bank-customer-churn-prediction-xgboost-gpu.ipynb

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    "# **BANK CUSTOMER CHURN PREDICTION**\n",
    "![Photo by <a href=\"https://unsplash.com/@clemono?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText\">Clem Onojeghuo</a> on <a href=\"https://unsplash.com/s/photos/churn?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText\">Unsplash</a>\n",
    "  ](https://images.unsplash.com/photo-1465125159090-b104362936e1?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1190&q=80)\n",
    "> Image credits: Clem Onojeghuo from Unsplash\n",
    "\n",
    "Churn prediction models have become a very important asset in determining the customer behaviour for a company. The churn rate drives decision making and makes the company analyse itself and the way they provide its services to the customer. Churn prediction consists of detecting which customers are likely to cancel a subscription to a service based on how they use the service. As it is more costlier to get new customers than to keep the old ones, Churn models have become very necessary.\n",
    "<br/>\n",
    "For a bank, predicting the churn rate is to determine which customers are closing their bank accounts and what factors are leading to their decisions. A Churn Prediction Model might be useful in this case to predict way before a person might close their account. The factors leading to customer churn are also very important to analyse closely and make important decisions in changing or modifying the current workings of the company or a service provider."
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   "source": [
    ">To create a churn model I have selected the `churn-modelling` dataset. The dataset consists of various information on the customers and a target column called `'Exited'` that tells whether they have closed their bank accounts. Let's start!"
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    "# Contents:\n",
    "1. <a href='#imports'>Imports</a>\n",
    "2. <a href='#dataanalysis'>Data Analysis</a>\n",
    "   1. <a href='#datareading'>Data Reading</a>\n",
    "   2. <a href='#datacleaning'>Data Cleaning</a>\n",
    "   3. <a href='#datawrangling'>Data Wrangling with EDA</a>\n",
    "3. <a href='#modeltraining'>Model Training</a>\n",
    "   1. <a href='#lrmodel'>Training a Logistic Regression model as a base model</a>\n",
    "   2. <a href='#xgboost'>Training on XG-Boost</a>\n",
    "   3. <a href='#hyperparam'>Hyperparameter tuning for XG-Boost</a>\n",
    "4. <a href='#results'>Results</a>"
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    "# 1. <a id='imports'>Imports</a>\n",
    "### Let us start by importing our usual libraries."
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     "text": [
      "/kaggle/input/churn-modelling/Churn_Modelling.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
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    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score, plot_roc_curve, plot_precision_recall_curve\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, RepeatedStratifiedKFold\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from numpy import mean\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "seed = 46"
   ]
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   "source": [
    "# 2. <a id='dataanalysis'>Data Analysis</a>\n",
    "### We will start by analysing the data. First we will read the dataset and see for any missing values using pandas. Then we will go through the dataset and replace all the values which are not in the right format."
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   "source": [
    "## 2.1. <a id='datareading'>Data Reading</a>"
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   "source": [
    "data = pd.read_csv('/kaggle/input/churn-modelling/Churn_Modelling.csv')"
   ]
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       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
       "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
       "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
       "9997       9998    15584532        Liu          709    France  Female   36   \n",
       "9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n",
       "9999      10000    15628319     Walker          792    France  Female   28   \n",
       "\n",
       "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "9995       5       0.00              2          1               0   \n",
       "9996      10   57369.61              1          1               1   \n",
       "9997       7       0.00              1          0               1   \n",
       "9998       3   75075.31              2          1               0   \n",
       "9999       4  130142.79              1          1               0   \n",
       "\n",
       "      EstimatedSalary  Exited  \n",
       "9995         96270.64       0  \n",
       "9996        101699.77       0  \n",
       "9997         42085.58       1  \n",
       "9998         92888.52       1  \n",
       "9999         38190.78       0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd220987",
   "metadata": {
    "papermill": {
     "duration": 0.05632,
     "end_time": "2021-12-01T09:05:46.877605",
     "exception": false,
     "start_time": "2021-12-01T09:05:46.821285",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    ">#### Creating a copy so that we can revert back to the original dataset whenever necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "89809ee0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:46.994695Z",
     "iopub.status.busy": "2021-12-01T09:05:46.994059Z",
     "iopub.status.idle": "2021-12-01T09:05:46.998644Z",
     "shell.execute_reply": "2021-12-01T09:05:46.998175Z",
     "shell.execute_reply.started": "2021-12-01T07:51:45.736846Z"
    },
    "papermill": {
     "duration": 0.063951,
     "end_time": "2021-12-01T09:05:46.998763",
     "exception": false,
     "start_time": "2021-12-01T09:05:46.934812",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29e66cde",
   "metadata": {
    "papermill": {
     "duration": 0.094214,
     "end_time": "2021-12-01T09:05:47.151407",
     "exception": false,
     "start_time": "2021-12-01T09:05:47.057193",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2.2. <a id='datacleaning'>Data Cleaning</a>\n",
    "### Let's first see if the target classes have equal number of entries or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee0d13d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:47.352011Z",
     "iopub.status.busy": "2021-12-01T09:05:47.351205Z",
     "iopub.status.idle": "2021-12-01T09:05:47.359440Z",
     "shell.execute_reply": "2021-12-01T09:05:47.360180Z",
     "shell.execute_reply.started": "2021-12-01T07:51:45.745688Z"
    },
    "papermill": {
     "duration": 0.106909,
     "end_time": "2021-12-01T09:05:47.360381",
     "exception": false,
     "start_time": "2021-12-01T09:05:47.253472",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7963\n",
       "1    2037\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Exited'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd9942f5",
   "metadata": {
    "papermill": {
     "duration": 0.092728,
     "end_time": "2021-12-01T09:05:47.545133",
     "exception": false,
     "start_time": "2021-12-01T09:05:47.452405",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"imb\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c06641a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:47.707672Z",
     "iopub.status.busy": "2021-12-01T09:05:47.706803Z",
     "iopub.status.idle": "2021-12-01T09:05:47.725336Z",
     "shell.execute_reply": "2021-12-01T09:05:47.725949Z",
     "shell.execute_reply.started": "2021-12-01T07:51:45.759015Z"
    },
    "papermill": {
     "duration": 0.088675,
     "end_time": "2021-12-01T09:05:47.726135",
     "exception": false,
     "start_time": "2021-12-01T09:05:47.637460",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class imbalance percentage: 25.580811252040686%\n"
     ]
    }
   ],
   "source": [
    "print('Class imbalance percentage: {}%'.format(len(df[df['Exited']==1])/(len(df[df['Exited']==0]))*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b5e76ad",
   "metadata": {
    "papermill": {
     "duration": 0.055752,
     "end_time": "2021-12-01T09:05:47.839623",
     "exception": false,
     "start_time": "2021-12-01T09:05:47.783871",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> #### The class 0 has around 6000 more values than the class 1 making this dataset skewed and the classes imbalanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4097f43e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:47.960390Z",
     "iopub.status.busy": "2021-12-01T09:05:47.959766Z",
     "iopub.status.idle": "2021-12-01T09:05:47.963772Z",
     "shell.execute_reply": "2021-12-01T09:05:47.963041Z",
     "shell.execute_reply.started": "2021-12-01T07:51:45.780862Z"
    },
    "papermill": {
     "duration": 0.067983,
     "end_time": "2021-12-01T09:05:47.963915",
     "exception": false,
     "start_time": "2021-12-01T09:05:47.895932",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Geography',\n",
      "       'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n",
      "       'IsActiveMember', 'EstimatedSalary', 'Exited'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "58fece48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:48.099008Z",
     "iopub.status.busy": "2021-12-01T09:05:48.098138Z",
     "iopub.status.idle": "2021-12-01T09:05:48.105394Z",
     "shell.execute_reply": "2021-12-01T09:05:48.104789Z",
     "shell.execute_reply.started": "2021-12-01T07:51:45.787714Z"
    },
    "papermill": {
     "duration": 0.080449,
     "end_time": "2021-12-01T09:05:48.105521",
     "exception": false,
     "start_time": "2021-12-01T09:05:48.025072",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72285e9f",
   "metadata": {
    "papermill": {
     "duration": 0.057322,
     "end_time": "2021-12-01T09:05:48.221369",
     "exception": false,
     "start_time": "2021-12-01T09:05:48.164047",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    ">#### No missing values found so we move on to the next step."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "480ac010",
   "metadata": {
    "papermill": {
     "duration": 0.056802,
     "end_time": "2021-12-01T09:05:48.334679",
     "exception": false,
     "start_time": "2021-12-01T09:05:48.277877",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2.3. <a id='datawrangling'>Data Wrangling with EDA</a>\n",
    "### Here, we will conduct some Exploratory Data Analysis to determine which features are necessary and which are just useless. We will also convert categorical features into numerical ones to make it easier for the model to understand our data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73862d5f",
   "metadata": {
    "papermill": {
     "duration": 0.056365,
     "end_time": "2021-12-01T09:05:48.448448",
     "exception": false,
     "start_time": "2021-12-01T09:05:48.392083",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> #### Features: CreditScore, Geography, Gender, Age, Tenure, Balance, NumOfProducts, HasCrCard, IsActiveMember, EstimatedSalary\n",
    "\n",
    "> #### Target: Exited"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec8647c0",
   "metadata": {
    "papermill": {
     "duration": 0.056428,
     "end_time": "2021-12-01T09:05:48.561252",
     "exception": false,
     "start_time": "2021-12-01T09:05:48.504824",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Heatmap for our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87d8aa79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:48.680756Z",
     "iopub.status.busy": "2021-12-01T09:05:48.680171Z",
     "iopub.status.idle": "2021-12-01T09:05:49.404831Z",
     "shell.execute_reply": "2021-12-01T09:05:49.405260Z",
     "shell.execute_reply.started": "2021-12-01T07:51:45.803015Z"
    },
    "papermill": {
     "duration": 0.788147,
     "end_time": "2021-12-01T09:05:49.405409",
     "exception": false,
     "start_time": "2021-12-01T09:05:48.617262",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "churn_corr = df.corr()\n",
    "matrix = np.triu(churn_corr)\n",
    "churn_corr_top = churn_corr.index\n",
    "sns.heatmap(df[churn_corr_top].corr(), annot=True, vmin=1.0, vmax=-1.0, mask=matrix, cmap=\"cool\", alpha=0.55)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b997ec2",
   "metadata": {
    "papermill": {
     "duration": 0.061673,
     "end_time": "2021-12-01T09:05:49.525581",
     "exception": false,
     "start_time": "2021-12-01T09:05:49.463908",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Class Imbalance shown visually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2c416374",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:49.653262Z",
     "iopub.status.busy": "2021-12-01T09:05:49.652679Z",
     "iopub.status.idle": "2021-12-01T09:05:49.782407Z",
     "shell.execute_reply": "2021-12-01T09:05:49.781646Z",
     "shell.execute_reply.started": "2021-12-01T07:51:46.558726Z"
    },
    "papermill": {
     "duration": 0.198035,
     "end_time": "2021-12-01T09:05:49.782564",
     "exception": false,
     "start_time": "2021-12-01T09:05:49.584529",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='Exited'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 3))\n",
