mid-boot-v2.4.final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Module Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import modules\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.ufunc size changed\")\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "plt.style.use('bmh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Defining Functions\n",
    "\n",
    "Here, we will be predefining and explaining the functions that will be used later in our notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Function to provide a list of unique values within each column'''\n",
    "\n",
    "def uniques(df):\n",
    "\n",
    "    for col in df:\n",
    "        print(df[col].unique())\n",
    "\n",
    "'''\n",
    "Function to provide a unique count of values from a dataframe\n",
    "'''\n",
    "\n",
    "def value_counts(df):\n",
    "    for col in df:\n",
    "        print(df[col].value_counts())\n",
    "\n",
    "'''Function that prints a summary of the descriptive statistics of\n",
    "a dataframe'''\n",
    "def eda(df): \n",
    "    data = {}\n",
    "    data['null_sum'] = df.isnull().sum()\n",
    "    data['null_pct'] = df.isnull().mean()\n",
    "    data['dtypes'] = df.dtypes\n",
    "    data['count'] = df.count()\n",
    "    data['mean'] = df.mean()\n",
    "    data['median'] = df.median()\n",
    "    data['min'] = df.min()\n",
    "    data['max'] = df.max()\n",
    "    \n",
    "    return pd.DataFrame(data)\n",
    "\n",
    "'''Creating a boxplot function to compare it against our target\n",
    "variable of Offer Accepted'''\n",
    "\n",
    "def box(df):\n",
    "    for col in df:\n",
    "        sns.boxplot(x=col, y = 'offer_accepted', data = data)\n",
    "        plt.show()\n",
    "\n",
    "'''\n",
    "Creating a Function to print an accuracy k chart\n",
    "High values can lead to slow processing times\n",
    "10 is set as a base\n",
    "'''\n",
    "\n",
    "def accuracy_k_chart(X_test,y_test,X_train,y_train):\n",
    "    scores = []\n",
    "\n",
    "    for i in range(2,10):\n",
    "        model = KNeighborsClassifier(n_neighbors=i)\n",
    "        model.fit(X_train, y_train)\n",
    "        scores.append(model.score(X_test, y_test))\n",
    "\n",
    "    plt.plot(range(2,10),scores,color = 'blue',linestyle='dashed', \n",
    "             marker='o',markerfacecolor='red', markersize=10)\n",
    "    plt.title('accuracy scores vs. K Value')\n",
    "    plt.xlabel('K')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.show()\n",
    "    \n",
    "'''Log Transform Function'''\n",
    "    \n",
    "def log_transfom_clean(x):\n",
    "    x = np.log(x)\n",
    "    if np.isfinite(x):  \n",
    "        return x\n",
    "    else:\n",
    "        return np.NAN\n",
    "\n",
    "'''The sample all function takes an input of x and y, x being the dependent\n",
    "variable, y being the independent variable\n",
    "\n",
    "The function runs various regression types and creates an output which allows you to decide\n",
    "which sampling technique is optimal for your dataset. Confusion matrices for each\n",
    "of the Logistic and and KNN Regressions.\n",
    "\n",
    "The output is in the following order:\n",
    "\n",
    "1. Classification score of Logistic Regression Following Sample Adjustment\n",
    "\n",
    "2. Logistic Regression Score on X_test, y_test\n",
    "\n",
    "3. Confusion Matrix of y_test\n",
    "\n",
    "4. Classification Report of the Logistic Regression on X_test, y_test\n",
    "\n",
    "5. KNN Model Score\n",
    "\n",
    "6. KNN Model Confusion Matrix\n",
    "\n",
    "7. KNN Model Classification Report\n",
    "\n",
    "8. KNN Model Accuracy Chart\n",
    "\n",
    "9. ROC Curve - Logistic Regression\n",
    "\n",
    "10. ROC Curve - KNN'''\n",
    "\n",
    "def sample_all (x, y):\n",
    "    \n",
    "    #Random OverSampler\n",
    "    ros = RandomOverSampler()\n",
    "    X = x\n",
    "    X_ros, y_ros = ros.fit_sample(X, y)\n",
    "    \n",
    "    classification = LogisticRegression(random_state=0, solver='lbfgs',\n",
    "                                        multi_class='ovr').fit(X_ros, y_ros)\n",
    "    print('\\n\\n\\nRandom Oversampling Classification Score:', classification.score(X_ros, y_ros))\n",
    "    \n",
    "    #Accuracy Score and Confusion Matrix\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_ros, y_ros, test_size = 0.3,random_state=100)\n",
    "    logreg = LogisticRegression()\n",
    "    logreg.fit(X_train,y_train)\n",
    "    y_pred = logreg.predict(X_test)\n",
    "    print ('\\nLogistic regression classifier on test set: {:.2F}'.format(logreg.score(X_test,y_test)))\n",
    "    print('\\n',confusion_matrix(y_test,y_pred))\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    model = KNeighborsClassifier(n_neighbors=4)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    print('\\nKNN Model Score: ',model.score(X_test, y_test), '\\n')\n",
    "    print('\\n',confusion_matrix(y_test,y_pred))\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    accuracy_k_chart(X_test,y_test,X_train,y_train)\n",
    "    \n",
    "    logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1], pos_label=2)\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, label='Logreg Regression (area = %0.2f)' % logit_roc_auc)\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.savefig('Log_ROC')\n",
    "    plt.show()\n",
    "    \n",
    "    logit_roc_auc = roc_auc_score(y_test, model.predict(X_test))\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test)[:,1], pos_label=2)\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, label='KNN Regression (area = %0.2f)' % logit_roc_auc)\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.savefig('Log_ROC')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    #Random Under Sampler\n",
    "    \n",
    "    rus = RandomUnderSampler()\n",
    "    X = x\n",
    "    X_rus, y_rus = rus.fit_sample(X, y)\n",
    "    \n",
    " \n",
    "    transformer = StandardScaler().fit(X_rus)\n",
    "    X = transformer.transform(X_rus)\n",
    "    classification = LogisticRegression(random_state=0, solver='lbfgs',\n",
    "                                        multi_class='ovr').fit(X_rus, y_rus)\n",
    "    print('\\n\\nRandom Undersampling Classification Score:', classification.score(X_rus, y_rus))\n",
    "    \n",
    "    #Accuracy Score and Confusion Matrix\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_rus, y_rus, test_size = 0.3)\n",
    "    logreg = LogisticRegression()\n",
    "    logreg.fit(X_train,y_train)\n",
    "    y_pred = logreg.predict(X_test)\n",
    "    print ('\\nLogistic regression classifier on test set: {:.2F}'.format(logreg.score(X_test,y_test)))\n",
    "    print('\\n',confusion_matrix(y_test,y_pred))\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    model = KNeighborsClassifier(n_neighbors=4)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    print('\\nKNN Model Score: ',model.score(X_test, y_test), '\\n')\n",
    "    print('\\n',confusion_matrix(y_test,y_pred))\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    accuracy_k_chart(X_test,y_test,X_train,y_train)\n",
    "    \n",
    "    logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1], pos_label=2)\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, label='Logreg Regression (area = %0.2f)' % logit_roc_auc)\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.savefig('Log_ROC')\n",
    "    plt.show()\n",
    "    \n",
    "    logit_roc_auc = roc_auc_score(y_test, model.predict(X_test))\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test)[:,1], pos_label=2)\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, label='KNN Regression (area = %0.2f)' % logit_roc_auc)\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.savefig('Log_ROC')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    \n",
    "    #SMOTE - outputs X_sm, y_sm\n",
    "\n",
    "    smote = SMOTE()\n",
    "    Xsmote = x\n",
    "    transformer = StandardScaler().fit(Xsmote)\n",
    "    Xsmote = transformer.transform(Xsmote)\n",
    "    X_sm, y_sm = smote.fit_sample(Xsmote, y)\n",
    "    \n",
    "    classification = LogisticRegression(random_state=0, solver='lbfgs',\n",
    "                        multi_class='ovr').fit(X_sm, y_sm)\n",
    "    print('\\n\\nSMOTE Classification Score:', classification.score(X_sm, y_sm))\n",
    "    \n",
    "    #Accuracy Score and Confusion Matrix\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_sm, y_sm, test_size = 0.3)\n",
    "    logreg = LogisticRegression()\n",
    "    logreg.fit(X_train,y_train)\n",
    "    y_pred = logreg.predict(X_test)\n",
    "    print ('\\nAccuracy of logistic regression classifier on test set: {:.2F}'.format(logreg.score(X_test,y_test)))\n",
    "    print('\\n',confusion_matrix(y_test,y_pred))\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    model = KNeighborsClassifier(n_neighbors=4)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    print('\\nKNN Model Score: ',model.score(X_test, y_test), '\\n')\n",
    "    print('\\n',confusion_matrix(y_test,y_pred))\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    accuracy_k_chart(X_test,y_test,X_train,y_train)\n",
    "    \n",
    "    logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1], pos_label=2)\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, label='Logreg Regression (area = %0.2f)' % logit_roc_auc)\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.savefig('Log_ROC')\n",
    "    plt.show()\n",
    "    \n",
    "    logit_roc_auc = roc_auc_score(y_test, model.predict(X_test))\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test)[:,1], pos_label=2)\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, label='KNN Regression (area = %0.2f)' % logit_roc_auc)\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.savefig('Log_ROC')\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3  Reading the File\n",
    "\n",
    "After preparation, we then load the dataset into a DataFrame for inspection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Number</th>\n",
       "      <th>Offer Accepted</th>\n",
       "      <th>Reward</th>\n",
       "      <th>Mailer Type</th>\n",
       "      <th>Income Level</th>\n",
       "      <th># Bank Accounts Open</th>\n",
       "      <th>Overdraft Protection</th>\n",
       "      <th>Credit Rating</th>\n",
       "      <th># Credit Cards Held</th>\n",
       "      <th># Homes Owned</th>\n",
       "      <th>Household Size</th>\n",
       "      <th>Own Your Home</th>\n",
       "      <th>Average Balance</th>\n",
       "      <th>Q1 Balance</th>\n",
       "      <th>Q2 Balance</th>\n",
       "      <th>Q3 Balance</th>\n",
       "      <th>Q4 Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1160.75</td>\n",
       "      <td>1669.0</td>\n",
       "      <td>877.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>147.25</td>\n",
       "      <td>39.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>276.50</td>\n",
       "      <td>367.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1219.00</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1211.00</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Number Offer Accepted     Reward Mailer Type Income Level  \\\n",
       "0                1             No  Air Miles      Letter         High   \n",
       "1                2             No  Air Miles      Letter       Medium   \n",
       "2                3             No  Air Miles    Postcard         High   \n",
       "3                4             No  Air Miles      Letter       Medium   \n",
       "4                5             No  Air Miles      Letter       Medium   \n",
       "\n",
       "   # Bank Accounts Open Overdraft Protection Credit Rating  \\\n",
       "0                     1                   No          High   \n",
       "1                     1                   No        Medium   \n",
       "2                     2                   No        Medium   \n",
       "3                     2                   No          High   \n",
       "4                     1                   No        Medium   \n",
       "\n",
       "   # Credit Cards Held  # Homes Owned  Household Size Own Your Home  \\\n",
       "0                    2              1               4            No   \n",
       "1                    2              2               5           Yes   \n",
       "2                    2              1               2           Yes   \n",
       "3                    1              1               4            No   \n",
       "4                    2              1               6           Yes   \n",
       "\n",
       "   Average Balance  Q1 Balance  Q2 Balance  Q3 Balance  Q4 Balance  \n",
       "0          1160.75      1669.0       877.0      1095.0      1002.0  \n",
       "1           147.25        39.0       106.0        78.0       366.0  \n",
       "2           276.50       367.0       352.0       145.0       242.0  \n",
       "3          1219.00      1578.0      1760.0      1119.0       419.0  \n",
       "4          1211.00      2140.0      1357.0       982.0       365.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading excel file\n",
