')\n",
        "df_final['client_catg']=df_final['client_catg'].astype('object')\n",
        "#df_final['region']=df_final['region'].astype('object')\n",
        "df_final['target']=df_final['target'].astype('object')\n",
        "df_final.groupby('target')['client_id'].count()\n",
        "\n",
        "\n"
      ],
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        {
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          "data": {
            "text/plain": [
              "target\n",
              "0.0    69876\n",
              "1.0     4673\n",
              "Name: client_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
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    {
      "cell_type": "code",
      "source": [
        "#%%split the data into testing and training\n",
        "df_final.drop('client_id',axis=1, inplace=True)\n",
        "X=df_final.columns[0:6]\n",
        "X\n",
        "encoded_df_final=pd.get_dummies(df_final[X],drop_first=True)\n",
        "list(encoded_df_final.columns)\n",
        "\n",
        "import statsmodels.api as sm\n",
        "\n",
        "df_finalY=df_final.target\n",
        "df_finalX=sm.add_constant(encoded_df_final)\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "df_finalX_train, df_finalX_test, df_finalY_train, df_finalY_test=train_test_split(df_finalX, df_finalY, test_size=0.25, random_state=6432)\n"
      ],
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      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-51-f1b2d48afc43>:5: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n",
            "  encoded_df_final=pd.get_dummies(df_final[X],drop_first=True)\n",
            "<ipython-input-51-f1b2d48afc43>:5: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n",
            "  encoded_df_final=pd.get_dummies(df_final[X],drop_first=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#%% ENSEMBLING\n",
        "\n",
        "# from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# df_final_smote = SMOTE()\n",
        "# df_finalX_train, df_finalY_train = df_final_smote.fit_resample(df_finalX_train, df_finalY_train)\n",
        "\n",
        "\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "#Bagging Classifier\n",
        "\n",
        "df_finalY_train = pd.Categorical(df_finalY_train).codes\n",
        "\n",
        "df_final_bagging=BaggingClassifier(base_estimator=DecisionTreeClassifier(),n_estimators=500, random_state=42)\n",
        "df_final_bagging.fit(df_finalX_train, df_finalY_train)\n",
        "\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T11:09:09.959613Z",
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          "height": 209
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        "outputId": "56290109-0328-4499-d1bd-a8abd34fdeeb"
      },
      "execution_count": 52,
      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "<ipython-input-52-b8e35e9847ab>:14: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n",
            "  df_finalY_train = pd.Categorical(df_finalY_train).codes\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
            "  warnings.warn(\n"
          ]
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          "data": {
            "text/plain": [
              "BaggingClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=500,\n",
              "                  random_state=42)"
            ],
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              "                  random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=500,\n",
              "                  random_state=42)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# %% TRAINING\n",
        "#%% MODEL PERFORMANCE TRAINING\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# df_final_tree=DecisionTreeClassifier(criterion='gini',max_depth=3,random_state=42)\n",
        "\n",
        "# df_final_tree.fit(df_finalX_train,df_finalY_train)\n",
        "\n",
        "\n",
        "\n",
        "df_finalY_predict=df_final_bagging.predict(df_finalX_train)\n",
        "\n",
        "\n"
      ],
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      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#%% Performance\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sn\n",
        "class_name=['no fraud','fraud']\n",
        "confusionmatrix=confusion_matrix(df_finalY_train,df_finalY_predict)\n",
        "sn.heatmap(confusionmatrix, annot=True, yticklabels=class_name, xticklabels=class_name, fmt='g', cmap=\"Greens\")\n",
        "\n",
        "#accuracy\n",
        "\n",
        "from sklearn import metrics\n",
        "print('Accuracy:',metrics.accuracy_score(df_finalY_train,df_finalY_predict))\n",
        "print('AUC-ROC: ',metrics.roc_auc_score(df_finalY_train, df_finalY_predict))"
      ],
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        },
        "id": "XkTxu2qCn3Dq",
        "outputId": "8da16921-88d4-40d0-f28a-192ae8388d96"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9998211443186493\n",