    "sns.countplot(y=df['Exited'], data=df, palette=\"cool\", alpha=0.55, saturation=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92099064",
   "metadata": {
    "papermill": {
     "duration": 0.062604,
     "end_time": "2021-12-01T09:05:49.909699",
     "exception": false,
     "start_time": "2021-12-01T09:05:49.847095",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### `Credit Scores`:\n",
    "#### Distribution plot for credit scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "00a00295",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:50.148546Z",
     "iopub.status.busy": "2021-12-01T09:05:50.147473Z",
     "iopub.status.idle": "2021-12-01T09:05:50.528148Z",
     "shell.execute_reply": "2021-12-01T09:05:50.528638Z",
     "shell.execute_reply.started": "2021-12-01T07:51:46.689371Z"
    },
    "papermill": {
     "duration": 0.556535,
     "end_time": "2021-12-01T09:05:50.528794",
     "exception": false,
     "start_time": "2021-12-01T09:05:49.972259",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.distplot(x=data[\"CreditScore\"], color=\"violet\", axlabel=\"Credit Scores\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2c65750",
   "metadata": {
    "papermill": {
     "duration": 0.059949,
     "end_time": "2021-12-01T09:05:50.650937",
     "exception": false,
     "start_time": "2021-12-01T09:05:50.590988",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    ">#### Here I have divided the Credit score column in 5 parts by using `pd.cut()`. The table below shows the 5 bands and the exiting value mean for those bands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7a9fe738",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:50.781175Z",
     "iopub.status.busy": "2021-12-01T09:05:50.780146Z",
     "iopub.status.idle": "2021-12-01T09:05:50.802450Z",
     "shell.execute_reply": "2021-12-01T09:05:50.801966Z",
     "shell.execute_reply.started": "2021-12-01T07:51:47.188469Z"
    },
    "papermill": {
     "duration": 0.090788,
     "end_time": "2021-12-01T09:05:50.802578",
     "exception": false,
     "start_time": "2021-12-01T09:05:50.711790",
     "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>CreditScoreBand</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(349.5, 450.0]</td>\n",
       "      <td>0.322751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(450.0, 550.0]</td>\n",
       "      <td>0.214385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(550.0, 650.0]</td>\n",
       "      <td>0.207780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(650.0, 750.0]</td>\n",
       "      <td>0.192496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(750.0, 850.0]</td>\n",
       "      <td>0.195870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CreditScoreBand    Exited\n",
       "0  (349.5, 450.0]  0.322751\n",
       "1  (450.0, 550.0]  0.214385\n",
       "2  (550.0, 650.0]  0.207780\n",
       "3  (650.0, 750.0]  0.192496\n",
       "4  (750.0, 850.0]  0.195870"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CreditScoreBand'] = pd.cut(df['CreditScore'], 5)\n",
    "df[['CreditScoreBand', 'Exited']].groupby(['CreditScoreBand'], as_index=False).mean().sort_values(by='CreditScoreBand', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "30110815",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:50.928477Z",
     "iopub.status.busy": "2021-12-01T09:05:50.927551Z",
     "iopub.status.idle": "2021-12-01T09:05:50.930296Z",
     "shell.execute_reply": "2021-12-01T09:05:50.929822Z",
     "shell.execute_reply.started": "2021-12-01T07:51:47.215368Z"
    },
    "papermill": {
     "duration": 0.066717,
     "end_time": "2021-12-01T09:05:50.930419",
     "exception": false,
     "start_time": "2021-12-01T09:05:50.863702",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_ = [df] #making a list so that 'for loops' can be easily implemented"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e32ba0b",
   "metadata": {
    "papermill": {
     "duration": 0.060433,
     "end_time": "2021-12-01T09:05:51.051312",
     "exception": false,
     "start_time": "2021-12-01T09:05:50.990879",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    ">#### Here what I have done is that I have replaced each Credit score band with a value (from 0 to 4) so that now only these values will be displayed instead of the actual credit scores in the table. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "26fd0190",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:51.199706Z",
     "iopub.status.busy": "2021-12-01T09:05:51.198633Z",
     "iopub.status.idle": "2021-12-01T09:05:51.224369Z",
     "shell.execute_reply": "2021-12-01T09:05:51.223788Z",
     "shell.execute_reply.started": "2021-12-01T07:51:47.223575Z"
    },
    "papermill": {
     "duration": 0.107736,
     "end_time": "2021-12-01T09:05:51.224533",
     "exception": false,
     "start_time": "2021-12-01T09:05:51.116797",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>CreditScoreBand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>2</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "      <td>(550.0, 650.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>2</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>(550.0, 650.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>1</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "      <td>(450.0, 550.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>3</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>(650.0, 750.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>4</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>(750.0, 850.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave            2    France  Female   42   \n",
       "1          2    15647311      Hill            2     Spain  Female   41   \n",
       "2          3    15619304      Onio            1    France  Female   42   \n",
       "3          4    15701354      Boni            3    France  Female   39   \n",
       "4          5    15737888  Mitchell            4     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited CreditScoreBand  \n",
       "0        101348.88       1  (550.0, 650.0]  \n",
       "1        112542.58       0  (550.0, 650.0]  \n",
       "2        113931.57       1  (450.0, 550.0]  \n",
       "3         93826.63       0  (650.0, 750.0]  \n",
       "4         79084.10       0  (750.0, 850.0]  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in df_:    \n",
    "    dataset.loc[(dataset['CreditScore'] > 349.5) & (dataset['CreditScore'] <= 450.0), 'CreditScore'] = 0\n",
    "    dataset.loc[(dataset['CreditScore'] > 450.0) & (dataset['CreditScore'] <= 550.0), 'CreditScore'] = 1\n",
    "    dataset.loc[(dataset['CreditScore'] > 550.0) & (dataset['CreditScore'] <= 650.0), 'CreditScore'] = 2\n",
    "    dataset.loc[(dataset['CreditScore'] > 650.0) & (dataset['CreditScore'] <= 750.0), 'CreditScore'] = 3\n",
    "    dataset.loc[(dataset['CreditScore'] > 750.0) & (dataset['CreditScore'] <= 850.0), 'CreditScore'] = 4\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8994cb7",
   "metadata": {
    "papermill": {
     "duration": 0.064296,
     "end_time": "2021-12-01T09:05:51.367571",
     "exception": false,
     "start_time": "2021-12-01T09:05:51.303275",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    ">#### Now, let's drop the CreditScoreBand column which has now done its purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2de3c353",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:51.496918Z",
     "iopub.status.busy": "2021-12-01T09:05:51.495970Z",
     "iopub.status.idle": "2021-12-01T09:05:51.498942Z",
     "shell.execute_reply": "2021-12-01T09:05:51.498472Z",
     "shell.execute_reply.started": "2021-12-01T07:51:47.256930Z"
    },
    "papermill": {
     "duration": 0.069615,
     "end_time": "2021-12-01T09:05:51.499071",
     "exception": false,
     "start_time": "2021-12-01T09:05:51.429456",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.drop(['CreditScoreBand'], axis=1)\n",
    "df_ = [df]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ddad15",
   "metadata": {
    "papermill": {
     "duration": 0.060537,
     "end_time": "2021-12-01T09:05:51.624718",
     "exception": false,
     "start_time": "2021-12-01T09:05:51.564181",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Thus, we have replaced the continuous distribution of the Credit scores into 5 discrete values. Similarly we will conduct such modifications on all the continuous features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0e099bc9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:51.790531Z",
     "iopub.status.busy": "2021-12-01T09:05:51.779828Z",
     "iopub.status.idle": "2021-12-01T09:05:52.258850Z",
     "shell.execute_reply": "2021-12-01T09:05:52.259302Z",
     "shell.execute_reply.started": "2021-12-01T07:51:47.265451Z"
    },
    "papermill": {
     "duration": 0.570524,
     "end_time": "2021-12-01T09:05:52.259461",
     "exception": false,
     "start_time": "2021-12-01T09:05:51.688937",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2)\n",
    "sns.distplot(x=data[\"CreditScore\"], color=\"violet\", axlabel=\"Credit Scores\", ax=ax[0])\n",
    "sns.distplot(x=df[\"CreditScore\"], color=\"violet\", axlabel=\"Credit Scores\", ax=ax[1])\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2908e0",
   "metadata": {
    "papermill": {
     "duration": 0.062323,
     "end_time": "2021-12-01T09:05:52.385094",
     "exception": false,
     "start_time": "2021-12-01T09:05:52.322771",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### `Geography`\n",
    "#### Plot for Geography with Exited:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3734044a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:52.523150Z",
     "iopub.status.busy": "2021-12-01T09:05:52.522348Z",
     "iopub.status.idle": "2021-12-01T09:05:52.672142Z",
     "shell.execute_reply": "2021-12-01T09:05:52.672606Z",
     "shell.execute_reply.started": "2021-12-01T07:51:47.763373Z"
    },
    "papermill": {
     "duration": 0.224515,
     "end_time": "2021-12-01T09:05:52.672757",
     "exception": false,
     "start_time": "2021-12-01T09:05:52.448242",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "sns.countplot(y=\"Geography\", hue=\"Exited\", data=data, palette=\"cool\", alpha=0.55)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f49464e",
   "metadata": {
    "papermill": {
     "duration": 0.062822,
     "end_time": "2021-12-01T09:05:52.799130",
     "exception": false,
     "start_time": "2021-12-01T09:05:52.736308",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Here, we can conduct One Hot Encoding and replace the categories: France, Germany and Spain into numbers. But, I went on a more raw approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a5086bb0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:52.943328Z",
     "iopub.status.busy": "2021-12-01T09:05:52.942537Z",
     "iopub.status.idle": "2021-12-01T09:05:52.945868Z",
     "shell.execute_reply": "2021-12-01T09:05:52.946387Z",
     "shell.execute_reply.started": "2021-12-01T07:51:47.951453Z"
    },
    "papermill": {
     "duration": 0.084744,
     "end_time": "2021-12-01T09:05:52.946531",
     "exception": false,
     "start_time": "2021-12-01T09:05:52.861787",
     "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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore  Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave            2          0  Female   42   \n",
       "1          2    15647311      Hill            2          2  Female   41   \n",
       "2          3    15619304      Onio            1          0  Female   42   \n",
       "3          4    15701354      Boni            3          0  Female   39   \n",
       "4          5    15737888  Mitchell            4          2  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in df_:\n",
    "    dataset['Geography'] = dataset['Geography'].map( {'France': 0, 'Germany': 1, 'Spain': 2} ).astype(int)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61d7a62f",
   "metadata": {
    "papermill": {
     "duration": 0.064193,
     "end_time": "2021-12-01T09:05:53.077675",
     "exception": false,
     "start_time": "2021-12-01T09:05:53.013482",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### `Gender`:\n",
    "#### Plot for Gender:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5cd0d219",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:53.223085Z",
     "iopub.status.busy": "2021-12-01T09:05:53.222155Z",
     "iopub.status.idle": "2021-12-01T09:05:53.321990Z",
     "shell.execute_reply": "2021-12-01T09:05:53.321497Z",
     "shell.execute_reply.started": "2021-12-01T07:51:47.971888Z"
    },
    "papermill": {
     "duration": 0.181311,
     "end_time": "2021-12-01T09:05:53.322108",
     "exception": false,
     "start_time": "2021-12-01T09:05:53.140797",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,3))\n",
    "sns.countplot(y=df['Gender'], orient=\"v\", data=df, palette=\"cool\", alpha=0.55)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d420c48a",
   "metadata": {
    "papermill": {
     "duration": 0.064132,
     "end_time": "2021-12-01T09:05:53.450121",
     "exception": false,
     "start_time": "2021-12-01T09:05:53.385989",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Exiting rate based on the gender:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "523f876e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:53.585246Z",
     "iopub.status.busy": "2021-12-01T09:05:53.584383Z",