    "data = pd.read_excel(\"creditcardmarketing_clean.xlsx\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Dataset\n",
    "##### The data set stores information about 18.0000 customers.The columns are defined as follows:\n",
    "- Customer Number:  A sequential number assigned to the customers (this column is hidden and excluded – this unique identifier will not be used directly).\n",
    "- Offer Accepted: Did the customer accept (Yes) or reject (No) the offer. \n",
    "- Reward: The type of reward program offered for the card.\n",
    "- Mailer Type: Letter or postcard.\n",
    "- Income Level: Low, Medium or High.\n",
    "- Bank Accounts Open: How many non-credit-card accounts are held by the customer.\n",
    "- Overdraft Protection: Does the customer have overdraft protection on their checking account(s) (Yes or No).\n",
    "- Credit Rating: Low, Medium, or High.\n",
    "- Credit Cards Held: The number of credit cards held at the bank.\n",
    "- Homes Owned: The number of homes owned by the customer.\n",
    "- Household Size: The number of individuals in the family.\n",
    "- Own Your Home: Does the customer own their home? (Yes or No).\n",
    "- Average Balance: Average balance for each quarter in the last year.\n",
    "- Q1-4 Balance: Average account balance (across all accounts over time) for Q1, Q2, Q3 and Q4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Header Standardization\n",
    "\n",
    "We opted to use Snake Casing for header standardization due to its simplicity. Snake Casing is a convention which replaces spaces with underscores and converts any upper-case letters to lower-case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_number', 'offer_accepted', 'reward', 'mailer_type',\n",
       "       'income_level', '#_bank_accounts_open', 'overdraft_protection',\n",
       "       'credit_rating', '#_credit_cards_held', '#_homes_owned',\n",
       "       'household_size', 'own_your_home', 'average_balance', 'q1_balance',\n",
       "       'q2_balance', 'q3_balance', 'q4_balance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Snake Casing - Lower case and replacing blanks with underscore\n",
    "new_cols = [col.replace(\" \", \"_\").lower() for col in data]\n",
    "data.columns = new_cols\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_cols2 = [col.replace(\"#_\", \"\") for col in data]\n",
    "data.columns = new_cols2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 Checking for and removing NaN Values\n",
    "\n",
    "\n",
    "At this point we checked for the percentage of null values per column. As we only found a small number of them we decided to drop those rows. \n",
    "\n",
    "Alternatively we could also replace them with the mean value, but percentages are so low that it is not necessary.<br> After dropping 24 rows we have now 17976 observations left. <br> We also decided to drop the rows with the index 38 and 101. This is done because they uniquely represent the household_size 8 and 9 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_number         0.000000\n",
       "offer_accepted          0.000000\n",
       "reward                  0.000000\n",
       "mailer_type             0.000000\n",
       "income_level            0.000000\n",
       "bank_accounts_open      0.000000\n",
       "overdraft_protection    0.000000\n",
       "credit_rating           0.000000\n",
       "credit_cards_held       0.000000\n",
       "homes_owned             0.000000\n",
       "household_size          0.000000\n",
       "own_your_home           0.000000\n",
       "average_balance         0.001333\n",
       "q1_balance              0.001333\n",
       "q2_balance              0.001333\n",
       "q3_balance              0.001333\n",
       "q4_balance              0.001333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for NaN in dataset\n",
    "data.isna().sum()/data.shape[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18000, 17)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping rows containing NaN values\n",
    "data.dropna(0, 'any', inplace =True)\n",
    "\n",
    "#alternative we can replace NaN with mean value. But the percentage are so low it is not necessary.\n",
    "#data['df_log'] = data['df_log'].fillna(np.mean(data['df_log']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17976, 17)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#after droping 24 rows we have a shape of 17976 rows\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping Data at Index 38,101\n",
    "data = data.drop(labels=[38,101], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Categorical Variables and Ordinal Numericals\n",
    "\n",
    "At this point we look more closely at our ordinal numericals as a well as at our categorical variables. We extracted the categoricals of type 'object' and the ordinal numericals of type 'int64' and stored them as \"categoricals\" and \"numericals\" respectively (the remaining numerical variables are in the form of floats). Afterwards, we defined a function that shows us the unique values for the categoricals and numericals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 17974 entries, 0 to 17999\n",
      "Data columns (total 17 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   customer_number       17974 non-null  int64  \n",
      " 1   offer_accepted        17974 non-null  object \n",
      " 2   reward                17974 non-null  object \n",
      " 3   mailer_type           17974 non-null  object \n",
      " 4   income_level          17974 non-null  object \n",
      " 5   bank_accounts_open    17974 non-null  int64  \n",
      " 6   overdraft_protection  17974 non-null  object \n",
      " 7   credit_rating         17974 non-null  object \n",
      " 8   credit_cards_held     17974 non-null  int64  \n",
      " 9   homes_owned           17974 non-null  int64  \n",
      " 10  household_size        17974 non-null  int64  \n",
      " 11  own_your_home         17974 non-null  object \n",
      " 12  average_balance       17974 non-null  float64\n",
      " 13  q1_balance            17974 non-null  float64\n",
      " 14  q2_balance            17974 non-null  float64\n",
      " 15  q3_balance            17974 non-null  float64\n",
      " 16  q4_balance            17974 non-null  float64\n",
      "dtypes: float64(5), int64(5), object(7)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's select the categorical variables, and discrete numeric variables, and check the unique values. We don't need to worry about outliers in the discrete variables though, we will handle those later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['No' 'Yes']\n",
      "['Air Miles' 'Cash Back' 'Points']\n",
      "['Letter' 'Postcard']\n",
      "['High' 'Medium' 'Low']\n",
      "['No' 'Yes']\n",
      "['High' 'Medium' 'Low']\n",
      "['No' 'Yes']\n",
      "[    1     2     3 ... 17998 17999 18000]\n",
      "[1 2 3]\n",
      "[2 1 3 4]\n",
      "[1 2 3]\n",
      "[4 5 2 6 3 1]\n"
     ]
    }
   ],
   "source": [
    "#Extract Categorical Variables\n",
    "categoricals = data.select_dtypes(np.object)\n",
    "\n",
    "#Extract Discrete Numerical Variables\n",
    "discrete = data.select_dtypes(np.int64)\n",
    "        \n",
    "uniques(categoricals)\n",
    "uniques(discrete)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Encoding\n",
    "\n",
    "Here, we will be label encoding our categorical columns using a temporary dataframe. We don't need to worry about the encoding specificity of reward or mailer type, so we're going to use a simpler method to encode. We also changed the type 'Reward' and 'Mailer Type' to category before we label encoded them, because otherwise Python would treat them as integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remaking categoricals for manipulation\n",
    "cat_E = categoricals\n",
    "\n",
    "#Label Encoding using a dictionary\n",
    "cat_E[\"income_level\"] = categoricals[\"income_level\"].map({\"Low\" : 3, \"Medium\" : 2, \"High\" : 1})\n",
    "cat_E[\"offer_accepted\"] = categoricals[\"offer_accepted\"].map({\"Yes\" : 2, \"No\" : 1})\n",
    "cat_E[\"credit_rating\"] = categoricals[\"credit_rating\"].map({\"Low\" : 3, \"Medium\" : 2, \"High\" : 1})\n",
    "cat_E[\"overdraft_protection\"] = categoricals[\"overdraft_protection\"].map({\"Yes\" : 1, \"No\" : 2})\n",
    "cat_E[\"own_your_home\"] = categoricals[\"own_your_home\"].map({\"Yes\" : 2, \"No\" : 1})\n",
    "\n",
    "#Label Encode Reward\n",
    "cat_E['reward'].astype('category')\n",
    "cat_E[\"reward\"] = categoricals[\"reward\"].map({\"Air Miles\" : 3, \"Cash Back\" : 1, \"Points\" : 2})\n",
    "#Label Encode Mail Type\n",
    "cat_E['mailer_type'] = cat_E['mailer_type'].astype('category')\n",
    "cat_E[\"mailer_type\"] = categoricals[\"mailer_type\"].map({\"Letter\" : 2, \"Postcard\" : 1})\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Merging the Tables\n",
    "\n",
    "Merging the tables is a step that must be taken using a join to avoid indexing issues later, which will cause NaNs equal to the number of removed rows to be found in an eventual concatenated numerical and categorical DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>offer_accepted</th>\n",
       "      <th>reward</th>\n",
       "      <th>mailer_type</th>\n",
       "      <th>income_level</th>\n",
       "      <th>overdraft_protection</th>\n",
       "      <th>credit_rating</th>\n",
       "      <th>own_your_home</th>\n",
       "      <th>bank_accounts_open</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>household_size</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  offer_accepted reward mailer_type income_level overdraft_protection  \\\n",
       "0              1      3           2            1                    2   \n",
       "1              1      3           2            2                    2   \n",
       "2              1      3           1            1                    2   \n",
       "3              1      3           2            2                    2   \n",
       "4              1      3           2            2                    2   \n",
       "\n",
       "  credit_rating own_your_home bank_accounts_open credit_cards_held  \\\n",
       "0             1             1                  1                 2   \n",
       "1             2             2                  1                 2   \n",
       "2             2             2                  2                 2   \n",
       "3             1             1                  2                 1   \n",
       "4             2             2                  1                 2   \n",
       "\n",
       "  homes_owned household_size  \n",
       "0           1              4  \n",
       "1           2              5  \n",
       "2           1              2  \n",
       "3           1              4  \n",
       "4           1              6  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add Customer Number \n",
    "cat_E['customer_number']= discrete['customer_number']\n",
    "#Merge Categorical Dataframes on Customer Number\n",
    "cat_F = pd.merge(cat_E,discrete,on='customer_number')\n",
    "#Drop Customer_number prior to analysis\n",
    "cat_F = cat_F.drop(['customer_number'], axis=1)\n",
    "\n",
    "#Checking the DataFrame\n",
    "cat_F = cat_F.astype('object')\n",
    "cat_F.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "categoricals = cat_F\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 Cleaning Summary\n",
    "\n",
    "We've now got a clean table of categorical and discrete numerical variables and can move on to the analysis of our categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    16954\n",
      "2     1020\n",
      "Name: offer_accepted, dtype: int64\n",
      "3    6046\n",
      "1    5995\n",
      "2    5933\n",
      "Name: reward, dtype: int64\n",
      "1    9132\n",
      "2    8842\n",
      "Name: mailer_type, dtype: int64\n",
      "2    9002\n",
      "1    4517\n",
      "3    4455\n",
      "Name: income_level, dtype: int64\n",
      "2    15299\n",
      "1     2675\n",
      "Name: overdraft_protection, dtype: int64\n",
      "1    6064\n",
      "2    5964\n",
      "3    5946\n",
      "Name: credit_rating, dtype: int64\n",
      "2    11636\n",
      "1     6338\n",
      "Name: own_your_home, dtype: int64\n",
      "1    13672\n",
      "2     4007\n",
      "3      295\n",
      "Name: bank_accounts_open, dtype: int64\n",
      "2    7933\n",
      "1    6145\n",
      "3    3381\n",
      "4     515\n",
      "Name: credit_cards_held, dtype: int64\n",
      "1    14501\n",
      "2     3288\n",
      "3      185\n",
      "Name: homes_owned, dtype: int64\n",
      "3    5705\n",
      "4    5532\n",
      "5    2851\n",
      "2    2809\n",
      "1     539\n",
      "6     538\n",
      "Name: household_size, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "value_counts(categoricals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  offer_accepted reward mailer_type income_level overdraft_protection  \\\n",
       "0              1      3           2            1                    2   \n",
