Random Forest.ipynb

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   "source": [
    "# Term Deposit Prediction: Random Forest"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7e18922b",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
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    "tags": []
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import (\n",
    "    classification_report,\n",
    "    confusion_matrix,\n",
    "    fbeta_score,\n",
    "    make_scorer,\n",
    "    recall_score,\n",
    ")\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac6530bc",
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     "start_time": "2023-04-03T06:54:53.024886",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3a76eb06",
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    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan   \n",
       "0   58    management  married   tertiary      no     2143     yes   no  \\\n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
       "4   33       unknown   single    unknown      no        1      no   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
       "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
       "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
       "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
       "4  unknown    5   may       198         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv(\"./train.csv\", sep=\";\")\n",
    "df_test = pd.read_csv(\"./test.csv\", sep=\";\")\n",
    "df_train.head()\n"
   ]
  },
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Check data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "514aa83f",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45211 entries, 0 to 45210\n",
      "Data columns (total 17 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   age        45211 non-null  int64 \n",
      " 1   job        45211 non-null  object\n",
      " 2   marital    45211 non-null  object\n",
      " 3   education  45211 non-null  object\n",
      " 4   default    45211 non-null  object\n",
      " 5   balance    45211 non-null  int64 \n",
      " 6   housing    45211 non-null  object\n",
      " 7   loan       45211 non-null  object\n",
      " 8   contact    45211 non-null  object\n",
      " 9   day        45211 non-null  int64 \n",
      " 10  month      45211 non-null  object\n",
      " 11  duration   45211 non-null  int64 \n",
      " 12  campaign   45211 non-null  int64 \n",
      " 13  pdays      45211 non-null  int64 \n",
      " 14  previous   45211 non-null  int64 \n",
      " 15  poutcome   45211 non-null  object\n",
      " 16  y          45211 non-null  object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f28951a",
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    "execution": {
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.936210</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>0.580323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.618762</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>2.303441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        balance           day      duration      campaign   \n",
       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000  \\\n",
       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  45211.000000  45211.000000  \n",
       "mean      40.197828      0.580323  \n",
       "std      100.128746      2.303441  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      871.000000    275.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
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     "start_time": "2023-04-03T06:54:53.585468",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Convert object data type to category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0baefdc1",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cols_to_category = [\n",
    "    \"job\",\n",
    "    \"marital\",\n",
    "    \"education\",\n",
    "    \"default\",\n",
    "    \"housing\",\n",
    "    \"loan\",\n",
    "    \"contact\",\n",
    "    \"month\",\n",
    "    \"poutcome\",\n",
    "]\n",
    "df_train[cols_to_category] = df_train[cols_to_category].astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47c8b980",
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    },
    "tags": []
   },
   "source": [
    "### Recode the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eb4c1e92",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train[\"y\"] = np.where(df_train[\"y\"] == \"no\", 0, 1)\n",
    "df_test[\"y\"] = np.where(df_test[\"y\"] == \"no\", 0, 1)\n"
   ]
  },
  {
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   "id": "7f06fe35",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8d82535b",
   "metadata": {
    "execution": {
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan   \n",
       "0   58    management  married   tertiary      no     2143     yes   no  \\\n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
       "4   33       unknown   single    unknown      no        1      no   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome  \n",
       "0  unknown    5   may       261         1     -1         0  unknown  \n",
       "1  unknown    5   may       151         1     -1         0  unknown  \n",
       "2  unknown    5   may        76         1     -1         0  unknown  \n",
       "3  unknown    5   may        92         1     -1         0  unknown  \n",
       "4  unknown    5   may       198         1     -1         0  unknown  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_seed = 45\n",
    "np.random.seed(rf_seed)\n",
    "\n",
    "train_x = df_train.iloc[:, :-1]\n",
    "train_y = df_train[[\"y\"]]\n",
    "train_y = np.ravel(train_y)\n",
    "\n",
    "train_x.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4afc268d",
   "metadata": {
    "execution": {
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   "source": [
    "### Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da2a87a5",
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