     "iopub.status.idle": "2021-12-01T09:05:53.710034Z",
     "shell.execute_reply": "2021-12-01T09:05:53.710492Z",
     "shell.execute_reply.started": "2021-12-01T07:51:48.094449Z"
    },
    "papermill": {
     "duration": 0.196334,
     "end_time": "2021-12-01T09:05:53.710644",
     "exception": false,
     "start_time": "2021-12-01T09:05:53.514310",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(y=\"Exited\", hue=\"Gender\", data=data, orient=\"v\", palette=\"cool\", alpha=0.55)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "37165a76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:53.862862Z",
     "iopub.status.busy": "2021-12-01T09:05:53.861941Z",
     "iopub.status.idle": "2021-12-01T09:05:53.865973Z",
     "shell.execute_reply": "2021-12-01T09:05:53.865546Z",
     "shell.execute_reply.started": "2021-12-01T07:51:48.266666Z"
    },
    "papermill": {
     "duration": 0.090329,
     "end_time": "2021-12-01T09:05:53.866112",
     "exception": false,
     "start_time": "2021-12-01T09:05:53.775783",
     "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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore  Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave            2          0       1   42   \n",
       "1          2    15647311      Hill            2          2       1   41   \n",
       "2          3    15619304      Onio            1          0       1   42   \n",
       "3          4    15701354      Boni            3          0       1   39   \n",
       "4          5    15737888  Mitchell            4          2       1   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in df_:\n",
    "    dataset['Gender'] = dataset['Gender'].map( {'Female': 1, 'Male': 0} ).astype(int)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df7de6de",
   "metadata": {
    "papermill": {
     "duration": 0.065775,
     "end_time": "2021-12-01T09:05:53.999740",
     "exception": false,
     "start_time": "2021-12-01T09:05:53.933965",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### `Age`:\n",
    "#### Distribution plot for age of the customers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "560b702f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:54.133341Z",
     "iopub.status.busy": "2021-12-01T09:05:54.132524Z",
     "iopub.status.idle": "2021-12-01T09:05:54.352590Z",
     "shell.execute_reply": "2021-12-01T09:05:54.353037Z",
     "shell.execute_reply.started": "2021-12-01T07:51:48.287045Z"
    },
    "papermill": {
     "duration": 0.288156,
     "end_time": "2021-12-01T09:05:54.353185",
     "exception": false,
     "start_time": "2021-12-01T09:05:54.065029",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.distplot(x=data[\"Age\"], bins=30, axlabel=\"Age\", color='violet')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dc90507d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:54.492765Z",
     "iopub.status.busy": "2021-12-01T09:05:54.491921Z",
     "iopub.status.idle": "2021-12-01T09:05:54.509979Z",
     "shell.execute_reply": "2021-12-01T09:05:54.510582Z",
     "shell.execute_reply.started": "2021-12-01T07:51:48.542807Z"
    },
    "papermill": {
     "duration": 0.091176,
     "end_time": "2021-12-01T09:05:54.510756",
     "exception": false,
     "start_time": "2021-12-01T09:05:54.419580",
     "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>AgeBand</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(17.926, 32.8]</td>\n",
       "      <td>0.076344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(32.8, 47.6]</td>\n",
       "      <td>0.188182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(47.6, 62.4]</td>\n",
       "      <td>0.529978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(62.4, 77.2]</td>\n",
       "      <td>0.214925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(77.2, 92.0]</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AgeBand    Exited\n",
       "0  (17.926, 32.8]  0.076344\n",
       "1    (32.8, 47.6]  0.188182\n",
       "2    (47.6, 62.4]  0.529978\n",
       "3    (62.4, 77.2]  0.214925\n",
       "4    (77.2, 92.0]  0.041667"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AgeBand'] = pd.cut(df['Age'], 5)\n",
    "df[['AgeBand', 'Exited']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "04f7af1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:54.654972Z",
     "iopub.status.busy": "2021-12-01T09:05:54.654022Z",
     "iopub.status.idle": "2021-12-01T09:05:54.672988Z",
     "shell.execute_reply": "2021-12-01T09:05:54.672569Z",
     "shell.execute_reply.started": "2021-12-01T07:51:48.567728Z"
    },
    "papermill": {
     "duration": 0.091907,
     "end_time": "2021-12-01T09:05:54.673104",
     "exception": false,
     "start_time": "2021-12-01T09:05:54.581197",
     "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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>AgeBand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.8, 47.6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>(32.8, 47.6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.8, 47.6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>(32.8, 47.6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>(32.8, 47.6]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore  Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave            2          0       1    1   \n",
       "1          2    15647311      Hill            2          2       1    1   \n",
       "2          3    15619304      Onio            1          0       1    1   \n",
       "3          4    15701354      Boni            3          0       1    1   \n",
       "4          5    15737888  Mitchell            4          2       1    1   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited       AgeBand  \n",
       "0        101348.88       1  (32.8, 47.6]  \n",
       "1        112542.58       0  (32.8, 47.6]  \n",
       "2        113931.57       1  (32.8, 47.6]  \n",
       "3         93826.63       0  (32.8, 47.6]  \n",
       "4         79084.10       0  (32.8, 47.6]  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in df_:    \n",
    "    dataset.loc[(dataset['Age'] > 17.926) & (dataset['Age'] <= 32.8), 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 32.8) & (dataset['Age'] <= 47.6), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 47.6) & (dataset['Age'] <= 62.4), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 62.4) & (dataset['Age'] <= 77.2), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 77.2, 'Age'] = 4\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "80601122",
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    {
     "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
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       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore  Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave            2          0       1    1   \n",
       "1          2    15647311      Hill            2          2       1    1   \n",
       "2          3    15619304      Onio            1          0       1    1   \n",
       "3          4    15701354      Boni            3          0       1    1   \n",
       "4          5    15737888  Mitchell            4          2       1    1   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['AgeBand'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e23741",
   "metadata": {
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     "duration": 0.068001,
     "end_time": "2021-12-01T09:05:54.967705",
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     "start_time": "2021-12-01T09:05:54.899704",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### `Tenure`:\n",
    ">#### Tenure has 11 unique values that are just number of years from 0 to 10. Hence, I kept it as it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cbd9dfac",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Tenure'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "799116f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:55.261847Z",
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     "shell.execute_reply.started": "2021-12-01T07:51:48.643287Z"
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  1,  8,  7,  4,  6,  3, 10,  5,  9,  0])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Tenure'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8333d43",
   "metadata": {
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     "duration": 0.066678,
     "end_time": "2021-12-01T09:05:55.400676",
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    },
    "tags": []
   },
   "source": [
    "### `Bank Balance`:\n",
    "#### Distribution plot for customer bank balance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "225cc15d",
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.distplot(x=data[\"Balance\"], axlabel=\"Bank Balance\", color=\"violet\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "200c4276",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:55.900858Z",
     "iopub.status.busy": "2021-12-01T09:05:55.900014Z",
     "iopub.status.idle": "2021-12-01T09:05:55.915094Z",
     "shell.execute_reply": "2021-12-01T09:05:55.914614Z",
     "shell.execute_reply.started": "2021-12-01T07:51:48.907849Z"
    },
    "papermill": {
     "duration": 0.089198,
     "end_time": "2021-12-01T09:05:55.915204",
     "exception": false,
     "start_time": "2021-12-01T09:05:55.826006",
     "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>BalanceBand</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-250.898, 50179.618]</td>\n",
       "      <td>0.142470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(50179.618, 100359.236]</td>\n",
       "      <td>0.199609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(100359.236, 150538.854]</td>\n",
       "      <td>0.257837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(150538.854, 200718.472]</td>\n",
       "      <td>0.217486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(200718.472, 250898.09]</td>\n",
       "      <td>0.593750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BalanceBand    Exited\n",
       "0     (-250.898, 50179.618]  0.142470\n",
       "1   (50179.618, 100359.236]  0.199609\n",
       "2  (100359.236, 150538.854]  0.257837\n",
       "3  (150538.854, 200718.472]  0.217486\n",
       "4   (200718.472, 250898.09]  0.593750"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BalanceBand'] = pd.cut(df['Balance'], 5)\n",
    "df[['BalanceBand', 'Exited']].groupby(['BalanceBand'], as_index=False).mean().sort_values(by='BalanceBand', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b0343277",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:56.059590Z",
     "iopub.status.busy": "2021-12-01T09:05:56.058807Z",
     "iopub.status.idle": "2021-12-01T09:05:56.061643Z",
     "shell.execute_reply": "2021-12-01T09:05:56.061196Z",
     "shell.execute_reply.started": "2021-12-01T07:51:48.943178Z"
    },
    "papermill": {
     "duration": 0.075445,
     "end_time": "2021-12-01T09:05:56.061757",
     "exception": false,
     "start_time": "2021-12-01T09:05:55.986312",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_ = [df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c5cf820b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:56.209004Z",
     "iopub.status.busy": "2021-12-01T09:05:56.208008Z",
     "iopub.status.idle": "2021-12-01T09:05:56.228911Z",
     "shell.execute_reply": "2021-12-01T09:05:56.228488Z",
     "shell.execute_reply.started": "2021-12-01T07:51:48.949248Z"
    },
    "papermill": {
     "duration": 0.098661,
     "end_time": "2021-12-01T09:05:56.229032",
     "exception": false,
     "start_time": "2021-12-01T09:05:56.130371",
     "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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>BalanceBand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "      <td>(-250.898, 50179.618]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>(50179.618, 100359.236]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "      <td>(150538.854, 200718.472]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>(-250.898, 50179.618]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>(100359.236, 150538.854]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore  Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave            2          0       1    1   \n",
       "1          2    15647311      Hill            2          2       1    1   \n",
       "2          3    15619304      Onio            1          0       1    1   \n",
       "3          4    15701354      Boni            3          0       1    1   \n",
       "4          5    15737888  Mitchell            4          2       1    1   \n",
       "\n",
       "   Tenure  Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
       "0       2        0              1          1               1        101348.88   \n",
       "1       1        1              1          0               1        112542.58   \n",
       "2       8        3              3          1               0        113931.57   \n",
       "3       1        0              2          0               0         93826.63   \n",
       "4       2        2              1          1               1         79084.10   \n",
       "\n",
       "   Exited               BalanceBand  \n",
       "0       1     (-250.898, 50179.618]  \n",
       "1       0   (50179.618, 100359.236]  \n",
       "2       1  (150538.854, 200718.472]  \n",