       "1              1      3           2            2                    2   \n",
       "2              1      3           1            1                    2   \n",
       "3              1      3           2            2                    2   \n",
       "4              1      3           2            2                    2   \n",
       "\n",
       "  credit_rating own_your_home bank_accounts_open credit_cards_held  \\\n",
       "0             1             1                  1                 2   \n",
       "1             2             2                  1                 2   \n",
       "2             2             2                  2                 2   \n",
       "3             1             1                  2                 1   \n",
       "4             2             2                  1                 2   \n",
       "\n",
       "  homes_owned household_size  \n",
       "0           1              4  \n",
       "1           2              5  \n",
       "2           1              2  \n",
       "3           1              4  \n",
       "4           1              6  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categoricals.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Checking for Correlations between Categorical Variables\n",
    "\n",
    "To check for correlations inside the categoricals we used the Chi2-Test. We wrote a for loop to check every categorical column against our independent variable, offers_accepted.\n",
    "\n",
    "Columns with P-values less than 0.05, indicating statistical significance are:\n",
    "\n",
    "1. Reward\n",
    "2. Income Type\n",
    "3. Mailer Type\n",
    "4. Credit Rating\n",
    "\n",
    "Remaining columns all had a P-value greater than 0.05\n",
    "\n",
    "We can take note of this and handle it later when we are looking to improve our model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "offer_accepted      1     2\n",
      "offer_accepted             \n",
      "1               16954     0\n",
      "2                   0  1020\n",
      "\n",
      "\n",
      "P-value:  0.0 \n",
      "\n",
      "\n",
      "reward             1     2     3\n",
      "offer_accepted                  \n",
      "1               5789  5582  5583\n",
      "2                206   351   463\n",
      "\n",
      "\n",
      "P-value:  1.0623451178000068e-22 \n",
      "\n",
      "\n",
      "mailer_type        1     2\n",
      "offer_accepted            \n",
      "1               8412  8542\n",
      "2                720   300\n",
      "\n",
      "\n",
      "P-value:  1.6011271841132057e-38 \n",
      "\n",
      "\n",
      "income_level       1     2     3\n",
      "offer_accepted                  \n",
      "1               4353  8533  4068\n",
      "2                164   469   387\n",
      "\n",
      "\n",
      "P-value:  1.4397292672885447e-25 \n",
      "\n",
      "\n",
      "overdraft_protection     1      2\n",
      "offer_accepted                   \n",
      "1                     2528  14426\n",
      "2                      147    873\n",
      "\n",
      "\n",
      "P-value:  0.6967328720491763 \n",
      "\n",
      "\n",
      "credit_rating      1     2     3\n",
      "offer_accepted                  \n",
      "1               5945  5695  5314\n",
      "2                119   269   632\n",
      "\n",
      "\n",
      "P-value:  4.108798473838473e-97 \n",
      "\n",
      "\n",
      "own_your_home      1      2\n",
      "offer_accepted             \n",
      "1               5979  10975\n",
      "2                359    661\n",
      "\n",
      "\n",
      "P-value:  0.9906936434622058 \n",
      "\n",
      "\n",
      "bank_accounts_open      1     2    3\n",
      "offer_accepted                      \n",
      "1                   12898  3773  283\n",
      "2                     774   234   12\n",
      "\n",
      "\n",
      "P-value:  0.44226452793168025 \n",
      "\n",
      "\n",
      "credit_cards_held     1     2     3    4\n",
      "offer_accepted                          \n",
      "1                  5785  7500  3183  486\n",
      "2                   360   433   198   29\n",
      "\n",
      "\n",
      "P-value:  0.7310107837602078 \n",
      "\n",
      "\n",
      "homes_owned         1     2    3\n",
      "offer_accepted                  \n",
      "1               13679  3099  176\n",
      "2                 822   189    9\n",
      "\n",
      "\n",
      "P-value:  0.8777264837287505 \n",
      "\n",
      "\n",
      "household_size    1     2     3     4     5    6\n",
      "offer_accepted                                  \n",
      "1               496  2658  5361  5222  2706  511\n",
      "2                43   151   344   310   145   27\n",
      "\n",
      "\n",
      "P-value:  0.09120659749695138 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# checking correlation with crosstab and chi2 p value\n",
    "\n",
    "for colname, col in categoricals.iteritems():\n",
    "    crosstab = pd.crosstab(categoricals['offer_accepted'], categoricals[colname])\n",
    "    print(crosstab)\n",
    "    [statistic, p_value, dof, arr] = stats.chi2_contingency(crosstab)\n",
    "    print(\"\\n\\nP-value: \", p_value, \"\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Numerical Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 5 numerical columns in the dataset. Four are average balances by yearly quarter and one is an overall average balance.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Selecting Numerical Variables\n",
    "numericals = data.select_dtypes(include = np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>null_sum</th>\n",
       "      <th>null_pct</th>\n",
       "      <th>dtypes</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>average_balance</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>float64</td>\n",
       "      <td>17974</td>\n",
       "      <td>940.539738</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>48.25</td>\n",
       "      <td>3366.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q1_balance</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>float64</td>\n",
       "      <td>17974</td>\n",
       "      <td>910.538055</td>\n",
       "      <td>772.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q2_balance</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>float64</td>\n",
       "      <td>17974</td>\n",
       "      <td>999.414488</td>\n",
       "      <td>1032.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3421.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q3_balance</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>float64</td>\n",
       "      <td>17974</td>\n",
       "      <td>1042.038333</td>\n",
       "      <td>945.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3823.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q4_balance</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>float64</td>\n",
       "      <td>17974</td>\n",
       "      <td>810.168076</td>\n",
       "      <td>703.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4215.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 null_sum  null_pct   dtypes  count         mean  median  \\\n",
       "average_balance         0       0.0  float64  17974   940.539738  1007.0   \n",
       "q1_balance              0       0.0  float64  17974   910.538055   772.0   \n",
       "q2_balance              0       0.0  float64  17974   999.414488  1032.0   \n",
       "q3_balance              0       0.0  float64  17974  1042.038333   945.5   \n",
       "q4_balance              0       0.0  float64  17974   810.168076   703.0   \n",
       "\n",
       "                   min      max  \n",
       "average_balance  48.25  3366.25  \n",
       "q1_balance        0.00  3450.00  \n",
       "q2_balance        0.00  3421.00  \n",
       "q3_balance        0.00  3823.00  \n",
       "q4_balance        0.00  4215.00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading information for numericals\n",
    "eda(numericals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>null_sum</th>\n",
       "      <th>null_pct</th>\n",
       "      <th>dtypes</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>offer_accepted</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "      <td>17974</td>\n",
       "      <td>1.056749</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reward</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "      <td>17974</td>\n",
       "      <td>2.002837</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mailer_type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "      <td>17974</td>\n",
       "      <td>1.491933</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_level</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "      <td>17974</td>\n",
       "      <td>1.996551</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>overdraft_protection</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "      <td>17974</td>\n",
       "      <td>1.851174</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit_rating</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "      <td>17974</td>\n",
       "      <td>1.993435</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>own_your_home</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "      <td>17974</td>\n",
       "      <td>1.647380</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bank_accounts_open</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "      <td>17974</td>\n",
       "      <td>1.255758</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit_cards_held</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "      <td>17974</td>\n",
       "      <td>1.903527</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>homes_owned</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "      <td>17974</td>\n",
       "      <td>1.203516</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_size</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "      <td>17974</td>\n",
       "      <td>3.498553</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      null_sum  null_pct  dtypes  count      mean  median  \\\n",
       "offer_accepted               0       0.0  object  17974  1.056749     1.0   \n",
       "reward                       0       0.0  object  17974  2.002837     2.0   \n",
       "mailer_type                  0       0.0  object  17974  1.491933     1.0   \n",
       "income_level                 0       0.0  object  17974  1.996551     2.0   \n",
       "overdraft_protection         0       0.0  object  17974  1.851174     2.0   \n",
       "credit_rating                0       0.0  object  17974  1.993435     2.0   \n",
       "own_your_home                0       0.0  object  17974  1.647380     2.0   \n",
       "bank_accounts_open           0       0.0  object  17974  1.255758     1.0   \n",
       "credit_cards_held            0       0.0  object  17974  1.903527     2.0   \n",
       "homes_owned                  0       0.0  object  17974  1.203516     1.0   \n",
       "household_size               0       0.0  object  17974  3.498553     3.0   \n",
       "\n",
       "                      min  max  \n",
       "offer_accepted        1.0  2.0  \n",
       "reward                1.0  3.0  \n",
       "mailer_type           1.0  2.0  \n",
       "income_level          1.0  3.0  \n",
       "overdraft_protection  1.0  2.0  \n",
       "credit_rating         1.0  3.0  \n",
       "own_your_home         1.0  2.0  \n",
       "bank_accounts_open    1.0  3.0  \n",
       "credit_cards_held     1.0  4.0  \n",
       "homes_owned           1.0  3.0  \n",
       "household_size        1.0  6.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading information for categoricals\n",
    "eda(categoricals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object     7\n",
       "float64    5\n",
       "int64      5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# which datatypes are in the dataframe\n",
    "data.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 EDA Analysis\n",
    "\n",
    "Unsurprisingly, average balance is correlated with the balance of each quarter. This is especially true with q2 and q3, when balances tend to be higher. However, after testing dropping various combinations of these columns it was found that this did not have a noticable impact on the model's accuracy (less than 0.01 change), so we decided to keep all numerical columns. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Cleaning Numerical Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Checking for multicolinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# there is not enough correlation to drop anything yet \n",
    "correlations_matrix = numericals.corr()\n",
    "mask = np.zeros_like(correlations_matrix)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "fig, ax = plt.subplots(figsize=(22, 12))\n",
    "ax = sns.heatmap(correlations_matrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Checking for Outliers and Skew\n",
    "\n",
    "We examined histograms of our numerical variables to quickly check for any outliers or skewness.\n",
    "\n",