            "AUC-ROC:  0.9985528219971056\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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Tt8q1iYsOfnNEC956QTsP7bWe+13wndq6d4fWPZOo23oP1vFT+Vr53pt6dctq824UuMLKy8p1+IvDNsmEk5OTBgcHaX/m/nrsGRzNzCTD1dW11qTCKCMjQydOnFC/fv2sxyoqKvTRRx8pPj5eW7duVVlZmYqKimyqGQUFBfL19ZUk+fr61lgFUr365NIY44qUgoICubu7q2nTpnJ2dpazs3OtMdVt1JVdczIuXLigzZs3y9nZ+eKHnZw0d+5cvffee1q6dKlatWpl18Xx6+3K+kwPPz9Ld8VM0GMr/iR/3w7674vvqkXT5jVivdxb6ZnxM/TK+29bj7k0cdE//5Sg2f/vLz+ZmOSdLtCjy6I1ZtFUjVk0VccKc7X9+fX6Tbde1pgufh312D0T9NXxbIXGjNdLm97SiohFeujO+82/aeAK+b7oYtnYq43tsIiXl5dOnjxVT73C1eqOO+7QgQMHlJmZaX0NGDBA48ePt/65SZMmSktLs37myJEjysnJUXBwsCQpODhYBw4csFkFkpqaKnd3dwUEBFhjLm2jOqa6DRcXF/Xv398mprKyUmlpadaYurKrknHddddp2rRpOnz4sF0XMSotLa056aWySnJi8xN7pez50PrnA9mHtevwZ/r27U/1h9vu0espa6znWjZroeRn39QX336lBW++YD0eN2muDud8pbfT3v3Ja3z53df68ruvre/Tv8hQ13adNWvMI3rorzMkSU4WJ+39cr+efv2vkqTMo4fUq3N3TRs5QW+m/su0+wWAK6E+9uJq2bKlevXqZXOsefPm8vLysh6fPHmyoqKi1Lp1a7m7uysyMlLBwcEaPHiwJGn48OEKCAjQhAkTtGTJEuXn52vevHmKiIiwVlOmTZum+Ph4zZkzR5MmTdK2bdu0bt06JScnW68bFRWl8PBwDRgwQIMGDdKyZctUUlKiiRMn2nVPdq8uGTRokDIzM+39mI24uDh5eHjYvJT9w69qExedKSnWl999rW7tOluPtWjaXCnP/UM/nDur+xZMsU4MlaTbf/Nb/X7ISJWnfKPylG+UtuRiYnLynf1a8NCTxuatdmdl2lwj7/QJfZHzlU3M4Zyv1LHt9ebcGFAPWnm2krOzs06dPG1z/NSpU2rTxqueeoUrwWKxmPYy04svvqiRI0dqzJgxGjJkiHx9ffXuu//3S6Kzs7N1xCE4OFgPPvigHnroIS1atMga4+/vr+TkZKWmpqpv375aunSpXn31VYWGhlpjxo4dq+eff17z589XYGCgMjMzlZKSUmMy6C+xe07G448/rqioKB07dkz9+/dX8+a2ZXnjxiG1qW0SjMd9Pe3tCmrR3K2Zuvp11lunL/6la9mshbbGva3S8jL9bv5ElZbbVpDGLJyqpq7/N8FoYPe+euOpF3TrrNE6mvftT14nsGuA8k7/Xznuk0N71b19F5uYG9t30bcF35lxW0C9aOLSRD0DemrXp7t0e8gwSRfLxrs+3a1xfxxbz73DtWD79u02793c3JSQkKCEhISf/EynTp30/vvv/2y7Q4cO1WefffazMdOnT9f06dPr3Nfa2J1kjBs3TpL0xBNPWI9ZLBZVVVXJYrGooqLipz5qVeskGIZKLsvfps7Tpk8/0LcF36mdl48WPvSkKior9M8PN6plsxb6z+LVaubaVA8ufkLuzVpa98goPHNKlZWV+tqQSLRxvzj2fDjnf9aVJDPum6zs/GM69O2XcnNx1ZQRD+j2wN9qeMwfrZ978Z3/p53LNyrmgelat2OzBnUP1NS7x2vqsugr9E0AjjHh4Qf1TMx83dQrQL1699I/3lytc+fOadR999Z31+BAPCDNHDyFtZFr38ZP//xTvLxatlLhmdP6+OBuDX7idzp55rRu6xOswT0vzlI++uYnNp/r/ODgOlcZXJq4aOmj83V9G1/9WHpO+78+rJDoB7T9853WmL1ffq77FkxR3OQYzX9wprLzj2nmSwu0etsG824WqAd3jQjV96e/18q/v6STJ0+pe4/uWvlygrwYLrmqkWSYo07bivfr109paWlq1aqVFi1apKeeekrNmpm7pSvbigM1sa04UDtHbyve/cW7TGvryKwU09pqbOo08fPw4cMqKSmRJC1cuFBnz579hU8AAIBrXZ2GSwIDAzVx4kTdcsstqqqq0vPPP68WLVrUGnvpk94AAGiMGC4xR52SjKSkJMXGxmrz5s2yWCzasmWLrruu5kctFgtJBgCg0SPJMEedkozu3btrzZqL+yc4OTkpLS1Nbdu2dWjHAABA42b36pLKykpH9AMAgAaDSoY57E4yAAC42pFjmMPubcUBAADqgkoGAAAGDJeYgyQDAAADkgxzXHaSkZGRYX3ke0BAgPr162dapwAAQONnd5Jx4sQJjRs3Ttu3b5enp6ckqaioSMOGDdOaNWvk7e1tdh8BALiiqGSYw+6Jn5GRkfrhhx906NAhnT59WqdPn9bBgwdVXFxs82RWAAAaK4vFvNe1zO5KRkpKij744AP17NnTeiwgIEAJCQkaPny4qZ0DAKA+UMkwh92VjMrKSjVp0qTG8SZNmrBRFwAAsLI7ybj99ts1Y8YM5ebmWo8dP35cs2bN0h133GFq5wAAqBeMl5jC7iQjPj5excXF6ty5s7p27aquXbvK399fxcXF+vvf/+6IPgIAcEVZLBbTXtcyu+dkdOjQQfv27dMHH3ygrKwsSVLPnj0VEhJieucAAEDjdVn7ZFgsFt1555268847ze4PAAD17hovQJjmspKMtLQ0paWl6cSJEzUme77++uumdAwAgPpyrQ9zmMXuJGPhwoVatGiRBgwYID8/P/6HAAAAtbI7yUhMTFRSUpImTJjgiP4AAFDv+AXaHHYnGWVlZbr55psd0RcAABoEkgxz2L2EdcqUKVq9erUj+gIAAK4idlcyzp8/r1deeUUffPCB+vTpU2P3zxdeeMG0zgEAUB8oZJjD7iRj//79CgwMlCQdPHjQ5hzlJQDA1YB/z8xhd5Lx4YcfOqIfAAA0GCQZ5rB7TgYAAEBdXNZmXAAAXM2oZJiDJAMAAAOSDHMwXAIAAByCSgYAAAYUMsxBkgEAgAHDJeZguAQAADgElQwAAAyoZJiDJAMAAAOSDHMwXAIAAByCSgYAAAYUMsxBkgEAgAHDJeYgyQAAwIgkwxTMyQAAAA5BJQMAAAOGS8xBkgEAgIETOYYpGC4BAAAOQSUDAAADhkvMQZIBAICBE0mGKRguAQAADkElAwAAA4ZLzEGSAQCAAWV+c5BkAABgwJwMc5CsAQAAh6CSAQCAAXMyzEGSAQCAAcMl5mC4BAAAOASVDAAADBguMQdJBgAABpT5zcH3CAAAHIJKBgAABkz8NAeVDAAADCwWi2kve7z00kvq06eP3N3d5e7uruDgYG3ZssV6/vz584qIiJCXl5datGihMWPGqKCgwKaNnJwchYWFqVmzZmrbtq1mz56tCxcu2MRs375d/fr1k6urq7p166akpKQafUlISFDnzp3l5uamoKAg7d692657kUgyAABoMNq3b6/FixcrIyNDe/fu1e233657771Xhw4dkiTNmjVLmzZt0vr167Vjxw7l5uZq9OjR1s9XVFQoLCxMZWVl2rlzp1atWqWkpCTNnz/fGpOdna2wsDANGzZMmZmZmjlzpqZMmaKtW7daY9auXauoqCjFxsZq37596tu3r0JDQ3XixAm77sdSVVVV9Su/E1NY7mxf310AGpxzKV/WdxeABsnNuZlD2//D+9NMa2vd3Ym/6vOtW7fW3/72N91///3y9vbW6tWrdf/990uSsrKy1LNnT6Wnp2vw4MHasmWLRo4cqdzcXPn4+EiSEhMTFR0drcLCQrm4uCg6OlrJyck6ePCg9Rrjxo1TUVGRUlJSJElBQUEaOHCg4uPjJUmVlZXq0KGDIiMjNXfu3Dr3nUoGAAAGFhNfpaWlKi4utnmVlpb+Yh8qKiq0Zs0alZSUKDg4WBkZGSovL1dISIg1pkePHurYsaPS09MlSenp6erdu7c1wZCk0NBQFRcXW6sh6enpNm1Ux1S3UVZWpoyMDJsYJycnhYSEWGPqiiQDAAADJ4vFtFdcXJw8PDxsXnFxcT957QMHDqhFixZydXXVtGnTtGHDBgUEBCg/P18uLi7y9PS0iffx8VF+fr4kKT8/3ybBqD5ffe7nYoqLi3Xu3DmdPHlSFRUVtcZUt1FXrC4BAMCBYmJiFBUVZXPM1dX1J+O7d++uzMxMnTlzRv/6178UHh6uHTt2OLqbDkGSAQCAgZlLWF1dXX82qTBycXFRt27dJEn9+/fXnj17tHz5co0dO1ZlZWUqKiqyqWYUFBTI19dXkuTr61tjFUj16pNLY4wrUgoKCuTu7q6mTZvK2dlZzs7OtcZUt1FXDJcAAGBQX0tYa1NZWanS0lL1799fTZo0UVpamvXckSNHlJOTo+DgYElScHCwDhw4YLMKJDU1Ve7u7goICLDGXNpGdUx1Gy4uLurfv79NTGVlpdLS0qwxdUUlAwCABiImJkYjRoxQx44d9cMPP2j16tXavn27tm7dKg8PD02ePFlRUVFq3bq13N3dFRkZqeDgYA0ePFiSNHz4cAUEBGjChAlasmSJ8vPzNW/ePEVERFirKdOmTVN8fLzmzJmjSZMmadu2bVq3bp2Sk5Ot/YiKilJ4eLgGDBigQYMGadmyZSopKdHEiRPtuh+SDAAADOprx88TJ07ooYceUl5enjw8PNSnTx9t3bpVd955pyTpxRdflJOTk8aMGaPS0lKFhoZq5cqV1s87Oztr8+bNeuyxxxQcHKzmzZsrPDxcixYtssb4+/srOTlZs2bN0vLly9W+fXu9+uqrCg0NtcaMHTtWhYWFmj9/vvLz8xUYGKiUlJQak0F/CftkAA0Y+2QAtXP0Phnh/5luWlurhseb1lZjw5wMAADgEAyXAABgwAPSzEGSAQCAAUmGORguAQAADkElAwAAAzP2twBJBgAANTBcYg6SDAAADEgxzMGcDAAA4BBUMgAAMGC4xBwkGQAAGJBkmIPhEgAA4BBUMgAAMGAJqzlIMgAAMKDMbw6+RwAA4BBUMgAAMGC4xBwkGQAAGLC6xBwMlwAAAIegkgEAgAGVDHOQZAAAYMCcDHM0mCTjXMqX9d0FoME5d6GkvrsANEhuzs0c2r4Tj0gzBXMyAACAQzSYSgYAAA0FwyXmIMkAAMCAiZ/mYLgEAAA4BJUMAAAMLEz8NAVJBgAABszJMAfDJQAAwCGoZAAAYMDET3OQZAAAYGCh0G8KvkUAAOAQVDIAADBguMQcJBkAABiwusQcJBkAABiwT4Y5mJMBAAAcgkoGAAAGzMkwB0kGAAAGzMkwB8MlAADAIahkAABg4MTv4KYgyQAAwIDhEnOQqgEAAIegkgEAgAGVDHOQZAAAYODEZlymYLgEAAA4BJUMAAAMGC4xB0kGAAAG7PhpDpIMAAAMeECaOZiTAQAAHIJKBgAABk4Wfgc3A0kGAAAGTPw0B6kaAABwCCoZAAAYMPHTHCQZAAAYsITVHAyXAAAAh6CSAQCAAcMl5iDJAADAgOESczBcAgAAHIJKBgAABhY24zIFSQYAAAbMyTAHSQYAAAbMyTAH9SAAABqIuLg4DRw4UC1btlTbtm01atQoHTlyxCbm/PnzioiIkJeXl1q0aKExY8aooKDAJiYnJ0dhYWFq1qyZ2rZtq9mzZ+vChQs2Mdu3b1e/fv3k6uqqbt26KSkpqUZ/EhIS1LlzZ7m5uSkoKEi7d++2635IMgAAMLBYLKa97LFjxw5FRETo008/VWpqqsrLyzV8+HCVlJRYY2bNmqVNmzZp/fr12rFjh3JzczV69Gjr+YqKCoWFhamsrEw7d+7UqlWrlJSUpPnz51tjsrOzFRYWpmHDhikzM1MzZ87UlClTtHXrVmvM2rVrFRUVpdjYWO3bt099+/ZVaGioTpw4Uffvsaqqqsqub8BBzlf8WN9dABqccxdKfjkIuAa1cvV2aPtvZL1sWlsTezx62Z8tLCxU27ZttWPHDg0ZMkRnzpyRt7e3Vq9erfvvv1+SlJWVpZ49eyo9PV2DBw/Wli1bNHLkSOXm5srHx0eSlJiYqOjoaBUWFsrFxUXR0dFKTk7WwYMHrdcaN26cioqKlJKSIkkKCgrSwIEDFR8fL0mqrKxUhw4dFBkZqblz59ap/1QyAABwoNLSUhUXF9u8SktL6/TZM2fOSJJat24tScrIyFB5eblCQkKsMT169FDHjh2Vnp4uSUpPT1fv3r2tCYYkhYaGqri4WIcOHbLGXNpGdUx1G2VlZcrIyLCJcXJyUkhIiDWmLkgyAAAwMHO4JC4uTh4eHjavuLi4X+xDZWWlZs6cqd/+9rfq1auXJCk/P18uLi7y9PS0ifXx8VF+fr415tIEo/p89bmfiykuLta5c+d08uRJVVRU1BpT3UZdsLoEAAADM/fJiImJUVRUlM0xV1fXX/xcRESEDh48qI8//ti0vlxpJBkAADiQq6trnZKKS02fPl2bN2/WRx99pPbt21uP+/r6qqysTEVFRTbVjIKCAvn6+lpjjKtAqlefXBpjXJFSUFAgd3d3NW3aVM7OznJ2dq41prqNumC4BAAAAydZTHvZo6qqStOnT9eGDRu0bds2+fv725zv37+/mjRporS0NOuxI0eOKCcnR8HBwZKk4OBgHThwwGYVSGpqqtzd3RUQEGCNubSN6pjqNlxcXNS/f3+bmMrKSqWlpVlj6oJKBgAABvYuPTVLRESEVq9erX//+99q2bKldf6Dh4eHmjZtKg8PD02ePFlRUVFq3bq13N3dFRkZqeDgYA0ePFiSNHz4cAUEBGjChAlasmSJ8vPzNW/ePEVERFgrKtOmTVN8fLzmzJmjSZMmadu2bVq3bp2Sk5OtfYmKilJ4eLgGDBigQYMGadmyZSopKdHEiRPrfD8kGQAANBAvvfSSJGno0KE2x9944w09/PDDkqQXX3xRTk5OGjNmjEpLSxUaGqqVK1daY52dnbV582Y99thjCg4OVvPmzRUeHq5FixZZY/z9/ZWcnKxZs2Zp+fLlat++vV599VWFhoZaY8aOHavCwkLNnz9f+fn5CgwMVEpKSo3JoD+HfTKABox9MoDaOXqfjLe/esO0tsbfUPff/K82VDIAADCor+GSqw1JBgAABvZO2ETtWF0CAAAcgkoGAAAGZm7GdS0jyQAAwMDCcIkpSNUAAIBDUMkAAMCA1SXmIMkAAMCA4RJzMFwCAAAcgkoGAAAGDJeYgyQDAAADNuMyB8MlAADAIahkAABgwHCJOUgyAAAwsFDoNwVJBgAABlQyzEGqBgAAHIJKBgAABmzGZQ6SDAAADJwYLjEFwyUAAMAhqGQAAGDAcIk5SDIAADBgdYk5GC4BAAAOQSUDAAADNuMyB0kGAAAGDJeYg1QNAAA4BJUMAAAMeNS7OUgyAAAwYLjEHHVKMkaPHl3nBt99993L7gwAAA0B+2SYo05zMjw8PKwvd3d3paWlae/evdbzGRkZSktLk4eHh8M6CgAAGpc6VTLeeOMN65+jo6P1hz/8QYmJiXJ2dpYkVVRU6PHHH5e7u7tjegkAwBXEcIk5LFVVVVX2fMDb21sff/yxunfvbnP8yJEjuvnmm3Xq1KnL6sj5ih8v63PA1ezchZL67gLQILVy9XZo+x/mbjWtrWHtQk1rq7GxewnrhQsXlJWVVeN4VlaWKisrTekUAABo/OxeXTJx4kRNnjxZR48e1aBBgyRJu3bt0uLFizVx4kTTOwgAwJXGo97NYXeS8fzzz8vX11dLly5VXl6eJMnPz0+zZ8/Wk08+aXoHAQC40lhdYg6752Rcqri4WJJMmfDJnAygJuZkALVz9JyMj/JSTWtriN+dprXV2PyqzbhYTQIAuBqxusQcdicZ/v7+P/vlf/3117+qQ7gyMvZmKOn1N3X40BcqLDypF1e8oNtDhlnPV1VVaWX8S3p3/Qb98MMPCvxNXz09/0/q1LlTPfYauHzvrN2gd9dtVF7uxWHeLl39NenRh3XzrcE2cVVVVZr1+FP69JNd+uuy53Tb7UNqtHWm6IwevP9hFZ4oVOrHW9TSvaUkKWPPPkVMfqJGfPK2f8urjZcD7gqOwnCJOexOMmbOnGnzvry8XJ999plSUlI0e/Zss/oFBzv34zl1736jRo2+V1FP1JxL88ZrSfrnP/6pPz+3SNe3v14JK1bqsakR2rDpHbm6utZDj4Ffp62PtyJmTlP7ju2lqiolv7dFc2bE6M11r6tLty7WuDX/WPeLv8X+JXaxut3YVYUnCms9v+691Wreorn1favWrcy5CaCRsTvJmDFjRq3HExISbHYBRcN2y5BbdMuQW2o9V1VVpbffXK1HHn1Ew+64WN14dvGfdfutIdqW9qFG3H3XlewqYIpbh9r+fX/siUe1Yd1GHdz/hTXJ+DLrK61etUZJa15V2O331trOO2svVvcmPzpR6R9/WmtMq9atrNUNNE4Ml5jDtEe9jxgxQu+8845ZzaEeHf/uuE6ePKmg4CDrsZYtW6p3n17an7m/HnsGmKOiokKpWz7QuXPn1bvvTZKk8+fOa/7chZr9dNRPDm1kH83W6y8nKfYv82Rx+ul/hCb8YaLCbr9XkVNn6vPP+JlpjJxM/O9aZtpTWP/1r3+pdevWZjWHenTy5ElJklcb2/89vby8dPLk5e3oCjQE//vyqB6ZME1lZWVq2qyp/rrsOfl39ZckLfvbCvXu20tDht1a62fLysr0TPQCTY96XL5+vjr+XW6NmDZt2ij6mafU86YeKisr13vvbtLjkyP12j9eUY+A7rW0ioaKSoY57E4yfvOb39h8+VVVVcrPz1dhYaFWrlxZpzZKS0tVWlpqc6zqugrG+gE4VCf/jnpz/RsqOXtW21K3a9G8v+il1/+uYznHtXf3Pr257vWf/OzK5S+rc5fOGjHyp7eI7uTfUZ38O1rf9wnsrePHcrXmH+u04LlnTL0XoDGwO8kYNWqUzXsnJyd5e3tr6NCh6tGjR53aiIuL08KFC22OPf3MnzQv9ml7uwMHaNOmjSTp1MnT8vb+v7Xop06dUvce/DaGxqtJkybq0LG9JKlHQA99cfCw1r69Xq6urjp+7Lju/O0Im/iYqHnq26+PXno9Xhm7M3T0q6/129Ttki7+giVJd902Ug9PeUiPREyu9ZoBvXoyZNIIsbrEHHYnGbGxsb/6ojExMYqKirI5VnVdxa9uF+a4vv31atOmjXZ9uks9el5MKs6ePasD+w/q9+N+X8+9A8xTVVmlsrJyPfL4ZP1u9D0258aPeUgzZkfq1tt+K0mKe+EvKj3/fxXYw4cO69n5cUpMStD17a//yWt8eeQreXmzfLWxYbjEHL9qTsb58+dVVlZmc6wuG3S5urrWGBphx88r68eSH5WTc8z6/vjx48o6fEQeHu7ya+en8Q/9Uf/v5VfVqVNH6xJW77beuv2OYT/TKtBwrVyeqODfDpaPn49+LPlR/9mSqn17P9OyxBfk1car1smevn4+ate+nSSpfQfbRKKoqEiS1Nm/k3UlyZq31qldez/5d/VXWWmZ3nt3kzJ279PyxBcce3NAA2V3klFSUqLo6GitW7eu1se6V1RQkWgMDh36QlMefsT6/vm/LpUk/W7UPfrzc4s0cfLDOnfunBbFPqsffvhBv+kXqJWvJDBvBo3W96e/18J5z+pU4Sm1aNFcXW/sqmWJLygoeKBp1ygvL9eK5+NVeKJQrm5u6nZjV/39lWXqP6ifadfAlcFwiTnsfnZJRESEPvzwQ/35z3/WhAkTlJCQoOPHj+vll1/W4sWLNX78+MvqCJUMoCaeXQLUztHPLtlb+IlpbQ3w/q1pbTU2dlcyNm3apDfffFNDhw7VxIkTdeutt6pbt27q1KmT3n777ctOMgAAwNXF7l1CTp8+rS5dLu6O5+7urtOnT0uSbrnlFn300Ufm9g4AgPpgsZj3uobZnWR06dJF2dnZkqQePXpo3bp1ki5WODw9PU3tHAAA9cFi4n/XMruTjIkTJ+rzzz+XJM2dO1cJCQlyc3PTrFmzeEAaAACwsnvip9G3336rjIwMdevWTX369Lnsdpj4CdTExE+gdo6e+LnvVO0Pv7sc/bwGm9ZWY2NXJaO8vFx33HGHvvrqK+uxTp06afTo0b8qwQAAoCFhuMQcdq0uadKkifbvZ3tcAMDV7VpPDsxi95yMBx98UK+99poj+gIAAK4idu+TceHCBb3++uv64IMP1L9/fzVv3tzm/AsvsH0uAKBx49kl5qhTkrF//3716tVLTk5OOnjwoPr1u7hF7pdffmkTx/8oAICrAcMl5qhTkvGb3/xGeXl5atu2rb799lvt2bNHXl48VRAAAPy0Os3J8PT0tG7A9c0336iystKhnQIAoD6xusQcdapkjBkzRrfddpv8/PxksVg0YMAAOTs71xr79ddfm9pBAACuNIb/zVGnSsYrr7yijRs36sknn1RVVZUeeeQRzZgxo9YXAAC4PB999JHuuecetWvXThaLRRs3brQ5X1VVpfnz58vPz09NmzZVSEiIzd5V0sVnjI0fP17u7u7y9PTU5MmTdfbsWZuY/fv369Zbb5Wbm5s6dOigJUuW1OjL+vXr1aNHD7m5ual37956//337b6fOq8uueuuuyRJGRkZmjFjhlq2bGn3xQAAaAzqa5ijpKREffv21aRJkzR69Oga55csWaIVK1Zo1apV8vf31zPPPKPQ0FB98cUXcnNzkySNHz9eeXl5Sk1NVXl5uSZOnKipU6dq9erVkqTi4mINHz5cISEhSkxM1IEDBzRp0iR5enpq6tSpkqSdO3fqgQceUFxcnEaOHKnVq1dr1KhR2rdvn3r16lXn+/nV24qbhW3FgZrYVhyonaO3Ff+iKNO0tgI8Ay/rcxaLRRs2bNCoUaMkXaxitGvXTk8++aSeeuopSdKZM2fk4+OjpKQkjRs3TocPH1ZAQID27NmjAQMGSJJSUlJ0991367vvvlO7du300ksv6emnn1Z+fr5cXFwkXXwW2caNG5WVlSVJGjt2rEpKSrR582ZrfwYPHqzAwEAlJibW+R7s3owLAADUXWlpqYqLi21epaWldreTnZ2t/Px8hYSEWI95eHgoKChI6enpkqT09HR5enpaEwxJCgkJkZOTk3bt2mWNGTJkiDXBkKTQ0FAdOXJE33//vTXm0utUx1Rfp65IMgAAMDBzdUlcXJw8PDxsXnFxcXb3KT8/X5Lk4+Njc9zHx8d6Lj8/X23btrU5f91116l169Y2MbW1cek1fiqm+nxd2b3jJwAAVzsz52TExMQoKirK5pirq6tp7TdkJBkAABiYuYTV1dXVlKTC19dXklRQUCA/Pz/r8YKCAgUGBlpjTpw4YfO5Cxcu6PTp09bP+/r6qqCgwCam+v0vxVSfryuGSwAAaAT8/f3l6+urtLQ067Hi4mLt2rVLwcHBkqTg4GAVFRUpIyPDGrNt2zZVVlYqKCjIGvPRRx+pvLzcGpOamqru3burVatW1phLr1MdU32duiLJAADAoL52/Dx79qwyMzOVmZkp6eJkz8zMTOXk5MhisWjmzJl69tln9d577+nAgQN66KGH1K5dO+sKlJ49e+quu+7SI488ot27d+uTTz7R9OnTNW7cOLVr106S9Mc//lEuLi6aPHmyDh06pLVr12r58uU2QzozZsxQSkqKli5dqqysLC1YsEB79+7V9OnT7fseWcIKNFwsYQVq5+glrF+dOWRaWzd43FTn2O3bt2vYsGE1joeHhyspKUlVVVWKjY3VK6+8oqKiIt1yyy1auXKlbrzxRmvs6dOnNX36dG3atElOTk4aM2aMVqxYoRYtWlhj9u/fr4iICO3Zs0dt2rRRZGSkoqOjba65fv16zZs3T998841uuOEGLVmyRHfffbdd906SATRgJBlA7a7WJONqw8RPAAAMeHaJOUgyAACogSTDDEz8BAAADkElAwAAA4ZLzEGSAQCAQX09hfVqw3AJAABwCCoZAAAYUMkwB0kGAAAGzMkwB0kGAAAGVDLMwZwMAADgEFQyAAAwoJJhDpIMAAAMmJNhDoZLAACAQ1DJAADAgOESc5BkAABgwHCJORguAQAADkElAwAAA4ZLzEGSAQBADSQZZmC4BAAAOASVDAAADKhjmIMkAwAAA1aXmIMkAwCAGkgyzMCcDAAA4BBUMgAAMKCOYQ6SDAAAaiDNMAPDJQAAwCGoZAAAYMDqEnNQyQAAAA5BkgEAAByC4RIAAAx4QJo5SDIAADAgyTAHwyUAAMAhSDIAAIBDMFwCAIABS1jNQSUDAAA4BEkGAABwCIZLAAAwYHWJOUgyAACogSTDDAyXAAAAh6CSAQCAAXUMc5BkAABgwBJWczBcAgAAHIJKBgAANVDJMANJBgAABqQY5mC4BAAAOASVDAAAaqCWYQaSDAAADFhdYg6GSwAAgEOQZAAAAIdguAQAAAMekGYOkgwAAGogyTADwyUAAMAhqGQAAGBAHcMcJBkAABiwhNUcDJcAAACHoJIBAEANVDLMQJIBAIABKYY5GC4BAAAOQSUDAIAaqGWYgUoGAAAGFovFtJe9EhIS1LlzZ7m5uSkoKEi7d+92wB1eGSQZAAA0EGvXrlVUVJRiY2O1b98+9e3bV6GhoTpx4kR9d+2yWKqqqqrquxOSdL7ix/ruAtDgnLtQUt9dABqkVq7eDm3/XIV5P3tNnZvXOTYoKEgDBw5UfHy8JKmyslIdOnRQZGSk5s6da1qfrhTmZAAAYGDmA9JKS0tVWlpqc8zV1VWurq42x8rKypSRkaGYmBjrMScnJ4WEhCg9Pd20/lxJDSbJcHNuVt9dgC7+MMTFxSkmJqbGDwCuPH4uGgZ+Lq49Zv7sLfjzAi1cuNDmWGxsrBYsWGBz7OTJk6qoqJCPj4/NcR8fH2VlZZnWnyupwQyXoGEoLi6Wh4eHzpw5I3d39/ruDtAg8HOBX6OulYzc3Fxdf/312rlzp4KDg63H58yZox07dmjXrl1XpL9majCVDAAArka1JRS1adOmjZydnVVQUGBzvKCgQL6+vo7qnkOxugQAgAbAxcVF/fv3V1pamvVYZWWl0tLSbCobjQmVDAAAGoioqCiFh4drwIABGjRokJYtW6aSkhJNnDixvrt2WUgyYMPV1VWxsbFMbgMuwc8FrpSxY8eqsLBQ8+fPV35+vgIDA5WSklJjMmhjwcRPAADgEMzJAAAADkGSAQAAHIIkAwAAOARJxjUsKytLgwcPlpubmwIDA+ulDxaLRRs3bqyXa+PaUVVVpalTp6p169ayWCzKzMy8otd/+OGHNWrUqCt6TaAhYHXJNSw2NlbNmzfXkSNH1KJFi/ruDuAwKSkpSkpK0vbt29WlSxe1adOmvrsEXBNIMq5hR48eVVhYmDp16vSTMeXl5WrSpMkV7BVgvqNHj8rPz08333xzrefLysrk4uJyhXsFXP0YLmkEhg4dqieeeEJz5sxR69at5evrW+PBOjk5Obr33nvVokULubu76w9/+EONrWkvZbFYlJGRoUWLFslisWjBggX65ptvZLFYtHbtWt12221yc3PT22+/rVOnTumBBx7Q9ddfr2bNmql379765z//adNe586dtWzZMptjgYGBNv386quvNGTIELm5uSkgIECpqam/9qsBftHDDz+syMhI5eTkyGKxqHPnzho6dKimT5+umTNnqk2bNgoNDZUkvfDCC+rdu7eaN2+uDh066PHHH9fZs2etbS1YsKDG0OKyZcvUuXNn6/uKigpFRUXJ09NTXl5emjNnjtgpANcqkoxGYtWqVWrevLl27dqlJUuWaNGiRdZ/pCsrK3Xvvffq9OnT2rFjh1JTU/X1119r7NixP9leXl6ebrrpJj355JPKy8vTU089ZT03d+5czZgxQ4cPH1ZoaKjOnz+v/v37Kzk5WQcPHtTUqVM1YcIE7d69u879r6ys1OjRo+Xi4qJdu3YpMTFR0dHRl/+FAHW0fPlyLVq0SO3bt1deXp727Nkj6eLPlIuLiz755BMlJiZKuvhY7RUrVujQoUNatWqVtm3bpjlz5th1vaVLlyopKUmvv/66Pv74Y50+fVobNmww/b6AxoDhkkaiT58+io2NlSTdcMMNio+PV1pamu68806lpaXpwIEDys7OVocOHSRJb775pm666Sbt2bNHAwcOrNGer6+vrrvuOrVo0cL64J2TJ09KkmbOnKnRo0fbxF+ahERGRmrr1q1at26dBg0aVKf+f/DBB8rKytLWrVvVrl07SdJzzz2nESNG2PlNAPbx8PBQy5Yt5ezsbPOQqRtuuEFLliyxiZ05c6b1z507d9azzz6radOmaeXKlXW+3rJlyxQTE2P9GUpMTNTWrVt/3U0AjRRJRiPRp08fm/d+fn46ceKEJOnw4cPq0KGDNcGQpICAAHl6eurw4cO1Jhk/Z8CAATbvKyoq9Nxzz2ndunU6fvy4ysrKVFpaqmbNmtW5zeo+VicYkhrtA39wdejfv3+NYx988IHi4uKUlZWl4uJiXbhwQefPn9ePP/5Yp7/vZ86cUV5enoKCgqzHrrvuOg0YMIAhE1yTGC5pJIyTLy0WiyorKx1yrebNm9u8/9vf/qbly5crOjpaH374oTIzMxUaGqqysjJrjJOTU43/Ey0vL3dI/wAzGP+ef/PNNxo5cqT69Omjd955RxkZGUpISJAk6991/p4D9iHJuAr07NlTx44d07Fjx6zHvvjiCxUVFSkgIOBXt//JJ5/o3nvv1YMPPqi+ffuqS5cu+vLLL21ivL29lZeXZ31fXFys7OzsGn28NObTTz/91X0DzJKRkaHKykotXbpUgwcP1o033qjc3FybGG9vb+Xn59skGpfuueHh4SE/Pz/t2rXLeuzChQvKyMhweP+Bhogk4yoQEhKi3r17a/z48dq3b592796thx56SLfddluNoY/LccMNNyg1NVU7d+7U4cOH9eijj9ZYuXL77bfrrbfe0n//+18dOHBA4eHhcnZ2tunjjTfeqPDwcH3++ef673//q6effvpX9w0wS7du3VReXq6///3v+vrrr/XWW29ZJ4RWGzp0qAoLC7VkyRIdPXpUCQkJ2rJli03MjBkztHjxYm3cuFFZWVl6/PHHVVRUdAXvBGg4SDKuAhaLRf/+97/VqlUrDRkyRCEhIerSpYvWrl1rSvvz5s1Tv379FBoaqqFDh8rX17fG7oUxMTG67bbbNHLkSIWFhWnUqFHq2rWr9byTk5M2bNigc+fOadCgQZoyZYr+8pe/mNI/wAx9+/bVCy+8oL/+9a/q1auX3n77bcXFxdnE9OzZUytXrlRCQoL69u2r3bt320yKlqQnn3xSEyZMUHh4uIKDg9WyZUvdd999V/JWgAaDR70DAACHoJIBAAAcgiQDAAA4BEkGAABwCJIMAADgECQZAADAIUgyAACAQ5BkAAAAhyDJAAAADkGSAQAAHIIkAwAAOARJBgAAcAiSDAAA4BD/Hz5KpY8ovsZwAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# %% TESTING\n",
        "#%% MODEL PERFORMANCE TESTING\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# df_final_tree=DecisionTreeClassifier(criterion='gini',max_depth=3,random_state=42)\n",
        "\n",
        "# df_final_tree.fit(df_finalX_train,df_finalY_train)\n",
        "\n",
        "df_finalY_predict=df_final_bagging.predict(df_finalX_test)"
      ],
      "metadata": {
        "id": "4RY4Z17WvVNw"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#%% Performance\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sn\n",
        "df_finalY_test = df_finalY_test.astype('int8')\n",
        "array_finalY_test = df_finalY_test.values\n",
        "df_finalY_test\n",
        "class_name=['no fraud','fraud']\n",
        "confusionmatrix=confusion_matrix(df_finalY_test,df_finalY_predict)\n",
        "sn.heatmap(confusionmatrix, annot=True, yticklabels=class_name, xticklabels=class_name, fmt='g', cmap=\"Greens\")\n",
        "\n",
        "#accuracy\n",
        "\n",
        "from sklearn import metrics\n",