       "3       0     (-250.898, 50179.618]  \n",
       "4       0  (100359.236, 150538.854]  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in df_:    \n",
    "    dataset.loc[dataset['Balance'] <= 50179.618, 'Balance'] = 0\n",
    "    dataset.loc[(dataset['Balance'] > 50179.618) & (dataset['Balance'] <= 100359.236), 'Balance'] = 1\n",
    "    dataset.loc[(dataset['Balance'] > 100359.236) & (dataset['Balance'] <= 150538.854), 'Balance'] = 2\n",
    "    dataset.loc[(dataset['Balance'] > 150538.854) & (dataset['Balance'] <= 200718.472), 'Balance'] = 3\n",
    "    dataset.loc[(dataset['Balance'] > 200718.472) & (dataset['Balance'] <=  250898.09), 'Balance'] = 4\n",
    "    df['Balance'] = df['Balance'].astype(int)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3d7b1301",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:56.375602Z",
     "iopub.status.busy": "2021-12-01T09:05:56.374579Z",
     "iopub.status.idle": "2021-12-01T09:05:56.378676Z",
     "shell.execute_reply": "2021-12-01T09:05:56.378253Z",
     "shell.execute_reply.started": "2021-12-01T07:51:48.979436Z"
    },
    "papermill": {
     "duration": 0.078752,
     "end_time": "2021-12-01T09:05:56.378790",
     "exception": false,
     "start_time": "2021-12-01T09:05:56.300038",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.drop(['BalanceBand'], axis=1)\n",
    "df_ = [df]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c67f8d92",
   "metadata": {
    "papermill": {
     "duration": 0.069924,
     "end_time": "2021-12-01T09:05:56.519130",
     "exception": false,
     "start_time": "2021-12-01T09:05:56.449206",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### `Estimated Salary`:\n",
    "#### Distribution plot for customer salary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5de4aa02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:56.685349Z",
     "iopub.status.busy": "2021-12-01T09:05:56.684265Z",
     "iopub.status.idle": "2021-12-01T09:05:56.866864Z",
     "shell.execute_reply": "2021-12-01T09:05:56.867456Z",
     "shell.execute_reply.started": "2021-12-01T07:51:48.987577Z"
    },
    "papermill": {
     "duration": 0.278911,
     "end_time": "2021-12-01T09:05:56.867641",
     "exception": false,
     "start_time": "2021-12-01T09:05:56.588730",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "sns.distplot(x=data[\"EstimatedSalary\"], color=\"violet\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f2927886",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:57.015911Z",
     "iopub.status.busy": "2021-12-01T09:05:57.015020Z",
     "iopub.status.idle": "2021-12-01T09:05:57.031488Z",
     "shell.execute_reply": "2021-12-01T09:05:57.031894Z",
     "shell.execute_reply.started": "2021-12-01T07:51:49.197557Z"
    },
    "papermill": {
     "duration": 0.092958,
     "end_time": "2021-12-01T09:05:57.032052",
     "exception": false,
     "start_time": "2021-12-01T09:05:56.939094",
     "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>EstimatedSalaryBand</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-188.401, 50006.805]</td>\n",
       "      <td>0.199348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(50006.805, 100002.03]</td>\n",
       "      <td>0.198660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(100002.03, 149997.255]</td>\n",
       "      <td>0.202348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(149997.255, 199992.48]</td>\n",
       "      <td>0.214664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       EstimatedSalaryBand    Exited\n",
       "0    (-188.401, 50006.805]  0.199348\n",
       "1   (50006.805, 100002.03]  0.198660\n",
       "2  (100002.03, 149997.255]  0.202348\n",
       "3  (149997.255, 199992.48]  0.214664"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EstimatedSalaryBand'] = pd.cut(df['EstimatedSalary'], 4)\n",
    "df[['EstimatedSalaryBand', 'Exited']].groupby(['EstimatedSalaryBand'], as_index=False).mean().sort_values(by='EstimatedSalaryBand', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "41933dec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:57.185883Z",
     "iopub.status.busy": "2021-12-01T09:05:57.184984Z",
     "iopub.status.idle": "2021-12-01T09:05:57.202539Z",
     "shell.execute_reply": "2021-12-01T09:05:57.202080Z",
     "shell.execute_reply.started": "2021-12-01T07:51:49.219675Z"
    },
    "papermill": {
     "duration": 0.096284,
     "end_time": "2021-12-01T09:05:57.202674",
     "exception": false,
     "start_time": "2021-12-01T09:05:57.106390",
     "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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>EstimatedSalaryBand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>(100002.03, 149997.255]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>(100002.03, 149997.255]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>(100002.03, 149997.255]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(50006.805, 100002.03]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(50006.805, 100002.03]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore  Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave            2          0       1    1   \n",
       "1          2    15647311      Hill            2          2       1    1   \n",
       "2          3    15619304      Onio            1          0       1    1   \n",
       "3          4    15701354      Boni            3          0       1    1   \n",
       "4          5    15737888  Mitchell            4          2       1    1   \n",
       "\n",
       "   Tenure  Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
       "0       2        0              1          1               1                2   \n",
       "1       1        1              1          0               1                2   \n",
       "2       8        3              3          1               0                2   \n",
       "3       1        0              2          0               0                1   \n",
       "4       2        2              1          1               1                1   \n",
       "\n",
       "   Exited      EstimatedSalaryBand  \n",
       "0       1  (100002.03, 149997.255]  \n",
       "1       0  (100002.03, 149997.255]  \n",
       "2       1  (100002.03, 149997.255]  \n",
       "3       0   (50006.805, 100002.03]  \n",
       "4       0   (50006.805, 100002.03]  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in df_:    \n",
    "    dataset.loc[(dataset['EstimatedSalary'] > -188.401) & (dataset['EstimatedSalary'] <= 50006.805), 'EstimatedSalary'] = 0\n",
    "    dataset.loc[(dataset['EstimatedSalary'] > 50006.805) & (dataset['EstimatedSalary'] <= 100002.03), 'EstimatedSalary'] = 1\n",
    "    dataset.loc[(dataset['EstimatedSalary'] > 100002.03) & (dataset['EstimatedSalary'] <= 149997.255), 'EstimatedSalary'] = 2\n",
    "    dataset.loc[(dataset['EstimatedSalary'] > 149997.255) & (dataset['EstimatedSalary'] <= 199992.48), 'EstimatedSalary'] = 3\n",
    "    df['EstimatedSalary'] = df['EstimatedSalary'].astype(int)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6fef773a",
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    {
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore  Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave            2          0       1    1   \n",
       "1          2    15647311      Hill            2          2       1    1   \n",
       "2          3    15619304      Onio            1          0       1    1   \n",
       "3          4    15701354      Boni            3          0       1    1   \n",
       "4          5    15737888  Mitchell            4          2       1    1   \n",
       "\n",
       "   Tenure  Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
       "0       2        0              1          1               1                2   \n",
       "1       1        1              1          0               1                2   \n",
       "2       8        3              3          1               0                2   \n",
       "3       1        0              2          0               0                1   \n",
       "4       2        2              1          1               1                1   \n",
       "\n",
       "   Exited  \n",
       "0       1  \n",
       "1       0  \n",
       "2       1  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['EstimatedSalaryBand'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efc62993",
   "metadata": {
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     "duration": 0.076393,
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    ">#### Removing other columns with very little to no information. "
   ]
  },
  {
   "cell_type": "code",
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    },
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   },
   "outputs": [
    {
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       "   CreditScore  Geography  Gender  Age  Tenure  Balance  NumOfProducts  \\\n",
       "0            2          0       1    1       2        0              1   \n",
       "1            2          2       1    1       1        1              1   \n",
       "2            1          0       1    1       8        3              3   \n",
       "3            3          0       1    1       1        0              2   \n",
       "4            4          2       1    1       2        2              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  \n",
       "0          1               1                2  \n",
       "1          0               1                2  \n",
       "2          1               0                2  \n",
       "3          0               0                1  \n",
       "4          1               1                1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['RowNumber', 'CustomerId', 'Surname', 'Exited'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bbe0f81",
   "metadata": {
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     "duration": 0.074169,
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     "start_time": "2021-12-01T09:05:57.751761",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "#### Here, we have a modified table with all the values converted into a format that our model can understand. Now, let's get it prepared to pass it into the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "11d849c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:58.015755Z",
     "iopub.status.busy": "2021-12-01T09:05:58.014887Z",
     "iopub.status.idle": "2021-12-01T09:05:58.020051Z",
     "shell.execute_reply": "2021-12-01T09:05:58.019632Z",
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     "exception": false,
     "start_time": "2021-12-01T09:05:57.935878",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df, data['Exited'], test_size=0.15, random_state=seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98567c8a",
   "metadata": {
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     "duration": 0.134221,
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     "start_time": "2021-12-01T09:05:58.108388",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 3. <a id='modeltraining'>Model Training</a>\n",
    "## 3.1. <a id='lrmodel'>Treating a Logistic Regression model as a base model</a>"
   ]
  },
  {
   "cell_type": "code",
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    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=46)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR_model = LogisticRegression(random_state=seed)\n",
    "LR_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e2a9dca1",
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   "source": [
    "y_pred = LR_model.predict(X_test)"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x7f1817fcff10>"
      ]
     },
     "execution_count": 44,
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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2)\n",
    "ax1.plot([0, 1], [0, 1], linestyle=\"--\", lw=2, color=\"r\", label=\"Chance\", alpha=0.8)\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(12)\n",
    "ax1.set_title('ROC Curve')\n",
    "ax2.set_title('Precision Recall Curve')\n",
    "plot_roc_curve(LR_model, X_test, y_test, ax=ax1)\n",
    "plot_precision_recall_curve(LR_model, X_test, y_test, ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d4fe64d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:59.608817Z",
     "iopub.status.busy": "2021-12-01T09:05:59.607967Z",
     "iopub.status.idle": "2021-12-01T09:05:59.616919Z",
     "shell.execute_reply": "2021-12-01T09:05:59.617986Z",
     "shell.execute_reply.started": "2021-12-01T07:51:49.626568Z"
    },
    "papermill": {
     "duration": 0.094923,
     "end_time": "2021-12-01T09:05:59.618168",
     "exception": false,
     "start_time": "2021-12-01T09:05:59.523245",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.98      0.88      1178\n",
      "           1       0.59      0.13      0.21       322\n",
      "\n",
      "    accuracy                           0.79      1500\n",
      "   macro avg       0.70      0.55      0.55      1500\n",
      "weighted avg       0.76      0.79      0.74      1500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a343c10",
   "metadata": {
    "papermill": {
     "duration": 0.07525,
     "end_time": "2021-12-01T09:05:59.768110",
     "exception": false,
     "start_time": "2021-12-01T09:05:59.692860",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3.2. <a id='xgboost'>Training on XG-Boost</a>\n",