    "We can see that there are quite a lot of outliers in the average balance column. Whether we excluded these outliers or not did not affect the accuracy of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='average_balance', ylabel='Density'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(numericals.average_balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='q1_balance', ylabel='Density'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q1KuXjNQq5pu09LLt1iMR+4nx8o6ubsbLGyagaEKwUjpn2VK9/sSf4i5o7tVLRmtFXk36ouRyuV7rtIXx8o6ubsbLGyagaIIVGz5n2VLR/xxKM69eUsuhLDYJKFNTU73WaQvj5R1d3YyXN0xA0YRIcfGcZX638ILmXr2kFkyXShWqy4bsTiaTfiitivHyjq5uxssbJqBoggqcGzTqfVB86tQIzb16STgYoD8SoKogWzq73Dgc7v3oy+1gvLyjq5vx8oYJKJpQCfeds0yHHEozr15TGyWgNgxNjaEh/4aFWQnj5R1d3YyXN0xA0YRwaemcZX73QYHmXr1mKNo8oMzNzfmhsyrGyzu6uhkvb/QsoIjIzSJyUEQOicidTdaLiHzaXX9ARK5ZbV8RGRWRB0TkZffviLt8u4gUROQp9/W53pzl2lmeEyjbVUqVKgGBeNi/uK9DDmXIzaFkTA5lXejqBfq6GS9v9OROJSJB4DPALcBVwHtF5Kplm90C7HRfHwI+28a+dwIPKqV2Ag+6n2u8opR6g/u6oztn1jkC1bNvljnL6f/RHwn6Om7Pci8/qAWU9LI6FMvSc54U4+UdXd2Mlzd69eh7HXBIKXVYKWUBdwN7lm2zB/iicngUGBaRjavsuwe4y31/F3Brl8+jawTss38gtVkKByL+Voov9/KDRIscSqFQ8ENnVYyXd3R1M17e6FXh/GbgWMPn48D1bWyzeZV9J5VSpwCUUqdEZEPDdjtE5EkgA3xMKfX95VIzMzPcfvvthEIhbNvmtttuY+/evSSTSfr7+wkGg2QyGSYmJpifn0cpxcTEBKdPn65PwZnNZpmcnGR2dhYRYXR0lNnZWRKJBLZtk8vlmJqaIplMEg6HGRoaYm5ujqGhISzLolAoMDU1hagqkcIClXCccGmJQsG5NIPBCmKXiRQXqQbDVIMRQlaOcnSQULmAVCtYsWF3fYRqIESonHfX55GqXV9vh6IgAYLlAuVogpCVRVBY0QSRYro+3W+wUsSKDdWHrg9USoRLGWcYFlUlWCnVj6kCQSrhPsKlJSrhPgLVCgHbalgfqp9TJdJPwLYI2OUG59XPaTToPPekC2Uihfn6Odm2zdzcXE+vUzKZJBKJMDg4SCqVYmRkhEKhQLFYrK8PhUJkMhkWFhYYGxtjaWkJy7Lq6+PxOJFIhHQ6zfj4OOl0mnK5XF/frd9eIpFgenp6TecUi8WIx+NdO6dYLEYqlerpdWrnnPr6+pienu7pdWrnnGzbJpPJ9Pw6rTb1sKhlbfu7gYj8LPAOpdQvup9/DrhOKfUrDdvcC3xCKfWI+/lB4D8Cl7baV0QWlVLDDcdYUEqNiEgUGFBKpUTkTcA3gKuVUmeNdLhv3z61a9eu7p24B779xAuU+ifqn/efWOL7ry7y+o0D3HjZiG9e0dzsWV5+oJTiM/tOYFcVv3zD5vq4Zq+N59i2bZuvbs2Ynp42Xh7R1c14NWf//v1P7N69+9rly3tV5HUc2NLw+RLgZJvbrLTvabdYDPfvDIBSqqSUSrnvnwBeAa7oyJl0CRU4O7NYK/Lq97nIa7mXH4gIQ27T6XTpTLFXJBLxS2lFjJd3dHUzXt7o1d3icWCniOwATgDvAf7Vsm3uAT4iInfjFGml3WKs2RX2vQf4IPBJ9+83AURkAphXStkicilORf/hbp7geqksG9VXlzqU5V5+kYiFmC9UyBQrTPQ7/0yDg4M+WzVnrV73vti8Kei7do2vR6eOrukF+roZL2/0JIeilKoAHwHuB14AvqKUek5E7hCRWgus+3Bu+oeAPwc+vNK+7j6fBG4SkZeBm9zPAD8BHBCRp4G/Ae5QSs13+TTXxfL+HvUcip8jDaNHPxQ4UzG/1NDSK5VK+aWzIsbLO7q6GS9v9Kw8Qyl1H07QaFz2uYb3Ctjb7r7u8hSwu8nyrwFfW6dyT6lE+s/6nNWkyGu5l18M1sf0OhNQRkb8q1taCePlHV3djJc3TE95TTDNhlemlg5LDXUoujadNF7e0dXNeHnDBBRNCNhnJpCyKlXKtiIUECJB/zo1wtleflKbE6Yxh1IsFv3SWRHj5R1d3YyXN0xA0YTGeUcaW3j52Use/J8PpcZg9Nwciq5zQhgv7+jqZry8YQKKJjTOO6JL/Qn4Px9KjYFoEMEZksauOn2ndJ0TYj1ehbLNs8ks331lgfsPpvjuKwv8/cEUM9n1Fz3qml6gr5vx8ob/nQwMAFSDZ+Y3OFN/4n+8b/Tyk4AI/dEg2ZJN1rIZioWIxWJ+azVlLV7FSpXvHV7g2WSOSvXszsYHTmUJCLxl2xA//6ZNbB1Z23nrml6gr5vx8oYJKJpQDZ7pqFQLKH0a5FAavfxm0A0oS6UKQ7EQ8bgefWSWs5rX8v4mi4UKf/fCHKm8U1+1bTjGluEo8XCQQtmmquAHxzI8ciTNY8cy/OtrN3Hbayc8F4fqml6gr5vx8oYJKJoQsnLY7lDx+fKZkYb9ptHLbwYjIU5h1SvmFxYWSCQSPludixevbMnm68/OsFSyGY6HuOXKMTYMnB3E37VrnFSuzF8+cZL7X5rn84+dYD5f5hev28R9B5v3R2jWGVLX9AJ93YyXN0xA0YRy9EzP13wthxL2P6A0evnN8r4oY2Njfuq0pF2vil3lnudnWSrZTA5EePdrJ4iGmhdzjvWH+Q8/sY1rL0nwye8e4avPzBCPBBmJt/8vrGt6gb5uxssb/hfSGwAIlc+0Ky+4ORQ/J9aq0ejlN8tbei0t6dGLfznteu07mmE2V2YoFuKfXj3eMpg08rZLR/jY7h0I8OX9pziZKXXcyw90dTNe3vD/jmUAQBomssqX9cmhiAYTbNVY3hdF10mG2vE6lSnx5IklBLj5ylFP1/rHtg/zL163gaqC+w+mKNvVjnn5ha5uxssbpshLExr7e+iUQ9GlHwqcm0PRtS3+al5KKR46vIAC3rR5kKnB6IrbNxs0cmIgwnh/mLlcmQOnsrzpktXL03VNL9DXzXh5w/87lgE4099DKdWQQ/H/8ujSDwXOBJSsm0PRtS3+al6H54vMZMv0hQNcv3VtFavBgPDW7c684j88voRVWT2Xomt6gb5uxssb/t+xDMCZ5rmWragqiASFUND/y6NTs+FoKEAoIFi2olSpatt0ciWvqlLsm04D8KNbEoTXcY23DsfYOBihWKny9Knsurz8Rlc34+UN/+9YBgCq7kRWtdxJXIP6EzjjpQMiUm9KnbNsbScZWsnrB8cypPJlBiJBXju18nSqqyEiXL/VyaUcSGZZbfZVXdML9HUzXt4wAUUTQuU8AHlLn/oTOOOlC41Nh9PptM82zVnJ657nZwF4w6YBQoH1j9O2dThKwu3weSy9cosvXdML9HUzXt7Q465lqPf3KGhUfwJ69UOBM8PYZ60K4+Odmcmw07TyOpEu8cPjSwQDwlWTnZlnRkR4jXus50/n1uSlA7q6GS9v6FOecZETKuexQrGGFl56FHnVvHRhoKFiPp1O09+vxwRgjaTTaR46dm7/nYcPLwBw5XhfR6/vazb089jRDIdSBUqVasv+LLqmF+jrZry8ocdjsAGpOjkTnVp4wRkvXTiTQ7Epl/WYq2U5zbyqSvHirFN8+CMbO3sjGIqF2JyIYlcVRxZaz5Oha3qBvm7Gyxt63LUM9f4euuVQdOqHAjDgdm7Mlmxt2+I38zq2WKRQrjISDzE50PkK1UtHnVzkkfnWIxvoml6gr5vx8oYJKJpQ6+9Rz6FoMHQ96NUPBRqKvCxb27b4zbxenHFyJ1dO9HVl0rTto04z0umFYsvWXrqmF+jrZry8ocddy4AdcnpL65ZDqXnpQr3Iq2RrWYYMnONVtqu84uYcrpzozsjNI/EQiWiQQqXK6RaTcemaXqCvm/HyhgkouiDOpdCtDqXmpQt94QABcSaksvF3euRWBINnPwxMLxQp24rJgQjD8e5MWCYi9VzKkfnm9SjLvXRCVzfj5Y227xYi8k9FZM2twkTkZhE5KCKHROTOJutFRD7trj8gItestq+IjIrIAyLysvt3ZNkxt4pIVkR+fa3evSLojupbsPTKoQQ1Gm0YnBtnLZdydFbPtviZTOasz6+knDS8fLy7vZu3uzM5Ti82DyjLvXRCVzfj5Q0vj5+/A5wSkT8Vkeu9fImIBIHPALcAVwHvFZGrlm12C7DTfX0I+Gwb+94JPKiU2gk86H5u5I+Ab3lx9YtyNEFVKQoVvTo2lqP6TeJTq0epxvRzA5iYmKi/r6ozLa8uHe1uQNmUiCLATNai2GRsr0Yv3dDVzXh5o+27llLq9cBPAwXga26O4WMisr2N3a8DDimlDiulLOBuYM+ybfYAX1QOjwLDIrJxlX33AHe57+8Cbq0dTERuBQ4Dz7V7jn4SsrJn6k9CAQJdqLhdCyFr9TGies1AxMkoH51d9FekBfPz8/X3J9MlipUqw/EQo33dKe6qEQ0FGO8PU1Xw0uy5nRwbvXRDVzfj5Q1Pj8FKqaeVUr8BbAH2Aj8LvCIiD4vI+0RaFrhvBo41fD7uLmtnm5X2nVRKnXLdTgEbAESkH/hN4Le9nJ+fCKreSz6uSQsvcLx0o5ZDmS/o1UemRmMrq1pl/GVdzp3U2JRwGlE816TX/GpjffmJrm7Gyxue60RE5DLg/e6rCvwWcBT4CPDPgNua7dZk2fIUabVNO/su57eBP1JKZVdqojkzM8Ptt99OKBTCtm1uu+029u7dSzKZpL+/n2AwSCaTYWJigvn5eZRSTExMcPr0aQYGnIH9stksk5OTzM7OIiKMjo4yOztLIpHAtm1yuRxTU1Mkk0nC4TBDQ0PMzc0xNDSEZVkUCgWmpqaQaoVyzqkT6A9UCZbzBGyLgF3Gig0TKS5SDYapBiOErBzl6CChcgGpVhrWR6gGQoTKeXd9Hqna9fV2KAoSIFguUI4mCFlZBIUVTRApprHdHvHBShErNkSklAFVJVApES5lsMNxUFWClVL9mCoQpBLuI1xaohLuI1CtELCthvUhKuG4sz7S35FzGsYZs2ouX2Zubq6n1ymZTBKJRBgcHCSVSjEyMkKhUKBYLNbXB4NBguU8ISvHkXmnA9qVfSXELnftOimESmSArdESTwMPvTzLhL1w1nWygxEOzrzAtZdt9HxOsViMeDzOwsICY2NjLC0tYVlWfX08HicSiZBOpxkfHyedTlMul+vrV/t/ikajpFKpnl6nds4pGo0yPT29pnPq5m/PsiwymUzPr1PtnFoh7UY6EdkL/BxwOfAVnOKpRxvW9wEzSqlzvlFE3gx8XCn1DvfzRwGUUp9o2ObzwENKqb92Px8EbgS2t9q3to1S6pRbPPaQUupKEfk+Ti4KYBg38Cml/rTRa9++fWrXrl1tnX+3+fYTL3Ag38ffH5xn53icd+7SY6yeaG6WUr9e5bUvz+W578UUb5gI8/t7Xuu3zjlMT0/zbKGfxUKFu544RTQU4EPXb+pJMeZSqcIXHj9FJCj80g2bz/rO2rV8lya/rUamp6fZtm2b3xrnYLyas3///id279597fLlXspWbgE+BWxSSn24MZgAKKXyNM+dADwO7BSRHSISAd4D3LNsm3uAD7itvW4A0m4x1kr73gN80H3/QeCbrsuPK6W2K6W2A38M/LflwUQ37FCsPtKwDlP/1rA1GserRm3E4XRZj3qm5dSe4qYXneKurcPRntWJDUZDDEaDWLYilT97eA4dr2WN1Z58/cJ4ecNLQHlIKfVVt2K8joj8+9p7pdQ/NNtRKVXBKRK7H3gB+IpS6jkRuUNE7nA3uw+nEv0Q8OfAh1fa193nk8BNIvIycJP7+bylXoeiSQsvXak1G54v6DPffTOm3dZd24Z7eyPf6E4pfHpJz3nHDRcuXupQfgv4gybLPwb84Wo7K6Xuwwkajcs+1/Be4VT0t7WvuzwF7F7lez++mpsOBCtF8mXnRqlTDiVYKVLRbAj7vkgQAdKlKmW7uq5ZD7tBNpvFrvZxfNGp69k20tuAsmEgzEtzTvPhRnS8ljWy2SxjY2N+a5yD8fLGqgFFRH6qtq2I/CRnV5JfCix1Q+xiw4oNUSg7lfI65VCs2JDfCucQcGduzFo2qXyZqUG9hoeZnJzk1FNzlKuKsb5wfUDLXrHBHXxyJnt2kZeO17LG5OSk3wpNMV7eaOeX/hfu3yjwhYblCkgCv9JpqYuRSClD3u2H0hfRJ4cSKWUo9elXiTsQdQLKXE6/gDI7O8vRtJM72Drce7daQJnLWdhVRdCdGVLXawlOmm3ZsmX1DXuM8fLGqgFFKbUDQES+qJT6QPeVLk4U0jAwpD45FKXpeFm1epTZnH7zQogIx93hT7b0uP4EnA6OQ7EQ6WKF+UKZiX4nwOh6LYGujMDcCYyXN7z0lDfBpItUIgMN0//qk0OpRPRsTVLr3JjK6VfxHBscIrlkIcDmhD+5pw0DTq/8xnoUXa8lwOjoqN8KTTFe3lgxoIjICw3vj4nI0Wav7mteBBTSWLYiIBAJ6vP0ES7pOQhdPYeS1y+Hsu/lJAqYGowQaTEdb7epFXvNNtSj6HotwSnC0RHj5Y3Virz+TcP793dT5GInSwwo0RcOapWdtcO9GTLEK7WK7jkNi7wO55wgcokPxV01zlTMn8mh6HotARIJPQf6NF7eWDGgKKUeaXj/ve7rXLzky06fCp3qTwBQ545aqwO1HMqchkVez552OjRuGfKvscB4v1PklcqXUUo5DymaXksA29ZzXDbj5Q0v86H8exF5g/v+Bre467A7rIphnRSLzo1Rp/oTgGCl5LdCU2q95XWrlE8XK0xnygQDwkaf6k/A+R3FwwEsW5G1nJuPrtcSIJc7dzBLHTBe3vDyOPzvgFfd95/A6cz4ezhDmxjWSUac4gjdcihWbNhvhab013rL58vYVX1GXn36pNMta9NghFDA36LLMXe4/JQbdHW9lgBTU1N+KzTFeHnDy91rSCmVFpFB4PXAnyil/gK4sjtqFxdWIQ/o1QcFIFJc9FuhKcGAkIgIVQULBX1yKU+ddOaP8bP+pEY9oLgNF3S9lgDJZNJvhaYYL294CSjHROQtOIMzPqyUskUkAehZmHeekXNTUbccigroFeAaGY3pV+z1pJtD8aND43JGlwUUna9lONzdycfWivHyhpcxIX4D+BvAwpn3BOCfAD/otNTFSM4OAhXt6lAq4T6/FVqyYTDKkUxFm5ZeM1mLE5kS8VCg3srKT5bnUHS+lkNDeg4LY7y84aVj431KqU3usPBPuIu/CvzT7qhdXBRLzj+9bjmUcEnfodr6A07LOF1aej3l5k52Dge1mMJ5rM95XpzPV1BKaX0t5+bm/FZoivHyhqdR60RkCKfOZHmX2+90zOgiJWc7NyCTQ2mfjUN9cKykTZFXrbjrms16jOgbCwfpjwTIWVUyJZuQxtdS1ydu4+WNtgOKiPw88BkgC+QbVimcUYcN6yCv4TheAIFqRdtKsoRbjKxDDkUpxZMnnICyayTEKU2mahntC5OzSqRyZcb79b2WluX/NWyG8fKGlxzK7wH/XCn1rW7JXKwopchXnKavfboFFFvPHy7AQL3Iy/8cyvRikflChdF4iNFQmVMV/yvlwalHObZYIpUvE4jpey0LhYLfCk0xXt7wcvcKAU1nZDSsj5xlU1XOGF4hzSaL0rnvws5LnDkhdCjyquVO3rh5kI0bN/psc4Zaxfx8vqz1tdS1X4Xx8oaXu9d/Bz4mInrd8S4AFou1YVf0qj8BvfsuVJZSgNOKqar87dy4vxZQNg1q1UegsaWXztdSpzRrxHh5w0uR178DpoD/KCKpxhVKqa0dtbrIWHTnRtetuAtABXo726AXBuJREtEgmZJNulBhpM+ftvmVquJA0unQ+MbNg1QyFmhSIlHrizJfqGCL/02ZWxGJ6OlmvLzh5W5hRhvuErWAomMOpaLxCLWDg4OM96fJlArM5su+BZSDMzkK5SpbhqJM9EfIqkFIF31xWU40FHBmtyzZpOwIerYNcq6ljhgvb7QdUMxow93jTJGXfjmUcGmJUsj/YUSakUqlmOgPc3i+4IxX5dPstvuXNRdOpVJAvz8yTRjrC5Mt2SxmsgwN6jnJViqVYmBAPzfj5Q0vow1HReT33BGG0+6yt4vIR7qnd3FQCyg6FnlVIvrcGJczMjLCmDtM+6yPTYcbK+RrXjpRq0eZKetbfKlbmtUwXt7wcgf7I+C1wPtw+p4APAf8cjs7i8jNInJQRA6JyJ1N1ouIfNpdf0BErlltXxEZFZEHRORl9++Iu/w6EXnKfT0tIu/2cJ49J12o9ZLXr8hL52bDhUKBcXe+dL+aDuctmxdmcgQEXr9xsO6lE40tvXRFtzSrYby84SWgvBv4V0qpfUAVQCl1Ati82o4iEsTpFHkLcBXwXhG5atlmtwA73deHgM+2se+dwINKqZ3Ag+5ngGeBa5VSbwBuBj4vIto+ntUr5SP65VACtr43oWKxyISbQ/Grc+MzySy2gisn+upD6heLetSf1Bh1h2CZy+varVG/NKthvLzh5Q5msazORUQmgFTzzc/iOuCQUuqwUsoC7gb2LNtmD/BF5fAoMCwiG1fZdw9wl/v+LuBWAKVUXilV66sc40yOSkt0bjase9+F8b5akZc/ge/x48487ddsPjMlq259BGotvRZKyvfm1a3QLc1qGC9veAkoXwXuEpEdAO7N/k9xbvCrsRk41vD5OOfmbFpts9K+k0qpUwDu3w21jUTkehF5DngGuKMhwGiHzs2Gde+7MOFjkZdSiseOOgHl+i1nAopufQQiwQCD0SC2cmaU1BHd0qyG8fKGl2Kg/4TTufEZoA94Gfj/gN9uY99mQ68uf1RqtU07+567gVKPAVeLyGtwAuG3lFJn5RNnZma4/fbbCYVC2LbNbbfdxt69e0kmk/T39xMMBslkMkxMTDA/P49SiomJCU6fPl1vYZHNZpmcnGR2dhYRYXR0lNnZWRKJBLZtk8vlmJqaIplMEg6HGRoaYm5ujqGhISzLolAo1CeIGrKXCFT6CZeWqET6CdgWAdvp4RwpLlINhqkGI4SsHOXoIKFyAalWGtZHqAZChMp5d30eqdr19XYoChIgWC5QjiYIWVkEhRVNECmmsd3WXMFKESs2RKSUcRwqJcKlDHY4DqpKsFKqH1MFglTCfY5zuI9AtULAthrWh6iE4105p1wux2DEuaHPZkvkcjkWFha6dp1q6yORCIODgxyYPs3prEUiEiCan6VUCpFMJrEsi2BZenqdFEIlMtDyOo1FYakES/MpFhfjLc8plUoxMjJCoVCgWCzW18diMeLxOAsLC4yNjbG0tIRlWfX18XicSCRCOp1mfHycdDpNuVyur1/t/6lSqZBKpbpyndZzTtVqlenp6TWdUzd/e7lcjkwm0/PrtFrLMlFtZoHdeosfB8aAIPANpdQzbe77ZuDjSql3uJ8/CqCU+kTDNp8HHlJK/bX7+SBwI7C91b61bZRSp9wc00NKqXNmkBSR7wK/oZT6YePyffv2qV27drV1/t3Crire+YWnUMCv/NglWgx73kiwnMfWdJTaH98UIZFIcOtdT5MvV/mb9/8IiVjvqsrufjrJFx4/xTuuGOU//MS2+vJMJsP3T+rVmOF7hxd46mSWt2wb4uM36TeWayaTIZFIrL5hjzFezdm/f/8Tu3fvvnb58lXLWNzWV1/AyZn8J+BngH8DPCki/1OkrTvg48BOEdkhIhGcWR/vWbbNPcAH3O+7AUi7xVgr7XsP8EH3/QeBb7rOO2qV8CKyDWfI/SNtePacTLGCAuIhtAsmACEr57dCSxYWFgB8K/Y6U9x1dnfBmpdO6N7SS8c0A+PllXYK7T+Ek1O4QSm1TSn1ZneolTfj5Fh+abUDuPUXHwHuB14AvqKUek5E7hCRO9zN7gMOA4eAPwc+vNK+7j6fBG4SkZeBm9zPAG8FnhaRp4C/BT6slNJyRhqdK+QBylE9e+QCjI2NATBea+mV712uYD5f5vnTOcIBOWf+k5qXTozEa0Ow6BlQdEwzMF5eaad84OeAX1VKPd64UCn1uIj8GvBR4HOrHUQpdR9O0Ghc9rmG9wrY2+6+7vIUsLvJ8i8BX1rNSQdqFfL9esYTQuUClqY95ZeWlhgYGKgHlF629HrkyCIKeNMlg/RFzr54S0tLgF5zfteaDi/kK1SV0i43XLuWumG8vNFOQLkKaDXsyvc4T27curJYdG6CfSE9m3NKVc9WQXBmkiE/iry+/+oiAIlYiHtfPDvzG80tQv9Ez1zaIR4O0heCfEUxlytrMed9I7pOGGW8vNFOkVdQKdV0Mmp3uX5tXc8jajmUWEzPXIDu/VCgocirR50b5/NlDpzKEg4Il46eO3imrmlWGzzz6KJ+neJ07VdhvLzRTjAIi8hPishPNXvhcV56w9nUAsqg6PnEoXs/FKDnRV6NxV3R0Ln/Qrqm2UTEmWZ6ekG/gKJrvwrj5Y12gsEM8IVV1hvWSL1SPqJnXK4G9SoaaSQed3IHvS7y+oeX5gG48dIRCpXqOet1TbPReBiwtcyh1K6lbhgvb6x6F1NKbe+Bx0VLvcgrrGlA0XiCrdokQ+MNIw4rpWivJfvaOJwq8NJcnoFIkB/bPsy3D82fs42uaeYUeRW1DCi6ThhlvLxh6j98plYpr2uRV6ic91uhJel0GoCBSJC+cIBCucpSqbsDIN7/sjN03Y2XjTQt7gJ902xD2PmNHV0s0m6H5l5Ru5a6Yby8YQKKz9RyKJE+Pecd0bkfyvi4M6OWiDA1GAXg1FKpa99nVap855DToezmK1r3A9A1zaL9g0SCwlLJrhe16kLtWuqG8fKGCSg+UxusLyHduxGuB12ftuHsp7SNg04RwMlM93J63z28QLpY4fKxODvHW5dh65pm4UqhPvLwUc0q5nV94jZe3jABxUdKlSr5cpVQQIii51wVUtXTC6BcPlMJvzHh5FCSXcqhKKX422ed9ifvfu3EivU0uqaZVG23Yl6/psON11InjJc3TEDxkVruZDgWohzXc0pPXftUwNlt8Te5AeVkpjsB5alTWQ7PFxmJh3jbpStfK13TzIoN13vMH13UK0esa78K4+UNE1B8pFZ/MhwPadt3QVcvOLst/pRb5HWqC0VeSin+ar/zXT9z1QSR4Mr/NrqmWaS4eKbIa1GvKWR17VdhvLyhZ/vGi4RaC6/heAi7RYshv7FDUb8VWtLff6YhQy2H0o1K+SdOLHEgmWUwGuTWq1avDNU1zexQlNGgE1CmNSvyaryWOmG8vKHnXewioZ5DiYVANL0UunoBweCZQRk3DEQIiNO50bLP7Wy4VpRSfOHxkwD8y9dNMhBt4xlM1zSTAIlokGhQmM9XyGjU0qvxWuqE8fKGpr/8i4MzRV5hgmW9iiBq6OoFziRDNUIBYcNABAWcXupcsdf3jyxyKFWgPxIgEhLufXHurFczdE2zYLmAiLBtxGmhdkSjll6N11InjJc3TEDxkcXGSvmofrPCAdp6AUxMnD2ib63pcKeKveyq4i9/eAqA67YMEV6l7qSGrmlW89ox6gxEemRBn8C3/FrqgvHyhgkoPlKbS344HiJkZX22aY6uXgDz82cPe7Kx3tKrMzmUB16e53i6xFAsxNWT7ZdZ65pmNa96DmVenxzK8mupC8bLGyag+Mh83smhjMTDCHoNhVFDVy/gnOFDahXzJ9Lrv1FalSpf2u/kTm7YmiAYaH98MF3TrOa1Y0S/HIpuQ8HUMF7eMK28fKSWQxntCzGraTGJpakXnJvt3zZcu1GuLaA01ok8eWKJ2VyZsb4wV070eTqOrmlW89o+eqYOpduDabaLrkU4xssbJofiIwuFMzmUSFHPoRR09QI4ffr0WZ+3uU/e653vw6pUefyYU+n5lm1Dnm+4uqZZzWs0HiIRDZK1bObyevS4Xn4tdcF4ecMEFJ+oVBWZYoWAwFAshK3pvO26egHnzKm9YSBCLBRgsVipj0KwFp48uUShUmXjYKRege0FXdOs5iUibNesHkXH+dHBeHnFBBSfSBcqKJxg4qV83tCagEhDLmVt9QOFss3+E86M12/Z7j13cr6w3Q2Ur87rU49iOP8xAcUn5t36kxF3sL5gRY8nxeXo6gWQzZ7bmmq99Sg/PL6EZSu2Dce4ZGhtOQ1d06zR6zK3HuUVTQJKs2upA8bLGz0LKCJys4gcFJFDInJnk/UiIp921x8QkWtW21dERkXkARF52f074i6/SUSeEJFn3L8/1ZuzbJ/GCnkAKzbkp05LdPUCmJycPGfZeupR8pbNgVPOP+qbt6/9vHVNs0avy8adhgaH5vQYar/ZtdQB4+WNngQUEQkCnwFuAa4C3isiVy3b7BZgp/v6EPDZNva9E3hQKbUTeND9DDAH/IxS6keADwJf6tKprZnGJsMAkZKePV919QKYnZ09Z1mtbmAtw7PvP7lEparYMRJjcmDtU6zqmmaNXtuHYwQEjqdLFMr+D7ff7FrqgPHyRq9yKNcBh5RSh5VSFnA3sGfZNnuALyqHR4FhEdm4yr57gLvc93cBtwIopZ5USp10lz8HxEREqxH7FupFXk4ORaFnWb2uXkDT+o1tI2sr8soUKxw46eROrtu6vma/uqZZo1ckFGDbcAyFHkOw6FpXZby80auAshk41vD5uLusnW1W2ndSKXUKwP27ocl3/zPgSaWUVhNALM+hVCJ6ttrQ1QtgdHT0nGUT/WH6I0HSxQpzufZ7zH/92RnKVcW2kVh9OuG1omuaLffSqdir2bXUAePljV51bGwWTpd39Wy1TTv7Nv9SkauB/w68vdn6mZkZbr/9dkKhELZtc9ttt7F3716SyST9/f0Eg0EymQwTExPMz8+jlGJiYoLTp0