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       "      <th>previous</th>\n",
       "      <th>job_blue-collar</th>\n",
       "      <th>job_entrepreneur</th>\n",
       "      <th>...</th>\n",
       "      <th>month_jul</th>\n",
       "      <th>month_jun</th>\n",
       "      <th>month_mar</th>\n",
       "      <th>month_may</th>\n",
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       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
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       "      <th>poutcome_success</th>\n",
       "      <th>poutcome_unknown</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2143</td>\n",
       "      <td>5</td>\n",
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       "      <td>1</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
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       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>1506</td>\n",
       "      <td>5</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  education  balance  day  duration  campaign  pdays  previous   \n",
       "0   58          3     2143    5       261         1     -1         0  \\\n",
       "1   44          2       29    5       151         1     -1         0   \n",
       "2   33          2        2    5        76         1     -1         0   \n",
       "3   47          0     1506    5        92         1     -1         0   \n",
       "4   33          0        1    5       198         1     -1         0   \n",
       "\n",
       "   job_blue-collar  job_entrepreneur  ...  month_jul  month_jun  month_mar   \n",
       "0            False             False  ...      False      False      False  \\\n",
       "1            False             False  ...      False      False      False   \n",
       "2            False              True  ...      False      False      False   \n",
       "3             True             False  ...      False      False      False   \n",
       "4            False             False  ...      False      False      False   \n",
       "\n",
       "   month_may  month_nov  month_oct  month_sep  poutcome_other   \n",
       "0       True      False      False      False           False  \\\n",
       "1       True      False      False      False           False   \n",
       "2       True      False      False      False           False   \n",
       "3       True      False      False      False           False   \n",
       "4       True      False      False      False           False   \n",
       "\n",
       "   poutcome_success  poutcome_unknown  \n",
       "0             False              True  \n",
       "1             False              True  \n",
       "2             False              True  \n",
       "3             False              True  \n",
       "4             False              True  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns = list(train_x.select_dtypes(include=\"category\").columns.values)\n",
    "categorical_columns.remove(\"education\")\n",
    "train_x = pd.get_dummies(\n",
    "    train_x, prefix=categorical_columns, columns=categorical_columns, drop_first=True\n",
    ")\n",
    "recode_education_var = {\"unknown\": 0, \"primary\": 1, \"secondary\": 2, \"tertiary\": 3}\n",
    "train_x[\"education\"] = train_x[\"education\"].replace(recode_education_var)\n",
    "train_x[\"education\"].value_counts(normalize=True)\n",
    "train_x[\"education\"] = pd.to_numeric(train_x[\"education\"])\n",
    "train_x.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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   },
   "outputs": [
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>4789</td>\n",
       "      <td>11</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
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       "      <td>1350</td>\n",
       "      <td>16</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>1476</td>\n",
       "      <td>3</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  education  balance  day  duration  campaign  pdays  previous   \n",
       "0   30          1     1787   19        79         1     -1         0  \\\n",
       "1   33          2     4789   11       220         1    339         4   \n",
       "2   35          3     1350   16       185         1    330         1   \n",
       "3   30          3     1476    3       199         4     -1         0   \n",
       "4   59          2        0    5       226         1     -1         0   \n",
       "\n",
       "   job_blue-collar  job_entrepreneur  ...  month_jul  month_jun  month_mar   \n",
       "0            False             False  ...      False      False      False  \\\n",
       "1            False             False  ...      False      False      False   \n",
       "2            False             False  ...      False      False      False   \n",
       "3            False             False  ...      False       True      False   \n",
       "4             True             False  ...      False      False      False   \n",
       "\n",
       "   month_may  month_nov  month_oct  month_sep  poutcome_other   \n",
       "0      False      False       True      False           False  \\\n",
       "1       True      False      False      False           False   \n",
       "2      False      False      False      False           False   \n",
       "3      False      False      False      False           False   \n",
       "4       True      False      False      False           False   \n",
       "\n",
       "   poutcome_success  poutcome_unknown  \n",
       "0             False              True  \n",
       "1             False             False  \n",
       "2             False             False  \n",
       "3             False              True  \n",
       "4             False              True  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x = df_test.iloc[:, :-1]\n",
    "test_y = df_test[[\"y\"]]\n",
    "test_y = np.ravel(test_y)\n",
    "\n",
    "test_x = pd.get_dummies(\n",