    ">#### Calling the XG-Boost model object and setting `tree_method='gpu_hist'` to be able to train the model on the GPU which is much faster than training it normally on CPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "947e444d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:05:59.924478Z",
     "iopub.status.busy": "2021-12-01T09:05:59.923727Z",
     "iopub.status.idle": "2021-12-01T09:06:03.794702Z",
     "shell.execute_reply": "2021-12-01T09:06:03.795275Z",
     "shell.execute_reply.started": "2021-12-01T07:51:49.641204Z"
    },
    "papermill": {
     "duration": 3.952916,
     "end_time": "2021-12-01T09:06:03.795478",
     "exception": false,
     "start_time": "2021-12-01T09:05:59.842562",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[09:06:03] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Your model is trained!\n"
     ]
    }
   ],
   "source": [
    "XGB_model = XGBClassifier(tree_method='gpu_hist')\n",
    "XGB_model.fit(X_train, y_train)\n",
    "print('Your model is trained!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0c41600e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:06:03.967302Z",
     "iopub.status.busy": "2021-12-01T09:06:03.959680Z",
     "iopub.status.idle": "2021-12-01T09:06:04.292681Z",
     "shell.execute_reply": "2021-12-01T09:06:04.292222Z",
     "shell.execute_reply.started": "2021-12-01T08:12:15.357266Z"
    },
    "papermill": {
     "duration": 0.417952,
     "end_time": "2021-12-01T09:06:04.292807",
     "exception": false,
     "start_time": "2021-12-01T09:06:03.874855",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x7f17a41cda10>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X0b5OeZ6/1E/62xtjgoYl0MYEmaTUdBbE7iEtIwOAZyatYs2OAwV67ZR7u9KgcgC3LOclJQWinD8OaNwYJk6E6Gh3YzJe8dbUdaRnKADr4w9aAm2MKXKWQBsTwP5cvYPFm/YetW/8vM3sOpB8TNmHzj3+giBhIvRvU4PKpV2eTs5btmyB22+HQYOOLMltyXPQykyeo8LDKOaPffCNMQHPEmhjAlRaegY3jlsAQPYJMTIUIsKECYM7EeY5UL9SScqViHIjTPetXg133AEJCTBhAvTp4/R9NkFt9LXtmbMhga8WbHY7FGNMELIE2pgAkpGhrNqeyJWj57A/KQ2AS9rV5KXL7RZ1rubMgfvvd1YZ7NgRXnjBkucQcG2nOvRufgpzNiS4HYoxJkhZAm1MgFiyeS9P/bSShf/tAaB4ZDg3d63PtZ3quByZn5o0CZ58EtLTnVbnxx+HyEi3ozJeFjuqn9shGGNCgCXQxrgoLT2DxyauIOFASr5lf12xPev521e34+zGlSkRZR/hXH39tbNACsB118HQodbybIwxpsjYt68xRSQxKbVAifDYWRuZuHQrEWHCrmzlG1ctnefrGlUtRf82NbipS33/XJzEn7RrB2XKOIujDBjgdjTGT2RkKAv+20OJqHBa1CjrdjjGmABmCbQxJ0lVmbxqJzd9vKBQr7v6tNoAREWEcevZpwbvDBi+kpFxpJW5fn34/nsniTYG53M67OtlfLMoDoClj/WmbAnr0mOMOTGWQBtzkjYlHMpKntvVLse1p+ffJ7lZtbI0PiXvFmdTCImJcN99Tl/niy929lnybLJ57IcVWckzQHJaOmAJtDHmxFgCbcxJevLHlQCMvLAFV59WGxHJ5xWmSO3Y4czxvGEDbN3qzPNsczybbN6bvj5rafqrTqvN53M35Vpu36FUikWGER0Z7svwjDEByBJoY07CvsOp/Ll6JwC9m1e15NnX1q93kuedO51uG2+8YcmzOcr+5DSe/WU1ABMGd2J9/LGrcqalZ3D/18v4dvEWqpYpxtyHevo6TGNMgLGRSMachHNf+wuAG86oS5XSlrj51KJFMHCgkzy3bQtjxkDVqm5HZfzIwk17sp6/eVVbOtWveEyZjAzlknf+5tvFWwDYkXjsKp7GGJOTJdDGnKDDKels2XsYgAfzWCbbeMGMGXDbbXDgAHTvDm+9ZX2ezTGWbt4LwF09G3Jeq+rHHE9Jy6DL81NZGrcPgL4tTiEq3L4WjTH5s5rCmELauT+Jp39eSdPHfgXgkX5NKRZhfSZ9qm5dKFkSLr/cme85KkSXKTd56lS/AgB39Wx0zLHEpFQ6PjM564/geQ/1oG6lkj6NzxgTuKwPtDEFsHp7YlZf5+d//Tdr/x3dT+WCNse2bBkvUIXMPua1a8P48VCp0pF95qSJSB/gNSAcGKOqo3IcrwOMBSoDCcA1qhp3zBv5iQmDTz/usZ4vzwAgOjKMWQ90p2Ipm0bSGFNwlkAbk4//dh+kz6t/HbXvtHoVGHlhCxrms/iJKSKpqTBiBDRtCldd5eyrXNndmIKMiIQDbwG9gDhgvohMVNWV2Yq9CHysqh+JSHfgWeBa30d74g6npGc9L1Usgr8f7E6Z6CPT2aWrcvcXS5gfm8Aj/ZrSp0U1N8I0xvg5S6CN8cjIUDbsOkiGqrOtyrUfzCN+vzOo6PKYmozo3wLAprnypUOHYNgwmDsXpk+Hvn2hfHm3owpGHYF1qroBQEQmAP2B7Al0M+Aez/OpwPe+DLAo/PzPtqzn8x7uQYmoI1+DiYdTSc9QvvMMKFwQu8cSaGNMriyBNiHvUEoak/7ZztiZG1m5LfGY49GRYdzevSG3nt3Apqnztd274Y474N9/oUIFeO01S569pwawOdt2HHBajjJLgYtxunlcBJQWkYqqujt7IREZDAwGqF27ttcCPhGNqpRm8aa9TL3v7KOSZ4DPjjM/tDHG5GQJtAlJyWnpPDtpNYmHU/ltxXYOZrut++ZVbRGcRDk8TOjSsBIli9lHxec2bYKhQ53FUWrVgjffhBo13I4q1N0HvCkiNwAzgC1Aes5CqjoaGA0QExOjvgwwP89e3JJ7z2mU67STPZtWYfKqnax48hw6Pj0ZgNT0DOZtTKBy6WI0si5bxhgPywpMSFFVdh1IYfX2RMb9HUulUlGUKxHFKWXDGHtDB6qUjqZ4lHXPcN3Klc4CKfv2QbNm1vLsG1uAWtm2a3r2ZVHVrTgt0IhIKeASVd3rqwCLQliYHHfO9jHXdzhqOyU9g/5vzsq6MxU7qp/X4zPGBAZLoE3QW/jfHnYkJgHw4ayNzI89srjCC5e1plvjKm6FZo6nXDmIiIDOnZ1p6ooXdzuiUDAfaCgi9XAS5yuBq7IXEJFKQIKqZgAP4szIEZTSMpSPZ/+X67Ft+w5z++eLKVciijevamtjIowJQZZAm6Cjqrw9bT27DiRzOCWdCfM3H1Pm6YtaUDwynDMaHLsymfED1avDhx9ClSpOIm28TlXTRGQo8BvONHZjVXWFiIwAFqjqROBs4FkRUZwuHLe5FrCXJadlZD2/+rTajJ/n9I+esSae68bOyzq2fV+SzR9tTAiybyYTVN6fsYExMzdkLcdbMiqcYhFhDDunMV0aOtOeVSldjPIlbeENv6IK77/vJMs33ujsq27za/uaqk4CJuXY91i2518DX/s6LjfNf7gnH8+OBeCrBZsZ9vUydwMyxvgFS6BNwEvPUBZv2kNyWgZPT1oFwDnNq/LQuU2pU9Fahvxeejo8+yx8/z2EhUHPns5CKca46MXLWtO+Tnkqly6GKmQoWcnz85e0IjJCuPuLpS5HaYxxiyXQJuA99+tqRs/YkLV9SbuavHR5axcjMgWWlAQPPgh//eUsxz1qlCXPxi9c2r5m1vOP/o7Nej7+pk6c3qAiX3q6hi3fus+6cBgTgiyBNgFrxdZ9vPDbv0z7Nx6A0de2p2KpKFrWKOduYKZg9u6Fu+6C5cuhTBl49VVo1crloIw5VpUyxdgfn8avd3WhySllAJi8agcAQz9fzHmtrLuRMaHGEmjj9/YnpXL5e3NYu2M/keFhWfsPpzrTzzarVoYbz6xH7+anuBWiKaytW505njdtgmrV4I03oG5dt6MyJleT7uzC3kOpVC1zZPq781tX5/eVO7K2l2/Zx6aEQ5zduPIxC7QYY4KPfcqNX0tJy6DlE79nbd94Zp2jjteqUIJrO9XJ+TLj7yIjISUFGjVy5niuXNntiIw5rmIR4VQtc/RUdee3rs66nQd4bcpafliyhTsnLAFg5IUtuMbqJGOCniXQxlVvTV3HqlyWz860wDNnc5jAgkd6UcFmzwgOlSvDO+84y3OXtP6jJjCpOossZibP4Ex/l7lfRNwIyxjjA5ZAG6/IyFC2JSZlfZEcz+tT1hIdGU7FUrknxsWjwmlUtRSfDepkyXOgmzQJ4uJg8GBnu1atvMsb4+c+yrbQyrvXtGPIp4t4d/p6nvppJTXKFeev+7sRFmZJtDHByBJoU+TS0jO4+J2/WRa3r0DlB59Vn3t7N/ZyVMY1qvDJJ/D66852p042WNAEhcZVSzMvNoEp93alUqliAMTvd+ag37L3MBmqhGEJtDHByKsJtIj0AV7DWdVqjKqOynG8NvARUM5TZrhnIn8ToJJS07nivdlZyfPzl+adKIWJ0L2JLaUdtDIy4OWXYcIEZ/ueeyx5NkHjyyGnZz3fsvdw1vPT6lVg7sYE5mxIoHn1MrZwkzFByGsJtIiEA28BvYA4YL6ITFTVldmKPQJ8qarviEgznBWw6norJuN9y7fsY6kneZ4xrBu1K5ZwOSLjmpQUePRRmDLFGTT45JPQu7fbURnjFdXLRlOhZBTvXtOebxbGMXdjAtd8MJfezaoy+roYt8MzxhQxb7ZAdwTWqeoGABGZAPQHsifQCpTxPC8LbPViPMYHklIzAPh04GmWPIey/fvh3nth0SJnkOBLL0GMJREmeIkIix7tBcB1Y+dm7T+YkuZWSMYYLwrLv8gJqwFszrYd59mX3RPANSISh9P6fLsX4zFetO9wKm9MWcs1HzhfHMWjvPmrZfxeairs3OnMtjFmjCXPJqS8cnkbykRH0L5OedLS8x5IbYwJTG4PIhwAjFPVl0TkdOATEWmhqhnZC4nIYGAwQG1b5tcvDf18EX+t3QVA+RKRtKlV3uWIjKsqVIA334SICDjFFrgxoaVvy2r0bVmNusN/BmDMXxsY1KU+ew+lUCwinOJR4fm8gzHG33kzgd4CZJ+nqqZnX3YDgT4AqjpbRKKBSsDO7IVUdTQwGiAmJsb+nHfRzsQktuw9zKdzNvHNojgiPFM0pWU4P5bVT/UhOtK+HELSokUwdy4MGQIiULOm2xEZ4xfWxx/k8R+W89Hs/ygWEcaKJ88hItzu0hkTyLyZQM8HGopIPZzE+UrgqhxlNgE9gHEi0hSIBuK9GJM5CdlX28p0c9f6Wc/7NK9myXOomjIFHnnE6brRtCmcfbbbERnjunkP9aDjM1MYP29T1r7ktAzSMpQIqyqNCWheS6BVNU1EhgK/4UxRN1ZVV4jICGCBqk4E7gXeF5G7cQYU3qD5rbxhXLFi676s5LlLw0rceGY96lYsSb1KtopcyPviC3jxRWe+58sug7POcjsiY/xClTLRR21f0Lo6E5faWHljgoFX+0B75nSelGPfY9merwQ6ezMGc/L2HU5l7oYEAF64tBWXxdgKcgZnjue33oKPPnK2b7sNbrjB6b5hjDnK7Ae7893iLUxc6nYkxpii4PYgQhMAer48PWt1rdPqVXQ5GuMXUlPhqaec5bnDw535ns87z+2ojPE739xyOjXKleCUstH5FzbGBAxLoE2ePpv7H/H7k+nZtCpXd6ptczsbR1ISrF4NxYvDc8/BGWe4HZExfql9nQpuh2CM8QJLoA0AB5PTuGrMXBIPpx51B35D/EEALo+pSbfGtuS28ShdGl5/HRISoFkzt6MxxhhjfMoSaEN6hhIzcjKHU9MBOK9VtaxjzauX5YYz6lgrioFNm+CXX2DwYKef8ymn2BzPxhhjQpIl0IYDSWkcTk2ndoUS/HTHmZSJjnQ7JONvli+Hu+6CvXuhShW46CK3IzImYE1fE0/b2uWoUtr6RRsTqGwmd8OIn1YCMKBjbUuezbH++gtuvtlJns84A845x+2IjAloN3+ykAe+XsbiTXv4c/UOUtMzjimjqtisrsb4L0ugQ1zcnkN8sygOgKs62jLpJofvv4d774XkZLjgAnj5ZShhA0mDlYj0EZF/RWSdiAzP5XhtEZkqIotFZJmInOtGnIGqQeVSWc+n/hvPRW//zY3jFvDX2qPXD5u6eif1HpxE40d+5b/dB5kfm0DCwRRfh2uMyYMl0CHukzn/AXBf70aULWGtz8ZDFd5/H0aOdOZ7HjjQmaouwnp9BSsRCQfeAvoCzYABIpJzhOgjwJeq2hZnddm3fRtlYDun+SnEjup3zP4bxy3gvq+WoqqMnbmR/42bD0BKegZdX5jGZe/O5o7xi30drjEmD/ZtGMLGztzIe9M3UKpYBLeefarb4Rh/kpwMU6dCWBg88ABcconbERnv6wisU9UNACIyAegPrMxWRoEynudlAVtW7yTc2aMhr01ZC8DXC+NYtGlP1sxHDauUYu3OA1ll9yenuRKjMSZ3lkCHoL2HUojbczir7/OgLvUIC7PV40w20dHONHWrV8OZZ7odjfGNGsDmbNtxwGk5yjwB/C4itwMlgZ65vZGIDAYGA9SubV3Dcnr3mvYAVC1TLCuBhiPThv56VxdmrdvNUz+tpEeTKqRmKImHU12J1RiTO0ugQ1Df1/5i274kAK4/vQ539WzkckTGL+zdC99+C//7nzNNXaVKljybnAYA41T1JRE5HfhERFqo6lGj4FR1NDAaICYmxkbC5dCnxZHpH3+4rTM3fbyAnfuTKR0dwcz7u1O2RCRNTilDTJ3ytK5VjuvGznMxWmNMbiyBDkF7D6XSs2kVLoupRddGld0Ox/iDrVth6FBnruewMLjhBrcjMr63BaiVbbumZ192A4E+AKo6W0SigUrATp9EGIRa1yrHc5e04qaPF7Ds8d5ItpWsWtcq515gxpg8WQIdArbuPcyVo+eQnqGEhcHh1HTqVy7FOc1tEQwD/Psv3HEH7N4NjRpBv2MHOZmQMB9oKCL1cBLnK4GrcpTZBPQAxolIUyAaiMeclG5NqrDuGZvQxJhAYrNwhIBL3vmbTQmHiIoIo0OdClzSriYXtK7udljGH8ybBzfd5CTPHTrA6NFQ2e5KhCJVTQOGAr8Bq3Bm21ghIiNE5AJPsXuBm0RkKTAeuEFtsmKv23c4lSWb99Lqid/4fcV2t8MxxmAt0EFpzobd/Lr8SCWb2d/5j7vPIiLc/mYyHr/8Ak88AenpzuIojz8OUVFuR2VcpKqTgEk59j2W7flKoLOv4wp1SzfvBSAxKY1flm+nV7OqR3X1MMb4niXQQURVSU7L4J1p6/lrbTylijk/3mIRYTxxQXNLns0RGRnw5ZdO8nzNNU4XjjD7/TDGH0VHhpGU6ozT/G7xFi5oXZ1uTaq4HJUxoc0S6CCQnJbO4k17uXPCYnYkJgPQrnY5vr3VGorMcYSFOasKzpgB/fu7HY0xJg+rn+oLwA9LtnDnhCXsPWyrEhrjNkugA1hiUiqfzdnEuL83ZiXOAPf3acxp9Sq6GJnxSykp8M03cMUVTgJdvrwlz8YEkNY1y7kdgjHGwxLoADZjTTzP/bo6a3v8TZ1oUaMMpaNtSW6Tw/79cO+9sGgRxMc7XTaMMcYYc0IsgQ5gW/ceBmDyPV1pULmkDSoxudu5E26/HdavdxZH6dPH7YiMMcaYgGajhgLUlr2HeWaS0/pcIirckmeTuw0bnEVR1q+HevXgww+duZ6NMQHr8R9W8PLv/2IzCBrjHkugA9T4uZsAuOGMulQvV9zlaIxfWrwYBg50WqBbtYIPPoBq1dyOyhhzgg4kpwHOdHav/7mOPYdSXY7ImNBlCXSA+mrhZgCeuKC5y5EYv6QKY8c6fZ/PPhveeQfKlHE7KmPMScjrRuPKrYks3rTHWqWN8RHrAx2AZq7dddSsG8YcQwSefhq+/trpwmFzPBsT8JpXL8uY62L4L+EQT/20kjsnLObidjVYt/MAb01dD8DPd5xJ8+plXY7UmOBn36oB6N3pTkX5xoC2Lkdi/Ioq/PijszgKOC3ON95oybMxQaRns6rM3bAbgL/W7uLuL5ZmJc8A/V6fSXKaUwes23mAmJGT6fnydH75ZxubEw5llVNVlm/Zx9od+317AcYEiQK3QItICVU9lH9J401fL4xj5rpdnFImmvNbV3c7HOMv0tJgxAiYNAn++QceesjtiIwxXpKWcWw3jZvPqs97MzYAsO9wKrPXb+fOCUsA2HUgmVs+W0R0ZBirn+pLUmo6V7w3m6Vx+wBn4a2L2tbg2tPr+uoSjAl4+SbQInIGMAYoBdQWkdbAzap6q7eDM8e6/+ulANzTy2ZSMB6HDsH998OcOVC8uNPn2RgTtN6+uh3Lt+zj2g/mcTg1nX+e6E3p6Eji9hzm53+2cdFbf7PFM81pdkmpGcxev5sB7885av+iTXtZtGkvDaqU4owGlXx1GcYEtILc230FOAfYDaCqS4GzvBmUOb5qZYvT5JTSXN6hltuhGH+QkACDBzvJc/ny8N57cMYZbkdljPGi6MhwYupWYNVTfYgd1S9r8aya5Z0ZmTKT54lDO7P26b50aViJq06rDZCVPHeoW57hfZsc9b5jZ2701SUYE/AK1IVDVTfnmGc43TvhmLykpWewZe9hLm5Xw+1QjD/YtAmGDoWtW6FmTXjjDahlf1gZE6rmbkwAoFKpKKYN60apYs5X/CcDT6Pu8J+zyn068DTObOi0NA/p2oDJK3cw6OMF2AQexhRcQVqgN3u6caiIRIrIfcAqL8dlcli9PZFTH/4FgHBbNMUAjB7tJM/NmjlT1lnybExIe2NAW3o2rcKCR3plJc+ZBp5ZD4BFj/bKSp4z9WxWlRY1bJpLYwqjIC3QQ4DXgBrAFuB3wPo/+9iEec68z/1aVuPBc5u6HI3xCw89BBUqwJAhUKKE29EYY1xWq0IJxlzfIddjj57XjEfPa+bjiIwJXgVpgW6sqleralVVraKq1wCWwflYZmvCm1e1pULJKJejMa6ZORNSPauPlSgB99xjybMxpkjtPZTCq5PX8PXCOLdDMcZvFaQF+g2gXQH2GS96c+o6AMS6b4QmVWcp7nffhT594Kmn8l6WzBhjCmH5lkSWk8htny/i52XbsvZf2r6mi1EZ47+Om0CLyOnAGUBlEbkn26EyQLi3AzPHyhxhbUJMejo89xx8+62TNLdpY8mzMcYrsifPxpjjy6sLRxTO3M8RQOlsj0TgUu+HZnYfSOaxH5Znzf18YRubfSPkJCXBsGFO8hwVBc8/D5fax88YU7S+vfXI9Jff3HI6d/RomGu5/3YfJOFgiq/CMsZvHbcFWlWnA9NFZJyq/ufDmAwQt+cQZz43NWu7RrnitK5Vzr2AjO/t2wd33w3LljnLcr/yCrRu7XZUxpgg1K52eb699QxqVyhBpVLFmL5mFwDr4w9w8dt/cygljdT0I/Pc/XbXWTQ+pbRb4RrjuoL0gT4kIi8AzYHozJ2q2t1rURluH78YgLa1y/HlzacTGV6Q8Z4mqLz/vpM8V63qzPFcv77bERljgli72uWznqelZwDQ46XpuZa9+ZMFTBvWzSdxGeOPCpKVfQasBuoBTwKxwHwvxhTyUtIyWLxpLwBfDznDkudQNXQo9O8PH35oybMxxqfG/R2b9fyO7qcCULl0MV64tBXAUa3RxoSigrRAV1TVD0TkzmzdOiyB9pKMDOW5X1cDcFq9CoSH2WCxkLJsGTRuDMWKQXQ0PPqo2xGZECIifXDm/Q8HxqjqqBzHXwEymx1LAFVUtZxPgzQ+8b/OdXlr6npmPtCNmuVLcE/vxlnHZm/YzTzPqofGhKqCJNCeSWfZJiL9gK1ABe+FFLqSUtPp8vxU4vcnA/DmVTZTYEj59Vd44gno0sWZdSPM7jwY3xGRcOAtoBcQB8wXkYmqujKzjKrena387UBbnwdqfGLYOU0Ydk6TXI99u2gLAG9MWcvtxxlsaEywK8g39EgRKQvcC9wHjAHuKsibi0gfEflXRNaJyPDjlLlcRFaKyAoR+byggQejnYnJxO9PpkeTKoy/qROVSxdzOyTjC6rwySfwyCOQlgbVq7sdkQlNHYF1qrpBVVOACUD/PMoPAMb7JDLjl+ZaK7QJYfm2QKvqT56n+/DcuhORzvm9riCtGSLSEHgQ6Kyqe0SkSuEvIXh8Pm8TAL2aVeX0BhVdjsb4REYGvPoqfO752/Huu+Hqq10NyYSsGsDmbNtxwGm5FRSROjjjYv70QVzGz6wa0Yfz3vjL7TCMcVVeC6mEA5fjVKq/qupyETkPeAgoTv637rJaMzzvl9masTJbmZuAt1R1D4Cq7jzRCwl0/+0+yLvT1wNwfmtrgQwJKSnw+OPwxx8QEQEjRkDv3m5HZUxBXAl8rarpuR0UkcHAYIDatWv7Mi7jA8WjwilXIsrtMIxxVV5dOD4ABgEVgddF5FPgReB5VS1Iv7fcWjNyrgTSCGgkIrNEZI5nAEtI2pxwGIABHWtRslhBuqabgPfpp07yXKKEM02dJc/GXVuAWtm2a3r25eZK8ui+oaqjVTVGVWMqV65chCEaY4x/yCtTiwFaqWqGiEQD24EGqrq7iM/fEDgbp7KeISItVXVv9kKh1JpxcbuabodgfOWaa2D9erj+emjUyO1ojJkPNBSRejiJ85XAVTkLiUgToDww27fhGX+SlqHE709GVRGx2aJM6MmrBTpFVTMAVDUJ2FDI5LkgrRlxwERVTVXVjcAanIT6KMHemrHrQDLXfDDX7TCML/z3Hxw65DyPioKnn7bk2fgFVU0DhgK/AauAL1V1hYiMEJELshW9EpigqjYRcAhbunkv/+7Yzxt/rnM7FGNckVcLdBMRWeZ5LkADz7YAqqqt8nnvgrRmfI8zkvtDEamE06VjQ+EuIfANGD0HgEZVS9G8ehmXozFes2SJM0iwZUt4+WWn37MxfkRVJwGTcux7LMf2E76Myfi3HYlJ/BO3jxd//5dTq5TikX5NrUXahIS8vsGbnswbq2qaiGS2ZoQDYzNbM4AFqjrRc6y3iKwE0oFhRdxFxO9lZChrdx4A4KfbuxAVYXP/BqU//3SmqUtJgchISE+3BNoYE7BiR/Wj3VN/8NncTXw215lBavqaeO7v05hiEeEuR2eM9x33G1xV/zvZN8+vNcNzC/AezyMkbUpwbuf3a1nNkudg9dVX8PzzznzPF18MDzwA4fYFY4wJbAkHU7KeVypVjF0Hkl2MxhjfsozNZfuT0gBn7mcTZFTh7bedVQVV4ZZb4MEHLXk2XicinUXkDxFZIyIbRGSjiIRc9zjjGwsf6UmbWmUBuO6DeWzbd5ibP1nAkE8Wsn1fksvRGeMddg/ZZfd8uQSAUjZ1XfD58UcYO9ZZkvuRR+CCC/J/jTFF4wPgbmAhTvc4Y4pc7Kh+Wc/j9jhTsc7dmMDpzx5ZX6d/m+r0bVnN57EZ420FaoEWkeIi0tjbwYSabfsOZ/V/7t4kpBdhDE7nngs9ejgDBi15Nr61T1V/UdWdqro78+F2UCZ4fTXkdMqViMzaPrVKKQBem7KWz+b+h03aYoJNvs2eInI+zgIqUUA9EWkDjFBVywhO0sy1uwC4s0dDwsJs1HJQSEhwpqcrVcoZJPjcc25HZELTVBF5AfgWyOqYqqqL3AvJBLPS0ZEseaw3k1fuICwMoiPCuWrMXFZv38/D3y3ny/mb+WHomW6HaUyRKUi/gSdwluWeBqCqSzxT05mTNOxrZ5bAi9rmXKDRBKRNm+D22+GUU5yVBaNsqVvjmtM8/8Zk26dAdxdiMSGkp2c8z7qd+4/avyH+oBvhGOM1BUmgU1V1X455He1ezEnKPnq5bqWSLkZiisSKFXDnnbB3L5Qp4yyWYgm0cYmqdnM7BhPaTq1SOquP9HVj5zFjTTwrtu6j6SllGDtrI+kZysAz6xERbnMZmMBUkAR6hYhcBYSLSEPgDuBv74YV3A6lpDFjTTwAj/Q7qem2jT+YNcuZmi4pCU4/3em2UaKE21GZECYiZYHHgbM8u6bjdL3b515UJlRlft/1e33mUfs7n1qJFjXKuhGSMSetIH/63Q40x+lH9zmwD7jLizEFvTZP/sFdXywBoH2d8u4GY07OxInO6oJJSXDeefDKK5Y8G38wFtgPXO55JAIfuhqRCVkvXJr7wsUp6Rms2pbIweQ0H0dkzMkrSAt0E1V9GHjY28GEgn/i9pGSngHAu9e0o1XNcu4GZE7c33/DiBHO8xtvdOZ5tiVsjX9ooKqXZNt+UkSWuBWMCW2XxdTKGvPzw22did+fzKCPF3Dx20duZl/ctgYjLmxBqWIRqCq/rdjBnA276d+mOm1rW0OT8T8FSaBfEpFTgK+BL1R1uZdjCkrvTl/Pt4viWLPDmbZuzHUxWYMtTIDq1MmZpi4mBi67zO1ojMnusIicqaozwVlYBTjsckwmhGWfM/q5X1cfc/zbxVtodEppzm5cmQvenEVKmtPQtOdQiiXQxi/lm0CrajdPAn058J6IlMFJpEd6PbogsO9wKr/8s41RvzgVRp/mp1CpdBTdbN7nwJSc7DzKlHEWSBk1ylqdjT+6BfjI0xdagATgBlcjMsajR5MqvDNtPa9d2YZ2tctz0duz2HUghVG/rM76rowIE9IyFJs+2virAi1/p6rbgddFZCpwP/AYYAl0HrbsPcyEeZt44891WfsePrcpN51V38WozEnZt8/p7wzwzjtQrJglz8YvqeoSoLWnwQNVTXQ3ImOOiKlb4agW6fkP96Teg5Oytp+7pCVXdKhNtxenAZCRoczdmECpYhG0rGmDDo1/KMhCKk2BK4BLgN3AF8C9Xo4roK3enkifV//K2q5ZvjgfXN+BRlVLuRiVOSnbtjlzPMfGQtWqsGsX1LD5u41/EZFrVPVTEbknx34AVPVlVwIzJg8iQuyofoz6ZTV39DiVElFHUpOF/+2h/kNHkutlT/SmTHRkbm9jjE8VpAV6LE7SfI6qbvVyPEHhEs/AiH6tqvHmgLaItVIGtjVr4I47nKT51FPh9dehinXBMX4pc1L50q5GYcwJGN63yVHbqsqWvUd33c/sG22M2wrSB/p0XwQSLDYnHOJgSjqli0Xw1lXt3A7HnKz58+Hee52FUdq3h5decpbpNsYPqep7nn+fdDsWY07W4LMa8NfaeC5oXZ34A8k89sMKNiccolSxCKIjw90Oz4S4484DLSJfev79R0SWZXv8IyLLfBdi4Fi8aQ9dnp8KwJCzG7gcjTlpq1c73TYOHYLevZ3luS15NgFARJ4XkTIiEikiU0QkXkSucTsuYwrjqtNq88417enbshqbEw4BcNHbfzPoowUuR2ZM3i3Qd3r+Pc8XgQQ6VeUiT9eNK2JqcVu3U12OyJy0Ro2gZ0+oUAHuusuZdcOYwNBbVe8XkYuAWOBiYAbwqatRGXOCVm/fn/V85rpdvPT7v9zbu7GLEZlQd9yMQFW3eZ7eqqr/ZX8At/omvMCxdqczv3P1stE8d5xVl0wAyMiA/Z6KOizMWSjlnnsseTaBJrNxpB/wlS3hbQLdO9e055UrWhMd6dTFb/y5jkn/bMvnVcZ4T0Gygl657Otb1IEEuh+XOuMrHzu/mcuRmBOWkgKPPOKsKHjIuV1oibMJUD+JyGqgPTBFRCoDSS7HZMwJK1Usgova1mT1U0fSj+Vb7O9C4568+kDfIiL/AI1z9IHeCFgf6GyWbN6bNd9zS1uaOzAdOOD0d/79d9i0yZmuzpgAparDgTOAGFVNBQ4C/d2NypiiETuqH5HhNruVcVdefaA/B34BngWGZ9u/X1UTvBpVAFmyeS9fzN8EwCP9mlKjXHGXIzKFtnOnM03dunVQsaIzWLBRI7ejMqbQRKS7qv4pIhdn25e9yLe+j8oYY4JPXgm0qmqsiNyW84CIVLAkGlZtS+TCt2YBEBUexoCOtV2OyBTaxo1Oy/P27VCnjpM8V6/udlTGnKiuwJ/A+bkcU/JJoEWkD/AaEA6MUdVRuZS5HHjC835LVfWqk4zZmEJLTVfenrYegDt6NLRp7YzP5dcCfR6wEKeizN6MoUDIr0nd73VntcHhfZtwYZsalCxWoJXRjb/Ytg0GDoTERGjVCl55BcraMrEmcKnq455//1fY14pIOPAWzriXOGC+iExU1ZXZyjQEHgQ6q+oeEbEVhYyr3p62nrMbV6FjvQpuh2JCTF6zcJzn+beeqtb3/Jv5CPnkedW2RDIUyhaPZEjXBpxSNtrtkExhnXIKdOsGZ50Fb79tybMJGiLyjIiUy7ZdXkRG5vOyjsA6Vd2gqinABI7tN30T8Jaq7gFQ1Z1FGLYxJ+Ty92bz4ayN/Lh0q61UaHwm3ykGRKSziJT0PL9GRF4WkZDuqxC35xB9X3Nan2/rZgumBJwkz2QEIvDQQ/DCCxBtfwCZoNJXVfdmbngS3nPzeU0NYHO27TjPvuwaAY1EZJaIzPF0+TDG52JH9WPMdTFZ20/+uJLbxy+m3+t/kZyW7mJkJlQUZI6ud4BDItIauBdYD3zi1aj83MFk58N5T69GXH9GXXeDMQWn6rQ0X3/9kbmew8OdhzHBJVxEimVuiEhxoFge5QsqAmgInA0MAN7P3tKd7XyDRWSBiCyIj48vgtMac6yezapya45Vf9fuPMDo6RtcisiEkoIk0Gmqqji38t5U1beA0t4Ny3+lpWewcpsz9+SpVUpRLMKSr4CQluYsijJ2rDNwcMkStyMyxps+w5n/eaCIDAT+AD7K5zVbgFrZtmt69mUXB0xU1VRV3QiswUmoj6Kqo1U1RlVjKleufMIXYUx+7u/ThNhR/Ygd1Y8WNcoAcCA5zeWoTCgoSAK9X0QeBK4FfhaRMCDSu2H5r9emrOXuL5YC0OSUkP07IrAcOuSsJvjjj05XjZdfhi5d3I7KGK9R1eeAkUBTz+MpVX0+n5fNBxqKSD0RiQKuBCbmKPM9TuszIlIJp0uHNfcZv/DT7U69/t6MDfy9fpfL0ZhgV5BpI64ArgJuVNXtnv7PL3g3LP+173AqJaPCGXdjR+pXLuV2OCY/CQlw112wciWUKwevvQbNm7sdlTG+sArnDuJkESkhIqVVdf/xCqtqmogMBX7DmcZurKquEJERwAJVneg51ltEVgLpwDBV3e2DazGmUK56fy4VS0bx571nU7ZEyLb5GS/KtwVaVbfj3A4sKyLnAUmq+rHXI/NjURFhdKhrU+b4vcREuPFGJ3muUcPpvmHJswkBInIT8DXwnmdXDZzW4zyp6iRVbaSqDVT1ac++xzzJM+q4R1WbqWpLVZ3gpUsw5oRMvqdr1vPdB1NoPeJ3F6Mxwawgs3BcDswDLgMuB+aKyKXeDsxf/bf7EGkZ6nYYpiBKl4YzzoCmTeHDD6F2SE8eY0LLbUBnIBFAVdcCNmezCXqnVilFz6ZVua/3kdVknWFcxhStgnTheBjokDnfp4hUBibjtG6ElAPJaUxfYyPK/V5aGkREONPU3XcfJCdDcVti3YSUZFVNyVzGW0QicBbAMibojbnemd7u49n/sXN/MkM+Xch718bk8ypjCqcggwjDckyWv7uArws6H/y1EYBzmld1ORJzXD/+CFddBXv3OtthYZY8m1A0XUQeAoqLSC/gK+BHl2Myxqc61a8IwLqdB1yOxASjgrRA/yoivwHjPdtXAJO8F5L/+n6JM6PTy5e3cTcQcyxVp5vG228723/+CRdf7G5MxrjnAWAQ8A9wM06dPcbViIzxsdcHtCU9Q/l3x3HHzhpzwvJNoFV1mIhcDJzp2TVaVb/zblj+qXKpYuxMTKJksYL83WF8JiMDnn8evv7a6bYxbJglzyZkiUg4sEJVmwDvux2PMcYEo+NmgiLSEHgRaIDTinGfquacVD9k/LBkC/NiE+hU32bf8CvJyfDwwzBtGkRFwciR0L2721EZ4xpVTReRf0WktqpucjseY9w0LzaB+P3J/L1uF2ecWsntcEwQyasv81jgJ+ASYCHwhk8i8lM/Lt0KQL9W1V2OxGRJSYFbb3WS59Kl4a23LHk2xlEeWCEiU0RkYubD7aCM8bX4/ckAjJ0V624gJujk1RehtKpm3v77V0QW+SIgf9a8ehmu7VTH7TBMpqgoaNMGtm2DN9+E+vXdjsgYf/Go2wEY4w9+uv1MzntjJjYJjSlqeSXQ0SLSFhDPdvHs26oaMgl1SloGk1ftpFm1Mm6HYsDp8xzmuXly221w7bXOKoPGhDgRiQaGAKfidL37QFXT3I3KGPe0qFEWgMmrdrJ2x34aVi3tckQmWOTVhWMb8DLwkuexPdv2i94PzX9c9t5sAEpF2+BB182fD9dcA7t2OdthYZY8G3PER0AMTvLcF6e+NsYAvV6ZwYLYBLfDMEHiuBmhqnbzZSD+an9SKks37wXg3WvauxtMqPv9d3jsMWehlC+/dPo/G2Oya6aqLQFE5AOcVWSNCWnvXxfDTR8vAGDVtkRi6tpkAObkeXVBFBHp4xkNvk5EhudR7hIRURHxu6WCvlkYB8CdPRpSoWSUy9GEsM8+g4cecpLnK6+EIUPcjsgYf5Sa+cS6bhjj6NWsKgse6el2GCbIeK1Pgmcu0reAXkAcMF9EJqrqyhzlSgN3AnO9FcvJmDB/MwBnNarsciQhKiMDXnvNSaAB7rjD6fMskvfrjAlNrUUk0fNccMauJHqeq6raQA5jjCkC3uzU2xFYp6obAERkAtAfWJmj3FPAc8AwL8ZywrbuPUzPplVpX6e826GEnowMePRR+O03iIiAxx+Hvn3djsoYv6Wq4W7HYIwxoSDfLhziuEZEHvNs1xaRjgV47xrA5mzbcZ592d+7HVBLVX8uRMw+s+9QKolJaew+mOx2KKEpLAzq1oUSJeD11y15NsYYc0JS0zMAePSHFdQd/jPDvlrqckQm0BWkD/TbwOnAAM/2fpyuGSdFRMJwZvW4twBlB4vIAhFZEB8ff7KnLrArRjuzb3RrXMVn5zSAZpuvc9Ag+OIL6FiQv9mMMcaYY5WIPPqG+1ee8U3GnKiCJNCnqeptQBKAqu4BCjKabgtQK9t2Tc++TKWBFsA0EYkFOgETcxtIqKqjVTVGVWMqV/ZdX+TV2/cDcEPnuj47Z8jbuBEGDoTt251tEahWzd2YjDHGBLSyJSKJHdWPFU+eQ7+WzndKwsEUklLTXY7MBKqC9IFO9QwIVAARqQxkFOB184GGIlIPJ3G+Ergq86Cq7gOyFqYXkWnAfaq6oMDRe1lEmHBz1/qUiY50O5TQsGwZ3HUXJCbCu+/CE0+4HZExxpggUrJYRFZ3jnZP/XHM8ZvPqs8FbarzzcItjJ21EYAhXRtwX+9GRIR7deIyE2AK8tvwOvAdUEVEngZmAs/k9yLPFEpDgd+AVcCXqrpCREaIyAUnEbNP/LZiO2kZtvSnz0yb5kxNl5gIZ50Fw48766ExxhhzwhZt2nvcY+/N2EC/12dmJc8A705fz6kP/8LC//ZwxrNT6P7SNOZs2E1qegYLYhPYtu+wD6I2/kZU808SRaQJ0ANnKqQpqrrK24EdT0xMjC5Y4L1G6vQM5ar35zB3o7Na0acDT+PMhpXyeZU5Kd98A88958y6cdFFTvIcbpMJmOAjIgtV1e/mu/cmb9fZxhRW/P5klm7eS89mVQG4+ZMFXNa+FoM+PvJ7emn7mjSvXoYxf21ky968E+TaFUow435bey5YHa/ezrcLh4jUBg4BP2bfp6qbijZE/3DPl0uykuf3rm1vybO3vfsujBnjPL/5ZmfQoM3xbIwxxksqly6WlTwDvHetkxvFjupHUmo6yWkZlC3udN38X+d69HhpGuvjD1K6WARvXt2O68cevcDnzv1JqCpi310hpSB9oH/G6f8sQDRQD/gXaO7FuFyzfMs+AOY93IMqpaNdjiYElCzpTFf30ENw4YVuR2OMMSaERUeGEx159B3Qb245g0Wb9tC9iZN0x47ql3Ws7vCfSUrNoN6DkzijQUXqVirJ4C71qVuppE/jNr6Xbx9oVW2pqq08/zbEWSBltvdDc0d4mND51IqWPPvKNdfAhAmWPBtjjPFL5UpEZSXPOZ3X6sgsUX+v383nczdx9ovT8u32YQJfoYeUquoi4DQvxOK6tPQM1uw44HYYwS0hAe6+G+I8c3CKQP367sZkjAFARPqIyL8isk5EjhnJKyI3iEi8iCzxPAa5Eacx/uLNq9rx+91nAVCt7JGGt14vTyfdJiIIagXpA31Pts0woB2w1WsRuShz1o1a5Uu4HEmQiouD22+HzZshNRXefNPtiIwxHp7pSt8CeuGsHDtfRCaq6socRb9Q1aE+D9AYP9Woaumsbh2T/tnGrZ8t4lBKOodT0ylVrCA9ZU0gKkgLdOlsj2I4faL7ezMot2ROSFKnovVdKnIrV8L//uckz02awIgRbkdkjDlaR2Cdqm5Q1RRgAkFa1xvjLee2rMbD5zYFnFm9du5P4lBKGm9MWUv3F6fxja2AGDTy/NPI0yJRWlXv81E8rjr39b8AsLnSi9jff8MDD8Dhw9CpEzz/PJSwVn5j/EwNYHO27Thy7653iYicBawB7lbVzbmUMSZkHfasbtj6yd+POXbvV0tpXqMMo35ZzbR/46lUKopvb+lMrQrFbRaPAHPcBFpEIlQ1TUQ6+zIgN23cdZDoyDAubFvD7VCCx08/wVNPQXo6nHsuPPooRNrKjsYEqB+B8aqaLCI3Ax8B3XMWEpHBwGCA2rVr+zZCY1z2/ZItx+y7tH1Nvva0Pvd59a+s/bsOpHDWC1MBqFWhONXKFmdnYhIXtq3B/zrX4+91u6hRvjitapbzSeym4PJqgZ6H0995iYhMBL4CDmYeVNVvvRybz0WGCzd2rmczcBSlpCQneb7hBrjtNpvj2Rj/tQWolW27pmdfFlXdnW1zDPB8bm+kqqOB0eAspFK0YRrj3167oi23fLaQn24/E1VnarziUeE0qlqKZyatBuC5S1rSoHIpLn33yKRmmxMOsznBmb3j1clreXXyWgCiIsJYM7Kv7y/E5Kkgvdujgd04rQyZ80ErEFQJ9NLNe0lNV9ILsDKjKYRLL4XGjaFlS7cjMcbkbT7QUETq4STOVwJXZS8gItVUdZtn8wLAtVVpjfFXLWuWZeYDx9yYYfBZDRh8VoOj9v1wW2eaVCvNh7NiGfXLavq1rMa6nQf4d8f+rDIZNpuHX8orga7imYFjOUcS50xB9dP8b/dB+r81C4B6NoDw5CQnO8tyX3st1Kvn7LPk2Ri/5+myNxT4DQgHxqrqChEZASxQ1YnAHSJyAZAGJAA3uBawMUGgda1yAAzp2oAhXY8k1yu3JtL4lNK0H/kHew+lcteExbx6ZVuXojS5ySuBDgdKcXTinCmoEuiZ63YBMKBjbS6LqZVPaXNciYnOHM9Ll8Lq1fDpp84qg8aYgKCqk4BJOfY9lu35g8CDvo7LmFDTrHoZAPq1rMZnczexZPNeWy7cz+SVQG9T1ZCYa2zexgQAhnY/lfAw++U8Idu3O3M8b9wIVarAyJGWPBtjjDEn4emLWvLZ3E3E7j7EqF9X82Dfpm6HZDzyynBCJpOM9MxbV62MDR48IWvXOnM8b9zorCr44Ye2uqAxxhhTBDqfWhGA/3YdcjkSk11eCXQPn0XhB2qUK06YtT4X3sKFMGgQxMdDu3bwwQdQtarbURljjDFB4bNBnQD4dcV2ur04jaWb97obkAHySKBVNcGXgZgAtXUrHDwIPXo4S3OXLu12RMYYY0xQ2rjrICu3JbodhqFg09gFvTU79pNu08ScmPPPh8qVoWNH6/NsjDHGeMFXQ07ncEo6142d53YoxiPkM57EpFSWxe0jMSnV7VACQ0YGvPMOrFlzZF+nTpY8G2OMMV7SoW4FGlW1O7z+JOSznthdzuKKg7rYoLd8pabCY485/ZzvvRdSUtyOyBhjjAkpD377D69OXsPuA8luhxLSQj6BjtvjLJvZ2P6yy9vBg3DnnfDrr1CiBDzyCERFuR2VMcYYExIOpaRlPX918lraj5zsYjQm5BPozHk3GlSxFQiPKz4ebroJ5s2DChXg/ffhtNPcjsoYY4wJGfUrl+LclqdQLCLkUze/YD8Fk7fYWLjxRqfPc+3aMG4cNG7sdlTGGGNMyHn76vb8O7Ivt5zdgKjwMOL3JzN3w26SUtPdDi3khPwsHCnpGW6H4N/Wr4dt26BlS3jlFShXzu2IjDHGmJC2fucBUtIz6PC0043jzh4NubtXI5ejCi0hn0DfOWEJAFHh1hifqx494KWXnC4b0bZSozHGGOO2hf/tAZzcJSU9g9emrCVMhJu71ic6Mtzl6EJDSCfQqdlan+tVsj7QWb77Dho1gubNne2uXd2NxxhjjDFZ/rz3bGJ3H6R1rXLUHf4zAK9MXsNb09aRkpZB5dLF6N2sKrG7D1K7QgmG92lK2RKRLkcdXEK62TVDncVT7u7ZCBFbxhtVePddePppuOsuSLTVjowxxhh/U7ZEJK1rlQPg0fOaER3ppHMpaU7DYPz+ZD6bu4lZ63Yzft5mWo/4nf223kWRCukE2pM/ExFuyTPp6fDUUzBmjLMoyq23QpkybkdljDHGmDwMPLMeq5/qy1MXtmD1U3145+p21ChXHICbzzqyxsWuA7Z2Q1EK6S4c/2zZB2CjVw8fhuHDYdYsKFYMnn0WzjrL7aiMMcYYU0DXdqoDQN+W1ejbslrW/qbVynDXF0tciip4hXQC/cX8zQCc0aCSy5G4aM8eZ4GUlSuhbFl49VVnxg1jjDHGGJOrkE2gU9MzmLxqBwDNqoVwV4VVq2D1aqheHd54A+rUcTsiY4wxxhi/FrIJ9LhZsew9lEqXhpVCe2TqGWfAM89A27ZQsaLb0RhjjDGmCC2Lc7qrdntxGue3rs4FravTq1lVl6MKfCGbQC/evAcReOny1m6H4nuzZzt9ndu1c7Z79nQ3HmOMMcZ4Raf6FRg7ayMAPy7dyo9LtwJQs3xxvh5yBqeUtTUeTkRIzsKxeNMeJv2znfqVSlKldIj94vz8szNF3b33OisMGmOMMSZo9W5+CpPu6HJMq3PcnsN0enYK+w7b9HYnIiRboNfuPADAgI61XY7Eh1Rh3Dh46y1n++KLoardwjHGGGOCXbPqZXj/uhh+WLKFXQdSOKd5Vc58bioAcXsOUbZ4WZcjDDwh2QK9wjN9XZ8Wp7gciY9kZMDzzzvJswgMGwa33+7M92yMMcaYkNC/TQ0GnlmPmuVLcE0npxGx3+szychQlyMLPCGZQR1MceZ9rlSqmMuR+EBysjPH81dfQWQkjBoFV1zhdlTGGD8kIn1E5F8RWSciw/Mod4mIqIjE+DI+Y0zRyX4XPnNlZlNwIZlAhwlUKxtNdGS426F437//wvTpUKqU0wLdo4fbERlj/JCIhANvAX2BZsAAEWmWS7nSwJ3AXN9GaIwpSs2rl+XeXo0ASLcEutBCMoEOKa1awdNPwwcfHJl1wxhjjtURWKeqG1Q1BZgA9M+l3FPAc0CSL4MzxhS975dsAaDxI7/y3K+rSUvPYOq/O5m1bpfLkfm/kBxEqOo8gta6dc4Kgx06ONs2TZ0xJn81gM3ZtuOA07IXEJF2QC1V/VlEhvkyOGNM0RvUpT4PfvsPAO9MW88709YfdfyNAW05v3V1N0LzeyHZAv3jsq2kpGe4HYZ3LFwIgwY509Rt2OB2NMaYICEiYcDLwL0FKDtYRBaIyIL4+HjvB2eMOSEDOtYmdlS/o/aFyZHnj/2w3McRBQ6vtkCLSB/gNSAcGKOqo3IcvwcYBKQB8cCNqvqfN2MCSErNoGqZIGx8/+MPeOwxSE2F7t2hZk23IzLGBI4tQK1s2zU9+zKVBloA00QE4BRgoohcoKoLsr+Rqo4GRgPExMQE8/0+Y4LC8ifPQYCSxZzc6HBKOpe88zcJB1PcDcyPea0FuoADUhYDMaraCvgaeN5b8WTae8j5ZahdoYS3T+Vb48fDQw85yfPllzuzbURFuR2VMSZwzAcaikg9EYkCrgQmZh5U1X2qWklV66pqXWAOcEzybIwJPKWKRWQlzwDFo8JpWcPmhs6LN7tw5DsgRVWnquohz+YcnBYPr0rzzHUYNOvAZ2TA66/DSy85Hbtvv92Z59nmeDbGFIKqpgFDgd+AVcCXqrpCREaIyAXuRmeMMf7Fm/0Y8h2QksNA4BcvxgPAsri9AKSkBUkf6NhYp/U5PBwefxzOPdftiIwxAUpVJwGTcux77Dhlz/ZFTMYY44/8oiOwiFwDxABdj3N8MDAYoHbtk1t++73pzsC6ro0qn9T7+I369Z1p6kqUgE6d3I7GGGOMMSboefM+f34DUgAQkZ7Awzh96ZJzeyNVHa2qMaoaU7nyiSe+KWkZzN2YQHiYcGqVUif8Pq6Lj4d5845sd+9uybMxxhhjikz8gWS2JyZxx/jFzNuY4HY4fsebCXSeA1IARKQt8B5O8rzTi7Ec5Z5ejfCMIg88sbFw441w992wYoXb0RhjjDEmCP252knLJi7dyjcL41yOxv94LYEu4ICUF4BSwFciskREJh7n7YrE4ZR0b7699y1b5iTP27ZBw4ZQo4bbERljjDEmCH1/W2fu7NGQiiVtRq/ceLUPdH4DUlTVp0vkTZi/CYCwQGx9njEDhg+HlBTo0gWeeQaKF3c7KmOMMcYEoTa1ytGmVjm+mL85/8IhyC8GEfrKlr2HAbiq48kNRPS5b7915nXOyIALL4QHH3Rm3TDGGGOMMT4XUgn04k17AShTPIAuOz4eXn7ZSZ4HD4abboJAbEE3xhhjjAkSAZRJnrxyJSKpXaFEYA0grFwZnn3WSaQvvtjtaIwxxhgTQrYnJvHFgs18sWAzV51Wm6f6tyA8LIDyKC8JqeXq/lq7i/IlIt0OI3+HD8OiRUe2u3Sx5NkYY4wxrvp87ibW7tzvdhh+IaQS6PAwoUSUnze679kDQ4bAbbcdnUQbY4wxxvhY7Kh+xI7qx9WnOePH0jPU5Yj8Q0gl0JHhQquaZd0O4/i2bIGBA535nStVgooV3Y7IGGOMMYbTGzg5yZwNtqgKhFACnXAwhaTUDDLUT/9yWrUK/vc/2LQJGjWCDz+EOnXcjsoYY4wxhuVbEgF46qeV1B3+M7+t2O5yRO4KmQQ6cxWdSqWKuRxJLmbPdmbYSEiAjh3h/fedFmhjjDHGGD9wT69GR23f/MlCLn93NuvjD7gUkbtCJoGOCHdGjF7crqbLkeRw8CA8/LAzcLBPH3jtNShZ0u2ojDHGGGOyREWEseGZc1n0aK+sffNiE+jx0nRWbN3nYmTuCJkEOlNkuJ9NvVKypLNIyvXXw4gREBkAs4QYY4wxJuSEhQkVSkbx571d+XrI6Vn7n520mowQG1wYcgm0X8jIgH/+ObLdsSPcfjuE2Y/DGGOMMf6tfuVSxNStwMShnQGYuW4Xq7eH1vR2IZOxrd7mJz/Y5GQYPhwGDXL6PhtjjDHGBKBWNcvRulY5AIZ+vojktHTUXydrKGIhk0DvPZwC4O480ImJzvzOf/4JxYtDMT8c0GiMMcYYU0B3dD8VgA27DtL4kV+598ulLkfkGyGTQEeEhXFqlVJERbh0ydu3O63OS5ZAlSrwwQfQrp07sRhjjDHGFIEeTatyc9f6WdvfLt7CmL82uBiRb4RMAr1sy173biusWwc33ggbNkD9+s4czw0auBOLMcYYY0wRerBvU2JH9cuaqGHkz6v4fO4ml6PyrpBJoPcdSmVHYrLvT5yWBvfcAzt3Qtu2MGYMVK3q+ziMMcYYY7xo7dPn0qWhs47Fsri97gbjZSGTQCenZXBeq2q+P3FEhDM9Xd++8NZbUKaM72MwxhhjjPGBTwaeBsCE+ZupO/xnPpnzn8sReYeLI+p8Z/eBZJLTMjiUku67k65ff6SbRps2zsMYY4wxJshFhgup6U632Ue/X07lUsU4p3lVRJwuHukZSoYqG3cdpHq54pQqFnjpaOBFfAIOJKcB0KpmWe+fLCMD3nwTPv0UXngBunb1/jmNMaYIiEgf4DUgHBijqqNyHB8C3AakAweAwaq60ueBGmP82tqnz2Xn/iTOfG4qKWkZDPl0YZ7lHz+/Gf/rXM9H0RWNkEigM1UoGeXdE6SmOt01fvkFwsOdZbpNkUlNTSUuLo6kpCS3QzEmT9HR0dSsWZPIAFpZVETCgbeAXkAcMF9EJuZIkD9X1Xc95S8AXgb6+DxYY4zfq1I6moWP9KT3KzPYti/v7+15GxMsgQ5ZBw/CsGEwb54zx/MLL0CnTm5HFVTi4uIoXbo0devWzboNZIy/UVV2795NXFwc9eoF1BdCR2Cdqm4AEJEJQH8gK4FW1cRs5UsCobFigjHmhJSOjmT2gz08C6xAdGT4MWV6vzLdhchOniXQRWHXLrjjDlizBipUgNdeg6ZN3Y4q6CQlJVnybPyeiFCxYkXi4+PdDqWwagCbs23HAaflLCQitwH3AFFAd9+EZowJZMUijk2cA13IzMLhNapw771O8ly7tjPHsyXPXmPJswkEwfx7qqpvqWoD4AHgkdzKiMhgEVkgIgsC8A8JY4wPrdlxgF+Wb2fhfwluh1IoIZFA/7NlH+CM+ixyInD//dChg7O6YI0aRX8O41e2b9/OlVdeSYMGDWjfvj3nnnsuo0eP5rzzznM7NGNOxhagVrbtmp59xzMBuDC3A6o6WlVjVDWmcuXKRRehMSZoXfLObBIOprgdRoGFRAKdlJoBQLPqRTgH89atR543bw5vvw3lyxfd+xu/pKpcdNFFnH322axfv56FCxfy7LPPsmPHDrdDM+ZkzQcaikg9EYkCrgQmZi8gIg2zbfYD1vowPmNMEIod1S/r+eSVgfNdGhIJdKYy0UU0Iv7bb+Gii+D334/sC+JbtuaIqVOnEhkZyZAhQ7L2tW7dmi5dunDgwAEuvfRSmjRpwtVXX521dPyIESPo0KEDLVq0YPDgwVn7zz77bB544AE6duxIo0aN+OuvvwBIT0/nvvvuo0WLFrRq1Yo33ngDgIULF9K1a1fat2/POeecw7Zt23x89SaYqWoaMBT4DVgFfKmqK0RkhGfGDYChIrJCRJbg9IO+3p1ojTHB5JtbTgcgQwNnXLINIiwMVRg9Gt5/39nevDnv8sa7YmKOf+yhh+Dii53n334Lzzxz/LILFhT4lMuXL6d9+/a5Hlu8eDErVqygevXqdO7cmVmzZnHmmWcydOhQHnvsMQCuvfZafvrpJ84//3wA0tLSmDdvHpMmTeLJJ59k8uTJjB49mtjYWJYsWUJERAQJCQmkpqZy++2388MPP1C5cmW++OILHn74YcaOHVvg2I3Jj6pOAibl2PdYtud3+jwoY0zQq16uuNshFFpIJNBb9x4++TdJT3eSsB9+gLAwGD78SIJmDNCxY0dq1qwJQJs2bYiNjeXMM89k6tSpPP/88xw6dIiEhASaN2+elUBf7Pkdat++PbGxsQBMnjyZIUOGEBHhfDwrVKjA8uXLWb58Ob169QKcVupq1VxYmt4YY4wpYilpTlfb4d/+wzeL4njpsjbUrljC5ajyFhIJ9Ib4A8BJdOE4fBgefBBmzoRixeDZZ+Gss4owQnNCCtpyfPHFRfbHTvPmzfn6669zPVasWLGs5+Hh4aSlpZGUlMStt97KggULqFWrFk888cRRC8Fkviaz/PGoKs2bN2f27NlFch3GGGOMv6hY6sj35/zYPZz1wlQA7uvdiKHdGx7vZa4KiT7Q0ZHhlC8RSdkSJ5hAP/qokzyXLQvvvGPJcwjr3r07ycnJjB49OmvfsmXLsvov55SZLFeqVIkDBw4cN/nOrlevXrz33ntZCXVCQgKNGzcmPj4+K4FOTU1lxYoVJ3s5xhhjjOtKFYsgdlQ/vrv1jKP2v/HnOg4mH79xyU0hkUBv3HWQ8LCTuNTBg6FhQxg7Flq1KrrATMAREb777jsmT55MgwYNaN68OQ8++CCnnHJKruXLlSvHTTfdRIsWLTjnnHPo0KFDvucYNGgQtWvXplWrVrRu3ZrPP/+cqKgovv76ax544AFat25NmzZt+Pvvv4v68owxxhjXtK1dnthR/bJm5khOy2DIpwu9Mw3xSRINoBGPADExMbqgEIO+AE5/dgrb9iUdNVVKvhISnFUFM2VkOH2fjWtWrVpFU1ukxgSI3H5fRWShquYx+jX4nEidbYwx3y/ewl1fLAHg1SvacGFbd9bZOF69HRIZYfHIcLo1LsRk/nPmwIUXwo8/HtlnybMxxhhjjE9c2LYGl7Z3BuYf8MNuHCGRFYpAiWIFHC85aRLceSccOgQLF3o3MGOMMcYYk6th5zQG4JHvl/P4D8vxp14TITELR4Gowscfg2fRCq67DoYOdTcmY4wJQqmpqcTFxR01I40xboiOjqZmzZpERhbRQmumSEVHhGc9/2j2f+w7nMrD/ZpRuXSxPF7lG5ZAg9O/+aWX4IsvnObqe+6BAQPcjsoYY4JSXFwcpUuXpm7duoit4mpcoqrs3r2buLg46tWr53Y4JhdlS0QSO6ofD367jPHzNvP9kq1MXxPPvId7EhnubieKkOjCka+XX3aS58hIZ7EUS56NMcZrkpKSqFixoiXPxlUiQsWKFe1OSAB4qn8LHunnDMrecyiVh779x+WILIF2XHIJVK8Ob74JnpXejDHGeI8lz8Yf2O9hYIgID2NQl/o8d0lLABIOprgcUSgn0AcPHnlerx58+y20b+9ePMYYY4wx5riu6FAbgCmrd7Jo0x5XYwnNBHr9erjsMvjqqyP7Iqw7uMnb5s2bqVevHgkJCQDs2bOHevXqERsbC8DatWs577zzaNCgAe3bt6dbt27MmDEDgHHjxlG5cmXatGlD8+bNufTSSzl06FDWe7/44os0adKENm3a0KFDBz7++GMAzj77bIpqDt0FCxZwxx13AJCcnEzPnj1p06YNX3zxBYMGDWLlypUn9f6vvvpqVtwAaWlpVK5cmeHDhx9Vrm7duuzatStre9q0aZx33nlZ27/88gsxMTE0a9aMtm3bcu+9955UXAALFy6kZcuWnHrqqdxxxx25juTet28f559/Pq1bt6Z58+Z8+OGHWcf69OlDuXLljooT4Morr2Tt2rUnHZ/xLfss562gn+Wzzz6bxo0b07p1azp37sy///57UucF+Oijj2jYsCENGzbko48+Om65N954gyZNmtC8eXPuv/9+AHbv3k23bt0oVaoUQ3NMAtCzZ0/27HE34TJF6+K3/+buL5Zw9Zg5TJi3ian/7uTzuZu498ulvDV1HXu83UqtqgH1aN++vRZW9xen6q2fLXQ2Fi5U7dpVtX171ZtuUk1PL/T7GXesXLnS7RD0ueee05tuuklVVQcPHqzPPPOMqqoePnxYGzZsqD/88ENW2X/++Uc//PBDVVX98MMP9bbbbss6NmDAAB07dqyqqr7zzjvau3dv3bdvn6qq7tu3T8eNG6eqql27dtX58+cX+XXMnj1be/ToccKvT0tLO2o7NTVVW7ZsqampqVn7Jk2apGeccYbWr19fMzIysvbXqVNH4+Pjs7anTp2q/fr1U1Xn/6x+/fq6atWqrPO8/fbbJxxnpg4dOujs2bM1IyND+/Tpo5MmTTqmzNNPP63333+/qqru3LlTy5cvr8nJyaqqOnnyZJ04cWJWnJmmTZumgwYNyvWcuf2+AgvUD+pRXz5yq7Pts1x03PwsZ7+m9957T88///wTjkNVdffu3VqvXj3dvXu3JiQkaL169TQhIeGYcn/++af26NFDk5KSVFV1x44dqqp64MAB/euvv/Sdd9456mekqjpu3DgdOXJkruf1h99HUzh1HvipwI/3Z6w/qXMdr972arOriPQBXgPCgTGqOirH8WLAx0B7YDdwharGei2gyZPh0UchNRW6d4eRI22BlAD15I8rWLk1sUjfs1n1Mjx+fvM8y9x99920b9+eV199lZkzZ/Lmm28C8Nlnn3H66adzwQUXZJVt0aIFLVq0OOY90tLSOHjwIOXLlwfgmWeeYdq0aZQpUwaAMmXKcP311x/zultuuYX58+dz+PBhLr30Up588kkAhg8fzsSJE4mIiKB37968+OKLfPXVVzz55JOEh4dTtmxZZsyYwbRp03jxxRcZO3Ys11xzDfHx8bRp04ZvvvmGgQMH8uKLLxITE8Pvv//O448/TnJyMg0aNODDDz+kVKlS1K1blyuuuII//viD+++/nyuvvDIrtj///JN27doRke1Ozvjx47nzzjt55513mD17NmeccUa+P4Pnn3+ehx9+mCZNmgAQHh7OLbfcku/r8rJt2zYSExPp1KkTANdddx3ff/89ffv2PaqciLB//35UlQMHDlChQoWs6+nRowfTpk075r27dOnCDTfcQFpa2lHXbgrOPsvB8Vk+66yzePXVV/P8P8/Pb7/9Rq9evajgWQW4V69e/PrrrwzIMbD/nXfeYfjw4RQr5kxlVqVKFQBKlizJmWeeybp164557wsuuIAuXbrw8MMPn1SMxj+MvSGGt6eup/EppRGBT+dsonHV0uw+mMKl7Wvy7vT1WWVH/ryKxKQ07unVqEhj8FqNLyLhwFtALyAOmC8iE1U1+72lgcAeVT1VRK4EngOu8EpAmzbBK0878z1ffjncd58lz6bQIiMjeeGFF+jTpw+///571tyhK1asoF27dnm+9osvvmDmzJls27aNRo0acf7555OYmMj+/fupX79+vud++umnqVChAunp6fTo0YNly5ZRo0YNvvvuO1avXo2IsHfvXgBGjBjBb7/9Ro0aNbL2ZapSpQpjxozhxRdf5Keffjrq2K5duxg5ciSTJ0+mZMmSPPfcc7z88ss89thjAFSsWJFFixYdE9usWbNon20MQVJSEpMnT+a9995j7969jB8/vkAJ9PLlywvUZWPq1Kncfffdx+wvUaIEf//991H7tmzZQs2aNbO2a9asyZYtW4557dChQ7nggguoXr06+/fv54svviAsnzoiLCyMU089laVLlx51/cb/2We5aD/LP/74Iy1btjxm/wsvvMBnn312zP6zzjqL119//ah9W7ZsoVatWlnbx/usrlmzhr/++ouHH36Y6OhoXnzxRTp06HBMuezKly9PcnIyu3fvpmLFinmWNf6ve5OqdG9SNWt75IVH/+4N79uEtPQMHv1hBePnbWLRf0XffcebTSYdgXWqugFARCYA/YHsCXR/4AnP86+BN0VEPE3mRScxEf7710mehw6F66935ns2ASu/1iVv+uWXX6hWrRrLly+n13FmbbnoootYu3YtjRo14ttvvwXgiiuu4M0330RVue2223jhhRe49dZbC3zeL7/8ktGjR5OWlsa2bdtYuXIlzZo1Izo6moEDB3Leeedl9dHt3LkzN9xwA5dffjkXX3xxgc8xZ84cVq5cSefOnQFISUnh9NNPzzp+xRW5/327bds2mjZtmrX9008/0a1bN4oXL84ll1zCU089xauvvkp4eHiuo94LOxK+W7duLFmypFCvyc9vv/1GmzZt+PPPP1m/fj29evWiS5cuWa2Jx1OlShW2bt1qCfQJss9y4H6WAa6++mqKFy9O3bp1eSNzIbJshg0bxrBhwwocd0GkpaWRkJDAnDlzmD9/PpdffjkbNmzItx7J/KxaAh0aIsLDePbilkyYv4mZ63Yx9d+ddGtcpcje35tNsDWAzdm24zz7ci2jqmnAPuCY32wRGSwiC0RkQXx8fOEjKVECoovBk0/CDTdY8mxO2JIlS/jjjz+YM2cOr7zyCtu2bQOgefPmR7XmfPfdd4wbNy5rkFJ2IsL555/PjBkzKFOmDKVKlWLDhg15nnfjxo28+OKLTJkyhWXLltGvXz+SkpKIiIhg3rx5XHrppfz000/06dMHgHfffZeRI0eyefNm2rdvz+7duwt0fapKr169WLJkCUuWLGHlypV88MEHWcdLliyZ6+uKFy9+1Fyq48ePZ/LkydStWzfr/H/++SfgtHxlH8yTkJBApUqVAOf/ceHChfnGOXXqVNq0aXPMI7eWsRo1ahAXF5e1HRcXR40aOasi+PDDD7n44osREU499VTq1avH6tWr840lKSmJ4sWL51vO+Bf7LJ/8ZxmcLi9Llizh+++/P6r1ONMLL7yQ62c1cxBkdjVq1GDz5iNpw/E+qzVr1sz6rHbs2JGwsLCjBiYfj31WQ1Nmk+zklTuK9H0Dog+Dqo5W1RhVjalcuXKhX//uDafxwGPXQ79+XojOhApV5ZZbbuHVV1+ldu3aDBs2jPvuuw+Aq666ilmzZjFx4sSs8tlH5uc0c+ZMGjRoAMCDDz7IbbfdRmKi0w/0wIEDR42AB0hMTKRkyZKULVuWHTt28Msvv2SV3bdvH+eeey6vvPIKS5cuBWD9+vWcdtppjBgxgsqVKx/1pZSXTp06MWvWrKw+hAcPHmTNmjX5vq5p06ZZr0lMTOSvv/5i06ZNxMbGEhsby1tvvcX48eMBZ+T+J598AkB6ejqffvop3bp1A5zWqmeeeSbrnBkZGbz77rvHnC+zBTrnI2f3DYBq1apRpkwZ5syZg6ry8ccf079//2PK1a5dmylTpgCwY8cO/v333wLdjl+zZk2u/WON/7LP8vEV5rNcEMOGDcv1s5qz+wbAOeecw++//86ePXvYs2cPv//+O+ecc84x5S688EKmTp0KOJ+/lJSUrD/Cj0dV2b59O3Xr1i1w7CY4LHykJ/Me7sGI/kVbT3uzC8cWIPufozU9+3IrEyciEUBZnMGERaph1dJF/ZYmBL3//vvUrl0761bvrbfeyocffsj06dPp2rUrP/30E/fccw933XUXVatWpXTp0jzyyCNZr8/sN5mRkUHNmjUZN24c4AwoOnDgAB06dCAyMpLIyMhj+gG3bt2atm3b0qRJE2rVqpV1W3b//v3079+fpKQkVJWXX34ZcL601q5di6rSo0cPWrduzfTp0/O9xsqVKzNu3DgGDBhAcnIyACNHjqRRo7wHX/Tt25drr70WcFrsunfvnjXAB6B///7cf//9JCcn8+ijj3LLLbfQunVrVJU+ffpwzTXXANCqVSteffVVBgwYwKFDhxCRY6aOOxFvv/02N9xwA4cPH6Zv375ZAwgzk/MhQ4bw6KOPcsMNN9CyZUtUleeeey7rS7lLly6sXr2aAwcOULNmTT744APOOeccduzYQfHixTnllFNOOkbjO/ZZPr7CfJaLWoUKFXj00Uez+jM/9thjWQMKBw0axJAhQ4iJieHGG2/kxhtvpEWLFkRFRfHRRx9ldd+oW7cuiYmJpKSk8P333/P777/TrFkzFi5cSKdOnWywbwiqWKpY/oVOgBR1d+OsN3YS4jVAD5xEeT5wlaquyFbmNqClqg7xDCK8WFUvz+t9Y2JitKjm0jSBZdWqVUf1zTP+5aKLLuL555+nYcOGbofiM6+88gplypRh4MCBxxzL7fdVRBaqaoyv4vMHudXZ9ln2b8H4Wb7zzju54IIL6NGjxzHH7PfR5OV49bbXunB4+jQPBX4DVgFfquoKERkhIpnzA30AVBSRdcA9wPDc380Y4+9GjRqV1Y80VJQrVy7XacqMCWTB+Flu0aJFrsmzMSfKq/cyVHUSMCnHvseyPU8CLvNmDMYY32jcuDGNGzd2Owyf+t///ud2CMYUuWD8LN90001uh2CCTEAMIjQmk7e6HBlTlAL191RE+ojIvyKyTkSOuSMoIveIyEoRWSYiU0SkzomeK1D/j0xwsd9Dc6IsgTYBIzo6mt27d1uFZ/yaqrJ7926io6PdDqVQsi1+1RdoBgwQkWY5ii0GYlS1Fc7c/c+fyLnss2z8QaB+Vo1/sOGoJmDUrFmTuLg4TmgucGN8KDo6+qjVDwNEvotfqerUbOXnANecyInss2z8RYB+Vo0fsATaBIzIyEjq1avndhjGBKvcFr86LY/yA4FfTuRE9lk2xgQ6S6CNMcYUiohcA8QAXY9zfDAwGJwFaowxJthYH2hjjDFQsMWvEJGewMPABaqa62oaJ7t6rDHG+DtLoI0xxoCz2FVDEaknIlHAlcDE7AVEpC3wHk7yvNOFGI0xxi94bSVCbxGReOC/E3hpJWBXEYfjL4L52iC4r8+uLXCdyPXVUVW/bZIVkXOBV4FwYKyqPi0iI4AFqjpRRCYDLYHMVTY2qeoFub9b1ntanX2sYL42CO7rC+Zrg+C+vhO9tlzr7YBLoE+UiCwI1iV0g/naILivz64tcAX79bktmP9/g/naILivL5ivDYL7+or62qwLhzHGGGOMMYVgCbQxxhhjjDGFEEoJ9Gi3A/CiYL42CO7rs2sLXMF+fW4L5v/fYL42CO7rC+Zrg+C+viK9tpDpA22MMcYYY0xRCKUWaGOMMcYYY05a0CXQItJHRP4VkXUiMjyX48VE5AvP8bkiUteFME9IAa7tHhFZKSLLRGSKiNRxI84Tkd+1ZSt3iYioiATUKOGCXJ+IXO75+a0Qkc99HeOJKsDvZW0RmSoiiz2/m+e6EeeJEJGxIrJTRJYf57iIyOuea18mIu18HWOgszo7MOtsCO562+psq7PzpapB88CZu3Q9UB+IApYCzXKUuRV41/P8SuALt+MuwmvrBpTwPL8lmK7NU640MAOYA8S4HXcR/+waAouB8p7tKm7HXYTXNhq4xfO8GRDrdtyFuL6zgHbA8uMcPxf4BRCgEzDX7ZgD6WF1dmDW2QW9Pk+5gKu3rc62Orsgj2Brge4IrFPVDaqaAkwA+uco0x/4yPP8a6CHiIgPYzxR+V6bqk5V1UOezTk4S/EGgoL83ACeAp4DknwZXBEoyPXdBLylqnsANHBWeSvItSlQxvO8LLDVh/GdFFWdASTkUaQ/8LE65gDlRKSab6ILClZnB2adDcFdb1udbXV2voItga4BbM62HefZl2sZVU0D9gEVfRLdySnItWU3EOevrECQ77V5brPUUtWffRlYESnIz64R0EhEZonIHBHp47PoTk5Bru0J4BoRiQMmAbf7JjSfKOzn0hzN6uwjAqnOhuCut63Otjo7XxFFEo7xKyJyDRADdHU7lqIgImHAy8ANLofiTRE4twTPxmmFmiEiLVV1r5tBFZEBwDhVfUlETgc+EZEWqprhdmDG+INgq7MhJOptq7NDXLC1QG8BamXbrunZl2sZEYnAuT2x2yfRnZyCXBsi0hN4GLhAVZN9FNvJyu/aSgMtgGkiEovTb2liAA1IKcjPLg6YqKqpqroRWINTOfu7glzbQOBLAFWdDUQDlXwSnfcV6HNpjsvq7MCssyG4622rs63OzlewJdDzgYYiUk9EonAGnEzMUWYicL3n+aXAn+rpWe7n8r02EWkLvIdTEQdKfyzI59pUdZ+qVlLVuqpaF6ev4AWqusCdcAutIL+X3+O0ZCAilXBuD27wYYwnqiDXtgnoASAiTXEq43ifRuk9E4HrPCO7OwH7VHWb20EFEKuzA7POhuCut63Otjo7f26OlvTGA2eE5RqcUaYPe/aNwPnggvOL8BWwDpgH1Hc75iK8tsnADmCJ5zHR7ZiL6tpylJ1GgIzmLsTPTnBud64E/gGudDvmIry2ZsAsnNHeS4DebsdciGsbD2wDUnFanAYCQ4Ah2X5ub3mu/Z9A+730h4fV2YFZZxfk+nKUDah62+psq7Pze9hKhMYYY4wxxhRCsHXhMMYYY4wxxqssgTbGGGOMMaYQLIE2xhhjjDGmECyBNsYYY4wxphAsgTbGGGOMMaYQLIE2XiMi6SKyJNujbh5lDxTB+caJyEbPuRZ5VlAq7HuMEZFmnucP5Tj298nG6HmfzP+X5SLyo4iUy6d8GxE5tyjObYwx/qqwdeMJvH+sZ87mIvnOMaHNEmjjTYdVtU22R6wPzjlMVdsAw3EWKCgUVR2kqis9mw/lOHbGyYcHHPl/aQEkALflU74NzrydxhgTzApbNxrjGkugjc+ISCkRmeJpHf5HRPrnUqaaiMzI1grRxbO/t4jM9rz2KxEplc/pZgCnel57j+e9lovIXZ59JUXkZxFZ6tl/hWf/NBGJEZFRQHFPHJ95jh3w/DtBRPpli3mciFwqIuEi8oKIzBeRZSJycwH+W2YDNTzv09FzjYtF5G8RaexZKWoEcIUnlis8sY8VkXmessf8PxpjTIDLXjc2EJFfRWShiPwlIk08+6uKyHeeenypiJzh2f+9p+wKERns4jWYIBbhdgAmqBUXkSWe5xuBy4CLVDXRcxttjohM1KNX87kK+E1VnxaRcKCEp+wjQE9VPSgiDwD34CSWx3M+8I+ItAf+B5yGswLRXBGZDtQHtqpqPwARKZv9xao6XESGelqzc/oCuBz42ZPg9gBuwVnxaJ+qdhCRYsAsEfldVTfmFqDn+noAH3h2rQa6qGqaiPQEnlHVS0TkMZzVkoZ6XvcMznLGN3pucc4TkcmqejCP/w9jjAkIudSNo3FWklsrIqcBbwPdgdeB6ap6kec1mQ0rN6pqgogUB+aLyDequtvHl2GCnCXQxpsOZ09ARSQSeEZEzgIycFoXqgLbs71mPjDWU/Z7VV0iIl3xLC0qIgBROK0TuXlBRB4B4nES2h7Ad5nJpYh8C3QBfgVeEpHngJ9U9a9CXNcvwGueJLkPMENVD4tIb6CViFzqKVcWaIjzx0N2mX9Y1ABWAX9kK/+RiDQEFIg8zvl7AxeIyH2e7Wigtue9jDEmUB1TN3ruNp4BfOWp/wGKef7tDlwHoKrpwD7P/jtE5CLP81o49bAl0KZIWQJtfOlqoDLQXlVTRSQWJ/nLoqozPAl2P2CciLwM7AH+UNUBBTjHMFX9OnNDRHrkVkhV14hIO5y+xSNFZIqq5tWinf21SSIyDTgHuAKYkHk64HZV/S2ftzisqm1EpATwG04/v9eBp4CpntaUusC047xegEtU9d+CxGuMMQEit7pxHLD3OHcDjyEiZwM9gdNV9ZCnro7O6zXGnAjrA218qSyw05M8dwPq5CwgInWAHar6PjAGaAfMATqLSGaf5pIi0qiA5/wLuFBESohISeAi4C8RqQ4cUtVPgRc858kp1dMSnpsvcLqGZLZmg1Ph35L5GhFp5DlnrlT1EHAHcK+IROD8/2zxHL4hW9H9QOls278Bt4unOUZE2h7vHMYYE2iy143AIWCjiFwGII7WnqJTcLrP4RmDUhanHt3jSZ6bAJ18fgEmJFgCbXzpMyBGRP7Bue22OpcyZwNLRWQxTuvua6oaj5NQjheRZTjdN5oU5ISquginBWMeMBcYo6qLgZY4fYeXAI8DI3N5+WhgWeYgwhx+B7oCk1U1xbNvDLASWCQiy3FmAcnzLo8nlmXAAOB54FnPtWd/3VSgWeYgQpyW6khPbCs828YYEzRy1I1XAwNFZCmwAsgcOH0n0M3znbIQp6vfr0CEiKwCRuE0wBhT5OTo8VvGGGOMMcaYvFgLtDHGGGOMMYVgCbQxxhhjjDGFYAm0McYYY4wxhWAJtDHGGGOMMYVgCbQxxhhjjDGFYAm0McYYY4wxhWAJtDHGGGOMMYVgCbQxxhhjjDGF8H9/U9wj17CQTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred1 = XGB_model.predict(X_test)\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2)\n",
    "ax1.plot([0, 1], [0, 1], linestyle=\"--\", lw=2, color=\"r\", label=\"Chance\", alpha=0.8)\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(12)\n",
    "ax1.set_title('ROC Curve')\n",
    "ax2.set_title('Precision Recall Curve')\n",
    "plot_roc_curve(XGB_model, X_test, y_test, ax=ax1)\n",
    "plot_precision_recall_curve(XGB_model, X_test, y_test, ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a3009d14",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:06:04.452443Z",
     "iopub.status.busy": "2021-12-01T09:06:04.451554Z",
     "iopub.status.idle": "2021-12-01T09:06:04.461212Z",
     "shell.execute_reply": "2021-12-01T09:06:04.461772Z",
     "shell.execute_reply.started": "2021-12-01T07:51:53.909749Z"
    },
    "papermill": {
     "duration": 0.091781,
     "end_time": "2021-12-01T09:06:04.461961",
     "exception": false,
     "start_time": "2021-12-01T09:06:04.370180",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.94      0.90      1178\n",
      "           1       0.65      0.43      0.52       322\n",
      "\n",
      "    accuracy                           0.83      1500\n",
      "   macro avg       0.75      0.69      0.71      1500\n",
      "weighted avg       0.81      0.83      0.81      1500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a55296",
   "metadata": {
    "papermill": {
     "duration": 0.079363,
     "end_time": "2021-12-01T09:06:04.620647",
     "exception": false,
     "start_time": "2021-12-01T09:06:04.541284",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3.3. <a id='hyperparam'>HyperParameter Tuning</a>\n",
    "#### Determining the best hyperparameters using GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3264e30e",
   "metadata": {
    "papermill": {
     "duration": 0.078503,
     "end_time": "2021-12-01T09:06:04.776128",
     "exception": false,
     "start_time": "2021-12-01T09:06:04.697625",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    ">`scale_pos_weight`: Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative instances) / sum(positive instances) ==> already calculated over<a href=\"#imb\"> here.</a>  \n",
    "\n",
    ">`alpha`: L1 regularization term on weights. Increasing this value will make model more conservative.  \n",
    "\n",
    ">`max_depth`: Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit.  \n",
    "\n",
    ">`gamma`: Minimum loss reduction required to make a further partition on a leaf node of the tree. The larger `gamma` is, the more conservative the algorithm will be. \n",
    "\n",
    ">`subsample`: Subsample ratio of the training instances. Setting it to 0.5 means that XGBoost would randomly sample half of the training data prior to growing trees. and this will prevent overfitting. \n",
    "\n",
    ">`learning_rate`: Step size shrinkage used in update to prevent overfitting. \n",
    "\n",
    ">`tree_method`: Basically to train XGBoost on cpu or gpu. Read more over in the <a href=\"https://xgboost.readthedocs.io/en/latest/gpu/index.html?highlight=tree_method\">documentation.</a>\n",
    "\n",
    ">`eval_metric`: Evaluation metrics for validation data, a default metric will be assigned according to objective. \n",
    "\n",
    ">`objective: 'binary:hinge'`: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities. *I tried using this but the score went below 72%.*  \n",
    "\n",
    ">`objective: 'binary:logistic'`: logistic regression for binary classification, output probability.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "02d46588",
   "metadata": {
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   "source": [
    "MAX_TREE_DEPTH = [4]#complex>>>\n",
    "TREE_METHOD = ['gpu_hist']\n",
    "SUBSAMPLE = [0.7]\n",
    "REGULARIZATION = [0.1, 0.01]\n",
    "GAMMA = [0.2, 0.3]\n",
    "POS_WEIGHT = [0.26]\n",
    "OBJ = ['binary:logistic']\n",
    "EVAL = ['auc']\n",
    "ETA = [0.11]\n",
    "\n",
    "param_grid = {'tree_method': TREE_METHOD, 'max_depth': MAX_TREE_DEPTH, 'alpha': REGULARIZATION,\n",
    "          'gamma': GAMMA, 'subsample': SUBSAMPLE, 'scale_pos_weight': POS_WEIGHT, 'learning_rate': ETA, \n",
    "          'objective': OBJ, 'eval_metric': EVAL}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c43b9eca",