/Xm+1ls1kmJyeZnZ1FRBgdHWV2dpZEIoFt2+RyOaampkgmk4TDYYaGhpibm+O0OxGUnVukVBoink1SjiaohOOES0tUIv0EbIuAXcaKDRMpLlINhqkGI4SsHOXoIKFyAalWGtZHqAZChMp5d30eqdr19XYoChIgWC5QjiYIWVkEhRVNECmm681Kg5UiVmyISClD0MpTHJgkXMpgh+OgqgQrpfoxVSBIJdznOIf7CFQrBGyrYX2oa+d06NAhNm3adM512jEY4NmUzfeem+bGy0ZXvU7pQrk+G+Nbx6tECvPrOqdgpUipb6yn10khVCIDK16nQKWECoRYXAxhWRZj4uRMnpqe5dqRCoODg6RSKUZGRigUChSLxfpvNxaLEY/HWVhYYGxsjKWlJSzLqq+Px+NEIhHS6TTj4+Ok02nK5XJ9/Wr/T4VCgYmJiTX/Pw0NDWFZFoVCob4+Eoms+5wWFhaIxWJrOqf13iNWOqfDhw+zffv2nl+n1ZorSy+68IvIm4GPK6Xe4X7+KIBS6hMN23weeEgp9dfu54PAjcD2VvvWtlFKnXKLxx5SSl3pbncJ8B3gF5RS/9jMa9++fWrXrl1dOOPV+fd/9xLPJnP8/jsv5w2bBrn/wBEtn2xDVlZLL4C3TAYZGTl39sQvP5nki0+c4t1XT/DLb75k1eN8af8pvrQ/yZahKLf9SLNMrjd0TbOa17t2OXO6PH1yid+47xBXTvTxJ3uu9NVtYWGh6bX0G+PVnP379z+xe/fua5cv71WR1+PAThHZISIR4D3APcu2uQf4gNva6wYg7RZjrbTvPTiV7rh/vwkgIsPAvcBHWwUTv1lwi7xqc3yjOjeHR0fR1Quw7eaVyVdvcAZyfO50btVj5Cybv33WqeBcb91JHV3TbJnXZWNOA4bD8wUqVX/Hhmp1Lf3GeHmjJwFFKVUBPgLcD7wAfEUp9ZyI3CEid7ib3QccBg4Bfw58eKV93X0+CdwkIi8DN7mfcbe/HPgvIvKU+1r/o2cHqVfKu82GgxWtqnjq6OoFkMs1Dxi7NvQREDiUyq/agukbz82StWxeNzWw5n4ny9E1zZZ7DURDbE5EKduKIz73R2l1Lf3GeHmjZ4NDKqXuwwkajcs+1/BeAXvb3dddngJ2N1n+u8DvrlO5axTKNvlylXBAGIg4M69ZsWF/pVqgqxfA1NRU0+XxcJBLR+McShU4OJvnDZsG6+saB4C0KlXuftoZE+ny8XjHvHRNs2ZeV070cSJT4sXZPJePexsEs5O0upZ+Y7y8YXrK+8CZmRpD9eZ/keKij0at0dULIJlMtlx39aRTh/HsCsVeT5/KUqpU2ZSIcMlQ51qV65pmzbxqIykfnPX3iXela+knxssbJqD4QG2E1/H+cH2ZCug5R7SuXgDhcLjlums2O7mSR15daLresqv1Mbuu29LZMbt0TbNmXrvc+qYXZ/1tOrzStfQT4+UNE1B8YC7nBJSxvjO9sSth/4obVkJXL4ChodZDnFx7ySCD0SCH54u8kjr3ZvnMqSxFd0ThrcOd7fOqa5o187psNE4oIBxdKJK3/KvoXela+onx8oYJKD5Q63A30ZBDCZeW/NJZEV29AObm5lquCwcD3Hip06zywUNn51KyJZsfuPOdXLc10fFex7qmWTOvSCjApaNxFPCSjx0cV7qWfmK8vGECig/UirzGGgLK+fRUqwurPaX99E6nU+N3XpnHqpxpMvu9wwtYtjNmV2104k6ia5q18qrVo7zoYz2Krk/cxssbJqD4QMot8hrvOxNQAtW1TwjVTXT1ArCslYdW2TXRx7aRGPP5Cn/26HEAfng8w6FUgXBAuPGyka6MiaRrmrXyek2t307Sv4Cy2rX0C+PlDTOnvA/U6lDG+8/UoQRsPX8gunoBHDiWojTf/JnoXbvGERF+823b+LX/8xL3vZjimVNZjqWdvhg3XjZCItadn7+uadbK67VTTkB5fiZHVSkCPgw8WCjoMS/LcoyXN0wOxQfm8s4/dmMrr/Op74IutON2+Xgf//atWwgKHEuXCAi844pRrprs99XLD1p5TQ5EGO8Ps1Sy1zSPTCfQtV+F8fKGCSg9pqpUvchrrKHI63zqu6AL7brdtHOML7/3tfzZrVfyr390U72prN9evaaVl4jwI1Nuv52kPzMB6tqvwnh5wwSUHpMuVLAVDEaDRENnkl8F9Cx91NULvLmN9YW5fLyP/kj3+4jommYreb3WzbGt1BG0m0Qia5/QrJsYL2+YgNJjZt0WXo1NhgEq4c4N/dFJdPUCfd3OR6/X+pxDGRwcXH0jHzBe3jABpcekmnRqhPOr74Iu6Op2PnptG4kxGA0ymytzKtP7wS1TqVTPv7MdjJc3TEDpMbVOjePLcyiR7pbrrxVdvUBft/PRKyDC6zc6uZQnT/Y+IOo45wgYL6/oWdh7AXOmyfDZASVgW9gadojT1Qv0dTtfvUJBp7nwfS/OnTMlam1Srm5RKBRIJDo0H00HMV7eMDmUHlMfGLJveUAp+6GzKrp6gb5u56vXVnfUgGOLJXoxk2sjxaI/zZVXw3h5wwSUHjOTdcfxGji7DuV867ugA7q6na9ew7EQA5EghUq1npPuFbr2qzBe3jABpcckl5yAMjV4dkA53/ou6ICubuerl4iwxR15uTaiQK/QtV+F8fKGqUPpIXZVMZuzEGDDshxKNajn/Aa6eoG+brp7Nc5auZytwzFemMkzvVCozynTC2Kxzg/S2QmMlzdMDqWHzOQsqsrpZBcJnp301aCeHZV09QJ93c5nr20jzo3qRLqEZVdX2bpzxON69t0xXt4wAaWHtCruAghZ/k7B2gpdvUBft/PZKx4OsnEwgq3g2GLvKn4XFprPrOk3xssbJqD0kJUCSjmqZ89XXb1AX7fz3Wv7qPP0++p87wLK2NhYz77LC8bLGyag9JDTS05F59TguVPOhsp6Dketqxfo63a+e+1wi72OLBR71nx4aUnP0QWMlzd6FlBE5GYROSgih0TkzibrRUQ+7a4/ICLXrLaviIyKyAMi8rL7d8RdPiYi3xWRrIj8aW/OcHVqOZTJJjkU0XRSJl29QF+3891rvD9MfyRIzrKZ7VHzYV0njDJe3uhJQBGRIPAZ4BbgKuC9InLVss1uAXa6rw8Bn21j3zuBB5VSO4EH3c8AReC/AL/erXNaC/Uir4FzA8r52nfBT3R1O9+9RITttVzKfG9yW7r2qzBe3uhVDuU64JBS6rBSygLuBvYs22YP8EXl8CgwLCIbV9l3D3CX+/4u4FYApVROKfUITmDRhmS2dZHX+dp3wU90dbsQvHaMnin26gW69qswXt7oVUDZDBxr+HzcXdbONivtO6mUOgXg/t3QQeeOYlWqzOcrBOXccbzg/G5q6he6ul0IXluGYwQFTi1ZFMp2F60cdG0Ga7y80auOjc0mqV5e29dqm3b2XRMzMzPcfvvthEIhbNvmtttuY+/evSSTSfr7+wkGg2QyGSYmJpifn0cpxcTEBKdPn2ZgwBmZNZvNMjk5yezsLCLC6Ogos7OzJBIJbNsml8sxNTXFU6+cAJxgcvzYUYaGhrAsi0KhwNTUFCErh6gqlXCccGmJSqSfgG0RsMtYsWEixUWqwTDVYISQlaMcHSRULiDVSsP6CNVAiFA5767PI1W7vt4ORUECBMsFytEEISuLoLCiCSLFNHbIeSoNVopYsSEipQxil7FDMcKlDHY4DqpKsFKqH1MFglTCfY5zuI9AtULAthrWh7p2TiErhxJpek7F4kDT6xTNz6EQKpGBrp0TShEs53t6ndo5J4UQzc22dU79tsWWgQBHlqqcSM5yeoNzE1tYWGBsbIylpSUsy2JqaopkMkk8HicSiZBOpxkfHyedTlMul+vrV/t/EhFSqVTb/0/JZJJwOMzQ0BBzc3Pn/D8lk0kikQiDg4OkUilGRkYoFAoUi8X6+lgstuo5WZbF9PT0ms7J6z3CyzktLCwQi8XWdE7ruU61c2qF9KIVh4i8Gfi4Uuod7uePAiilPtGwzeeBh5RSf+1+PgjcCGxvtW9tG6XUKbd47CGl1JUNx/x54Fql1Eeaee3bt0/t2rWrw2fbnP87vcjHH3iVay8Z5L/dfPk567/9xAuU+id64uKFaG5WSy9Y2a3V6Lgr9RLvFLqmmVevJ08s8fCri+wcj/OZW7v7fzI9Pc22bdu6+h1rwXg1Z//+/U/s3r372uXLe1Xk9TiwU0R2iEgEeA9wz7Jt7gE+4Lb2ugFIu8VYK+17D/BB9/0HgW92+0TWylG3k9iW4eZDJpzvfRf8QFe3C8XrsjGnWOXIfJFSpbu95sfHuzs8/loxXt7oSUBRSlWAjwD3Ay8AX1FKPScid4jIHe5m9wGHgUPAnwMfXmlfd59PAjeJyMvATe5nAETkCPCHwM+LyPEmrcp6ytFFp0J+W4uAEirne6nTNrp6gb5uF4pXIhZiw0CYclXxw+OZLlk5pNPprh5/rRgvb/RscEil1H04QaNx2eca3itgb7v7ustTwO4W+2xfh27HOeq2ltnaIqBItfsVn2tBVy9Y2a0XRVut0DXN1uK1c7yPmWya77+6yI9tH+68lEu5rOccMsbLG6anfA9QStWLvFoFlPO974If6Op2IXld7hZ7PXo03dXBInXtV2G8vGECSg+YzZUpVqoMx0IkYs0zhRdC34Veo6vbheQ1HA8z3h8mX67y5InuDfeha78K4+UNE1B6QC13UhsavBl26NzOjjqgqxfo63ahee10cynff3WxgzZn09/f37Vjrwfj5Q0TUHrA9MLKLbwAEE0vha5eoK/bBeZ1+XgfAP93Ok25S8VewWCwK8ddL8bLG5r+8i8sVqs/AQhqOkKtrl6gr9uF5jXaF2bbSIysZfP0qWyHrRwyme62IlsrxssbJqD0gMPuAHsrFXmVo4le6XhCVy/Q1+1C9Ppxt4XXw4cXOyOzjIkJ/TqCgvHyigkoXcayqxxOFRDgCrfooBkhqztPfutFVy/Q1+1C9PqJS4cB+P6RRawudHKcn5/v+DE7gfHyhgkoXeaVVIFyVbF1OEZ/pHW5p3RmeLKOo6sX6Ot2IXptH4lz+VicnGXz6NHOd6rr1UReXjFe3jABpcu8OOPM471rQ+vcCYClaTGJrl6gr9uF6nXTzlEAHni580/HuhbhGC9vmIDSZV6cdYa7uHJi5WZ+kaKeQyno6gX6ul2oXjdeNkJA4IfHMywUOttT+/Tp0x09XqcwXt4wAaXLHJx1ciivWSWHUhuSXDd09QJ93S5Ur5F4mB+9JIGt4B9e6mwuZbVh0f3CeHnDBJQuki5WOJmxiIYCbB/Rc0Icg8EL/+Q1zii39744R1XTcnyDf5iA0kWeSTqtaq4Y7yMYaDZP2BmCFa1mK66jqxfo63Yhe117SYLJgQjJJYsnjnduKJZsVs+WccbLGyagdJHH3NYw116y+jwUVmyo2zprQlcv0NftQvYKBoR37hoD4J7nZ9d9vBqTk5MdO1YnMV7eMAGlS1SV4rGjTm/WG7au/o8cKenZ81VXL9DX7UL3uvnKMSJB4bFjGQ6nOjMqwOxs54JTJzFe3jABpUscnM2zWKwwORBh+wo95GsoVi4S8wtdvUBftwvdayQe5pYrnbqU/32gM62NRPRMM+PlDRNQusSj005x1w1bh9q6+JWInq02dPUCfd0uBq+ffd0GggLfO7zA8fT662ZGR0c7YNV5jJc3TEDpAnZV8b1XFwC4YWt7ncnCmhaT6OoF+rpdDF4bBiK8/Yoxqgo+/+iJdR9P1yIc4+UNE1C6wGPH0pzMWEwNRnjDptUr5AHssJ7NinX1An3dLhavn3/TRvrCAR47luEHx9bXaTKR0HN0AePljZ7NKX8x8bVnnKeHW6+eWLW5cB3VvelV14WuXqCv2wXode+Lc02Xv/+ajfy/j53gT/7xOH/27n4Go2u7pdi29/nue4Hx8obJoXSY505neSaZpS8c4B1XjLW9X7BS6qLV2tHVC/R1u5i8br16givG+zidtfjUw0fXPGhhLpfrsFlnMF7eMAGlg5QqVT718FEA9lw1seLowsuxYsNdslofunqBvm4Xk1coIPznn9pOfyTI/51O8/nHTqwpqExNTXXcrRMYL2+YgNIhqkrxZ/uOczxdYttwjPe90dsFjxQXuyO2TnT1An3dLjavjYkoH/3JbYQCwtefneWPHzlGyeOcKclksitu68V4eaNnAUVEbhaRgyJySETubLJeROTT7voDInLNavuKyKiIPCAiL7t/RxrWfdTd/qCIvKOb51Yo2/zB96b51sEUoYDwGzduIxLylrQP3Pd/umS3PnT1An3dLkav67YM8Vs/vYNwQPjWwRR7v3GQR44sYlfby6184xvf6JrbejBe3uhJQBGRIPAZ4BbgKuC9InLVss1uAXa6rw8Bn21j3zuBB5VSO4EH3c+4698DXA3cDPyZe5yOUbarvJLK89dPJfmFrzzPtw8tEAsF+K9vv3TFmRlb8e377umkXsfQ1Qv0dbtYvW7YOsQf/cwVbBmKcnSxyH/99qu8/+7n+NTD09zz/Cw/OJbmhZkcJ9JFFvJllkoVCmWbSlXx9a9/vatua8V4eaNXrbyuAw4ppQ4DiMjdwB7g+YZt9gBfVE4B7KMiMiwiG4HtK+y7B7jR3f8u4CHgN93ldyulSsCrInLIddi33hMpVar88t++yMlMicaHryvG+/jVt25ZUzABkKqerTZ09QJ93S5mrysm+vizd+/iWwdTfO2ZGU5nLe5/aZ77VxnuPvpzf8w7v/AUoYAQDgqhgDS8D9Q/1161vsIiuP3/5azP9fVIwzbeCb7tdv7L/a+sce/u0Qmvn7lqnOu2dHbcOenFVJIi8s+Bm5VSv+h+/jngeqXURxq2+Tvgk0qpR9zPD+IEh+2t9hWRRaXUcMMxFpRSIyLyp8CjSqkvu8v/AviWUupvGr3uu+++pVOnTtVzaYlEYnZ0dLR5+8guMz8/P+7Xd6+Erl6gr5vx8o6ubsarJdt27959zrSRvcqhNHtAWB7JWm3Tzr5r+T7e+c53ttfr0GAwGAyr0qtK+ePAlobPlwAn29xmpX1Pu8ViuH9nPHyfwWAwGDpIrwLK48BOEdkhIhGcCvPlNYT3AB9wW3vdAKSVUqdW2fce4IPu+w8C32xY/h4RiYrIDpyK/h906+QMBoPBACilevIC3gm8BLwC/Gd32R3AHe57wWnN9QrwDHDtSvu6y8dwWne97P4dbVj3n93tDwK39Oo815g2N7ueh4A7ffj+I26aPwX80F02Cjzgpu0DwEjD9h91XQ8C7+igxxdwcpnPNizz7AG8yT2fQ8CncesKO+z1ceCEm2ZPAe/0wWsL8F3gBeA54N9qlGat3HxNNyCG83D5tOv12zqk2Qpevv/OPJ1Hr77IvFr+kII4ge9SIOL+oK7qscMRYHzZst/HDW44zbH/u/v+KtcxCuxw3YMd8vgJ4BrOvnF79nD/Md+M85DyLdb5QNHC6+PArzfZtpdeG4Fr3PeDOA9dV2mSZq3cfE039xgD7vsw8Bhwg99ptoKX778zLy/TU95/6k2qlVIWUGsW7Td7cJpi4/69tWH53UqpklLqVZynoOs68YVKqYeB5e1LPXm4dWkJpdQ+5fx3fbFhn056taKXXqeUUvvd90s4uYHN6JFmrdxa0RM35VCbkD3svhQ+p9kKXq3o2bX0ggko/rMZONbw+Tgr/+N1AwX8g4g8ISIfcpdNKqcOC/fvBnd5r329emx23/fC7yPuqA5faBilwRcvEdkOvBHnyVarNFvmBj6nm4gEReQpnGLMB5RSWqRZCy/Q6He2Giag+M9amkV3mh9TSl2DMxrBXhH5iRW21cEXOtvMfC18FrgMeANwCviUX14iMgB8Dfg1pdRKs2jp4OZ7uimlbKXUG3Baf14nIq9dYXO/vXxPLy+YgOI/vjdxVkqddP/OAH+LU4SlS5Nsrx7H3fdd9VNKnXZvAFXgzzlT7NdTLxEJ49yw/0opVRuPQ4s0a+amS7q5Los4o2vcjCZpttxLp/RqBxNQ/KedJtVdQ0T6RWSw9h54O/As+jTJ9uThFlcsicgNIiLABxr26Ri1m4/Lu3HSrKde7nH+AnhBKfWHDat8T7NWbn6nm4hMiMiw+z4O/DTwIj6nWSsvv9PLM72q/TevFVt4NG0W3aPvvhSntUituWKtSXfPm2QDf42TrS/jPGndvhYP4Fqcf7xXgD9l/U1gm3l9Cadp5gGcf+6NPni9Fac44wANzUo1SbNWbr6mG/A64En3+58Ffmutv/ceefn+O/Py6slYXgaDwWC48DFFXgaDwWDoCCagGAwGg6EjmIBiMBgMho5gAorBYDAYOoIJKAaDwWDoCCagGAxdRkT+UkR+d437flxEvtxpJ4OhG5iAYjC0iYhERORvROSIiCgRudFvJ4NBJ0xAMRi88QjwfiDpt4jBoBsmoBgMyxCRN4rIfhFZEpH/LSJ3i8jvKqUspdQfK6UeAWyPhx0XkQfcY35PRLY1fN//EJFjIpJxR3z+8RXcvioiSRFJi8jDInJ1w7q/FJHPiMi97vc8JiKXNay/2nWYF5HTIvKf3OUBEblTRF4RkZSIfEVERj2en8FgAorB0Ig7nto3cIa8GAW+CvyzDhz6fcDvAOM4w5D8VcO6x3FGkx0F/hfwVRGJtTjOt3DGbdoA7F92HID3Ar8NjODMkfF7AO54bd8G/h7YBFyOM8QIwK/izJnxNnfdAs7sqQaDJ0xAMRjO5gacyY3+WClVVkr9Dc4Nf73cq5R6WClVwhmD6c0isgVAKfVlpVRKKVVRSn0KZxa+K5sdRCn1BaXUknucjwOvF5Ghhk2+rpT6gVKqghNs3uAu/ydAUin1KaVU0T1Gbb6NX8IZw+14w3H/uYiEOnDehosIE1AMhrPZBJxQZw9yN92B49YnQ1LOzHzz7nchIv9BRF5wi7EWgSGcnMxZiDMB0yfdoqkMztTNLNu2sW4nDwy477fgDBbYjG3A34rIovv9L+AU6U16OkPDRY8JKAbD2ZwCNrtDf9fY2oHj1ueuEGfSqVHgpFtf8pvAvwBGlFLDQJrmEyX9K5ypX38aJ+hsrx2yje8/hjNRU6t1tyilhhteMaXUiTaOazDUMQHFYDibfUAF+FURCYnIbZyZ1Ah3/ola/UZERGLLgk8r3ikib3XraH4HeEwpdQwYdL9vFgiJyG8BiRbHGARKQAroA/6bh/P6O2BKRH7NPYdBEbneXfc54PdqDQXcuTn2eDi2wQCYgGIwnIVSygJuA34ep3L6XwJfb9jkIFDAmaf7fvf9NlbnfwH/D05R15twKulxj/EtnPlwpoEiZ88V3sgX3W1OAM8Dj7Z3VqCUWgJuAn4Gp1jsZeAn3dX/A2eujX8QkSX3uNc3O47BsBJmPhSDYRVE5C+B40qpj/ntYjDojMmhGAwGg6EjmIBiMHQAEXlORLJNXu9bfW+D4cLAFHkZDAaDoSOYHIrBYDAYOoIJKAaDwWDoCCagGAwGg6EjmIBiMBgMho5gAorBYDAYOoIJKAaDwWDoCP8/In9OXob0QIwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(numericals.q1_balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='q2_balance', ylabel='Density'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(numericals.q2_balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='q3_balance', ylabel='Density'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(numericals.q3_balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='q4_balance', ylabel='Density'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(numericals.q4_balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=numericals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Comparing to our Target\n",
    "\n",
    "Now we can check the numerical columns against the dependent variable. We see that balance does not have a huge impact on whether a customer accepts the offer or not, but customers with higher balances were less likely to accept the offer. Almost all of the outliers rejected the offer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2C70TtCxU9RYAZwJYiOhZRR+At6qq2/86jhA5aq4mE9DbL/T2C7394tI7ScsCqnoXgA8523sD6e7uDq2QCnr7hd5+obdfXHon6Tq7TkReV1b2OhH5njMbh4yMjIRWSAW9/UJvv9DbLy69kzzgPgPAL8vKbgfwBmc2Dpk3b15ohVTQ2y/09gu9/eLSO0myOACg/NvaXXjmOxdNxaFDh0IrpILefqG3X+jtF5feSZLFRgBfF5F5ABD//DKADc5sHLJv377QCqmgt1/o7Rd6+8Wld5Jk8Q8A5gEYE5GnAIwBmA/go85sHMIJ1v1Cb7/Q2y/0TtZ1dlxVzwJwEoCzAJykqn+hqk87s3EIJ1j3C739Qm+/0DtB11kRGQCwVVUfRDRrHkTk+QCeo6o/cWbkiLa2ttAKqaC3X+jtF3r7xaV3kttQXwGwp6xsT1zedMyfPz+0Qiro7Rd6+4XefnHpnSRZnKCq5fNtPwGgKW/mjY6OhlZIBb39Qm+/0NsvLr2TJItHKsyx/XoAjzqzcQivBPxCb7/Q2y/0Tjbcx2UA1onIdQAeBnAagHfHr6bj4MGDoRVSQW+/0Nsv9PaLS+8kvaF+CGAA0Rfzzop/Lo/Lm46JiYnQCqmgt1/o7Rd6+8Wld9KBBO8EcKezvTcQ9ov2C739Qm+/0DvZMwuIyMtEZKWIrBGRzxRfzmwcwn7RfqG3X+jtF3onG3X2/QB+AaAfwCoAL0H0re4lzmwc0t7eHlohFfT2C739Qm+/uPRO0rL4nwBWqOpbAEzEP9+GJh1IcO7cuaEVUkFvv9DbL/T2i0vvJM8sTlDVn8XLh0VklqquF5G8MxuHFAoFdHV1hdZIjE/vgYEBL/uphespXpPA88Qv9PaLS+8kyWKHiDxXVbcCeBDAOSIyCqAp+5QtWLAgtEIqrHpbxWq86e0XeidLFp8H8AIAWwF8BsD3ALQDuNCZjUMmJiZMTlgSwvv8x3XGddx4oiSuq/iZkPA88Qu9/eLSu+5koao3lCyvF5EFANpVdW+xXEReq6q/cGI2Qw4cOBBaIRVWva1iNd709gu9E3adLUVVD5Ymipj1M/RxBvtFk3qwGm96+4XeM0gWVQh/XyGG/aJJPViNN739Qm/3yWLmN78dMXv27NAKqbDqbRWr8aa3X+jtPlk0DR0dHaEVUmHV2ypW401vv9A7w8lifHw8tEIqrHpbxWq86e0XeteZLCTiVBFpqbWpAycn9PT0hFZIhVVvq1iNN739Qu86k4WqKoDfosYzCVVtmu/E79lTPgOsDax6W8VqvOntF3onuw11N4DnOdtzg+FkJaQerMab3n6hd7JvcN8GYIOI3ABgO0paGap6vTMjR7BfNKkHq/Gmt1/onaxl8VpE822fAeA8AO+MX+c5s3EI+0WTerAab3r7hd7Jhvt4g7O9eoBd3Ug9WI03vf1C7+Qz5fWIyDtF5B/j9RNF5CRnNg7hZCWkHqzGm95+oXeymfLOAPAAgByAT8XFpwP4mjMbh+zatSu0QiqselvFarzp7Rd6J2tZfBHAuaq6AsBUXHYHgFc6s3FIb29vaIVUWPW2itV409sv9E6WLJ6rqpvi5WJPqINI1qPKG7wSIPVgNd709gu9kyWL+0RkeVnZGxF9Wa/pmJxsyqnBa2LV2ypW401vv9A7WavgHwDcIiK3AugQka8D+AsA5zizcQj7RZN6sBpvevuF3glaFqr6/wC8FMDvAFyP6DsXr1TVXzmzcQj7RZN6sBpvevuF3jVaFiLyuKqeGC9fr6rvQTQXd9PT2dnZ8H0MDAwAAAYHB53V6cObPEOaeDfi954Uq+cJvf3i0rtWy6JNRIrDFr7N2V490NJSa4Dc5sSqt1WsxpvefqF37WTxdQDbReT3AOaIyO8rvZzZOGT37t2hFVJh1dsqVuNNb7/Qu8ZtKFX9pIhcC+C5AAYRjQVlgoULF4ZWSIVVb6tYjTe9/ULv2s8s/k1VzwWwQ0Q+qqo/dbbnBjM2NoY5c+aE1kiMVW+rWI03vf1C79q3oZaLSHH2uy842aMnovma7GHV2ypW401vv9C79vcsfgbgdhF5EMBsEbmxitD5zowcwWYjqQer8aa3X+hdu2Xx1wC+jGgAQQXwcJVX07Fz587QCqmw6m0Vq/Gmt1/oXfsB9wEANwGAiLQB+DaAvwVwIoDHAHxHVR90ZuOQrq6u0AqpsOptFavxprdf6J1sbKhfx6/nARgD8HwAvxKRv3RmQwghpClJkiyuAHCOquZU9WJVzSEaF+qKxqjNjL1794ZWSIVVb6tYjTe9/ULvZMniJEQPvEv5eVzedCxatCi0QiqselvFarzp7Rd6J0sW9yAaebaUj8XlTcfIyEhohVRY9baK1XjT2y/0TjZE+f8A8GMRuQjAdgAnA9gHoCmfWTzz9RBbWPW2itV409sv9E6QLFR1WEReAODViHpDPQ7gDlVtyllBuru7Qyukwqq3VazGm95+oXey21BQ1SlV/bmqfjf+2ZSJAmCzkdSH1XjT2y/0TpgsLDFv3rzQCqmw6m0Vq/Gmt1/oneFkcejQodAKqbDqbRWr8aa3X+id4WSxb9++0AqpsOptFavxprdf6J2sN1Qq4lFrfwbgc6q6Pi77GwDvUdUVjdpvtYnKC4UC1qxZAwC46KKL8KUvfQnj4+PYuXMn2tra0NnZiaeffjrRvu6++268/OUvn6kyALsTw1vFVby3bNmCj3/843jve