    "    test_x, prefix=categorical_columns, columns=categorical_columns, drop_first=True\n",
    ")\n",
    "test_x[\"education\"] = test_x[\"education\"].replace(recode_education_var)\n",
    "test_x.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "897e5c9f",
   "metadata": {
    "papermill": {
     "duration": 0.03817,
     "end_time": "2023-04-03T06:55:21.626537",
     "exception": false,
     "start_time": "2023-04-03T06:55:21.588367",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c98e310b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.800e+01, 3.000e+00, 2.143e+03, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       [4.400e+01, 2.000e+00, 2.900e+01, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       [3.300e+01, 2.000e+00, 2.000e+00, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00],\n",
       "       ...,\n",
       "       [5.500e+01, 1.000e+00, 1.946e+03, ..., 0.000e+00, 0.000e+00,\n",
       "        0.000e+00],\n",
       "       [4.600e+01, 1.000e+00, 4.410e+02, ..., 0.000e+00, 1.000e+00,\n",
       "        0.000e+00],\n",
       "       [5.300e+01, 2.000e+00, 8.890e+02, ..., 0.000e+00, 0.000e+00,\n",
       "        1.000e+00]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "oversampling = SMOTE(random_state=1)\n",
    "train_x, train_y = oversampling.fit_resample(train_x, train_y)\n",
    "SimpleImputer(missing_values=np.nan, strategy=\"mean\").fit_transform(train_x, train_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "08730c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>job_blue-collar</th>\n",
       "      <th>job_entrepreneur</th>\n",
       "      <th>...</th>\n",
       "      <th>month_jul</th>\n",
       "      <th>month_jun</th>\n",
       "      <th>month_mar</th>\n",
       "      <th>month_may</th>\n",
       "      <th>month_nov</th>\n",
       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
       "      <th>poutcome_other</th>\n",
       "      <th>poutcome_success</th>\n",
       "      <th>poutcome_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>2143</td>\n",
       "      <td>5</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>1506</td>\n",
       "      <td>5</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79839</th>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>1971</td>\n",
       "      <td>10</td>\n",
       "      <td>386</td>\n",
       "      <td>1</td>\n",
       "      <td>181</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79840</th>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>-485</td>\n",
       "      <td>16</td>\n",
       "      <td>996</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79841</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>1946</td>\n",
       "      <td>2</td>\n",
       "      <td>224</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79842</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>441</td>\n",
       "      <td>20</td>\n",
       "      <td>154</td>\n",
       "      <td>1</td>\n",
       "      <td>181</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79843</th>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>889</td>\n",
       "      <td>20</td>\n",
       "      <td>267</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79844 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  education  balance  day  duration  campaign  pdays  previous   \n",
       "0       58          3     2143    5       261         1     -1         0  \\\n",
       "1       44          2       29    5       151         1     -1         0   \n",
       "2       33          2        2    5        76         1     -1         0   \n",
       "3       47          0     1506    5        92         1     -1         0   \n",
       "4       33          0        1    5       198         1     -1         0   \n",
       "...    ...        ...      ...  ...       ...       ...    ...       ...   \n",
       "79839   35          2     1971   10       386         1    181         1   \n",
       "79840   42          2     -485   16       996         1     -1         0   \n",
       "79841   55          1     1946    2       224         1    122         4   \n",
       "79842   46          1      441   20       154         1    181         1   \n",
       "79843   53          2      889   20       267         2     -1         0   \n",
       "\n",
       "       job_blue-collar  job_entrepreneur  ...  month_jul  month_jun   \n",
       "0                False             False  ...      False      False  \\\n",
       "1                False             False  ...      False      False   \n",
       "2                False              True  ...      False      False   \n",
       "3                 True             False  ...      False      False   \n",
       "4                False             False  ...      False      False   \n",
       "...                ...               ...  ...        ...        ...   \n",
       "79839            False             False  ...      False      False   \n",
       "79840            False             False  ...      False      False   \n",
       "79841            False             False  ...      False      False   \n",
       "79842            False             False  ...      False      False   \n",
       "79843            False             False  ...      False      False   \n",
       "\n",
       "       month_mar  month_may  month_nov  month_oct  month_sep  poutcome_other   \n",
       "0          False       True      False      False      False           False  \\\n",
       "1          False       True      False      False      False           False   \n",
       "2          False       True      False      False      False           False   \n",
       "3          False       True      False      False      False           False   \n",
       "4          False       True      False      False      False           False   \n",
       "...          ...        ...        ...        ...        ...             ...   \n",