        "print('Accuracy:',metrics.accuracy_score(df_finalY_test,df_finalY_predict))\n",
        "print('AUC-ROC: ',metrics.roc_auc_score(df_finalY_test, df_finalY_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "8bQ3XlLawPG0",
        "outputId": "efab6a21-cba5-4d9a-ac02-ce151a5e9988"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9340594484386737\n",
            "AUC-ROC:  0.5019751092962622\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**BOOSTING TECHNIQUE**"
      ],
      "metadata": {
        "id": "4lsUyeMjzieg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "df_final_boosting=GradientBoostingClassifier(n_estimators=100,learning_rate=1.0,random_state=0)\n",
        "df_final_boosting.fit(df_finalX_train, df_finalY_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "HZ_g5ymiwS9e",
        "outputId": "3cb997d8-60ac-4fc5-d868-1fd78636c3f2"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(learning_rate=1.0, random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=1.0, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=1.0, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# df_final_tree=DecisionTreeClassifier(criterion='gini',max_depth=3,random_state=42)\n",
        "\n",
        "# df_final_tree.fit(df_finalX_train,df_finalY_train)\n",
        "\n",
        "df_finalY_predict=df_final_boosting.predict(df_finalX_test)"
      ],
      "metadata": {
        "id": "Oewj1RPF0QCx"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sn\n",
        "df_finalY_test = df_finalY_test.astype('int8')\n",
        "array_finalY_test = df_finalY_test.values\n",
        "df_finalY_test\n",
        "class_name=['no fraud','fraud']\n",
        "confusionmatrix=confusion_matrix(df_finalY_test,df_finalY_predict)\n",
        "sn.heatmap(confusionmatrix, annot=True, yticklabels=class_name, xticklabels=class_name, fmt='g', cmap=\"Greens\")\n",
        "\n",
        "#accuracy\n",
        "\n",
        "from sklearn import metrics\n",
        "print('Accuracy:',metrics.accuracy_score(df_finalY_test,df_finalY_predict))\n",
        "print('AUC-ROC: ',metrics.roc_auc_score(df_finalY_test, df_finalY_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "_XqlKeF10ddS",
        "outputId": "8555e772-75fd-4f50-d69a-a0b1e95fa366"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9291769503165576\n",
            "AUC-ROC:  0.5016540073410892\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SMOTE**"
      ],
      "metadata": {
        "id": "fLyNmT3y2hJ_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install imblearn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXWYp4jp2g1Y",
        "outputId": "b9631d3e-9c7c-4797-bfd3-de1849bab85e"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting imblearn\n",
            "  Downloading imblearn-0.0-py2.py3-none-any.whl (1.9 kB)\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.10/dist-packages (from imblearn) (0.10.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.11.4)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.2.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (3.2.0)\n",
            "Installing collected packages: imblearn\n",
            "Successfully installed imblearn-0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "df_final_smote=SMOTE()\n",
        "df_finalX_train, df_finalY_train=df_final_smote.fit_resample(df_finalX_train, df_finalY_train)\n",
        "\n",
        "#importing packages\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "df_final_tree=DecisionTreeClassifier(criterion='gini',max_depth=3,random_state=42)\n",
        "df_final_tree.fit(df_finalX_train,df_finalY_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "2G2RAXSm2pHL",
        "outputId": "8e11620c-48c1-42e1-ece6-b1c94d994d92"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=3, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# df_final_tree=DecisionTreeClassifier(criterion='gini',max_depth=3,random_state=42)\n",
        "\n",
        "# df_final_tree.fit(df_finalX_train,df_finalY_train)\n",
        "\n",
        "df_finalY_predict=df_final_tree.predict(df_finalX_test)"
      ],
      "metadata": {
        "id": "_r8Y8RgP3iiJ"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sn\n",
        "df_finalY_test = df_finalY_test.astype('int8')\n",
        "array_finalY_test = df_finalY_test.values\n",
        "df_finalY_test\n",
        "class_name=['no fraud','fraud']\n",
        "confusionmatrix=confusion_matrix(df_finalY_test,df_finalY_predict)\n",
        "sn.heatmap(confusionmatrix, annot=True, yticklabels=class_name, xticklabels=class_name, fmt='g', cmap=\"Greens\")\n",
        "\n",
        "#accuracy\n",
        "\n",
        "from sklearn import metrics\n",
        "print('Accuracy:',metrics.accuracy_score(df_finalY_test,df_finalY_predict))\n",
        "print('AUC-ROC: ',metrics.roc_auc_score(df_finalY_test, df_finalY_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "gO1t4zK_3w7X",
        "outputId": "20fa064b-cb32-4c61-928f-4d389b2b26ee"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7227170297242194\n",