9+La665Bm1tbVi8eDFaWlrQ2tqKCy64AGvWrMFJJ52Eyy+//KgHj4VCAVdccQUuueSSuh5KWj1P6O0Xl94Nb1loNEbuBwH8k4jMFpFOAJ8D8OFG7rfaROX5fB7Dw8MYHh7G2rVrMTw8PD3Y1uTkZOJEAQCXX375TFSPwOrE8FZxFe8rr7wS+/fvxzXXXANVxcGDB/Hoo49iy5YtGB4exmc/+1lMTEzgoYceQj6fP+rz+XwemzdvrvheI719Q2+/uPT2chtKVTcD+DGAVQA+jWhwwktE5FcicreInAMAIvIiEblTRO4RkXtF5PS0+2xrazuqrFAoYOPGjdPr27ZtS1v9Eezduxd33323k7oqeZPG4SLeW7ZsmT6Xqs0fUDrswoYNGzA2Nja9XigUMDg4CFXFxo0bj3ivGlbPE3r7xaV3w29DlbAGwF0ADgK4BcCQqr5HRI4HcKeI/F9ELZAvqWpeRNoBpJ5tfP78+UeV5fN5TE1Npa3ymKxataoh9f4xMDAwEFphRlx55ZWJtp+cnEQ+n8fKlSsBROfl4cOHAQCHDx8+4r1qVDq/LUBvv7j09vaAW1X3Afg3AN8C8N8BrBaRewDcBmA2gOcAuB3AJ0RkFYA+VZ0or2d0dBT9/f3TrxtuuKHi/kZHR48qGxoaMjvjFWle0rRQN23aNL08NDQ0fREzNTV1xHvVqHR+W4DefnHp7bNlAQCH45cA+CtVfaDs/ftF5A4AZwHYKCLvVdWh0g16e3sxNDSEWlTKqP39/bj11lsbkjC6urqwbt26Gdfz9NNP4/jjj5+5UB00yxX94OBgsH2niXd53Pr6+hInjGXLlk0v9/f3Y8OGDZiamkJra+sR71WDV7p+oXe4rrMbAawszu8tIi+Pf54K4BFVvRrAjwC8NO0ODh48eFRZLpdDa2tj8uOll17qpJ5K3qRxuIh30luQbW1tyOVy0+u5XA6zZkV/irNmzTrivWpYPU/o7ReX3qGSxeUA2gDcKyKb43UAOBfA5vj21FIAFef8roeJiaPuYKGnpwfLly+fXu/r60tb/RF0dXU56zpbyZs0DhfxXrJkyfS5VG3gttIZy1asWHFE99ienh4MDAxARLB8+fK6us5aPU/o7ReX3l6ThapepqpfUNUJVf2Aqr5EVV+sqmfH7/8vVX2Rqr5MVVeoau1uIVWo1r84l8th6dKlWLp0KVavXo2lS5dOj/ne1taW6haQq1YFYLc/t1VcxXvVqlWYM2cOVq5cCRFBe3s7TjnlFCxZsgRLly7FJz/5SXR0dOD000+v2HLI5XJ48YtfXFerwqW3b+jtF5fevp9ZeOPJJ5+s2HLo6enB1VdfPb1eupyU4r1rV60KoLo3aQyu4r1kyRLcfPPNAICzzz674jY//OEPq36+p6cHV111Vd37s3qe0NsvLr0zO9xHe3t7aIVUWPW2itV409sv9M5wspg7d25ohVRY9baK1XjT2y/0znCyKBQKoRVSYdXbKlbjTW+/0DvDyWLBggWhFVJh1dsqVuNNb7/QO8PJgl3dSD1YjTe9/ULvDCeLAwcOhFZIhVVvq1iNN739Qu8MJwv2iyb1YDXe9PYLvTOcLDj+PKkHq/Gmt1/oneFkMXv27NAKqbDqbRWr8aa3X+id4WTR0dERWiEVVr2tYjXe9PYLvTOcLMbHx0MrpMKqt1WsxpvefqF3hpNFT09PaIVUWPW2itV409sv9M5wstizZ09ohVRY9baK1XjT2y/0znCy4GQlpB6sxpvefqF3hpMF+0WTerAab3r7hd4ZThbsF03qwWq86e0Xemc4WbCrG6kHq/Gmt1/oneGZ8nxMVjI4OOi8TquTrFglTbwb8XtPitXzhN5+4eRHdbBr167QCqmw6m0Vq/Gmt1/oneFk0dvbG1ohFVa9rWI13vT2C70znCx4JUDqwWq86e0Xemc4WUxOToZWSIVVb6tYjTe9/ULvDCcL9osm9WA13vT2C70znCzYL5rUg9V409sv9M5wsujs7AytkAqr3laxGm96+4XeGU4WLS0toRVSYdXbKlbjTW+/0DvDyWL37t2hFVJh1dsqVuNNb7/QO8PJYuHChaEVUmHV2ypW401vv9A7w8libGwstEIqrHpbxWq86e0Xemc4WahqaIVUWPW2itV409sv9M5wsmCzkdSD1XjT2y/0znCy2LlzZ2iFVFj1torVeNPbL/TOcLLo6uoKrZAKq95WsRpvevuF3hlOFoQQQtyR2WSxd+/e0AqpsOptFavxprdf6J3hZLFo0aLQCqmw6m0Vq/Gmt1/oneFpVUdGRnDyySeH1khMCO8bT5SmrMsHPE/8Qm+/uPTObMtCxNY/rSJWva1iNd709gu9M9yy6O7uDq2QCp/eg4ODzurav38/5syZ46w+X/A88Qu9/eLSO7Mti5GRkdAKqaC3X+jtF3r7xaV3ZpPFvHnzQiukgt5+obdf6O0Xl96ZTRaHDh0KrZAKevuF3n6ht19cemc2Wezbty+0Qiro7Rd6+4XefnHpndlkwQnW/UJvv9DbL/TOcLLgBOt+obdf6O0Xemc4Wdx8882hFVJBb7/Q2y/09otL78wmi3Xr1oVWSAW9/UJvv9DbLy69M5sspqamQiukgt5+obdf6O0Xl95ibbrATZs2jQDYVmu7sbGx3u7u7lEPSk6ht1/o7Rd6+6XMu2/ZsmWpp84zlywIIYT4J7O3oQghhLiDyYIQQkhNMpksRGSFiDwgIltEZHVon3JEZKuI/FZE7hGR/4rLukXkJyLyUPxzQcn2F8fH8oCILPfoeb2IPCUim0vKEnuKyJ/Ex7tFRK6WBo73XMX5MhF5LI73PSLypmZyjvd3soj8h4jcLyK/E5GL4vJmj3c176aOuYjMFpE7ReQ3sfeauLzZ413Nu/HxVtVMvQC0AHgYwKkA2gH8BsALQ3uVOW4F0FtW9nkAq+Pl1QCujJdfGB/DcQBOiY+txZPnnwN4BYDNM/EEcCeA1wAQAOsBnOnZ+TIAH6+wbVM4x/t7NoBXxMtzATwY+zV7vKt5N3XM4310xcttAO4A8GoD8a7m3fB4Z7Fl8UoAW1T1EVU9COA7AM4J7FQP5wD4Zrz8TQBvLin/jqr+QVUfBbAF0TE2HFX9TwBjM/EUkWcDmKeqt2t0ht5Y8hlfztVoCmcAUNUnVPWueHkPgPsBLEbzx7uadzWaxVtVtThBdVv8UjR/vKt5V8OZdxaTxWIA20vWd+DYJ28IFMCgiPxaRN4fly1S1SeA6A8QwAlxebMdT1LPxfFyeblvPiIi98a3qYq3FprSWUSeC+DliK4azcS7zBto8piLSIuI3APgKQA/UVUT8a7iDTQ43llMFpXuuzVb/+DXquorAJwJ4MMi8ufH2NbC8QDVPZvB/2sATgPwMgBPALgqLm86ZxHpAvB9AB9V1d3H2rRCWTD3Ct5NH3NVPaSqLwNwEqKr7RcfY/Nm9254vLOYLHYAKJ2h/CQAjwdyqYiqPh7/fArADxDdVtoZNw0R/3wq3rzZjiep5454ubzcG6q6M/4DOwzgG3jmNl5TOYtIG6J/uHlVLY7T0PTxruRtJeax69MAbgOwAgbiXaTU20e8s5gsfgXgdBE5RUTaAbwdwI8CO00jIp0iMre4DGAAwGZEju+KN3sXgB/Gyz8C8HYROU5ETgFwOqIHU6FI5Bk35feIyKvj3hbnl3zGC8U//pi3IIp3UznH+7kOwP2q+k8lbzV1vKt5N3vMRWShiBwfL3cAeCOAYTR/vCt6e4l3o57ah3wBeBOiXhkPA7gktE+Z26mIeif8BsDvin4AegBsAvBQ/LO75DOXxMfyABrcK6fM9V8RNWknEV2J/F0aTwB/Gp+8DwP4MuKRAzw6fwvAbwHcG//xPLuZnOP9/Rmi2wD3Argnfr3JQLyreTd1zAG8FMDdsd9mAJ+Ky5s93tW8Gx5vDvdBCCGkJlm8DUUIIcQxTBaEEEJqwmRBCCGkJkwWhBBCasJkQQghpCZMFoQ0CBG5QER+nvKzrxeRHbW3JMQPTBaEEEJqwmRBMo+ItIZ2IMQ6TBYkOCKyWkQeFpE9InKfiLwlHp7g6dLB3eKhDiZE5IR4/ex4openReSXIvLSkm23isgqEbkXwD4Raa20n5LtW0TkKhEZFZFHReQjIqLFRCMi80XkOhF5QqJJZj4rIi31HZ5cIyK7RGRYRJaVvPFuiSYN2iMij4jIB5LEqOS9C0Tk5yLyBREZj/3PLHm/W0T+RUQej9+/ueS9qjEkpBQmC9IMPAzgdQDmA1gD4CYA3QDWAXhHyXZ/A+CnqvqUiLwCwPUAPoBoiIavA/iRiBxXsv07AJwF4HhVnaq0n5Ixdd6HaBTglyGaPOnNZY7fBDAFYAmiYbgHALy3jmN7FYBHAPQC+DSAdSLSHb/3FICzAcwD8G4A/xwfVyWO5V7czwPxfj4P4Lp4zB8gGgpiDoAXIRpy+58BoM4YEhLRyPFu+OIrzQvR+ELnIBok7ZGS8l8AOD9e/hqAy8s+9wCAM+LlrQDeU89+4uUhAB8oee+NiMY8agWwCMAfAHSUvP8OAP9Ro/4LEI3kKSVldwJ4Z5XtbwZwUbz8egA76nS/ANGEX8X35sTuz0I0k91hAAsq1HHMGPLFV+mL93JJcETkfAAfA/DcuKgL0RXyjwF0iMirADyJ6Kr/B/E2fQDeJSIrS6pqB3BiyXrppC/H2g/iz5VuX7rch2hGsieeuVjHrPL6q/CYqpYOwLat6BjfKvo0gOfF9c1BNBjcUdRwB6L4AABUdX/s2YWohTamquMVqq0nhoQAAJMFCYuI9CEaf38ZgNtV9ZBEs4CJqh4Wke8iuorfCeAWjabuBKJ/1J9T1c8do/rpf9LH2k+8yRM4cnz/0jkAtiNqWfRqdDsrCYtFREoSxnPwzK2e7yMeGlpVJ+NnCUdNSlOH+7HYDqBbRI7XaP6D8vdqxZAQAHxmQcLTieif+ggQPfQFUDpj2bcBnAsgFy8X+QaAD4rIqySiU0TOkniukBT7+S6Ai0RkcTxfwKriGxqN/T8I4CoRmScis0TkNBE5o47jOwHAhSLSJiJ/DeAFAP4d0RX8cbHPVNzKGEjpXpXYfT2Ar4rIgtijODNj0hiSP2KYLEhQVPU+RFNA3o6o9fASRM8miu/fAWAfolsj60vK/wvRQ+kvAxhHNBH9BWn3g+gf5yCi+QDuRvQPfQrAofj98xH9g78v3t/3ED0PqMUdiCacGQXwOQBvU9VC3EK6EFGSGgfwt6gySVcd7rV4J6L5PYYRPVT/aFxvohiSP244nwUhFYiv9K9V1b7QLoQ0A2xZEIJoikoReVP8fYzFiB48/6DW5wj5Y4HJgpAIQfT9hXFEt6HuB/Cpmh8SuVZE9lZ4XdtgX0K8wttQhBBCasKWBSGEkJowWRBCCKkJkwUhhJCaMFkQQgipCZMFIYSQmjBZEEIIqcn/B5vi4Kf1og9AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "box(numericals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 Removing Skewness\n",
    "\n",
    "Here, we will dive into eliminating skew from our numerical variables. For every numerical column, most of the data was in the lower half of the table, so we used a log transformation to remove the right skew.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numericals['average_balance'] = list(map(log_transfom_clean, numericals['average_balance']))\n",
    "sns.distplot(numericals.average_balance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['q1_balance'] = list(map(log_transfom_clean, numericals['q1_balance']))\n",
    "sns.distplot(numericals.q1_balance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['q2_balance'] = list(map(log_transfom_clean, data['q2_balance']))\n",
    "sns.distplot(data.q2_balance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['q3_balance'] = list(map(log_transfom_clean, numericals['q3_balance']))\n",
    "sns.distplot(numericals.q3_balance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['q4_balance'] = list(map(log_transfom_clean, numericals['q4_balance']))\n",
    "sns.distplot(numericals.q4_balance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Scaling Numericals\n",
    "\n",
    "We opted to use a MinMax scaler due to the distribution shape being non-normal. It ensures that our variables are scaled to values within 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_balance</th>\n",
       "      <th>q1_balance</th>\n",
       "      <th>q2_balance</th>\n",
       "      <th>q3_balance</th>\n",
       "      <th>q4_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.749189</td>\n",
       "      <td>0.483768</td>\n",
       "      <td>0.256358</td>\n",
       "      <td>0.286424</td>\n",
       "      <td>0.237722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.262826</td>\n",
       "      <td>0.011304</td>\n",
       "      <td>0.030985</td>\n",
       "      <td>0.020403</td>\n",
       "      <td>0.086833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.411248</td>\n",
       "      <td>0.106377</td>\n",
       "      <td>0.102894</td>\n",
       "      <td>0.037928</td>\n",
       "      <td>0.057414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.760723</td>\n",
       "      <td>0.457391</td>\n",
       "      <td>0.514469</td>\n",
       "      <td>0.292702</td>\n",
       "      <td>0.099407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.759172</td>\n",
       "      <td>0.620290</td>\n",
       "      <td>0.396668</td>\n",
       "      <td>0.256866</td>\n",
       "      <td>0.086595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_balance  q1_balance  q2_balance  q3_balance  q4_balance\n",
       "0         0.749189    0.483768    0.256358    0.286424    0.237722\n",
       "1         0.262826    0.011304    0.030985    0.020403    0.086833\n",
       "2         0.411248    0.106377    0.102894    0.037928    0.057414\n",
       "3         0.760723    0.457391    0.514469    0.292702    0.099407\n",
       "4         0.759172    0.620290    0.396668    0.256866    0.086595"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Scaling numericals with Min-Max Scaler\n",
    "from sklearn import preprocessing\n",
    "scaler = preprocessing.MinMaxScaler()\n",
    "scaled_df = scaler.fit_transform(numericals)\n",
    "scaled_df = pd.DataFrame(scaled_df, columns = numericals.columns)\n",
    "scaled_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "numericals = scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_balance</th>\n",
       "      <th>q1_balance</th>\n",
       "      <th>q2_balance</th>\n",
       "      <th>q3_balance</th>\n",
       "      <th>q4_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.749189</td>\n",
       "      <td>0.483768</td>\n",
       "      <td>0.256358</td>\n",
       "      <td>0.286424</td>\n",
       "      <td>0.237722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.262826</td>\n",
       "      <td>0.011304</td>\n",
       "      <td>0.030985</td>\n",
       "      <td>0.020403</td>\n",
       "      <td>0.086833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.411248</td>\n",
       "      <td>0.106377</td>\n",
       "      <td>0.102894</td>\n",
       "      <td>0.037928</td>\n",
       "      <td>0.057414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.760723</td>\n",
       "      <td>0.457391</td>\n",
       "      <td>0.514469</td>\n",
       "      <td>0.292702</td>\n",
       "      <td>0.099407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.759172</td>\n",
       "      <td>0.620290</td>\n",
       "      <td>0.396668</td>\n",
       "      <td>0.256866</td>\n",
       "      <td>0.086595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_balance  q1_balance  q2_balance  q3_balance  q4_balance\n",
       "0         0.749189    0.483768    0.256358    0.286424    0.237722\n",
       "1         0.262826    0.011304    0.030985    0.020403    0.086833\n",
       "2         0.411248    0.106377    0.102894    0.037928    0.057414\n",
       "3         0.760723    0.457391    0.514469    0.292702    0.099407\n",
       "4         0.759172    0.620290    0.396668    0.256866    0.086595"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numericals.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Concatenate Clean Data\n",
    "\n",
    "Here, we will check DataFrame shape before concatenating the two cleaned datasets into a new DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17974, 5)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numericals.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17974, 11)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categoricals.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat((categoricals, numericals), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>offer_accepted</th>\n",
       "      <th>reward</th>\n",
       "      <th>mailer_type</th>\n",
       "      <th>income_level</th>\n",
       "      <th>overdraft_protection</th>\n",
       "      <th>credit_rating</th>\n",
       "      <th>own_your_home</th>\n",
       "      <th>bank_accounts_open</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>household_size</th>\n",
       "      <th>average_balance</th>\n",
       "      <th>q1_balance</th>\n",
       "      <th>q2_balance</th>\n",
       "      <th>q3_balance</th>\n",
       "      <th>q4_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.749189</td>\n",
       "      <td>0.483768</td>\n",
       "      <td>0.256358</td>\n",
       "      <td>0.286424</td>\n",
       "      <td>0.237722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.262826</td>\n",
       "      <td>0.011304</td>\n",
       "      <td>0.030985</td>\n",
       "      <td>0.020403</td>\n",
       "      <td>0.086833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.411248</td>\n",
       "      <td>0.106377</td>\n",
       "      <td>0.102894</td>\n",
       "      <td>0.037928</td>\n",
       "      <td>0.057414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.760723</td>\n",
       "      <td>0.457391</td>\n",
       "      <td>0.514469</td>\n",
       "      <td>0.292702</td>\n",
       "      <td>0.099407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.759172</td>\n",
       "      <td>0.620290</td>\n",
       "      <td>0.396668</td>\n",
       "      <td>0.256866</td>\n",
       "      <td>0.086595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  offer_accepted reward mailer_type income_level overdraft_protection  \\\n",
       "0              1      3           2            1                    2   \n",
       "1              1      3           2            2                    2   \n",