       "79839      False      False      False      False      False           False   \n",
       "79840      False      False      False      False      False           False   \n",
       "79841      False      False      False      False      False           False   \n",
       "79842      False      False      False      False      False           False   \n",
       "79843      False      False      False      False      False           False   \n",
       "\n",
       "       poutcome_success  poutcome_unknown  \n",
       "0                 False              True  \n",
       "1                 False              True  \n",
       "2                 False              True  \n",
       "3                 False              True  \n",
       "4                 False              True  \n",
       "...                 ...               ...  \n",
       "79839             False             False  \n",
       "79840             False              True  \n",
       "79841             False             False  \n",
       "79842              True             False  \n",
       "79843             False              True  \n",
       "\n",
       "[79844 rows x 40 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8cef7ae1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T06:55:21.706160Z",
     "iopub.status.busy": "2023-04-03T06:55:21.705375Z",
     "iopub.status.idle": "2023-04-03T06:55:21.709980Z",
     "shell.execute_reply": "2023-04-03T06:55:21.709144Z"
    },
    "papermill": {
     "duration": 0.046445,
     "end_time": "2023-04-03T06:55:21.712049",
     "exception": false,
     "start_time": "2023-04-03T06:55:21.665604",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation scores: [0.33892786 0.88026052 0.99599299 0.99348861 0.99373747 0.99073146\n",
      " 0.99273547 0.99574148 0.99348697 0.99373747]\n",
      "0.92 recall with a standard deviation of 0.20\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(\n",
    "    n_jobs=-1, random_state=rf_seed, class_weight=\"balanced_subsample\", max_depth=18\n",
    ")\n",
    "recall = make_scorer(recall_score)\n",
    "cross_val = cross_val_score(\n",
    "    random_forest, train_x, train_y, cv=10, n_jobs=-1, scoring=recall\n",
    ")\n",
    "print(\"Cross validation scores: {}\".format(cross_val))\n",
    "print(\n",
    "    \"%0.2f recall with a standard deviation of %0.2f\"\n",
    "    % (cross_val.mean(), cross_val.std())\n",
    ")\n",
    "\n",
    "random_forest.fit(train_x, train_y)\n",
    "pred = random_forest.predict(train_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "83089cd6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T06:55:52.201561Z",
     "iopub.status.busy": "2023-04-03T06:55:52.200326Z",
     "iopub.status.idle": "2023-04-03T06:55:52.447781Z",
     "shell.execute_reply": "2023-04-03T06:55:52.446330Z"
    },
    "papermill": {
     "duration": 0.291044,
     "end_time": "2023-04-03T06:55:52.451412",
     "exception": false,
     "start_time": "2023-04-03T06:55:52.160368",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9905    0.9765    0.9835     39922\n",
      "           1     0.9769    0.9907    0.9837     39922\n",
      "\n",
      "    accuracy                         0.9836     79844\n",
      "   macro avg     0.9837    0.9836    0.9836     79844\n",
      "weighted avg     0.9837    0.9836    0.9836     79844\n",
      "\n",
      "The train recall score is 0.9907\n",
      "The F2 score is 0.99\n"
     ]
    }
   ],
   "source": [
    "sns.heatmap(\n",
    "    confusion_matrix(train_y, pred),\n",
    "    annot=True,\n",
    "    fmt=\"g\",\n",
    "    cbar=False,\n",
    "    cmap=\"Reds\",\n",
    "    annot_kws={\"size\": 15},\n",
    ")\n",
    "plt.title(\"Confusion matrix (Train set)\", fontsize=16)\n",
    "plt.yticks(rotation=0)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()\n",
    "print(classification_report(train_y, pred, digits=4))\n",
    "print(\"The train recall score is {}\".format(np.round(recall_score(train_y, pred), 4)))\n",
    "print(\"The F2 score is {}\".format(np.round(fbeta_score(train_y, pred, average=\"binary\", beta=2), 2)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a4e2815b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T06:55:52.730081Z",
     "iopub.status.busy": "2023-04-03T06:55:52.729051Z",
     "iopub.status.idle": "2023-04-03T06:55:52.914554Z",
     "shell.execute_reply": "2023-04-03T06:55:52.912994Z"
    },
    "papermill": {
     "duration": 0.229309,
     "end_time": "2023-04-03T06:55:52.917508",
     "exception": false,
     "start_time": "2023-04-03T06:55:52.688199",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9929    0.9748    0.9837      4000\n",
      "           1     0.8300    0.9463    0.8843       521\n",
      "\n",
      "    accuracy                         0.9715      4521\n",
      "   macro avg     0.9114    0.9605    0.9340      4521\n",
      "weighted avg     0.9741    0.9715    0.9723      4521\n",
      "\n",
      "The test recall score is 0.9463\n",
      "The F2 score is 0.92\n"
     ]
    }
   ],
   "source": [
    "pred = random_forest.predict(test_x)\n",
    "\n",
    "sns.heatmap(\n",
    "    confusion_matrix(test_y, pred),\n",
    "    annot=True,\n",
    "    fmt=\"g\",\n",
    "    cmap=\"Reds\",\n",
    "    cbar=False,\n",
    "    annot_kws={\"size\": 15},\n",
    ")\n",
    "plt.title(\"Confusion matrix (Test set)\", fontsize=16)\n",
    "plt.yticks(rotation=0)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()\n",
    "print(classification_report(test_y, pred, digits=4))\n",
    "print(\"The test recall score is {}\".format(np.round(recall_score(test_y, pred), 4)))\n",
    "print(\"The F2 score is {}\".format(np.round(fbeta_score(test_y, pred, average=\"binary\", beta=2), 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c7e1de",
   "metadata": {
    "papermill": {
     "duration": 0.03939,
     "end_time": "2023-04-03T06:55:53.075889",
     "exception": false,