   "metadata": {
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     "duration": 0.085627,
     "end_time": "2021-12-01T09:06:05.107257",
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     "start_time": "2021-12-01T09:06:05.021630",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    ">#### The above parameters are what I found to give the greatest score (by trial and error)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfdaa6d1",
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     "start_time": "2021-12-01T09:06:05.185372",
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   },
   "source": [
    "### Introducing Cross Validation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "64e14539",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3f48b3d2",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-12-01T07:51:53.942609Z"
    },
    "papermill": {
     "duration": 27.63156,
     "end_time": "2021-12-01T09:06:33.141308",
     "exception": false,
     "start_time": "2021-12-01T09:06:05.509748",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 790 ms, sys: 101 ms, total: 891 ms\n",
      "Wall time: 27.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "grid = GridSearchCV(estimator=XGB_model, param_grid=param_grid, scoring='roc_auc', cv=cv, n_jobs=-1)\n",
    "grid_fit = grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af234d2",
   "metadata": {
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     "duration": 0.076166,
     "end_time": "2021-12-01T09:06:33.294068",
     "exception": false,
     "start_time": "2021-12-01T09:06:33.217902",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    ">### The training process has been completed!!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94d059a3",
   "metadata": {
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     "duration": 0.076192,
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     "start_time": "2021-12-01T09:06:33.370807",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 4. <a id=\"results\">Results</a>\n",
    "### Thus, I have successfully trained the XG Boost model using cross validation and finally fine tuned the hyperparameters, with the best scores as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ac927d93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:06:33.607533Z",
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     "shell.execute_reply.started": "2021-12-01T07:52:21.228398Z"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: 85.42 % with these parameters: {'alpha': 0.1, 'eval_metric': 'auc', 'gamma': 0.3, 'learning_rate': 0.11, 'max_depth': 4, 'objective': 'binary:logistic', 'scale_pos_weight': 0.26, 'subsample': 0.7, 'tree_method': 'gpu_hist'})\n"
     ]
    }
   ],
   "source": [
    "best_score = grid_fit.best_score_\n",
    "best_params = grid_fit.best_params_\n",
    "\n",
    "print('Best Score: {:.2f} % with these parameters: {})'.format(best_score*100, best_params))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71232e12",
   "metadata": {
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     "end_time": "2021-12-01T09:06:33.768386",
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    "tags": []
   },
   "source": [
    "### Other parameters which I tried:\n",
    "- Best Score: **85.40%** with these parameters: {'alpha': 0.01, 'eval_metric': 'auc', 'gamma': 0.2, 'learning_rate': 0.1, 'max_depth': 4, 'objective': 'binary:logistic', 'scale_pos_weight': 0.26, 'subsample': 0.5, 'tree_method': 'gpu_hist'}), \n",
    "- Best Score: **85.43%** with these parameters: {'alpha': 0.01, 'eval_metric': 'auc', 'gamma': 0.3, 'learning_rate': 0.1, 'max_depth': 4, 'objective': 'binary:logistic', 'scale_pos_weight': 0.26, 'subsample': 0.6, 'tree_method': 'gpu_hist'}),\n",
    "- Best Score: **85.44%** with these parameters: {'alpha': 0.01, 'eval_metric': 'auc', 'gamma': 0.2, 'learning_rate': 0.11, 'max_depth': 4, 'objective': 'binary:logistic', 'scale_pos_weight': 0.26, 'subsample': 0.7, 'tree_method': 'gpu_hist'}),  \n",
    "- Best Score: **85.44%** with these parameters: {'alpha': 0.1, 'eval_metric': 'auc', 'gamma': 0.3, 'learning_rate': 0.11, 'max_depth': 4, 'objective': 'binary:logistic', 'scale_pos_weight': 0.26, 'subsample': 0.7, 'tree_method': 'gpu_hist'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "976ce224",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "#Passing our final model with the test data\n",
    "y_pred2 = grid_fit.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cb8c96c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:06:34.114702Z",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.99      0.90      1178\n",
      "           1       0.89      0.25      0.39       322\n",
      "\n",
      "    accuracy                           0.83      1500\n",
      "   macro avg       0.86      0.62      0.65      1500\n",
      "weighted avg       0.84      0.83      0.79      1500\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2)\n",
    "ax1.plot([0, 1], [0, 1], linestyle=\"--\", lw=2, color=\"r\", label=\"Chance\", alpha=0.8)\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(12)\n",
    "ax1.set_title('ROC Curve')\n",
    "ax2.set_title('Precision Recall Curve')\n",
    "plot_roc_curve(grid_fit, X_test, y_test, ax=ax1)\n",
    "plot_precision_recall_curve(grid_fit, X_test, y_test, ax=ax2)\n",
    "print('Classification Report: \\n', classification_report(y_test, y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "78f75471",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:06:34.641801Z",
     "iopub.status.busy": "2021-12-01T09:06:34.640793Z",
     "iopub.status.idle": "2021-12-01T09:06:35.157800Z",
     "shell.execute_reply": "2021-12-01T09:06:35.157063Z",
     "shell.execute_reply.started": "2021-12-01T08:28:13.740544Z"
    },
    "papermill": {
     "duration": 0.646183,
     "end_time": "2021-12-01T09:06:35.157934",
     "exception": false,
     "start_time": "2021-12-01T09:06:34.511751",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f17a0457210>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x540 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2)\n",
    "fig.set_figheight(7.5)\n",
    "fig.set_figwidth(18)\n",
    "fig.suptitle('ROC Curve for Different models')\n",
    "\n",
    "#ROC curves for Training set\n",
    "ax1.plot([0, 1], [0, 1], linestyle=\"--\", lw=2, color=\"r\", label=\"Chance\", alpha=0.6)\n",
    "ax1.set_title(\"Train Data Results\")\n",
    "plot_roc_curve(LR_model, X_train, y_train, ax=ax1)\n",
    "plot_roc_curve(XGB_model, X_train, y_train, ax=ax1)\n",
    "plot_roc_curve(grid_fit, X_train, y_train, ax=ax1)\n",
    "\n",
    "#ROC curves for Testing set\n",
    "ax2.plot([0, 1], [0, 1], linestyle=\"--\", lw=2, color=\"r\", label=\"Chance\", alpha=0.6)\n",
    "ax2.set_title(\"Test Data Results\")\n",
    "plot_roc_curve(LR_model, X_test, y_test, ax=ax2)\n",
    "plot_roc_curve(XGB_model, X_test, y_test, ax=ax2)\n",
    "plot_roc_curve(grid_fit, X_test, y_test, ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6dff557",
   "metadata": {
    "papermill": {
     "duration": 0.080228,
     "end_time": "2021-12-01T09:06:35.319591",
     "exception": false,
     "start_time": "2021-12-01T09:06:35.239363",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The above two plots give the ROC AUC curve for the training set and for the testing set. It can be observed that the curve for training set is more smooth as compared to the curves for the testing sets.\n",
    "#### For the XG-Boost model trained **without** cross validation and hyperparameter tuning, the training AUC score is **0.95** but while testing it falls to **0.81**, suggesting that the model must have overfit to the data. \n",
    "#### For the XG-Boost model which used cross validation during training and was hyper tuned, the training score was **0.88** and the testing score was **0.84**, suggesting that the model did not overfit to the training data and hence the testing score was the highest among all three models.\n",
    "#### Overall, the Hypertuned XG-Boost model performs the best among other models on testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d18b8b81",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:06:35.504259Z",
     "iopub.status.busy": "2021-12-01T09:06:35.503517Z",
     "iopub.status.idle": "2021-12-01T09:06:36.025764Z",
     "shell.execute_reply": "2021-12-01T09:06:36.025135Z",
     "shell.execute_reply.started": "2021-12-01T08:26:57.105791Z"
    },
    "papermill": {
     "duration": 0.624743,
     "end_time": "2021-12-01T09:06:36.025900",
     "exception": false,
     "start_time": "2021-12-01T09:06:35.401157",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x7f17a032e850>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x540 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2)\n",
    "fig.set_figheight(7.5)\n",
    "fig.set_figwidth(18)\n",
    "fig.suptitle('Precision Recall Curve for Different models')\n",
    "\n",
    "#ROC curves for Training set\n",
    "ax1.set_title(\"Train Data Results\")\n",
    "plot_precision_recall_curve(LR_model, X_train, y_train, ax=ax1)\n",
    "plot_precision_recall_curve(XGB_model, X_train, y_train, ax=ax1)\n",
    "plot_precision_recall_curve(grid_fit, X_train, y_train, ax=ax1)\n",
    "\n",
    "#ROC curves for Testing set\n",
    "ax2.set_title(\"Test Data Results\")\n",
    "plot_precision_recall_curve(LR_model, X_test, y_test, ax=ax2)\n",
    "plot_precision_recall_curve(XGB_model, X_test, y_test, ax=ax2)\n",
    "plot_precision_recall_curve(grid_fit, X_test, y_test, ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91cbacfd",
   "metadata": {
    "papermill": {
     "duration": 0.086748,
     "end_time": "2021-12-01T09:06:36.197278",
     "exception": false,
     "start_time": "2021-12-01T09:06:36.110530",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### An excerpt from [this](http://acutecaretesting.org/en/articles/precision-recall-curves-what-are-they-and-how-are-they-used) article referenced:  \n",
    ">#### The example presented above clearly shows that an imbalanced data set with a large fraction of persons without disease in the test population will make a ROC curve look better than it would in a balanced data set with fewer persons without disease.   \n",
    "\n",
    "#### This suggests that the ROC curves automatically shows better performance when the dataset is imbalanced as compared to when the dataset in not imbalanced. Hence, relying just on ROCs would not be good for understanding the model performance.  \n",
    "#### The above two figures show Precision Recall Curves for training and testing datasets on different models. The curve above the other curve has a better performance level.  \n",
    "#### First figure shows the XGBClassifier model as performing the best but it is clearly overfitting to the data as seen in the second figure where the GridSearchCV i.e. the hypertuned and cross-validated model comes on top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3643b9a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-01T09:06:36.392337Z",
     "iopub.status.busy": "2021-12-01T09:06:36.391430Z",
     "iopub.status.idle": "2021-12-01T09:06:37.045851Z",
     "shell.execute_reply": "2021-12-01T09:06:37.045391Z",
     "shell.execute_reply.started": "2021-12-01T08:47:31.459577Z"
    },
    "papermill": {
     "duration": 0.764606,
     "end_time": "2021-12-01T09:06:37.045981",
     "exception": false,
     "start_time": "2021-12-01T09:06:36.281375",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no. of testing samples:  1500\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(ncols=3)\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(20)\n",
    "fig.suptitle('Confusion Matrices for Different models')\n",
    "\n",
    "ax1.set_title('Logistic Regression')\n",
    "LR_conf = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(LR_conf, annot=True, fmt='d', ax=ax1, linewidths=3, linecolor='white')\n",
    "\n",
    "ax2.set_title('XG-Boost Model')\n",
    "XG_conf_1 = confusion_matrix(y_test, y_pred1)\n",
    "sns.heatmap(XG_conf_1, annot=True, fmt='d', ax=ax2, linewidths=3, linecolor='white')\n",
    "\n",
    "ax3.set_title('Hypertuned XG-Boost Model')\n",
    "XG_conf_2 = confusion_matrix(y_test, y_pred2)\n",
    "sns.heatmap(XG_conf_2, annot=True, fmt='d', ax=ax3, linewidths=3, linecolor='white')\n",
    "\n",
    "print('Total no. of testing samples: ', len(y_test))"
   ]
  },
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   "source": [
    "### Thus, we can say after looking at the ROC and PRC charts that the performance improved substantially when XG-Boost was paired with fine tuning and cross validation. \n",
    "### Though the overall performance was not that great we can still improve it by using more advanced methods such as synthetic data creation to remove the imbalance in the dataset and see if the performance improves further."
   ]
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   "source": [
    "# Referenced:\n",
    "[1] https://machinelearningmastery.com/xgboost-for-imbalanced-classification/  \n",
    "[2] https://xgboost.readthedocs.io/en/latest/parameter.html?highlight=learning%20rate#learning-task-parameters  \n",
    "[3] https://acutecaretesting.org/en/articles/precision-recall-curves-what-are-they-and-how-are-they-used"
   ]
  }
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