       "2              1      3           1            1                    2   \n",
       "3              1      3           2            2                    2   \n",
       "4              1      3           2            2                    2   \n",
       "\n",
       "  credit_rating own_your_home bank_accounts_open credit_cards_held  \\\n",
       "0             1             1                  1                 2   \n",
       "1             2             2                  1                 2   \n",
       "2             2             2                  2                 2   \n",
       "3             1             1                  2                 1   \n",
       "4             2             2                  1                 2   \n",
       "\n",
       "  homes_owned household_size  average_balance  q1_balance  q2_balance  \\\n",
       "0           1              4         0.749189    0.483768    0.256358   \n",
       "1           2              5         0.262826    0.011304    0.030985   \n",
       "2           1              2         0.411248    0.106377    0.102894   \n",
       "3           1              4         0.760723    0.457391    0.514469   \n",
       "4           1              6         0.759172    0.620290    0.396668   \n",
       "\n",
       "   q3_balance  q4_balance  \n",
       "0    0.286424    0.237722  \n",
       "1    0.020403    0.086833  \n",
       "2    0.037928    0.057414  \n",
       "3    0.292702    0.099407  \n",
       "4    0.256866    0.086595  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "offer_accepted          0\n",
       "reward                  0\n",
       "mailer_type             0\n",
       "income_level            0\n",
       "overdraft_protection    0\n",
       "credit_rating           0\n",
       "own_your_home           0\n",
       "bank_accounts_open      0\n",
       "credit_cards_held       0\n",
       "homes_owned             0\n",
       "household_size          0\n",
       "average_balance         0\n",
       "q1_balance              0\n",
       "q2_balance              0\n",
       "q3_balance              0\n",
       "q4_balance              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.1 Defining Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we can apply a model we are setting x and y prior to creating a training and test set.\n",
    "- x are our features excluding household_size and overdraft_protection \n",
    "- y is our independent variable for \"offer_accepted\" Yes(1) or No(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting x and y prior to creating a training and test set\n",
    "y = df['offer_accepted']\n",
    "x = df.drop(['offer_accepted', 'household_size', 'overdraft_protection'], axis=1)\n",
    "\n",
    "#converting y from object to int - Python didn't want to accept the object type\n",
    "y = y.astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Packages for the Logistic Regression Function\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import TomekLinks\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2 Logistic Regression Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: Test and Training sets are regenerated for each regression to avoid errors.\n",
    "\n",
    "We are going to use our previously created sample_all loop that will iterate through a Logistic Regression (logreg) and a KNN model (KNN) with diffrent sampling methods. \n",
    "Each model will take a different set of values for the sampling strategy:\n",
    "- Random OverSampler\n",
    "- Random Under Sampler\n",
    "- SMOTE\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "\n",
    "The function will also provide:\n",
    "- Accuracy score for logreg and KNN\n",
    "- Confusion Matrix\n",
    "- Receiver Operating Characteristic Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Random Oversampling Classification Score: 0.7013684086351304\n",
      "\n",
      "Logistic regression classifier on test set: 0.70\n",
      "\n",
      " [[3427 1709]\n",
      " [1326 3711]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.72      0.67      0.69      5136\n",
      "         2.0       0.68      0.74      0.71      5037\n",
      "\n",
      "    accuracy                           0.70     10173\n",
      "   macro avg       0.70      0.70      0.70     10173\n",
      "weighted avg       0.70      0.70      0.70     10173\n",
      "\n",
      "\n",
      "KNN Model Score:  0.9267669320751007 \n",
      "\n",
      "\n",
      " [[4391  745]\n",
      " [   0 5037]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       1.00      0.85      0.92      5136\n",
      "         2.0       0.87      1.00      0.93      5037\n",
      "\n",
      "    accuracy                           0.93     10173\n",
      "   macro avg       0.94      0.93      0.93     10173\n",
      "weighted avg       0.94      0.93      0.93     10173\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Random Undersampling Classification Score: 0.6936274509803921\n",
      "\n",
      "Logistic regression classifier on test set: 0.71\n",
      "\n",
      " [[208  98]\n",
      " [ 78 228]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.73      0.68      0.70       306\n",
      "         2.0       0.70      0.75      0.72       306\n",
      "\n",
      "    accuracy                           0.71       612\n",
      "   macro avg       0.71      0.71      0.71       612\n",
      "weighted avg       0.71      0.71      0.71       612\n",
      "\n",
      "\n",
      "KNN Model Score:  0.6176470588235294 \n",
      "\n",
      "\n",
      " [[217  89]\n",
      " [145 161]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.60      0.71      0.65       306\n",
      "         2.0       0.64      0.53      0.58       306\n",
      "\n",
      "    accuracy                           0.62       612\n",
      "   macro avg       0.62      0.62      0.61       612\n",
      "weighted avg       0.62      0.62      0.61       612\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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rmOVrkiu4w3f7dmHq1ELWrv2sRdK0aSFOPbWTla920njuxRxd9A4BaefoondoPPdiVr7ayW9+08ZzzzWx115h1q3zMXZsMWvWuGcwbKfK1j05zCBubZ/dEyb5muQKZvma5AqZ93399WwuuKCQqqosGhuF559vQiJuBEaO7OLXc+HXc8N0dDQRPb/MoYeGeemlJi6+uIAXXvDT3Oyeuwij+0G4Hbe2z+4Jk3xNcgWzfE1yhcz5qsKiRblMnlxEVVUWRx8d4qGHmj8XHKLpyXXgQOWxx5pZvryJU04J7VofDqfaOjmcKlsvQAB5eXmZVkgKk3xNcgWzfE1yhcz4trTAxRcXcPXV+XR2Cpde2s7jjzczdGj8FknxXLOyYOzYz8Zl+sc/sjnmmGLefTdzHemcKlsvQACBQCDTCklhkq9JrmCWr0mukH5fVZg2rYhly3IoKFDuu6+ZX/2qjUTm1knG9fbb8/jww2wmTCjisccy0xnQqbL1AgSwY8eOTCskhUm+JrmCWb4muUL6fUVg9ux29t8/zOrVjXz3u6Hed7JJxvWPf2zhe9/roK1NuOSSAq68MkAwzR2unSpbL0AApaWlmVZICpN8TXIFs3xNcoX0+HZ2WlU+3UyaFOLVVxs54ICupI6TjGsgAP/3f63cfnsLOTnKffflcfLJRfz3v+l7iO1U2XoBAmhqasq0QlKY5GuSK5jla5IrOO9bUyNMm1bIqacWfS5I9GW65mRdReC884I880wTw4eHWbvWx3e/W0RnmqaQcKpsvQABBNN9P9hPTPI1yRXM8jXJFZz1ffPNbMaOLeaVV/wMHKh0JXfDsBt9dR092moKO25ciJtvbsOXpo4ETpWtFyDIfPvsZDHJ1yRXMMvXJFdwxlcV7rsvl1NOKeLTT7M48shOXnqpkaOO6l+70/64lpYqS5c2M2HCZ888Vq7009jYL6W4eP0gHMRrT+4cJrmCWb4muULqfVtb4dJL87nyynxCIeFHP2pnxYomhg3r/6B6/XWN7GPx+uvZnHNOAePGFfOf/zhzyfX6QTiI11zQOUxyBbN8TXKF1PvW1GTx3HN+8vOVe+9t5pZbEmvCmgipdB0yRDnggDAbNmRz4onFLFuWIskIvGauDuLWiUx6wiRfk1zBLF+TXCH1viNGdHH//S2sWtXI1KmJN2FNhFS67rNPFytXNnHWWR20tgoXXVTInDmpbQrrTRjkIA0NDZlWSAqTfE1yBbN8TXKF/vuGw/DrX+exaNFnYySNHdvJgQf284l0DFJdtvn5MH9+K7//fQt+v3LPPXlMnlzEp5+mpimsU+eCN1gfMHjw4EwrJIVJvia5glm+JrlC/3zr6oSLLipgzRo/ubnKaacFKS93bgIfJ8pWBC64IMghh4Q5//xCPvwwi2BQgP7nw6lzwbuD4Mv3SyydmOQKZvma5Ap99127NtseXtvP4MFdPPZYs6PBAZwt229+M8yaNY08+mgzI0ZYdz+q1quvOOXrBQggFEpt/aXTmORrkiuY5WuSKyTvqwoPPJBj90rO4hvfsJqwfvvbzvc+c7psBw9Wjjzys6a4d9+dy/nnF9DX/m5O+XoBAq89uZOY5Apm+ZrkCsn73n57Hj/9aQHBoDBjRjtPP93EV76Snnmh01m2jY1wxx15rFiRwwknFPPBB8lflr1+EA7yZW9P7iQmuYJZvia5QvK+06YF2WOPLhYubGHu3Dai5u9xlHSWbXExPP98EwceGOajj7I54YRiHn88uaawXj8IBykoKMi0QlKY5GuSK5jla5IrJOb7r39l76qL32uvLv75zwbOPDP9Q4qku2z33beL559vZNq0DlpahBkzCrn22gCJ1hw55esFCCA7O3MTffQFk3xNcgWzfE1yhfi+4TDcckse48YVM2/eZ7cKmZoTKRNlW1AAixa18tvftuLzKQsW5HHttYl1gHPKN20BQkQmisgHIvKxiMzpIc1YEXlLRN4TkZejtmWLyL9E5OlUuzU6OUiKA5jka5IrmOVrkiv07FtfL0yfXsjcuQFENG0joMYjU2UrAhdf3MFTTzVx8MGdzJ7dkdB+TvmmpR+EiGQD84DxwFbgTRFZoar/iUgzAJgPTFTVzSIyJOowlwPrgeJU+5WVlaX6kI5ikq9JrmCWr0muENv3rbeyOe+8ArZsyWbQoC7uvbeF73wn8xEi02V75JFhXn65iSz7J7wqPPusn5NOCsWcS9sp33TdQRwBfKyqG1U1CCwBTotK8z1guapuBlDV6u4NIjIcOBlY7IRcfX29E4d1DJN8TXIFs3xNcoXdfR98MIeTTipiy5ZsRo/uZM2aRlcEB3BH2WZFXJ3vuiuXH/ygkAsvLKC5efe0TvmmK0B8BdgSsbzVXhfJ/sBAEVkjImtF5NyIbXcAVwGp71MPaH96qGQAk3xNcgWzfE1w3bQpi2t+ns0Be+Vy2KEHc8BeuVzz82w++iiLP/0pl44O4Yc/7OBvf2ti+HD35MdtZTtyZBeFhcrjj1tNYT/8MKvHst20KXWX9XQNtRFrwJHoT8AHfAMYBwSAChF5HStwVKvqWhEZ29MbVFdXM2PGDHw+H+FwmClTpjBr1iyqqqooKCggOzubxsZGysrKqK+vR1UpKytj+/bt5ObmUldXR3NzM0OHDqWmpgYRYdCgQdTU1FBcXEw4HKalpYXy8nKqqqrw+/2UlJRQW1tLSUkJwWCQtra2XdtzcnIoKiqirq6OgQMH0tbWRnt7+67teXl5BAIBduzYQWlpKU1NTQSDwV3bA4EAOTk5NDQ0MHjwYBoaGgiFQpSXlxMMBqmtrY2bp8LCQgBX5Kmjo6PXPCXyOaUjTyJCZWVlSj4np/MUDAbZuXNnWs+9ZPL03HPCjVcN5cLQAio6FzKCSiqbR7D4zzOZ9Ogl/HjOf/nhD0s49thPqKvL3PcpVp7C4TBbtmxxzffp+ONLefTRKn784+F8+GEexx1XSEDbmNn1+bK9988zOfHRS7jxd7VMmNCV0OcU98KdjkgpImOAG1V1gr18NYCq3hKRZg6Qp6o32sv3Ac8Bo4FzgE4gD+sZxHJV/UHke1RUVOioUaP65FdZWcmIESP6tG8mMMnXJFcwy9fNrps2ZTHhWB8rWk9gDK/vtr2Co5icv5qVr3YycqQjFQP9wq1l29wMM2YU8NqqEC+QmrJdt27d2nHjxh0ea1u6qpjeBPYTkZEikgNMB1ZEpXkSOFZEfCKSDxwJrFfVq1V1uKrube/3YnRw6C+JRFI3YZKvSa5glq+bXe+dJ1wYmh/zAgYwhteZEVrA4vmpGc001bi1bAsLYd8925mdlZ6yTUuAUNVO4DJgJVZLpKWq+p6IzBSRmXaa9Vh3DO8A/wAWq+q76fDz8PBILcuW+rgwtDBumotCC1i21Ky+HG5g2VIfF3elp2zT1g9CVZ9R1f1VdV9V/bW9bqGqLoxIM1dVD1LVr6rqHTGOsUZVT0m1W3OsZgEuxiRfk1zBLF83u9a15DGCyrhp9mIzdc0Z6gnXC17ZWng9qYGhQ4dmWiEpTPI1yRXM8nWza2lBO5XEr8PfzF6UFranySg5vLK18AIEUFNTk2mFpDDJ1yRXMMvXza5Tz+xkUdbMuGnu9V/C1DPDcdNkCreX7WJ/esrWCxCAxOqa6GJM8jXJFczydbPrRbOUP+ZeSgVHxdxewVHc57+ECy91V3+Dbtxetov96SlbL0AAgwYNyrRCUpjka5IrmOXrZteRI7uY/0AXk/NXM8c/lw3sQwgfG9iHOf65TM5fzbz7u1zZxBXcX7bz7k9P2XoBAnffTsbCJF+TXMEsX7e6PvJIDi0tMH58Jytf7aTx3Is5uugdAtLO0UXv0Hjuxax8tZPx490xrEYs3Fq23aSrbNPSUS4d9Kej3I4dOxg4cGCKjZzDJF8TXDdtyuLeecKypT7qWvIoLWhn6pmdXDRLXfsLF9xZtsuX+7nwwkIOOaSTF19sInIUajf69oRJrtA/Xzd0lHM14bA7H5T1hEm+bnddtcrHhGN9DHhwERXNh9ChOVQ0H8KABxcx4Vgfq1alazSa5HFb2f73v8LPfpYPwHnndRA9RYHbfONhkis45+sFCKClpSXTCklhkq+bXTdtymLW+VmsaD2BW0JXsS8b8RFmXzZyS+gqVrSewKzzs1I6+FkqcVPZdnXBZZcV0NCQxfjxIc4/f/dZ4Nzk2xsmuYJzvu4889PMF33y90ziZlfTh4NwU9kuWpTLyy/7KS3t4q67WmLOWeAm394wyRWc8/UCBF/8yd8ziZtdTR8Owi1l+5//ZHHTTdbUmHfe2crQobGfa7rFNxFMcgXnfL0AAfj9/kwrJIVJvm52NX04CLeU7ZNP5tDRIfzgBx1MmhTqMZ1bfBPBJFdwzte9T+DSSElJSaYVksIkXze7lha0U9k8gn3Z2GMaNw8H4Zayvfrqdg4+OMx3vtNzcAD3+CaCSa7gnK93BwHU1tZmWiEpTPJ1s+vUMzu5J9vc4SDcVLaTJ4coKoqfxk2+vWGSKzjn6wUIvF8LTuJm16lnw93h+EMW3Jvt3uEgMlm2jY0wfXoB772X+PMZN58L0ZjkCt4dhKMEg7s3yXMzJvm61VUV/vCHPFop4MSs3YcsuFLmMo7VZBXmUVDgzgCRybL93//N5/nnc7jyygCJ9rV167kQC5NcwTlfL0AAbW1tmVZICpN83er64IM5PPtsDsXFXTz2VDjmkAX7fjWX9vYsNm5059ckU2X7+ON+Hnssl0BAufPO1phNWmPh1nMhFia5gnO+3kNqvDbPTuJG140bs7j2WqvH79y5bYwZE2bMGPj13DAdHU3k5uYCUF3dTG2tcNBB7hxuIxNlu23bZ72lb765lf32S7xs3Hgu9IRJruD1g3AUr82zc7jR9dprA7S0CN/9bpBp0z5/ax7pO2SIfi44uK1HdbrLtqsLZs0qYOfOnntLx8ON50JPmOQKXj8IR8nJycm0QlKY5OtG1zvuaOXsszu47bbdq0d68n3ooRyOPLKYhx5yT37SXbb33NN7b+l4uPFc6AmTXME5Xy9AAEW9tc9zGSb5utF16FBl3rxWBgzY/elqT76dndDZKfz0p/m8/LI7ambTXbYlJUphoXLHHT33lo6HG8+FnjDJFZzz9QIEUFdXl2mFpDDJ1y2uLS3WeEGdvQyT35PvD38YZPbsdjo7hfPOK+CDDzL/1Ul32Z59dpB16xo4+eT4HeJ6wi3nQiKY5ArO+Wb+LHcBJo37Dmb5usX1+uvzufrqfH7yk/y46eL53nBDG6ecEqSxMYuzziqkpiazg/ilq2x37vwsn4MH973Jr1vOhUQwyRWc8/UCBF6TNidxg+vzz/u4//5ccnKUH/2oI27aeL5ZWbBwYQujR3eyeXM23/9+IZnMXjrK9u9/9/G1r5XwwAP9r+N2w7mQKCa5gnO+XoAA2tvdOdZOT5jkm2nXmhph9uwCAK69to2vfjX+sBm9+ebnw8MPNzN8eJiPP85i48bMjfTqdNk2NsLMmfk0NwvbtvX/UpHpcyEZTHIF53zd8bQtw3htnp0jk66qcMUV+dTUZPGtb4WYNSv+3QMk5jt0qPLYY834fCTVDyDVOF22//u/+Wzdms3o0Z1ceWX/L0DeeescXj8IB/HaPDtHJl0je0vPn99CVgJne6K+Bx7Y9bngUFWV/ucRTpZtZG/pBQtaSMVo0t556xzG94MQkYki8oGIfCwic3pIM1ZE3hKR90TkZXtdnoj8Q0Tettf/MtVueXnuHO+/J0zyzZSrKjz3nHVVmzu3jeHDE3u42hffe+7JZfToEl57Lb035E6VbX96S8fDO2+dwynftAQIEckG5gEnAQcBZ4vIQVFpBgDzgcmqejBwhr2pAzheVQ8FDgMmikjs4Tf7SCAQSOXhHMck30y5isBDD7Xw8MPNu/WWjkdffCsrs2hvF849t4CPPkrfTblTZfvzn+f3ubd0PLzz1jmc8k3X2XwE8LGqblTVILAEOC0qzfeA5aq6GUBVq+2/qqrNdhq//Urp8Jo7duxI5eEcxyTfTLpmZcFJJ4WS6vHbF9+bbmpj0qQgO3dmMX16IXV16alucqpsr7++je98J9Sn3tLx8M5b53DKN133xF8BtkQsbwWOjEqzP+AXkTVAEXCnqv4Zdt2BrAX+B5inqm9Ev0F1dTUzZszA5/MRDoeZMmUKs2bNoqqqioKCArKzs2lsbKSsrIz6+npUlbKyMrZv347P56Ouro7m5maGDh1KTU0NIsKgQYOoqamhuLiYcDhMS0sL5eXlVFVV4ff7KSkpoba2lpKSEoLBIG1tbbu25+TkUFRURF1dHQMHDqStrY329vZd2/Py8ggEAuzYsYPS0lKampoIBoO7tgcCAXJycmhoaGDw4ME0NDQQCoUoLy8nGAxSW1sbN0+FhYUArshTR0dHr3lK5HNKJE//+pePP/xhAHfd1U529rak8wRQWVmZ9Of0i19sYcuWUfz73zmccYaPxx9vZufO1OSpp88pGAyyc+fOlJ97gwYFuO8+63NqaUnd51RQUEBlZaXrvk+x8hQOh9myZYsrv0+x8pSfn8+2bdv69DnFQzTRwdz7gYicAUxQ1Qvt5XOAI1R1dkSau4HDgXFAAKgATlbVDyPSDAAeB2ar6ruR71FRUaGjRo3qk9+nn37KsGHD+rRvJjDJN52uLS3wne8U8/HH2VxxRRvXX598y5v++H76qTB+fDHbtmUxbVoHixYlPhR2394vdWUbDMLKlX5OOSW5O65k8M5b5+iP77p169aOGzfu8Fjb0lXFtBXYM2J5OLAtRprnVLVFVWuBV4BDIxOo6k5gDTAxlXLe5CDOkU7XG24I8PHH2Rx4YJirrupbs8z++A4bZjV/LSxU1qzxs3Wrs1+vVJbtLbcEOO+8Qn7xC+fq3r3z1jlMnzDoTWA/ERkpIjnAdGBFVJongWNFxCci+VhVUOtFpMy+c0BEAsAJwPuplPPaPDtHulyff97HH/+YR06OsmhRC31t1NFf34MPDvPQQ82sWtXEnns620ciVWX797/7uOuuXLKylNNOc+7C6J23zmF0PwhV7QQuA1YC64GlqvqeiMwUkZl2mvXAc8A7wD+AxXY10jDgJRF5ByvQrFLVp1Pp57V5do50uNbWCj/+sdVb+ppreu8tHY9U+H77252MGPFZcIgcyyiVpMK1u7e