     "start_time": "2023-04-03T06:55:53.036499",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Random Forest Model 2 (Splitting the training data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "37eb7772",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T06:55:53.264560Z",
     "iopub.status.busy": "2023-04-03T06:55:53.263449Z",
     "iopub.status.idle": "2023-04-03T06:55:53.272290Z",
     "shell.execute_reply": "2023-04-03T06:55:53.271393Z"
    },
    "papermill": {
     "duration": 0.052586,
     "end_time": "2023-04-03T06:55:53.274398",
     "exception": false,
     "start_time": "2023-04-03T06:55:53.221812",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45211, 16), (45211,))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = df_train.iloc[:, :-1]\n",
    "y = df_train[[\"y\"]]\n",
    "\n",
    "y_train = np.ravel(y)\n",
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f972fd86",
   "metadata": {
    "papermill": {
     "duration": 0.040149,
     "end_time": "2023-04-03T06:55:53.841658",
     "exception": false,
     "start_time": "2023-04-03T06:55:53.801509",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d38dbd16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T06:55:53.927282Z",
     "iopub.status.busy": "2023-04-03T06:55:53.926422Z",
     "iopub.status.idle": "2023-04-03T06:55:53.936421Z",
     "shell.execute_reply": "2023-04-03T06:55:53.935397Z"
    },
    "papermill": {
     "duration": 0.055905,
     "end_time": "2023-04-03T06:55:53.938914",
     "exception": false,
     "start_time": "2023-04-03T06:55:53.883009",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>job_blue-collar</th>\n",
       "      <th>job_entrepreneur</th>\n",
       "      <th>...</th>\n",
       "      <th>month_jul</th>\n",
       "      <th>month_jun</th>\n",
       "      <th>month_mar</th>\n",
       "      <th>month_may</th>\n",
       "      <th>month_nov</th>\n",
       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
       "      <th>poutcome_other</th>\n",
       "      <th>poutcome_success</th>\n",
       "      <th>poutcome_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>2143</td>\n",
       "      <td>5</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>1506</td>\n",
       "      <td>5</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  education  balance  day  duration  campaign  pdays  previous   \n",
       "0   58          3     2143    5       261         1     -1         0  \\\n",
       "1   44          2       29    5       151         1     -1         0   \n",
       "2   33          2        2    5        76         1     -1         0   \n",
       "3   47          0     1506    5        92         1     -1         0   \n",
       "4   33          0        1    5       198         1     -1         0   \n",
       "\n",
       "   job_blue-collar  job_entrepreneur  ...  month_jul  month_jun  month_mar   \n",
       "0            False             False  ...      False      False      False  \\\n",
       "1            False             False  ...      False      False      False   \n",
       "2            False              True  ...      False      False      False   \n",
       "3             True             False  ...      False      False      False   \n",
       "4            False             False  ...      False      False      False   \n",
       "\n",
       "   month_may  month_nov  month_oct  month_sep  poutcome_other   \n",
       "0       True      False      False      False           False  \\\n",
       "1       True      False      False      False           False   \n",
       "2       True      False      False      False           False   \n",
       "3       True      False      False      False           False   \n",
       "4       True      False      False      False           False   \n",
       "\n",
       "   poutcome_success  poutcome_unknown  \n",
       "0             False              True  \n",
       "1             False              True  \n",
       "2             False              True  \n",
       "3             False              True  \n",
       "4             False              True  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns = list(X_train.select_dtypes(include=\"category\").columns.values)\n",
    "categorical_columns.remove(\"education\")\n",
    "X_train = pd.get_dummies(\n",
    "    X_train, prefix=categorical_columns, columns=categorical_columns, drop_first=True\n",
    ")\n",
    "recode_education_var = {\"unknown\": 0, \"primary\": 1, \"secondary\": 2, \"tertiary\": 3}\n",
    "X_train[\"education\"] = X_train[\"education\"].replace(recode_education_var)\n",
    "X_train[\"education\"].value_counts(normalize=True)\n",
    "X_train[\"education\"] = pd.to_numeric(X_train[\"education\"])\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d9da3bee",
   "metadata": {},
   "outputs": [],
   "source": [
    "oversampling = SMOTE(random_state=1)\n",
    "X_train, y_train = oversampling.fit_resample(X_train, y_train)\n",
    "SimpleImputer(missing_values=np.nan, strategy=\"mean\").fit_transform(X_train, y_train)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_train, y_train, test_size=0.10, stratify=y_train, random_state=rf_seed\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c9a937ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-03T06:55:54.912607Z",
     "iopub.status.busy": "2023-04-03T06:55:54.911803Z",
     "iopub.status.idle": "2023-04-03T06:56:22.569692Z",
     "shell.execute_reply": "2023-04-03T06:56:22.567739Z"
    },
    "papermill": {
     "duration": 27.705306,
     "end_time": "2023-04-03T06:56:22.572371",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation scores: [0.94489285 0.94210966 0.94823267 0.94461453 0.9412747  0.94628444\n",
      " 0.94377957 0.9471194  0.94990259 0.94459911]\n",
      "0.95 recall with a standard deviation of 0.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9878    0.9728    0.9803     35930\n",
      "           1     0.9732    0.9880    0.9806     35929\n",
      "\n",
      "    accuracy                         0.9804     71859\n",
      "   macro avg     0.9805    0.9804    0.9804     71859\n",