            "AUC-ROC:  0.45993139644709385\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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1xNBe1/SduP088MADf/uIDovFohkzZmjcuHHq0ePyWqIPP/xQPj4+WrFihXr37q3k5GTFxsZq27ZtCgwMlCS9++676tatm9566y35+/tr8eLFysnJ0YIFC+Tq6qq77rpLO3fu1PTp061hZObMmeratatGjBghSXr11VcVFxen2bNnKyYmptDX41Alo2TJknr55ZdtEtO1yM7Oltlstnkpz3JdfeLvbfhpi5q9HKK7I0IVu22jlo6bp8reFfO186/oq9ip/6dlm1brf2uWFNjXI+26qlwZDy36ZpnN8XubBOk/Tw7UgHfHqsUrD+iRSf3UvU1njQsb4pRrAhxx5mSmPp/zpZ75z5Mq5Vq4CoS9TSs268L5bN3/5H2Fan9g5y9a8tan6j30UfnV8r2m70TRMZmMexX0Oy872/EF8ampqUpLS1NwcLD1mJeXl9q0aaOEhARJUkJCgry9va0BQ5KCg4Pl4uKixMREa5sOHTrI1dXV2iYkJET79+/XmTNnrG2u/p4rba58T2E5vPCzdevW2rlzp2rW/Od5y38SFRWVfxdKQDmpjuc194m/d/7Cnzp4/JAOHj+kxOQd+nnhd+rbtbemffJXNcKvoo82vLVUW/ZtV/93Rv5tX/0eeFKrfojXyczfbY6/+txwfbTuC72/5mNJ0p5DKfJwL6P5EW/o9SWzZLEQIlF0jh74TVmZWXrr5ZnWY3l5eTq4O1Xfrdiit9dEyaXEP/8314Eff9Gh5MMa9oDtM5reHjBLLTs319OjeluP/fLTQc0f94Eeeflhte4SaN8VbgJGbj0t6HfexIkTNWnSJIf6SUtLkyT5+PjYHPfx8bGeS0tLU5UqVWzOlyxZUhUqVLBpExAQkK+PK+fKly+vtLS0f/yewnI4ZAwYMECRkZE6evSoWrZsKQ8PD5vzTZo0+dc+xowZo8hI2y2RXo80cHQouEYuJpPcSrlZ3/tX9NWGt5Yq6cAuPf9W5N8Gglq+1dWp6d16eMLz+c6VcSutPEuezbFLeZckXf7LSshAUbqzeV2N+u8wm2NL3vxUPjWqqPMTnf41YEhSz/Ae6vZ8V+t78+mzmjf6f+ozLky1Gvx1j6ADOw9q/rgFevjFbrr7wbbGXQRuWgX9znNzc/ub1rcWh0PGlUWfgwf/Na955ZeIyWTSpUuX/rUPNze3/D9gF/YkXwsP9zKqW7WW9X2Ab3U1rdNQGeZMnf7jjMY+NVhfJcTpxOl0VfKqoPCH+6hqJV8t27RK0uWAsfHtZTqc/puGv/eaKnv9NY2SfuaUzXe9EPKETmSc1JptG/KNY+UP6xTZ60X9+MseJab8qLr+tfRqnxFa+UOc8vLy8rUHbiT3Mu7yD7CdsnBzd5WHZxnrcXOGWeaMP/T78ctVuhOpJ+RW2k3lq5SXh2cZVfApb/v50pdLzZX8K8q7sreky1Mk88ctUIdH2qtp+8YyZ5glSSVKlpSHZxlnXiIMZmQlo8DfedfA1/fyn9X09HT5+f21BTs9PV3NmjWztjl58qTN5y5evKiMjAzr5319fZWenm7T5sr7f2tz5Xxh8RTWm1zgnU218e2/1ke888okSdLCb5bq5RljVL96XfW5/zFV8iyv03+c0bb9P6n90F7ad/hnSdL9LdvrjqoBuqNqgI59st2mb9P91f76d5NJz3V5XAu/WVpgaHht8UxZLBa99txIVa3kq1NnT2vlD3EauyD/tiigONq88gfFfhRnfT9r6DxJ0lMjHlebkFaF6mPrN9uVcyFX6z5er3Uf/7Wzqm6T2ho0/RVjBwynKo536gwICJCvr6/i4+OtocJsNisxMVGvvHL5z1dQUJAyMzOVlJSkli1bSpLWr1+vvLw8tWnTxtpm7Nixys3NValSl9coxcXFqV69eipfvry1TXx8vCIiIqzfHxcXp6CgIIfGbLIUoo7dokULxcfHq3z58poyZYqGDx+uMmWMTeVX/0IDcNmaBXOLeghAsdS1+sNO7b/eO13/vVEh7R9a8H2MCpKVlaVffvlFktS8eXNNnz5dnTp1UoUKFVSjRg298cYbmjZtmhYtWqSAgACNHz9eu3bt0r59++Tu7i7p8g6V9PR0xcTEKDc3V88//7wCAwO1ZMnlBf1nz55VvXr11KVLF40aNUp79uzRCy+8oHfeece6u2TLli269957NW3aNHXv3l2ffPKJpk6dqh07dqhRo0aFvp5C7S5JTk7WuXPnJEmTJ0/Od1MPAABw/bZv367mzZurefPmkqTIyEg1b95cEyZMkCSNHDlSgwYNUv/+/dWqVStlZWUpNjbWGjAkafHixapfv746d+6sbt26qV27djb3wPDy8tI333yj1NRUtWzZUsOGDdOECRNs7qVx9913a8mSJZo/f76aNm2qzz77TCtWrHAoYEiFrGQEBQWpbNmyateunSZPnqzhw4erbNmyBba98oNwFJUMID8qGUDBnF3JqD+j4HtVXIuUiH++Tf2trFBrMhYuXKiJEydq1apVMplMWrNmjUqWzP9Rk8l0zSEDAIDiojiuybgZFSpk1KtXT598cvk5FS4uLoqPj8+3DxcAAOBqDu8uYTsiAOBWRyXDGA6HDAAAbnVkDGM4/BRWAACAwqCSAQCAHaZLjEHIAADADiHDGNccMpKSkpScnCxJatiwoVq0aGHYoAAAwM3P4ZBx8uRJ9e7dWxs3bpS3t7ckKTMzU506ddInn3yiypUrGz1GAABuKCoZxnB44eegQYP0xx9/aO/evcrIyFBGRob27Nkjs9ls82RWAABuViaTca/bmcOVjNjYWK1bt04NGjSwHmvYsKHmzJmjLl26GDo4AACKApUMYzhcycjLy7M+GvZqpUqV4kZdAADAyuGQcd9992nIkCE6fvy49dixY8c0dOhQde7c2dDBAQBQJJgvMYTDIWP27Nkym82qVauW6tSpozp16iggIEBms1nvvvuuM8YIAMANZTKZDHvdzhxek1G9enXt2LFD69atU0pKiiSpQYMGCg4ONnxwAADg5nVN98kwmUy6//77df/99xs9HgAAitxtXoAwzDWFjPj4eMXHx+vkyZP5FnsuWLDAkIEBAFBUbvdpDqM4HDImT56sKVOmKDAwUH5+fvwPAQAACuRwyIiJidHChQv1zDPPOGM8AAAUOf4D2hgOh4ycnBzdfffdzhgLAADFAiHDGA5vYe3Xr5+WLFnijLEAAIBbiMOVjAsXLmj+/Plat26dmjRpku/un9OnTzdscAAAFAUKGcZwOGTs2rVLzZo1kyTt2bPH5hzlJQDArYDfZ8ZwOGRs2LDBGeMAAKDYIGQYw+E1GQAAAIVxTTfjAgDgVkYlwxiEDAAA7BAyjMF0CQAAcAoqGQAA2KGQYQxCBgAAdpguMQbTJQAAwCmoZAAAYIdKhjEIGQAA2CFkGIPpEgAA4BRUMgAAsEMhwxiEDAAA7DBdYgxCBgAA9ggZhmBNBgAAcAoqGQAA2GG6xBiEDAAA7LiQMQzBdAkAAHAKKhkAANhhusQYhAwAAOy4EDIMwXQJAABwCioZAADYYbrEGIQMAADsUOY3Bj9HAADsuJhMhr0ccenSJY0fP14BAQEqXbq06tSpo1dffVUWi8XaxmKxaMKECfLz81Pp0qUVHBysAwcO2PSTkZGhsLAweXp6ytvbW3379lVWVpZNm127dql9+/Zyd3dX9erVFR0dfe0/sL9ByAAAoJh44403NG/ePM2ePVvJycl64403FB0drXfffdfaJjo6WrNmzVJMTIwSExPl4eGhkJAQXbhwwdomLCxMe/fuVVxcnFatWqVNmzapf//+1vNms1ldunRRzZo1lZSUpDfffFOTJk3S/PnzDb0epksAALBTVGsytmzZoh49eqh79+6SpFq1aunjjz/W1q1bJV2uYsyYMUPjxo1Tjx49JEkffvihfHx8tGLFCvXu3VvJycmKjY3Vtm3bFBgYKEl699131a1bN7311lvy9/fX4sWLlZOTowULFsjV1VV33XWXdu7cqenTp9uEketFJQMAADtGTpdkZ2fLbDbbvLKzswv83rvvvlvx8fH6+eefJUk//fSTvv/+ez3wwAOSpNTUVKWlpSk4ONj6GS8vL7Vp00YJCQmSpISEBHl7e1sDhiQFBwfLxcVFiYmJ1jYdOnSQq6urtU1ISIj279+vM2fOGPdzNKwnAACQT1RUlLy8vGxeUVFRBbYdPXq0evfurfr166tUqVJq3ry5IiIiFBYWJklKS0uTJPn4+Nh8zsfHx3ouLS1NVapUsTlfsmRJVahQwaZNQX1c/R1GYLoEAAA7Rk6XjBkzRpGRkTbH3NzcCmy7dOlSLV68WEuWLLFOYURERMjf3199+vQxbEw3CiEDAAA7Rpb53dzc/jZU2BsxYoS1miFJjRs31uHDhxUVFaU+ffrI19dXkpSeni4/Pz/r59LT09WsWTNJkq+vr06ePGnT78WLF5WRkWH9vK+vr9LT023aXHl/pY0RmC4BAKCYOH/+vFxcbH81lyhRQnl5eZKkgIAA+fr6Kj4+3nrebDYrMTFRQUFBkqSgoCBlZmYqKSnJ2mb9+vXKy8tTmzZtrG02bdqk3Nxca5u4uDjVq1dP5cuXN+x6CBkAANgpqvtkPPTQQ3r99de1evVqHTp0SMuXL9f06dP1yCOPSLo8jRMREaHXXntNX331lXbv3q1nn31W/v7+Cg0NlSQ1aNBAXbt21YsvvqitW7dq8+bNGjhwoHr37i1/f39J0lNPPSVXV1f17dtXe/fu1aeffqqZM2fmm9a5XkyXAABgp6i2sL777rsaP368BgwYoJMnT8rf318vvfSSJkyYYG0zcuRInTt3Tv3791dmZqbatWun2NhYubu7W9ssXrxYAwcOVOfOneXi4qJevXpp1qxZ1vNeXl765ptvFB4erpYtW6pSpUqaMGGCodtXJclkufo2YkXIdH+1oh4CUOysWTC3qIcAFEtdqz/s1P4fXf2SYX191v09w/q62VDJAADADo96NwYhAwAAO0QMYxAyAACwQyXDGOwuAQAATkElAwAAO1QyjEHIAADATlFtYb3VMF0CAACcgkoGAAB2mC4xBiEDAAA7RAxjMF0CAACcgkoGAAB2mC4xBiEDAAA7hAxjMF0CAACcgkoGAAB2uE+GMQgZAADYYbrEGIQMAADsEDGMwZoMAADgFFQyAACww3SJMQgZAADYIWQYg+kSAADgFFQyAACwwxZWYxAyAACwQ5nfGPwcAQCAU1DJAADADtMlxiBkAABgh90lxmC6BAAAOAWVDAAA7FDJMAYhAwAAO6zJMEaxCRkZq34s6iEAxU7pkh5FPQTgtuTCI9IMwZoMAADgFMWmkgEAQHHBdIkxCBkAANhh4acxmC4BAABOQSUDAAA7JhZ+GoKQAQCAHdZkGIPpEgAA4BRUMgAAsMPCT2MQMgAAsGOi0G8IfooAAMApqGQAAGCH6RJjEDIAALDD7hJjEDIAALDDfTKMwZoMAADgFFQyAACww5oMYxAyAACww5oMYzBdAgBAMXLs2DE9/fTTqlixokqXLq3GjRtr+/bt1vMWi0UTJkyQn5+fSpcureDgYB04cMCmj4yMDIWFhcnT01Pe3t7q27evsrKybNrs2rVL7du3l7u7u6pXr67o6GjDr4WQAQCAHRcD/3HEmTNndM8996hUqVJas2aN9u3bp7ffflvly5e3tomOjtasWbMUExOjxMREeXh4KCQkRBcuXLC2CQsL0969exUXF6dVq1Zp06ZN6t+/v/W82WxWly5dVLNmTSUlJenNN9/UpEmTNH/+/Ov/4V3FZLFYLIb2eI3OZJ8q6iEAxU7pkh5FPQSgWHIvUcap/U/bEWVYX6NbjCl829GjtXnzZn333XcFnrdYLPL399ewYcM0fPhwSdLZs2fl4+OjhQsXqnfv3kpOTlbDhg21bds2BQYGSpJiY2PVrVs3/fbbb/L399e8efM0duxYpaWlydXV1frdK1asUEpKynVe8V+oZAAA4ETZ2dkym802r+zs7ALbfvXVVwoMDNRjjz2mKlWqqHnz5vrvf/9rPZ+amqq0tDQFBwdbj3l5ealNmzZKSEiQJCUkJMjb29saMCQpODhYLi4uSkxMtLbp0KGDNWBIUkhIiPbv368zZ84Ydu2EDAAA7JhMJsNeUVFR8vLysnlFRRVcKfn11181b9483XHHHVq7dq1eeeUVDR48WIsWLZIkpaWlSZJ8fHxsPufj42M9l5aWpipVqticL1mypCpUqGDTpqA+rv4OI7C7BAAAOy4G3oxrzJgxioyMtDnm5uZWYNu8vDwFBgZq6tSpkqTmzZtrz549iomJUZ8+fQwb041CJQMAACdyc3OTp6enzevvQoafn58aNmxoc6xBgwY6cuSIJMnX11eSlJ6ebtMmPT3des7X11cnT560OX/x4kVlZGTYtCmoj6u/wwiEDAAA7Bg5XeKIe+65R/v377c59vPPP6tmzZqSpICAAPn6+io+Pt563mw2KzExUUFBQZKkoKAgZWZmKikpydpm/fr1ysvLU5s2baxtNm3apNzcXGubuLg41atXz2Yny/UiZAAAYMfFZDLs5YihQ4fqhx9+0NSpU/XLL79oyZIlmj9/vsLDwyVdDj8RERF67bXX9NVXX2n37t169tln5e/vr9DQUEmXKx9du3bViy++qK1bt2rz5s0aOHCgevfuLX9/f0nSU089JVdXV/Xt21d79+7Vp59+qpkzZ+ab1rlerMkAAMBOUT0grVWrVlq+fLnGjBmjKVOmKCAgQDNmzFBYWJi1zciRI3Xu3Dn1799fmZmZateunWJjY+Xu7m5ts3jxYg0cOFCdO3eWi4uLevXqpVmzZlnPe3l56ZtvvlF4eLhatmypSpUqacKECTb30jAC98kAijHukwEUzNn3yXjnp7cM62to0+GG9XWzoZIBAIAdFxOrCYxAyAAAwA4PSDMGUQ0AADgFlQwAAOwU1cLPWw0hAwAAO45uPUXBmC4BAABOQSUDAAA7TJcYg5ABAIAdpkuMwXQJAABwCioZAADYMXEzLkMQMgAAsMOaDGMQMgAAsMOaDGNQDwIAAE5BJQMAADs8u8QYhAwAAOy4sCbDEEyXAAAAp6CSAQCAHaZLjEHIAADADvfJMAY/RQAA4BRUMgAAsMPCT2MQMgAAsMOaDGMwXQIAAJyCSgYAAHZ4dokxCBkAANhhusQYhAwAAOyw8NMYrMkAAABOQSUDAAA73IzLGIQMAADssPDTGEQ1AADgFFQyAACww+4SYxAyAACww3SJMZguAQAATkElAwAAO0yXGIOQAQCAHW7GZQymSwAAgFNQyQAAwA7TJcYgZAAAYMdEod8QhAwAAOxQyTAGUQ0AADgFlQwAAOxwMy5jEDIAALDjwnSJIZguAQAATkElAwAAO0yXGIOQAQCAHXaXGIPpEgAA4BRUMgAAsMPNuIzBTxEAADsmk8mw17WaNm2aTCaTIiIirMcuXLig8PBwVaxYUWXLllWvXr2Unp5u87kjR46oe/fuKlOmjKpUqaIRI0bo4sWLNm02btyoFi1ayM3NTXXr1tXChQuveZz/hJABAEAxs23bNr333ntq0qSJzfGhQ4dq5cqVWrZsmb799lsdP35cPXv2tJ6/dOmSunfvrpycHG3ZskWLFi3SwoULNWHCBGub1NRUde/eXZ06ddLOnTsVERGhfv36ae3atYZfh8lisVgM7/UanMk+VdRDAIqd0iU9inoIQLHkXqKMU/uP+22VYX3dX+1Bh9pnZWWpRYsWmjt3rl577TU1a9ZMM2bM0NmzZ1W5cmUtWbJEjz76qCQpJSVFDRo0UEJCgtq2bas1a9bowQcf1PHjx+Xj4yNJiomJ0ahRo3Tq1Cm5urpq1KhRWr16tfbs2WP9zt69eyszM1OxsbGGXbdEJQMAgHyMnC7Jzs6W2Wy2eWVnZ//td4eHh6t79+4KDg62OZ6UlKTc3Fyb4/Xr11eNGjWUkJAgSUpISFDjxo2tAUOSQkJCZDabtXfvXmsb+75DQkKsfRipUAs/ry7F/JsvvvjimgcDAEBxYOR9MqKiojR58mSbYxMnTtSkSZPytf3kk0+0Y8cObdu2Ld+5tLQ0ubq6ytvb2+a4j4+P0tLSrG2uDhhXzl85909tzGaz/vzzT5UuXdqh6/snhQoZXl5e1n+3WCxavny5vLy8FBgYKOlyusrMzHQojAAAcDsYM2aMIiMjbY65ubnla3f06FENGTJEcXFxcnd3v1HDc6pChYwPPvjA+u+jRo3S448/rpiYGJUoUULS5YUmAwYMkKenp3NGCQDADWTkzbjc3NwKDBX2kpKSdPLkSbVo0cJ67NKlS9q0aZNmz56ttWvXKicnR5mZmTbVjPT0dPn6+kqSfH19tXXrVpt+r+w+ubqN/Y6U9PR0eXp6GlrFkK5hTcaCBQs0fPhwa8CQpBIlSigyMlILFiwwdHAAABQFk1wMexVW586dtXv3bu3cudP6CgwMVFhYmPXfS5Uqpfj4eOtn9u/fryNHjigoKEiSFBQUpN27d+vkyZPWNnFxcfL09FTDhg2tba7u40qbK30YyeGbcV28eFEpKSmqV6+ezfGUlBTl5eUZNjAAAG4n5cqVU6NGjWyOeXh4qGLFitbjffv2VWRkpCpUqCBPT08NGjRIQUFBatu2rSSpS5cuatiwoZ555hlFR0crLS1N48aNU3h4uLWa8vLLL2v27NkaOXKkXnjhBa1fv15Lly7V6tWrDb8mh0PG888/r759++rgwYNq3bq1JCkxMVHTpk3T888/b/gAAQC40Yrro97feecdubi4qFevXsrOzlZISIjmzp1rPV+iRAmtWrVKr7zyioKCguTh4aE+ffpoypQp1jYBAQFavXq1hg4dqpkzZ6patWr63//+p5CQEMPH6/B9MvLy8vTWW29p5syZOnHihCTJz89PQ4YM0bBhw2ymURzBfTKA/LhPBlAwZ98nY9OJOMP66uB3v2F93Wyu62ZcZrNZkgxZ8EnIAPIjZAAFI2TcHK7rAWnsJgEA3Ip41LsxHA4ZAQEB//jD//XXX69rQLh+P27fqf9buET7k/fr91On9caMqbr3vg7W8xaLRf+d+76+/Hylsv74Q42bNdbIccNVo2Z1SdLxYyf0wfyF2p64QxmnT6tS5Urq2j1Ez/V/VqVKlbLpZ8mij7Xi86+Udjxd3uW91PPxR/R8/z43/JqBa5G0PUkLF3yo5L37dOrU73pn1nTdF9zJev7076c1Y/pMJWxO0B9/ZKlFYAuN/s9I1axVU5J0NvOs5s6ep4QtPyjtRJrKly+vTp07KnzwAJUrV66oLgsGMPJmXLczh0PG1U+Dk6Tc3Fz9+OOPio2N1YgRI4waF67Dn3/+qTvq1dVDj3TX6KFj853/6IPFWrrkM014baz8qvpp/uz/KeLlSH284v/k5uamw6mHlZdn0egJI1StRlUdPJCqqMlv6M8//9Tg4QOt/Ux/Y6a2btmqQZEDVfeO2jKbzTp79o8beanAdfnz/J+qV+9OhfbsocjBw2zOWSwWRQwaqpIlS2rG7BkqW9ZDHy78P73U92V9sfILlSlTWidPndKpU6cUOWKo6tSprePHT+i1ya/r1KlTenvGW0V0VUDxYdgD0ubMmaPt27fb3LjLEazJcI62TdrZVDIsFose7Byqp559QmHPPSVJyvojS906Pazxr/5H9z8QXGA///fBEn2xdLm+WLNMkpT66yE9/WgfLfn8I9UMqHFjLuY2xJqMG6dpw+Y2lYxDhw6rR7dQff7lZ6p7Rx1Jlxe+39chWIMjBqrnowXf4fib2Dj9Z9RY/ZC0RSVLXteMNP6Bs9dkbE5fb1hf9/jcZ1hfNxvDHpD2wAMP6PPPPzeqOzjJ8WPHdfr302rVtpX1WNlyZXVX44ba/dOev/1cVlaWPL3+WoPz/cbNqlrVX99v2qxHuj6m0K6P6vWJ03T2rNmp4wdulNycHEmSm5ur9ZiLi4tcXV31446df/u5rKw/VLasBwHjJudi4D+3M8Ou/rPPPlOFChWM6g5Ocvr3DElShYrlbY5XqFhep09nFPiZo0d+07KPP1fooz2sx44fO660E+la/80GTXx9nMa/+h/tT96v/0SOc97ggRuoVkAt+fn5atY778p81qzcnFwt+N8HSk9L16lTvxf4mTNnzmj+vP+q12O9bvBoYTQjn8J6O3M4ajdv3tzmh2axWJSWlqZTp07Z3BDkn2RnZ+d7zG22sgt1b3fcWCfTT2noK8N03/2dFProw9bjeXl5ysnJ0cTXx6lGrcvTJf+ZNFrP9e6rw6lHmELBTa9UqVKaPuttTRo3We2D7lWJEiXUJqiN2rW/RwXNMmdlZWngy4NVu05tvRz+UhGMGCh+HA4ZoaGhNu9dXFxUuXJldezYUfXr1y9UHwU99nbk2OEaPX6ko8OBgypWulxtyjh9RpUqV7Iezzh9RnfUq2vT9tTJ3xXeb5AaN22kMRNt/7epVKmSSpQsYQ0YklSrdi1JUlpaOiEDt4SGdzXU0uWf6o8//lBubq4qVKigsCee0V2NGtq0O3funAb0D5eHRxm98+50m11YuDmxu8QYDoeMiRMnXveXFvTY2/NiLv9G8K/qr4qVKmpb4nbdWf8OSdK5rHPau3ufej4eam13Mv2UwvsNUv0G9TTu1f/IxcV2Zq1J88a6dPGSfjt6TNWqV5UkHT18RJLk5+dzYy4GuEGubEc9fOiw9u3dp/DBA6znsrKy9MqLA+Tq6qqZc2ZQkb1F3O7THEa5rpVJFy5cUM7/Xxx1RWFu0FXQY28v2U2f4NqdP39evx05Zn1//NgJ/ZxyQJ5e5eTr56snnn5MC+cvUvUa1eVf1U/z5/xPlSpXVIf72ku6HDAG9B0kXz8fDRo2UJlnMq19VaxUUZLUqm2g6jW4U69PiFLEyMGyWPL05uvT1TqolU11AyjOzp87ryNHjlrfHzt2TCnJ++Xl5Sk/fz99Exun8hXKy8/PVwd+PqDoqDfVqXNH3X3P5adVZmVl6eV+A3ThwgVNfeN1ncs6p3NZ5yRJ5SuUv+bHLAC3Coe3sJ47d06jRo3S0qVLdfr06XznL126dE0DYQurcZK27VB438H5jnd7+AFNeG2s9WZcKz77Sll/ZKlJ88YaOXaYNRys+vJrvTZ+aoF9/7Dre+u/nzr5u96OekdbE7bKvXRpBbVrq8HDB8rLizvBGoUtrM61bet29XvuxXzHHw59SK9OnaLFHy3Rog8+1OnfT6ty5Up6sMeDeunl/irlWuofPy9JX8etVtWq/k4d/+3M2VtYt536/t8bFVKryu0M6+tm43DICA8P14YNG/Tqq6/qmWee0Zw5c3Ts2DG99957mjZtmsLCwq5pIIQMID9CBlAwZ4eM7ac2G9ZXYOV7DOvrZuPwdMnKlSv14YcfqmPHjnr++efVvn171a1bVzVr1tTixYuvOWQAAIBbi8P3ycjIyFDt2rUlXV5/kZFx+d4K7dq106ZNm4wdHQAARcFkMu51G3M4ZNSuXVupqamSpPr162vp0qWSLlc4vL29DR0cAABFwWTgP7czh0PG888/r59++kmSNHr0aM2ZM0fu7u4aOnQoD0gDAABW1/2AtMOHDyspKUl169ZVkyZNrrkfFn4C+bHwEyiYsxd+7jj9g2F9tajY1rC+bjYOVTJyc3PVuXNnHThwwHqsZs2a6tmz53UFDAAAihOmS4zh0O6SUqVKadeuXc4aCwAAxcLtHg6M4vCajKefflrvv/++M8YCAABuIQ7fJ+PixYtasGCB1q1bp5YtW8rDw3bOePr06YYNDgCAosCzS4xRqJCxa9cuNWrUSC4uLtqzZ49atGghSfr5559t2vE/CgDgVsB0iTEKFTKaN2+uEydOqEqVKjp8+LC2bdumihUrOntsAADgJlaoNRne3t7WG3AdOnRIeXl5Th0UAABFid0lxihUJaNXr16699575efnJ5PJpMDAwL99hPGvv/5q6AABALjRmP43RqFCxvz589WzZ0/98ssvGjx4sF588UWVK1fO2WMDAAA3sULvLunataskKSkpSUOGDCFkAABuWbf7NIdRHN7C+sEHHzhjHAAAFBtMlxjD4ZtxAQAAFIbDlQwAAG51TJcYg5ABAIAdQoYxCBkAANhhTYYxWJMBAACcgkoGAAB2mC4xBiEDAAA7hAxjMF0CAACcgkoGAAB2WPhpDEIGAAD5EDKMwHQJAABwCioZAADYYbrEGIQMAADssLvEGEyXAAAAp6CSAQCAHSoZxiBkAABghzUZxmC6BAAAOyYD/3FEVFSUWrVqpXLlyqlKlSoKDQ3V/v37bdpcuHBB4eHhqlixosqWLatevXopPT3dps2RI0fUvXt3lSlTRlWqVNGIESN08eJFmzYbN25UixYt5Obmprp162rhwoXX9LP6J4QMAACKiW+//Vbh4eH64YcfFBcXp9zcXHXp0kXnzp2zthk6dKhWrlypZcuW6dtvv9Xx48fVs2dP6/lLly6pe/fuysnJ0ZYtW7Ro0SItXLhQEyZMsLZJTU1V9+7d1alTJ+3cuVMRERHq16+f1q5da+j1mCwWi8XQHq/RmexTRT0EoNgpXdKjqIcAFEvuJco4tf8jWQcN66tG2TrX/NlTp06pSpUq+vbbb9WhQwedPXtWlStX1pIlS/Too49KklJSUtSgQQMlJCSobdu2WrNmjR588EEdP35cPj4+kqSYmBiNGjVKp06dkqurq0aNGqXVq1drz5491u/q3bu3MjMzFRsbe30XfBUqGQAA2DGZTIa9srOzZTabbV7Z2dmFGsfZs2clSRUqVJAkJSUlKTc3V8HBwdY29evXV40aNZSQkCBJSkhIUOPGja0BQ5JCQkJkNpu1d+9ea5ur+7jS5kofRiFkAADgRFFRUfLy8rJ5RUVF/evn8vLyFBERoXvuuUeNGjWSJKWlpcnV1VXe3t42bX18fJSWlmZtc3XAuHL+yrl/amM2m/Xnn39e03UWhN0lAADYMXIL65gxYxQZGWlzzM3N7V8/Fx4erj179uj77783bCw3GiEDAAA7Rm5hdXNzK1SouNrAgQO1atUqbdq0SdWqVbMe9/X1VU5OjjIzM22qGenp6fL19bW22bp1q01/V3afXN3GfkdKenq6PD09Vbp0aYfG+k+YLgEAoJiwWCwaOHCgli9frvXr1ysgIMDmfMuWLVWqVCnFx8dbj+3fv19HjhxRUFCQJCkoKEi7d+/WyZMnrW3i4uLk6emphg0bWttc3ceVNlf6MAq7S4BijN0lQMGcvbvk+PnDhvXlX6ZmodsOGDBAS5Ys0Zdffql69epZj3t5eVkrDK+88oq+/vprLVy4UJ6enho0aJAkacuWLZIub2Ft1qyZ/P39FR0drbS0ND3zzDPq16+fpk6dKunyFtZGjRopPDxcL7zwgtavX6/Bgwdr9erVCgkJMerSCRlAcUbIAArm/JBxxLC+/MvUKHTbv5um+eCDD/Tcc89JunwzrmHDhunjjz9Wdna2QkJCNHfuXOtUiCQdPnxYr7zyijZu3CgPDw/16dNH06ZNU8mSf62S2Lhxo4YOHap9+/apWrVqGj9+vPU7jELIAIoxQgZQsFs1ZNxqWPgJAIAdnlxiDEIGAAB2eECaMQgZAADkQ8gwAltYAQCAU1DJAADADnUMYxAyAADIh5hhBKZLAACAU1DJAADADrtLjEElAwAAOAUhAwAAOAXTJQAA2DGx8NMQhAwAAOwQMozBdAkAAHAKQgYAAHAKpksAALDDFlZjUMkAAABOQcgAAABOwXQJAAB22F1iDEIGAAD5EDKMwHQJAABwCioZAADYoY5hDEIGAAB22MJqDKZLAACAU1DJAAAgHyoZRiBkAABgh4hhDKZLAACAU1DJAAAgH2oZRiBkAABgh90lxmC6BAAAOAUhAwAAOAXTJQAA2OEBacYgZAAAkA8hwwhMlwAAAKegkgEAgB3qGMYgZAAAYIctrMZgugQAADgFlQwAAPKhkmEEQgYAAHaIGMZgugQAADgFlQwAAPKhlmEEQgYAAHbYXWIMpksAAIBTEDIAAIBTMF0CAIAdHpBmDJPFYrEU9SBQfGRnZysqKkpjxoyRm5tbUQ8HKBb4ewFcG0IGbJjNZnl5eens2bPy9PQs6uEAxQJ/L4Brw5oMAADgFIQMAADgFIQMAADgFIQM2HBzc9PEiRNZ3AZchb8XwLVh4ScAAHAKKhkAAMApCBkAAMApCBkAAMApCBm3sZSUFLVt21bu7u5q1qxZkYzBZDJpxYoVRfLduH1YLBb1799fFSpUkMlk0s6dO2/o9z/33HMKDQ29od8JFAc8u+Q2NnHiRHl4eGj//v0qW7ZsUQ8HcJrY2FgtXLhQGzduVO3atVWpUqWiHhJwWyBk3MYOHjyo7t27q2bNmn/bJjc3V6VKlbqBowKMd/DgQfn5+enuu+8u8HxOTo5cXV1v8KiAWx/TJTeBjh07avDgwRo5cqQqVKggX19fTZo0yabNkSNH1KNHD5UtW1aenp56/PHHlZ6e/rd9mkwmJSUlacqUKTKZTJo0aZIOHTokk8mkTz/9VPfee6/c3d21ePFinT59Wk8++aSqVq2qMmXKqHHjxvr4449t+qtVq5ZmzJhhc6xZs2Y24zxw4IA6dOggd3d3NWzYUHFxcdf7owH+1XPPPadBgwbpyJEjMplMqlWrljp27KiBAwcqIiJClSpVUkhIiCRp+vTpaty4sTw8PFS9enUNGDBAWVlZ1r4mTZqUb2pxxowZqlWrlvX9pUuXFBkZKW9vb1WsWFEjR44UdwrA7YqQcZNYtGiRPDw8lJiYqOjoaE2ZMsX6SzovL089evRQRkaGvv32W8XFxenXX3/VE0888bf9nThxQnfddZeGDRumEydOaPjw4dZzo0eP1pAhQ5ScnKyQkBBduHBBLVu21OrVq7Vnzx71799fzzzzjLZu3Vro8efl5alnz55ydXVVYmKiYmJiNGrUqGv/gQCFNHPmTE2ZMkXVqlXTiRMntG3bNkmX/065urpq8+bNiomJkSS5uLho1qxZ2rt3rxYtWqT169dr5MiRDn3f22+/rYULF2rBggX6/vvvlZGRoeXLlxt+XcDNgOmSm0STJk00ceJESdIdd9yh2bNnKz4+Xvfff7/i4+O1e/dupaamqnr16pKkDz/8UHfddZe2bdumVq1a5evP19dXJUuWVNmyZeXr6ytJ+v333yVJERER6tmzp037q0PIoEGDtHbtWi1dulStW7cu1PjXrVunlJQUrV27Vv7+/pKkqVOn6oEHHnDwJwE4xsvLS+XKlVOJEiWsf9aly3+PoqOjbdpGRERY/71WrVp67bXX9PLLL2vu3LmF/r4ZM2ZozJgx1r9DMTExWrt27fVdBHCTImTcJJo0aWLz3s/PTydPnpQkJScnq3r16taAIUkNGzaUt7e3kpOTCwwZ/yQwMNDm/aVLlzR16lQtXbpUx44dU05OjrKzs1WmTJlC93lljFcChiQFBQU5NC7ASC1btsx3bN26dYqKilJKSorMZrMuXryoCxcu6Pz584X683727FmdOHFCbdq0sR4rWbKkAgMDmTLBbYnpkpuE/eJLk8mkvLw8p3yXh4eHzfs333xTM2fO1KhRo7Rhwwbt3LlTISEhysnJsbZxcXHJ93+iubm5ThkfYAT7P+eHDh3Sgw8+qCZNmujzzz9XUlKS5syZI0nWP+v8OQccQ8i4BTRo0EBHjx7V0aNHrcf27dunzMxMNWzY8Lr737x5s3r06KGnn35aTZs2Ve3atfXzzz/btKlcubJOnDhhfW82m5WamppvjFe3+eGHH657bIBRkpKSlJeXp7fffltt27bVnXfeqePHj9u0qVy5stLS0myCxtX33PDy8pKfn58SExOtxy5evKikpCSnjx8ojggZt4Dg4GA1btxYYWFh2rFjh7Zu3apnn31W9957b76pj2txxx13KC4uTlu2bFFycrJeeumlfDtX7rvvPn300Uf67rvvtHv3bvXp00clSpSwGeOdd96pPn366KefftJ3332nsWPHXvfYAKPUrVtXubm5evfdd/Xrr7/qo48+si4IvaJjx446deqUoqOjdfDgQc2ZM0dr1qyxaTNkyBBNmzZNK1asUEpKigYMGKDMzMwbeCVA8UHIuAWYTCZ9+eWXKl++vDp06KDg4GDVrl1bn376qSH9jxs3Ti1atFBISIg6duwoX1/ffHcvHDNmjO699149+OCD6t69u0JDQ1WnTh3reRcXFy1fvlx//vmnWrdurX79+un11183ZHyAEZo2barp06frjTfeUKNGjbR48WJFRUXZtGnQoIHmzp2rOXPmqGnTptq6davNomhJGjZsmJ555hn16dNHQUFBKleunB555JEbeSlAscGj3gEAgFNQyQAAAE5ByAAAAE5ByAAAAE5ByAAAAE5ByAAAAE5ByAAAAE5ByAAAAE5ByAAAAE5ByAAAAE5ByAAAAE5ByAAAAE5ByAAAAE7x/wB32JYql/hRAgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tEoejXGP30ri"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}