      "weighted avg     0.9805    0.9804    0.9804     71859\n",
      "\n",
      "The train recall score is 0.99\n",
      "The F2 score is 0.99\n"
     ]
    },
    {
     "data": {
      "image/png": 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zNFaVKlXk5+enQ4cO6fz589ft++yzz0qSPvjgA02ePFmSNHDgwMwdxDXSesyaN28uPz8/LV++PFNv3V4rICBA/fv311133aWkpCRt3bo13XVca/v27ZKunqnF7Yuggptu0qRJKlu2rPbv369GjRpp1apVLn3i4+P1ySefqEaNGilCQPL78qNHj9aWLVuc240x+te//qXIyEiFhobq8ccfz/4D0dUPR5OuLn1+7TUdUVFRioiIcPuWxLx587Ry5UqXt4USEhK0ePFiSe5/Mf9To0aNVK9ePV26dElPPvmkLl686Gw7efKknnzySUlS165dnX8N32qSr1+YNGlSiu27d+9W//79U92vaNGikqTff/89W+o6ceKEunXrpsTERE2ePFlVqlRRsWLFNGPGDOdF3VFRUekeLzAwUPXr11dSUlKan23Vpk0blS1bVosXL9aWLVtUpkwZtW7d+gaP6O/HbMeOHW7bCxQooEGDBik2NlZt27bVtm3bXPrExcVpwYIF2rVrl3PbW2+95Vzh+Vq7du1ynhG79vmeVh3XSr5ot1mzZmn2xS3Mg3cc4Q527NgxEx4e7rxFtFSpUqZ9+/amW7duplmzZiY4ONi5xsW6deuc+yUlJTnXYvHx8THNmzc33bp1MxUqVHAuOOVutdBrV6Z1J7XbUNO6VXLfvn0mNDTUeWtp586dzb333msCAwNNixYtTIMGDVxuT05efyNfvnymZcuWpkePHqZdu3bOhc+KFCmS4nZiY9K34Fv+/PlNly5dTPv27Z0rrF5vwTd3K46mNd/1XG+f5BVjS5Qo4bY9tZrmzp1rHA6Hcw2Wrl27mmbNmhlfX1/TrFkzt4+vMcZs2bLFeHl5GS8vL9OiRQvTt29f069fP/Ptt99m6BjdPS8SExPNfffdl+rzYtiwYUaSqVu3rss6OdeTvCbICy+8kGbfd99911n/xIkT0z3H9cTFxTkX0qtRo4bp3bu36devn5kwYYKzT0JCgunevbvztvcaNWqYzp07m0ceecQ0bNjQ5MiRw0hKsehbSEiIkWQqVqxoOnbsaLp3727Cw8ONj4+PkWR69+6d4TqMYWXaOwlBBR61aNEi07t3b1O2bFkTHBxsfH19TcGCBU3Lli3Nu+++m+oL0KxZs0x4eLgJDQ01vr6+plixYqZPnz5m165dbvtnV1AxxpgdO3aYTp06mdy5cxt/f39ToUIF869//cvEx8e7XUdl8+bNZsSIEaZRo0amSJEixs/Pz4SFhZlatWqZsWPHmpMnT7rMcb1fqslL6FeqVMkEBASYoKAgU6NGDTN+/Pg0l9BPjS1BxRhjVq5caZo3b27y5ctngoKCTNWqVc2YMWNMXFxcqh9RYIwx8+bNMw0bNjQ5c+Z0hp3UltBPjbvnxejRo40kU7lyZRMbG+uyT3x8vHPF48GDB193/GudOXPG5MiRwxQuXDjNVZt37tzpXEPnn6sY34ht27aZdu3ambCwMOPl5ZXqz+SHH34wnTp1ci6JHxoaaipVqmS6du1qZs2aleJx+eyzz0zfvn1N1apVTZ48eYy/v78pUaKEad26tZk3b55JSkrKVB3Ja/NkdO0d3HocxtzA5fIAgCwzcOBATZ48WQsWLEh1zR9JeumllzRmzBg98cQT+uijj25ihfZo27atFi5cqE2bNunuu+/2dDnIRgQVALDEiRMnVL58eZUtW1br16932+fo0aOqXLmyYmJitH37do9+urinrF+/XnXr1lWfPn00bdo0T5eDbMbFtABgibCwML366qvasGGD5syZk6JtxIgR6tWrl2rWrKmzZ8/qiSeeuCNDinR1fZ2cOXNq3Lhxni4FNwFnVADgFlCyZEkdOHBABQsW1COPPKLx48fL39/fbd/x48enuPPmeipWrKgRI0ZkZalAliKoAMBtJjw83O3nO7nTpEkT5+dGATYiqAAAAGtxjQoAALAWQQUAAFjrlv9QQkerW3NpcABpu7gwfReEArj1BHqn7xPiOaMCAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANby8XQBuL0M6fS4GlWpo2olKyp/aF4F+Por+swJrdi2Vm/O+Ujbo3al6G8WH0xzzJ8jV6v5iK7O75vcVV/LJ3ydav+1OzfpniHtU2zLH5pPD9ZrrgfrtlCd8tWVPzSvLsZd0pZ9O/XJj7P16U9zMnikAJJdunRJa1av1crlK7V5U6SOHjkqL28vFS9eTM1bNleviJ4KyhGU5jhPPtpf69b+Jkn638+LVKBgAbf9Nm+K1McfTdW2LduUkJCg0mVLq2v3R9S2/YNZelywA0EFWerFRwYqR0CQtu7fqW3/P5RUKVFevVt0Udcm7dRp9BNa+NtSZ//pS1IPHA/Uaaaw0Lz6Zftvbtv3HInSqt/Xu2zfe/Qvl20Tn3hZPZt1UsKVBG34c6tW/b5eRfIVVKMqddTkrvp6sF5zdR33tJKSkjJ6yMAdb9HCxXr9ldGSpNKlS6lJ0yaKjb2gLZu3asoHH2rxD4s1dcbHypM3T6pjfDtvgdat/U0Oh0PGmFT7/fTjUg0fNkJJSUmqWbumcucO1bq1v+nlka/oz91/augLQ7L8+OBZDnO9Z8QtwNGqmKdLwDUaVK6tjX9uU1xCXIrtTz3YW/8eOEbRp4+raM+6SkxKvO44ITlyKfqLjQrwC1C5Rxtrz5EoZ1vyGZXpS75W34lD01XXu/1f1bEzJ/XfxbN08txp5/ba5avrp3GzFJIjl554b7j+u2hW+g8W2e7iwl1pd4LHLZj/nbZs3qIevburdJnSzu0nTpzQoP7PatfOXWr1QCuNf3Os2/1Pnz6jjg90UuWqlRS1/y8dPXLU7RmVc2fP6YH72urChQua+N6bat6yuSTp1MlT6tPzUR08cFD/nf4f1albO/sOFlkm0DtHuvpxjQqy1K87NriEFEma8v2n2nMkSgXz5FflEuXSHOehxg8owC9Aa3ZuTBFSMmvwh69q3OwPUoQUSdrwxxaN/+rfkqRu4e3d7QogDe06tNXLr72UIqRIUlhYmEa+PFyS9POSn5UQn+B2/zfHvaXLly/rxZdHXneeb+bO04ULFxTeLNwZUiQpb768GvLcs5KkmdNn3sihwEJWBJXJkyerZMmSCggIUL169fTbb+5P9ePWlnDl6otUfIL7F6tr9WzWSZI0c+k32VqTJG3Zt0OSVDiv+/fDAWRe+QrlJUnx8fE6e+6sS/vqX1Zr0cJFeuzJfipW/PpnyFetWCVJanlfc5e2xvc2lr+/v9at+U1xca5/LOHW5fGgMnv2bA0dOlSjRo3Spk2bVL16dd1///06fvy4p0tDFurZvJMqFC2jPw7t059H9l+3b7Gwwmpcta7iE+I1e8WCVPuVK1xSY/sO10fPjNeYPsPVuk5TORyODNdWumBxSVL06RMZ3hfA9R0+dFiS5OPjo5CQkBRtly5e0pjXx6lU6ZLq82hEmmPt3v2HJKli5Uoubb5+vipTrozi4uL0V5TrdWq4dXn8Ytq3335bjz/+uPr27StJ+vDDD7Vw4UJ98sknGjFihIerQ2Y91+VJVSlRQTkCAlWpWDlVLVlBh09Gq9v4gWlesNqjWUd5eXlp0brlOn3+bKr9Glapo4ZV6qTYtnX/TnUe/US63y7y8fbRgAd7S5K+XftjuvYBkH6fz/xCktSwcQP5+fmlaPv3pCk6cviIPp7xX/n6+V53nAsXLujC+QuSpAIF87vtU6BAAe3YvkNHjxx1nsnBrc+jQSU+Pl4bN27UyJF/vy/p5eWlFi1aaM2aNR6sDDfq/lpN1KJGY+f3UccOqvebQ7Rpz7Y09+3ZrKMkaebSuW7bz8We14Svp2juqh/05+EoSdLdZSprTJ8XdE+lWvpx7Oe6e0ArxVw8n+Zco3s/p8olymvf0b/04ULe2way0i8rVmn+3Pny8fHRgEEDUrTt3LFTsz77Qm07tFXtOrXSHOvixUvOfwcEBLjtExh4dXts7MUbqBq28ehbPydPnlRiYqIKFEh5bUCBAgUUHR3toaqQFVqO7C5Hq2IK7VxFjYd11p+H92vlW3P0YtdB192vRtmqqlKigs6cP6vv1v3ktk/k3t81fOpY/bY7UmcunNWZC2e1bMuvajSsk1ZuW6dSBYtrQNveadb4SJN2euGhp3Qp7rK6vzFIl+IuZ+pYAbjav2+//m/4SzLGaMjzg1Wh4t9nOBITE/Xay6OVM2dODX1+sOeKxC3B49eoZERcXJxiYmJSfCnplr67+rZ3LjZGq37/TW1ejtCGP7ZqdO/nVLt89VT7J19E+/UvCxWfEJ+huZKSkvTG11fv4Lm/VpPr9m1avYGmD5uoJJOkbuMHat2uzRmaC0Dqjh07rgFPDFRMTIx6RfRUj17dU7R//uks7dq5S4OHPaPcuXOna8ygoEDnvy9fdv9HxaVLV7fnSMficrh1ePStn3z58snb21vHjh1Lsf3YsWMqWLCgS/9x48bptddeS7mxTE6pbIhLX9jlSuIVzV65QLXL36W29Vpowx9bXPp4eXmpa5N2kqTPfp6XqXn+PHz1Qt1Cedy/hy1dXTvl21FT5efjp37vPK9v1/wvU3MBcHXu7Dk99dgAHT1yVO07tnO7ANuK5SvlcDj03bff67sFC1O0nTp5SpL0/JAX5Ovnp0cf66OGjRsqODhYwTmDdeH8BR2LPq7gssEu4yb/LilUuFA2HBk8xaNnVPz8/FSrVi0tXfr3SqVJSUlaunSp7rnnHpf+I0eO1Llz51J8qXSum1kybsDJc2ckSWEhed22N7+7kQrnLaCoYwf1y/Z1mZojd/DV0Bp72f171JWKl9Oi0Z8qZ1Cwhv7ndU1f8lWm5gHg6mLsRT395CDt27tPzVs20yuvv5zqnXjGGG3csEkb129M8RUff/VM6tYt27Rx/Uad/P/BRZIq/P8LZHft2OkyXkJCgvb+uVf+/v4qUbJENhwdPMXjd/0MHTpUERERql27turWrat3331XsbGxzruAruXv7y9/f/+UG70yfjsqPKPJXfUluV/iXvr7ItrMnk2RpM6N2kiSNu3Z7tJWokBR/Tjmc+ULyaNRMyfqvflTMz0PgJTi4+M1eOAQbd+2XQ0a3aPxb46Tt7e3275TZ/w31XFat3gg1ZVpGzVppI0bNmnJj0v1QLsHUrT9suIXxcXF6d7wxq6/J3BL8/g1Ko888ojeeustvfLKK7r77rsVGRmpxYsXu1xgC/s1qFxb99cKd/kLysfbRwPb9VGvZp108fIlt2ujBPoHqGODVpJSv9sn2bMd+qloPtdTu0+06aEhHR9TUlKSpnyf8g6esJC8+nHM5yoaVkhvzflIr3/+bgaPDkBqEhMTNeK5kfpt3XrVrFVDE997K83bjTOjU+eOCg4O1vKfl2vpkr/PxJ8+dVrvvPWeJKlXn15ZPi88y+NnVCRp4MCBGjhwoKfLwA0qV6SUpg97WyfOntLGPdt0KuaM8oXkUbWSFVU4bwFdirusPhOH6tDJoy77drinlXIGBeu33ZH649C+684zuGM/vfX4S9q0Z7v2Rx9UgJ+/qpWsoNKFSigxMVHPTHnF5Tboj54Zr/JFSyv28kXlC8mjacPedhn35LnTev7jf93YgwDcgb6cNVs//7RMkhSaO1RjR49322/o84PTffGsOyGhIXr1X6P0wtDhem7wC6pdt5ZCQkK1bu06nY85r14RPfmcn9uQFUEFt4cVW9dqzBeT1OSuerqrVCXly5Vb8VcSFHXskOasWqj350/T3qNRbvf9+22ftJfMnzj3P7qv5r2qUqK8KhcvJ19vHx09c1wzl87V+99Oc3uhbu6cV69dyREQpD4tH3I7btSxgwQVIBNizsU4/50cWNzp//STNxRUJKnFfc019dOP9fGHH2vr1m1KSEhQ6TKl1bX7I2rXoe0NjQ078enJAKzFpycDty8+PRkAANzyCCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWj7p6bRgwYJ0D9iuXbtMFwMAAHCtdAWVDh06pGswh8OhxMTEG6kHAADAKV1BJSkpKbvrAAAAcME1KgAAwFrpOqPyT7GxsVqxYoUOHDig+Pj4FG3PPPNMlhQGAACQ4aCyefNmtWnTRhcvXlRsbKzy5MmjkydPKigoSPnz5yeoAACALJPht36GDBmitm3b6syZMwoMDNTatWv1119/qVatWnrrrbeyo0YAAHCHynBQiYyM1LBhw+Tl5SVvb2/FxcWpWLFimjBhgl588cXsqBEAANyhMhxUfH195eV1dbf8+fPrwIEDkqSQkBAdPHgwa6sDAAB3tAxfo1KjRg2tX79e5cqVU5MmTfTKK6/o5MmTmjlzpqpWrZodNQIAgDtUhs+ojB07VoUKFZIkjRkzRrlz59ZTTz2lEydO6D//+U+WFwgAAO5cDmOM8XQRN8LRqpinSwCQTS4u3OXpEgBkk0DvHOnqx4JvAADAWhm+RqVUqVJyOByptu/bt++GCgIAAEiW4aAyePDgFN8nJCRo8+bNWrx4sZ5//vmsqgsAACDjQeXZZ591u33y5MnasGHDDRcEAACQLMuuUWndurXmzp2bVcMBAABkXVCZM2eO8uTJk1XDAQAAZG7Bt2svpjXGKDo6WidOnNC///3vLC0OAADc2TIcVNq3b58iqHh5eSksLEzh4eGqWLFilhaXHpcW7r7pcwK4OQJblfd0CQCyiVlyKF39bvkF3y4nXvR0CQCyCUEFuH2lN6hk+BoVb29vHT9+3GX7qVOn5O3tndHhAAAAUpXhoJLaCZi4uDj5+fndcEEAAADJ0n2Nyvvvvy9Jcjgc+vjjjxUcHOxsS0xM1MqVKz1yjQoAALh9pTuovPPOO5KunlH58MMPU7zN4+fnp5IlS+rDDz/M+goBAMAdK91BZf/+/ZKkpk2b6ptvvlHu3LmzrSgAAAApE7cnL1u2LDvqAAAAcJHhi2k7d+6sN954w2X7hAkT9NBDD2VJUQAAAFImgsrKlSvVpk0bl+2tW7fWypUrs6QoAAAAKRNB5cKFC25vQ/b19VVMTEyWFAUAACBlIqhUq1ZNs2fPdtn+5ZdfqnLlyllSFAAAgJSJi2lffvllderUSXv37lWzZs0kSUuXLtWsWbM0Z86cLC8QAADcuTIcVNq2bav58+dr7NixmjNnjgIDA1W9enX9/PPPypMnT3bUCAAA7lA3/KGEMTEx+uKLLzR16lRt3LhRiYmJWVVbuvChhMDtiw8lBG5f2fahhMlWrlypiIgIFS5cWBMnTlSzZs20du3azA4HAADgIkNv/URHR2v69OmaOnWqYmJi9PDDDysuLk7z58/nQloAAJDl0n1GpW3btqpQoYK2bt2qd999V0eOHNGkSZOyszYAAHCHS/cZlUWLFumZZ57RU089pXLlymVnTQAAAJIycEZl1apVOn/+vGrVqqV69erpgw8+0MmTJ7OzNgAAcIdLd1CpX7++/vvf/+ro0aN68skn9eWXX6pw4cJKSkrSkiVLdP78+eysEwAA3IFu6Pbk3bt3a+rUqZo5c6bOnj2rli1basGCBVlZX5q4PRm4fXF7MnD7yvbbkyWpQoUKmjBhgg4dOqQvvvjiRoYCAABwccMLvnkaZ1SA2xdnVIDb1005owIAAJCdCCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC0fTxeAO8OlS5e0ZvVarVi+Qps3RerokaPy8vZS8eLF1Lxlc/WO6KWgHEEp9ok+Gq0Vy1dq+7bt2rZ1u6L2R8kYo4+n/1d16tZ2O88fu//QnK+/0Y7tOxR99KjOnj0nf39/lS5TSq0faK2HHukiX1/fm3HIwG1nSOfH1ahqXVUrVVH5Q/MqwNdf0WdOaMXWtXrzqw+1PWpXiv5myaE0x/x582o1f+GRFNv8fP00uNNjeqRJW5UvWlq+3j46cuqYftq8SuO++ED7ow+4HatyifJ6tfdQhd91j4IDc2jPkShNXfSF3p//iYwxmT9weJTD3OI/vcuJFz1dAtLhmznf6LVXRkuSSpcupbLlyupCbKy2bN6i2NhYlSpdSlNnfKy8efM49/ns08/15vi3XMa6XlD54vMvNX7MGypcuJCKFS+m3Hly68zpM4rcvEVxcXGqXaeWPvzvFPn6EVZuBYGtynu6BFzjxJytyhEQpK37durwqWhJUpUS5VWhWBnFJ8Sr02uPa+G6pc7+055/O9WxHqjbXGGhefXazHf06qcTndv9ff217K2vdE/lWjpz/qx+3bFRl+PjVLNsVZUqVFwxsefV9PmHtenPbSnGq1+pppZOmK2ggECt27lZUccO6t5q9VQobwF9teI7PfKvp7L40cCNSk+QlQgquEkWzF+gyM1b1bN3d5UuU9q5/cSJExrY/xnt2rlLrR9opfFvjnO2Lf95udb/tkFVqlZRlaqVNW7MG1qzes11g8qhg1ef+EWLFU2x/dTJU3qiX3/t+XOPRvzfcHXr0TUbjhJZjaBilwZVamvjH9sUlxCXYvtTbXvr38+MVfTp4yrarY4SkxKvO05IjlyK/mqTAvwCVK5PY+05vN/ZNqjDo3r/6df1265ItRzeTTEXz0uSvLy89O5Tr2lQh75asXWtwod1ce7j4+2j3dNWqHShEhoy5VW9+83HkqQcAUH6cfwsNahSW33eHKIZP36dVQ8FskB6gwrXqOCmaNehnV557aUUIUWSwsLC9OLLIyRJS5f8rIT4BGdbeLNwPT/iObV5sLVKlCwhh8OR5jxFixV1CSmSlDdfXvXt10eS9Nu69TdwJMCd69ffN7iEFEma8t2n2nM4SgXz5FflEmmHy4fufVABfgFas2NjipAiSfdWqydJenvuf5whRZKSkpL0yoyrZ1jrlK+eYp+ODVupdKESitz7uzOkSFLs5Ysa+MFLkqRhXZ5M51HCNh4NKitXrlTbtm1VuHBhORwOzZ8/35PlwEPKV7j6whYfH6+z585m2zw+vlcvyfL15dIsIKslJF79IyP+SnyafXu26CRJmvnTXJe2uIS09z8VcybF9w/Uay5JmrNyoUvfzXu2a++RKFUrVVElCrj+EQP7eTSoxMbGqnr16po8ebIny4CHHTp0WJLk4+OjkJCQbJkj5lyMPp0+U5LUuEnjbJkDuFP1bNFZFYqW0R+H9unPf5wh+adiYYXVuGpdxSfEa/byBS7tP25cIUka2vkJ5QrK6dzu5eWl1yOekyRNXfxlin2ql6ksSdq0Z7vbOZO331W6UjqPCDbx6J+WrVu3VuvWrT1ZAiwwa+YsSVLDxg3k5+eXJWP+FfWXPv7PVCUlJenUqdPasnmLLl68qIce6aIHHmyTJXMAd6rnHuqvKiXLK0dAkCoVK6uqpSrq8MlodRv7tJKSkq67b4/mHeXl5aVFa5fp9PmzLu2fLf1GreqEq1vTDor6bI1W/75Bl+PjVKtcNRXIHaYJs6do9OfvptineFgRSdKhE0fdzpm8vUR+zqjcijgHDo/6ZcUvmjd3vnx8fPT0oAFZNu6pU6e1YP53KbZ179lNTz8zQF5eXJoF3Ij7azdRi5p/n5mMij6o3hMGu9yJ407P5slv+3zjtj0pKUk9xz+jA8ePaPgjA/Rg/RbOto1/bNXSyFUuYSg48OrSBhfjLrkdM/by1ZsucgblSLM+2OeWesWOi4tTTExMiq+4ONcLu3Br2L9vv14c/pKMMRr6/BBVqFghy8auWauGtuzYrE3bNmjhj99r2AtDtWD+d+r2UA8dPnwky+YB7kQth3eTo2VRhXaorMZDOunPw/u18u25erH7oOvuV6NsVVUpWUFnzp/Vd2uXuO0TGhyipRNma2C7Pnpm8isq0rW2cnesovavPKqw0Lz64V+f6uEmbbPjsGCpWyqojBs3TiEhISm+3K2zAfsdO3ZcA554WjExMeoV0VM9enXPlnm8vb1VtGgR9e7TS6+NeVUH/jqg8WPeyJa5gDvNudgYrdr+m9r8X29t+GOLRkc8r9r/uCPnWslnU75euVDxqVw0+85ToxRe/R7937Q3NGn+JzpyKlpnL5zTgjU/qtNrj8vhcGjik6/Ix/vvNwQuXLp6xiTIP9DtmDkCrp5xOX8xNlPHCc+6pYLKyJEjde7cuRRfz494ztNlIYPOnT2n/o89pSNHjqp9x/Ya9sLQmzJv8xbNFBQUpF9X/ZriNmgAN+ZK4hXNXv6dvLy81Paelm77eHl5qWvTdpKuXoeSWp9u4e0lSXN++cGlfeMfW7U/+oCKhhVS6ULFndsPnLh6QX7RsEJux03e/tfx9K3bAbvcUkHF399fuXLlSvHl7+/v6bKQARdjL2rAkwO1b+8+NW/ZTKNefzld66NkBYfDoZCQXLpy5YpiYmJuypzAneJkzGlJUlhIHrftzWs0UuG8BRUVfVC/bFvntk/+0Hzy97v6mn4u1v3/0XOxV9dWyZ0z1Llty94dkqSaZau63Sd5+9Z9O9M4CtjIo0HlwoULioyMVGRkpCRp//79ioyM1IED7j/HAbe2+Ph4PTtwiLZv264GjRrojTfHy9vb+6bNf+jgIUVHH1NwcLBCc4fetHmBO0GTu+pLkvYe+ctte/LbPqmdTZGk0+fPKi7+6nWH7t5CyhkUrApFy0iS/jr299mR5GX7u9z7gMs+d5epojKFS2rb/l0p9sGtw6NBZcOGDapRo4Zq1KghSRo6dKhq1KihV155xZNlIRskJiZq+HMj9du631SzVg29/d5b2fJ5O7M++0InT5x02R61P0ojnn9Rxhg92O6BmxqQgNtBgyq1dX/tcJczoD7ePhrYvq96Ne+si5cvafYK17VRAv0D1LFhK0nuF3lLFp8Qr8UblkuS3u7/igrmye9s8/f1178HjVWOwCCt2v6bok8fd7bNW71Y+47+pbvLVNHgTo85twcFBGryoDGSpIlzPsr4QcMKfNYPborPZ87ShHFvSpKatWim4GD3twkOfX6IcufOLenq5wANGTTM2Ra1f7/On7+g0qVLKUdwsCSpcZNGevKpJ5x9Wrdoo+joYypfobyKFy8mY4yOHD2qnb/vVFJSkmrVrqkPpkxy+aRm2InP+rFHxH0Pafrz7+jE2VPa+Oc2nYo5o3whuVWtVEUVzltQl+IuK2LCYH298nuXfbs17aBZL36g33ZFqt6gB687T+lCJbT63XkqmCe/YmLPa83OjboUd1l1KtytIvkK6lTMGTUZ1kW/R+1Osd89lWvppze+VFBAoNbu3KS/jh1S42p1VThvQX298ns9PLp/lj4euHHp/awf1lHBTRFz7u/3m3/+6edU+/V/ur8zqMTHJ2jbVtd1Gfbt+3vly1KlS6ZoG/jsQK1auUq//75Dv65eo8uXLyskJET1G9RX6zb368F2D7KOCpAJK7au1ZhZ76vJXfV1V+mKypcrj+KvJCgq+qDmrPxB78//RHuPRLndN3nJ/Ou97ZNs39G/VP3J+zT8kQFqXbep7q1WXw6HdPDEUX3w7XSN/3KyDp90XdhtzY6NqjPwAb3We5jCq9+j6qUra++RKL351Yd6b97UGzp2eBZnVABYizMqwO2LT08GAAC3PIIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANZyGGOMp4sA0iMuLk7jxo3TyJEj5e/v7+lyAGQh/n8jNQQV3DJiYmIUEhKic+fOKVeuXJ4uB0AW4v83UsNbPwAAwFoEFQAAYC2CCgAAsBZBBbcMf39/jRo1igvtgNsQ/7+RGi6mBQAA1uKMCgAAsBZBBQAAWIugAgAArEVQAQAA1iKo4JYxefJklSxZUgEBAapXr55+++03T5cE4AatXLlSbdu2VeHCheVwODR//nxPlwTLEFRwS5g9e7aGDh2qUaNGadOmTapevbruv/9+HT9+3NOlAbgBsbGxql69uiZPnuzpUmApbk/GLaFevXqqU6eOPvjgA0lSUlKSihUrpkGDBmnEiBEerg5AVnA4HJo3b546dOjg6VJgEc6owHrx8fHauHGjWrRo4dzm5eWlFi1aaM2aNR6sDACQ3QgqsN7JkyeVmJioAgUKpNheoEABRUdHe6gqAMDNQFABAADWIqjAevny5ZO3t7eOHTuWYvuxY8dUsGBBD1UFALgZCCqwnp+fn2rVqqWlS5c6tyUlJWnp0qW65557PFgZACC7+Xi6ACA9hg4dqoiICNWuXVt169bVu+++q9jYWPXt29fTpQG4ARcuXNCePXuc3+/fv1+RkZHKkyePihcv7sHKYAtuT8Yt44MPPtCbb76p6Oho3X333Xr//fdVr149T5cF4AYsX75cTZs2ddkeERGh6dOn3/yCYB2CCgAAsBbXqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAWCFPn36qEOHDs7vw8PDNXjw4Jtex/Lly+VwOHT27NmbPjcAVwQVANfVp08fORwOORwO+fn5qWzZsnr99dd15cqVbJ33m2++0ejRo9PVl3AB3L74rB8AaWrVqpWmTZumuLg4/fDDD3r66afl6+urkSNHpugXHx8vPz+/LJkzT548WTIOgFsbZ1QApMnf318FCxZUiRIl9NRTT6lFixZasGCB8+2aMWPGqHDhwqpQoYIk6eDBg3r44YcVGhqqPHnyqH379oqKinKOl5iYqKFDhyo0NFR58+bVCy+8oH9+msc/3/qJi4vT8OHDVaxYMfn7+6ts2bKaOnWqoqKinJ8Vkzt3bjkcDvXp00fS1U/ZHjdunEqVKqXAwEBVr15dc+bMSTHPDz/8oPLlyyswMFBNmzZNUScAzyOoAMiwwMBAxcfHS5KWLl2q3bt3a8mSJfr++++VkJCg+++/Xzlz5tQvv/yi1atXKzg4WK1atXLuM3HiRE2fPl2ffPKJVq1apdOnT2vevHnXnbN379764osv9P7772vnzp366KOPFBwcrGLFimnu3LmSpN27d+vo0aN67733JEnjxo3Tp59+qg8//FC///67hgwZop49e2rFihWSrgaqTp06qW3btoqMjNRjjz2mESNGZNfDBiAzDABcR0REhGnfvr0xxpikpCSzZMkS4+/vb5577jkTERFhChQoYOLi4pz9Z86caSpUqGCSkpKc2+Li4kxgYKD53//+Z4wxplChQmbChAnO9oSEBFO0aFHnPMYY06RJE/Pss88aY4zZvXu3kWSWLFnitsZly5YZSebMmTPObZcvXzZBQUHm119/TdG3X79+plu3bsYYY0aOHGkqV66con348OEuYwHwHK5RAZCm77//XsHBwUpISFBSUpK6d++uV199VU8//bSqVauW4rqULVu2aM+ePcqZM2eKMS5fvqy9e/fq3LlzOnr0qOrVq+ds8/HxUe3atV3e/kkWGRkpb29vNWnSJN0179mzRxcvXlTLli1TbI+Pj1eNGjUkSTt37kxRhyTdc8896Z4DQPYjqABIU9OmTTVlyhT5+fmpcOHC8vH5+6UjR44cKfpeuHBBtWrV0ueff+4yTlhYWKbmDwwMzPA+Fy5ckCQtXLhQRYoUSdHm7++fqToA3HwEFQBpypEjh8qWLZuuvjVr1tTs2bOVP39+5cqVy22fQoUKad26dbr33nslSVeuXNHGjRtVs2ZNt/2rVaumpKQkrVixQi1atHBpTz6jk5iY6NxWuXJl+fv768CBA6meialUqZIWLFiQYtvatWvTPkgANw0X0wLIUj169FC+fPnUvn17/fLLL9q/f7+WL1+uZ555RocOHZIkPfvssxo/frzmz5+vXbt2acCAAdddA6VkyZKKiIjQo48+qvnz5zvH/OqrryRJJUqUkMPh0Pfff68TJ07owoULypkzp5577jkNGTJEM2bM0N69e7Vp0yZNmjRJM2bMkCT1799ff/75p55//nnt3r1bs2bN0vTp07P7IQKQAQQVAFkqKChIK1euVPHixdWpUydVqlRJ/fr10+XLl51nWIYNG6ZevXopIiJC99xzj3LmzKmOHTted9wpU6aoS5cuGjBggCpWrKjHH39csbGxkqQiRYrotdde04gRI1SgQAENHDhQkjR69Gi9/PLLGjdunCpVqqRWrVpp4cKFKlWqlCSpePHimjt3rubPn6/q1avrww8/1NixY7Px0QGQUQ6T2tVrAAAAHsYZFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACs9f8APQpU88t1FyYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9463    0.9399    0.9431      3992\n",
      "           1     0.9403    0.9467    0.9435      3993\n",
      "\n",
      "    accuracy                         0.9433      7985\n",
      "   macro avg     0.9433    0.9433    0.9433      7985\n",
      "weighted avg     0.9433    0.9433    0.9433      7985\n",
      "\n",
      "The test recall score is 0.95\n",
      "The F2 score is 0.95\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(\n",
    "    n_jobs=-1, random_state=rf_seed, class_weight=\"balanced_subsample\", max_depth=17\n",
    ")\n",
    "recall = make_scorer(recall_score)\n",
    "\n",
    "cross_val = cross_val_score(\n",
    "    random_forest, X_train, y_train, cv=10, n_jobs=-1, scoring=recall\n",
    ")\n",
    "print(\"Cross validation scores: {}\".format(cross_val))\n",
    "print(\n",
    "    \"%0.2f recall with a standard deviation of %0.2f\"\n",
    "    % (cross_val.mean(), cross_val.std())\n",
    ")\n",
    "random_forest.fit(X_train, y_train)\n",
    "\n",
    "pred = random_forest.predict(X_train)\n",
    "\n",
    "sns.heatmap(\n",
    "    confusion_matrix(y_train, pred),\n",
    "    annot=True,\n",
    "    fmt=\"g\",\n",
    "    cbar=False,\n",
    "    cmap=\"Greens\",\n",
    "    annot_kws={\"size\": 15},\n",
    ")\n",
    "plt.title(\"Confusion matrix (y_train)\", fontsize=16)\n",
    "plt.yticks(rotation=0)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()\n",
    "print(classification_report(y_train, pred, digits=4))\n",
    "print(\"The train recall score is {}\".format(np.round(recall_score(y_train, pred), 2)))\n",
    "print(\"The F2 score is {}\".format(np.round(fbeta_score(y_train, pred, average=\"binary\", beta=2), 2)))\n",
    "\n",
    "\n",
    "# Get predictions test set\n",
    "pred = random_forest.predict(X_test)\n",
    "\n",
    "sns.heatmap(\n",
    "    confusion_matrix(y_test, pred),\n",
    "    annot=True,\n",
    "    fmt=\"g\",\n",
    "    cmap=\"Greens\",\n",
    "    cbar=False,\n",
    "    annot_kws={\"size\": 15},\n",
    ")\n",
    "plt.title(\"Confusion matrix (y_test)\", fontsize=16)\n",
    "plt.yticks(rotation=0)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()\n",
    "print(classification_report(y_test, pred, digits=4))\n",
    "print(\"The test recall score is {}\".format(np.round(recall_score(y_test, pred), 2)))\n",
    "print(\"The F2 score is {}\".format(np.round(fbeta_score(y_test, pred, average=\"binary\", beta=2), 2)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "04b1bdc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9850    0.9685    0.9767      4000\n",
      "           1     0.7857    0.8868    0.8332       521\n",
      "\n",
      "    accuracy                         0.9591      4521\n",
      "   macro avg     0.8854    0.9276    0.9049      4521\n",
      "weighted avg     0.9620    0.9591    0.9601      4521\n",
      "\n",
      "The test recall score is 0.89\n",
      "The F2 score is 0.86\n"
     ]
    }
   ],
   "source": [
    "pred = random_forest.predict(test_x)\n",
    "\n",
    "sns.heatmap(\n",
    "    confusion_matrix(test_y, pred),\n",
    "    annot=True,\n",
    "    fmt=\"g\",\n",
    "    cmap=\"Greens\",\n",
    "    cbar=False,\n",
    "    annot_kws={\"size\": 15},\n",
    ")\n",
    "plt.title(\"Confusion matrix (test_y)\", fontsize=16)\n",
    "plt.yticks(rotation=0)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()\n",
    "print(classification_report(test_y, pred, digits=4))\n",
    "print(\"The test recall score is {}\".format(np.round(recall_score(test_y, pred), 2)))\n",
    "print(\"The F2 score is {}\".format(np.round(fbeta_score(test_y, pred, average=\"binary\", beta=2), 2)))\n"
   ]
  }
 ],
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