0qvCTn7RzxBF9L7ve8M5b53DKN20Nt1X1GeCZqHULo5bnAnOj1r0DfN1JN69Jm3M47drdW7q6Ootjjkmst3Q8UumrCnfdlcvdd+fx3HNN7Ltvau8oUuHa3Vv661/v7HO1XKJ4561zmN7M1dV4k4M4h9OuqnDooWFKS7tYsKCF7H4OjZRK364ueP11H3V11uiv9fWpvZPor2tkb+mFC1PTWzoe3nnrHN6EQQ7S0NCQaYWkMMnXadesLLjyynbWrm1IuLd0PFLpm50N997bwte+1snGjdmcc04BHf27wfkc/XFVhXvvtebevummtrSMKeWdt87hlK8XIIDBgwdnWiEpTPJ1yrWzk8/Nx1BcnJrjptq3sBAeeaSZYcO6qKjwc/nl+aSqZXl/XEVg2bJmbruthQsuSGHUioN33jqHU74JBQgR+Zoj7+4SvF8LzuGU6x135HH00cWsWpXax2hO+O6xh7JkSTMFBcrSpbnMnZuacXP66xoIwPnnBx3tqxGJd946R6bvIF6wB8v7uYiY03skQUKhvk2hmE42bcrimp9nc8BeuXztkAM5YK9crvl5Nps2ufsm0ImyXbcum1tvzaOuLotUV706dS4cckiYxYtbyMpS/vSnXBob+3/MvriuX5/F7Nn5KXn/ZDHhe9aNSa7gnG+iV5dhwPVYfRM+EpHnReQHdn8F43F7m+dVq3xMONbHgAcXUdF8CB2aQ0XzIQx4cBETjvWl/Fd0Kkl12ba0wMyZBYTDwiWXtHPccb1MMp0kTp4LEyaEWLSoheefb0xJlViyrsGgVXYPP5zLbbelv5WO279nkZjkChnuB6Gqnar6pKqegTWu0lLgKmC7iPxZRI5xxC5NuLnN86ZNWcw6P4sVrSdwS+gq9mUjPsLsy0ZuCV3FitYTmHV+lmvvJFJdtpG9pa+7LvXTLDp9LkydGuIrX/nsIURLS9+PlazrLbcE+Pe/fey9d5if/zz9U2q6+XsWjUmu4JL5IESkEDgdqyf0cKxRWT8CHhaReSm3SxMFBQWZVuiRe+cJF4bmM4bXY24fw+vMCC1g8fz0T1iTCKks21T1lo5Hus4FVbjlljyOP76YHTv69tkl4xrZW3rBghaKivr0lv3Czd+zaExyBed8E31IfbKILAH+C5wFLAb2UNWLVPVmYDRwniOGaSC7v43nHWTZUh8XhhbGTXNRaAHLlrozD6kq2/Z2uOKK1PSWjke6zoW2NnjmGT8ffZTNeecV0JehdBJ1je4tfeSRzvWWjoebv2fRmOQKzvkmegfxW6zhtkep6iRVXaKqu7pdqmo9cIUDfmmhMRNP7BKkriWPEVTGTbMXm6lrducMWKkq27w8uO++FqZP7+h3b+l4pOtcyM+HRx9tpry8i9de8/OTnyTf/DVR13T2lo6Hm79n0ZjkCs75JvoM4hBVnauqn8ZJszh1WumlrKws0wo9UlrQTiUj4qbZzF6UFmbuix+PVJbtmDGdzJ/f2u/e0vFI57kwfLjyyCPN5Ocrjz6ayx/+kFyQT8S1sxNyc0lbb+l4uPl7Fo1JruCcb6JVTMtF5NiodceKyF8dsUoz9fX1mVbokalndrLYPzNumvlcwsDBWVRXu+85RH/LdtOmLF58MX2ttNJ9Lhx2WJh77mlBRPn1rwMsX574FTwRV58P7rijlTfeaEhLb+l4uPl7Fo1JruCcb6JVTMcBf49aVwF8J7U6mSEdkyb1lYtmKYv9l1JB7Gm4KziKhVzCB5sCHH10cdqmu0yU/pRtZ6fVLHPatCKWLEnP2DiZOBcmTQpx001Wq6Jbbw2QaJP2eK5dXdDa+tlyKoYh6S9u/p5FY5IrOOebaIBoB6IfkxcCZvUm6QE3306OHNnF3HldnMBqfsZcNrAPIXxsYB/m+OcyOX81v7sbxo4NccopIUpL3XVi96ds77gjjzff9DFsWBcTJ6bnVMvUuXDppR3cfHMrTz3VlHA1UDzXe+/N5bjjilm3zj0PW938PYvGJFfIcBUT1jwOi0SkGMD+ezfW/A3Gs3379kwrxKW5OYtWCvjr0FkcXfQOAWnn6KJ3aDz3Yla+2sn3vhdk2bJmbr31s5+MFRU+7rsvl67M1ir0uWy7e0sDzJ/fwoAB6Ql8mToXRGDWrA6GDPksn721bOrJdf36LG68McCGDdl8+ql7+se4/XsWiUmu4JxvomfPz4BioF5EqoF6oASDWy5Fksjk3Znksces6pWfXdPF+5VB3v9wE+9XBvn13DAjR1oRQMR6GAnWheXHP87nyivzOfXUQj7+OHMXib6UbWRv6ZkzU99bOh5uOBe6uuC66wKceWZh3CARy7W7t3RHh/D973dw8snuucl3Q9kmikmu4Jxvoq2YdqjqyVjzSp8MDFfVU+05oj0cZMuWLF57zU9eXuLTQebkwHXXtTFkiDWC6LHHFnPnnbl0pu862y+6e0uPGhXm+uvT3+M309TUCH/5Sw6vvOLnZz9LrvlrZG/p3/ymtfcdPDzikNRPS7uZ6z+BahHJEhH33L/2g+bm5kwr9Mj772dRXNzFpEmhXeP3JOI7eXKIiopGzj67g44O4Ze/zGf8+CLefTe9ddLJlu3OncKzz+bg9yv33ONMb+l4uOFcGDrUav4aCCgPP5zLnXfmxkwX7eqG3tLxcEPZJopJruCcb6LNXPcQkcdFpA7oxHo43f0ynqFDh2ZaoUfGj+/k/fcb+NWvPvs1mKjvwIHKvHmt/PWvTey5Z5i33/Zx1lmFKZ20pjeSLdsBA5RXX23kgQdaHOstHQ+3nAujR4dZuNBq/nrTTfk88cTuT64jXVtb3dFbOh5uKdtEMMkVnPNN9A5gERAExgHNWENrrADiN9A3hJqamkwrxCUvD8rLP6tnSNb3+OM7+X//r5GLL27n5ptbdz2rSAd9KdtBg5STTsrMbw83nQunnhrihhusKrZLLy3gzTc/f/cX6ZqfDzfc0MZxx4Uy2ls6Hm4q294wyRWc8000QBwNXKCqbwGqqm8DM7AeXhuPpGvGlCRZty475mifffEtLITf/raNKVM+u/D+/vd5zJkTwMm76URdH344h5tuyuvTmESpxG3nwuzZHZx7bgft7cINNwQ+9zwi2nXq1BDLlzdntLd0PNxWtvEwyRWc8020i2oYq2oJYKeIlAGNWEN/G8+gQYMyrbAbwSCccUYhwaDw+usNnxsiOhW+9fXCHXfk0doqPPusn9tvb+X441P/FDsR102bspgzJ5+WFuEb3whntOWN284FEZg7t5WBA5XZs9v55JMs7p0nLFvqo67lYAblt3P8+DD/e10XI0d2pW12uL7gtrKNh0mu4JxvoncQbwCT7P9XAo8By7EeWBuPG28nV63ys2NHFiNGhNljj883Y0mF76BByrPPNvG1r3WyZUs206YVMWtWPjt3pvYK05trd2/plhbh9NODTJqU2cdabjwX/H6r+mjt2uzdJo56veUQ9nxiIePGuHviKHBn2faESa6Q+Sqmc4CX7f+vAF4E3gW+54BT2ilO1Yz3KaS778NZZ+0+Z3CqfA85JMzq1U3ccEMrubnWgHFjxhTz1FOpq6PozTWyt/Rtt7Vm/BewG88FiD9x1O+5imeC7p44CtxbtrEwyRWc8+31bBKRbOBOoAVAVdtU9Veq+r/xRnc1iXDYXS0+duwQVq70k5WlTJu2e6V8Kn19Prj88g5eeaWRo44KsX17Fn/8Y27SQ0/3RDzXf/0rm9/9zmrHOm9eCwMHZn6YELedC92YPnEUuLdsY2GSKzjn22uAUNUwcCLQr0EbRGSiiHwgIh+LyJwe0owVkbdE5D0Redlet6eIvCQi6+31l/fHIxYt/Zn30QEef9xPKCQcd1wnw4btftF0wne//bp4+ulm5s5t5Y47Pvsl39hIv4JFT65Ws8wCOjut3tJjx7qjF5/bzoVuTJ84CtxbtrEwyRWc80200vJ24JcicoOqJl1JbN+FzAPGA1uBN0Vkhar+JyLNAGA+MFFVN4vIEHtTJ/AzVV0nIkXAWhFZFblvf3HbBOVLlljtUKdPj92kx7EJyrNgxozPOkl0dcH3v19ITg7cfnsre+2V/G+Enlzb24WRI8NkZ+Oq3tJuOxe6SW7iqAw3BesBt5ZtLExyBQevCQmmmw1cCTSJyBYR2dz9SnD/I4CPVXWjqgax5rI+LSrN94DlqroZQFWr7b+fquo6+/8mYD0pbj3lpgnKW1qsC3NhoTJpUuwverp8N23K4j//yeall/wcc0wx99yT/OB/PbkOGqQ8+mgLK1Y0pb23dDzcdC5EYvrEUeDeso2FSa7gnG+iAeIHwAnABPv/cyJeifAVYEvE8lZ2v8jvDwwUkTUislZEzo0+iIjsDXwdq1VVyvC7qOF4QQGsXt3EP//ZQE/zkKfLd999u6ioaOT004O0tAhz5uRz8slFfPhh4g9Co10bG9k1JpQIDB6c+ecOkbjpXIgkkYmj7vVfwtQz3Vt37tayjYVJruCcb0JVTKr6cu+p4hLryVn0lcEHfAOrt3YAqBCR11X1QwARKQSWAVeo6m4TsFZXVzNjxgx8Ph/hcJgpU6Ywa9YsqqqqKCgoIDs7m8bGRsrKyqivr0dVKSsrY/v27fj9furq6mhubmbo0KHU1NQgIgwaNIiamhqKi4sJh8O0tLRQXl5OVVUVfr+fkpISamtrKSkpIRgM0tbWtmt7Tk4ORUVF1NXVMXDgQNra2mhvb9+1PS8vj0AgwI4dOygtLaWpqYlgMLhreyAQYOfOHBoaGhg8eDANDQ2EQiHKy8tpbW2ltrY2bp66R3dMRZ4WLCjn299u4ze/Gc4bb/g59tgirryygXPO2d5rnlpbW+no6LC3B7j88uF8+iksXNhISUn9rjwl8jmlMk89fU6dnZ1UVlYm9Tnl5MT+nFKZp0mnhzj/0UuYHFoe80F1BUex2DeT+0/bzKef0u9zz4k8BQIBKisrM/Z9SiZP7e3tbNmyJa3nXn/yVFBQwLZt2/r0OcW9cCcyE5GI3NTTNlW9PoH9xwA3quoEe/lqe99bItLMAfJU9UZ7+T7gOVX9i4j4gaeBlar6h1jvUVFRoaNGjeo1L7GorKxkxIj4t+/pYMOGLIJBOPDA+PU4mfLduVO4/voADz2Uyy9+0cZPf9p7dUak60MP5fDjHxdQVKS89loje+6Z4ckqYuCWcyEWq1b5mHV+FjNCC7gotIC92Mxm9uJe/yXc57+Eefd3MX68Ox72x8LNZRuNSa7QP99169atHTdu3OGxtiVaV7Bn1OubwM+BfRPc/01gPxEZKSI5wHSssZwieRI4VkR8IpIPHAmsF6sP+X3A+p6CQ38pKSlx4rBJc+edeRxzTAkLF8YfLClTvgMGKHfd1crf/tbE7NmfBYe3386mrYfnzN2umzZlcfXV+YDVM9iNwQHccy7EYvz4Tla+2knjuRfHnDjKzcEB3F220ZjkCs75JlrFdH70OhGZCJyd4P6dInIZVi/sbOCPqvqeiMy0ty9U1fUi8hzwDlaT2sWq+q6IfAvrWce/ReQt+5DXqOozibx3IgQzPQAQ0NYGTzxhdY47/vj4DcUy7TtmzGcXopoaYcqUQkpLlbvuauGoo8Js2hQ5HMQISgvayQ3Irt7SZ5yR+fLuiUyXbW+MHNnFr+fCr+eGqa7eypAhQ3rfySW4vWwjMckVnPPtT9/857GG3EgI+4L+TNS6hVHLc4G5UeteI/YzjJTR1tPP3zTyzDN+mpuF0aM72X//+L+u3eDbTV2dMGSI8sEH2UyaVMyECUH++UqIizrnUxFayAgqqWwewYLmmSzkUk49VTPeWzoebirb3jDJFczyNckVnPNNKECIyD5Rq/KxmqVuiZHcONzQ5vmxx6xqpbPO6v2XgBt8uxk1qos1axq57bY8br89j1dWhniBEz73ILV7OIipLGfy7NUcdljWrqlS3YabyrY3THIFs3xNcoXM94P4GPjI/vsx8DpwLHCeI1ZpJtNtnrdvF1580YfPp0yZ0nuAyLRvNLm5cM017Uw7tZlZmD0chNvKNh4muYJZvia5Qob7Qahqlqpm23+zVLVQVY9V1bWOWKWZnJycjL7/X/+aQ1eXcOKJIUpLe29VlmnfnnhxdTYzMXs4CLeWbSxMcgWzfE1yBed8E51y9DAR2TNq3Z4icqgjVmmmKMOT97a3C4WFmlD1EmTetyeSGw7Cnbi1bGNhkiuY5WuSKzjnm2gV00NAdFe9HODB1Opkhrq6uoy+/89+1s777+9k4sTEhrnKtG9PfBGGg3Br2cbCJFcwy9ckV3DON9EAsZeqboxcoaobgL1TbpQBBg4cmGkF8vNJeKpIN/jG4oswHIRbyzYWJrmCWb4muYJzvokGiK0iMjpyhb28LfVK6SdTTdo6O+GJJ/w9djLrCbc2wbtolrLYfykVHBVzewVHcZ//Ei681F3jL0Xi1rKNhUmuYJavSa7gnG+iAeJ24EkRmS0ik0RkNvA44EjP5nTT3p6ZKo+XX/ZxwQWFTJqUXP1hpnx7Y+TILubd38Xk/NXM8c9lA/sQwscG9mGOfy6T81cz7/4u1zZxBfeWbSxMcgWzfE1yBed8E+1Jfa+I7ARmYA21sQVrjoa/OmKVZjLV5rl7WtFk52F2cxttaziILBbPv5ijl15CXXMepYXtTD0zzMpLO10dHMDdZRuNSa5glq9JrpD5fhCo6l9UdaKqHmz//UIEB8hMm+emJvjb36wAceaZyXWTd3sbbWs4iDDvVwb557p/835lkF/PDbs+OID7yzYSk1zBLF+TXCHD/SBE5C4ROTpq3dEicocjVmkmLwMz1jz1VA5tbcLRR4cYMSK5C2cmfPuKSa5glq9JrmCWr0mu4JxvoncQZwP/jFq3Fmu4DeMJBAJpf8/u6qVE+z5EkgnfvmKSK5jla5IrmOVrkis455togNAYabOT2N/V7NixI63vt2VLFq++6icvTznttOQDRLp9+4NJrmCWr0muYJavSa7gnG+iF/hXgV+JSBaA/feX9nrjKS0tTev7VVcLhx7ayUknhSguTn7/dPv2B5NcwSxfk1zBLF+TXME530SH+74ca0a3T0WkEhiB1QfiVEes0kxTU1NC0++lim98I8xLLzXR15Zp6fbtDya5glm+JrmCWb4muYJzvok2c+3uKHcEVjPX7cDpwD+APVJulWYyNTlIX58rmTSZiUmuYJavSa5glq9JruCcbzLPEEqxpgG9BngJGI11Z2E86Wzz/Nxzfj78sH+Pbkxqo22SK5jla5IrmOVrkitkqB+EiPhFZKqIPAX8F/gRsBzYCZypqn9xxCrNpKvNczAIl12Wz1FHlfDBB30PEia10TbJFczyNckVzPI1yRUy1w9iO7AI+AA4SlUPUtWbAbPuv3ohXU3aVq/2U1+fxYEHhnudVjQeJjXBM8kVzPI1yRXM8jXJFTLXzPUdYABW1dI3RcSsIQ4TJF2TgyxZ0t33oaNf8zKbNJmJSa5glq9JrmCWr0mukKEJg1R1LLAv8Dzwc6DKrm4qYPf5IYyloaHB8ffYsUNYudJPVpZyxhn9uwFLh2+qMMkVzPI1yRXM8jXJFZzz7bUiXFUrVfVmVd0PGAd8CnQBb4vI7xyxSjODBw92/D0ef9xPKCQcd1wnw4b1b7jrdPimCpNcwSxfk1zBLF+TXME536SelKrqa6p6MVAOzAYOccQqzaTj18KSJbkATJ/e/8c3Jv26MckVzPI1yRXM8jXJFTJ4BxELVW1X1UdV9aRUC2WCUCi54baTPz7st1+Y8vIuJk3qf4Bw2jeVmOQKZvma5Apm+ZrkCs75JtqT+guN022e/X6YN6+Vzk7wpaDETWqjbZIrmOVrkiuY5WuSK7hgPogvMulq85yK4ABmtdE2yRXM8jXJFczyNckVMjwfRCoQkYki8oGIfCwic3pIM1ZE3hKR90Tk5Yj1fxSRahF51wm3goICJw4LwHvvZfPEE/4+j7sUCyd9U41JrmCWr0muYJavSa7gnG9aAoSIZAPzgJOAg4CzReSgqDQDgPnAZFU9GDgjYvMDwESn/LKzs506NIsW5XLBBYXcdlvqJvRw0jfVmOQKZvma5Apm+ZrkCs75pusO4gjgY1XdqKpBYAlwWlSa7wHLVXUzgKpWd29Q1VeAeqfkGhsbHTluWxs8+aTVgWXatNR1PnfK1wlMcgWzfE1yBbN8TXIF53zT9ZD6K8CWiOWtWL2zI9kf8IvIGqAIuFNV/5zoG1RXVzNjxgx8Ph/hcJgpU6Ywa9YsqqqqKCgoIDs7m8bGRsrKyqivr0dVKSsrY/v27fj9furq6mhubmbo0KHU1NQgIgwaNIiamhqKi4sJh8O0tLRQXl5OVVUVfr+fkpISamtrKSkpIRgM0tbWtmt7Tk4OL700hKYm4dBDOxgw4FMqK9t3bc/LyyMQCLBjxw5KS0tpamoiGAzu2h4IBMjJyaGhoYHBgwfT0NBAKBSivLycUChEbW1t3Dx1D/2b6jwVFRVRV1fHwIEDaWtro709fp5CoRAdHR295imRzykdecrKyqKysjIln5PTeQqFQuzcuTMln1M68lRUVERlZWXazr3+5Kmrq4stW7a47vvUU54KCwvZtm1bnz6neIhq/zptJYKInAFMUNUL7eVzgCNUdXZEmruBw7E64wWACuBkVf3Q3r438LSqfjXWe1RUVOioUaP65Ld161aGDx/ep33jcdZZhaxa5efWW1u56KKOlB3XKV8nMMkVzPI1yRXM8jXJFfrnu27durXjxo07PNa2dN1BbMWaR6Kb4VgTDkWnqVXVFqBFRF4BDgU+dFrOiSC5fbvw4os+fD5lypTUjm2YjqCeKkxyBbN8TXIFs3xNcgXnfNP1DOJNYD8RGSkiOcB0YEVUmieBY0XEJyL5WFVQ69MhV1ZWlvJjLluWQzgsjB8forQ0tR+eE75OYZIrmOVrkiuY5WuSKzjnm5YAoaqdwGXASqyL/lJVfU9EZorITDvNeuA5rBFk/wEsVtV3AUTkUawqpwNEZKuIzEil3/bt21N5OACGDOnioIM6Oeus1I+M7oSvU5jkCmb5muQKZvma5ArO+aatJ7WqPgM8E7VuYdTyXGBujH3PdtLNiblcp00LMW1aCCfu/EyaK9ckVzDL1yRXMMvXJFdwztfrSe0w/Zn3wcPDwyOTeAECq+laqgiH4Q9/yGPDBueKNpW+TmOSK5jla5IrmOVrkis45+sFCGDo0KEpO9bLL/v41a8CnHVWoSPVS5BaX6cxyRXM8jXJFczyNckVnPP1AgRQU1OTsmM99pjVc/rMM4OOVS+l0tdpTHIFs3xNcgWzfE1yBed8vQABSIqu5E1N8PTTnwUIp0iVbzowyRXM8jXJFczyNckVnPP1AgQwaNCglBznqadyaGsTxowJsffeXSk5ZixS5ZsOTHIFs3xNcgWzfE1yBed8vQBB6m7PuquXnOj7EIlJt78muYJZvia5glm+JrmCV8XkKMXFxf0+xtatwmuv+cjNVU4/3dkAkQrfdGGSK5jla5IrmOVrkis45+tNOQqEw+F+HyMYFKZNC+L3g9PnVip804VJrmCWr0muYJavSa7gnK93BwG0tLT0+xj77NPFokWt3H13awqM4pMK33RhkiuY5WuSK5jla5IrOOfrBQi8CcqdxCRXMMvXJFcwy9ckV3DO1wsQ9H/C7wcfzOGpp/x0pG7Kh7iYNKG6Sa5glq9JrmCWr0mu4Jyv9wwC8Pv9fd43GIRf/jJAfX0WL73UyKGHOl932R/fdGOSK5jla5IrmOVrkis45+vdQQAlJSV93nf1aj/19VkceGCYr30tPQ+2+uObbkxyBbN8TXIFs3xNcgXnfL0AAdTW1vZ538/6PnSkbeTW/vimG5NcwSxfk1zBLF+TXME5Xy9A0Pfou2OHsHKlHxFl2jRn+z5EYtKvG5NcwSxfk1zBLF+TXMG7g3CUYLBvF/cnnvATDArHHdfJHnukbw7bvvpmApNcwSxfk1zBLF+TXME5Xy9AAG1tbX3ab8mSXACmT0/vydRX30xgkiuY5WuSK5jla5IrOOfrtWKib22Iu7rg5JODhMPW33RiUhttk1zBLF+TXMEsX5NcwesH4Sh9aUOclQU//nEHq1c3UVDggFQcTGqjbZIrmOVrkiuY5WuSKzjn6wUIICcnJ9MKSWGSr0muYJavSa5glq9JruCcrxcggKKioqTSr1uXza235lFZmZniS9Y3k5jkCmb5muQKZvma5ArO+XoBAqirq0sq/Z/+lMuttwb4858z8ysjWd9MYpIrmOVrkiuY5WuSKzjn6wUIYODAgQmnbWuDJ55wflrReCTjm2lMcgWzfE1yBbN8TXIF53y9AEFyTcSefdZPU5MwenQnBxzg3LSi8TCpCZ5JrmCWr0muYJavSa7gnG/aAoSITBSRD0TkYxGZ00OasSLyloi8JyIvJ7Nvf2hvb0847WOPWX0fnJ5WNB7J+GYak1zBLF+TXMEsX5NcwTnftPSDEJFsYB4wHtgKvCkiK1T1PxFpBgDzgYmqullEhiS6b39JtA3x9u3Ciy/68PmUKVMyFyBMaqNtkiuY5WuSK5jla5IrmN8P4gjgY1XdqKpBYAlwWlSa7wHLVXUzgKpWJ7Fvv0i0DfGyZTmEw8KJJ4YoLU3f0BrRmNRG2yRXMMvXJFcwy9ckVzC/H8RXgC0Ry1vtdZHsDwwUkTUislZEzk1i336Rl5eXULrRozuZMiXI97+f2XFaEvV1Aya5glm+JrmCWb4muYJzvukaaiPWQNjRP8F9wDeAcUAAqBCR1xPcl+rqambMmIHP5yMcDjNlyhRmzZpFVVUVBQUFZGdn09jYSFlZGfX19agqZWVlbN++nezsbOrq6mhubmbo0KHU1NQgIgwaNIiamhqKi4sJh8MMG9bCvHnlVFVVsW2bn5KSEmpraykpKSEYDNLW1kZ5ubU9JyeHoqIi6urqGDhwIG1tbbS3t+/anpeXRyAQYMeOHZSWltLU1EQwGNy1PRAIkJOTQ0NDA4MHD6ahoYFQKER5eTkNDQ1kZ2fHzVNhYSFAr3lqaWnZ9Z5+f+rz1NTUxIABA3rNUyKfU7ryVFlZmZLPyek8NTQ0kJubm9Zzrz95ysnJobKyMm3nXn/y1NzcTDAYdN33qac85eXlsW3btj59TnEv3KrOV5WIyBjgRlWdYC9fDaCqt0SkmQPkqeqN9vJ9wHNYdwxx9wWoqKjQUaNG9cmvsrKSESNG9GnfTGCSr0muYJavSa5glq9JrtA/33Xr1q0dN27c4bG2pauK6U1gPxEZKSI5wHRgRVSaJ4FjRcQnIvnAkcD6BPftF6WlpXG3h8Mwe3Y+zzzjpyszLVs/R2++bsIkVzDL1yRXMMvXJFdwzjctAUJVO4HLgJVYF/2lqvqeiMwUkZl2mvVYdwzvAP8AFqvquz3tm0q/pqamuNtfftnHww/nct11gbTNGheP3nzdhEmuYJavSa5glq9JruCcb9qG+1bVZ4BnotYtjFqeC8xNZN9U0ttkG59NKxp0RYAwaTITk1zBLF+TXMEsX5NcwZswyFHitSFuaoK//S2zQ2tEY1IbbZNcwSxfk1zBLF+TXMH8fhCuJl4b4qefzqG1VRgzJsTee7vgAQRmtdE2yRXM8jXJFczyNckVzO8H4WoCgUCP2yKrl9xCPF+3YZIrmOVrkiuY5WuSKzjn6wUIep5sY+tW4dVXfeTmKqef7p4AYdJkJia5glm+JrmCWb4muYI3YZCjNDQ0xFwfCMC117bzox91UFycZqk49OTrRkxyBbN8TXIFs3xNcgXnfNPWisnNDB48OOb60lLlpz9136iOPfm6EZNcwSxfk1zBLF+TXME5X+8OAu/XgpOY5Apm+ZrkCmb5muQK3h2Eo4RCod3W3XlnLp2dwjnndDBkSOZGbo1FLF+3YpIrmOVrkiuY5WuSKzjn6wUIdm9DHAzC3XfnUVeXxYknhhgyJJwhs9iY1EbbJFcwy9ckVzDL1yRX8PpBOEp0G+IXXvBTV5fFgQeG+epX3RUcwKw22ia5glm+JrmCWb4muYLXD8JRCgoKPre8ZEl334cOVwytEU20r5sxyRXM8jXJFczyNckVnPP1AgSQnZ296/+dO4WVK/1kZSlnnOGevg+RRPq6HZNcwSxfk1zBLF+TXME5Xy9AAI2Njbv+f+IJP8GgcNxxnQwb5q6H091E+rodk1zBLF+TXMEsX5NcwTlfL0AAZWVlu/5fsiQXgOnT3Xn3AJ/3dTsmuYJZvia5glm+JrmCc75egADq6+t3/X/FFe1MnRpk0iT3BohIX7djkiuY5WuSK5jla5IrOOfrNXMFIqddnTgxxMSJ7m4DnY5pYlOFSa5glq9JrmCWr0mu4JyvdweBdzvpJCa5glm+JrmCWb4muYJXxeQo27dv5403spkxo4DXXnP/TdX27dszrZAwJrmCWb4muYJZvia5gnO+XoAACgsLeeSRXB5/PIeXXnJ/gCgsLMy0QsKY5Apm+ZrkCmb5muQKzvm6/2roEJs2ZXHvPGHZUh91LfuQqx3k0MHRR3dmWs3Dw8PDFXwp7yBWrfIx4VgfAx5cREXzIXRoDv/mq/yYu7jkvCxWrXJ33Gxubs60QsKY5Apm+ZrkCmb5muQKzvm6+0roAJs2ZTHr/CxWtJ7AGF7ftX5fNjKXq5jSupzJ569m5atZjBzpjjmooxk6dGimFRLGJFcwy9ckVzDL1yRXcM73S3cHce884cLQ/M8Fh0jG8DozQgtYPN+FgzDZ1NTUZFohYUxyBbN8TXIFs3xNcgXnfL90AWLZUh8XhhbGTXNRaAHLlrp3LBZx4wiCPWCSK5jla5IrmOVrkis45/ulCxB1LXmMoDJumr3YTF1zXpqMkmfQoEGZVkgYk1zBLF+TXMEsX5NcwTnfL12AKC1op5IRcdNsZi9KC903F3U3Jt3+muQKZvma5Apm+ZrkCl+AKiYRmSgiH4jIxyIyJ8b2sSLSICJv2a/rI7ZdLiLvish7InJFfzymntnJYv/MuGnu9V/C1DPdN1FQN8XFxZlWSBiTXMEsX5NcwSxfk1zBOd+0BAgRyQbmAScBBwFni8hBMZK+qqqH2a+b7H2/ClwEHAEcCpwiIvv11eWiWcpi/6VUcFTM7RUcxX3+S7jwUveOxRIOuzd4RWOSK5jla5IrmOVrkis455uuO4gjgI9VdaOqBoElwGkJ7nsg8LqqtqpqJ/Ay8N2+iowc2cW8+7uYnL+aOf65bGAfQvjYwD7M8c9lcv5q5t3f5domrgAtLS2ZVkgYk1zBLF+TXMEsX5NcwTnfdAWIrwBbIpa32uuiGSMib4vIsyJysL3uXeDbIlIqIvnAJGDP/siMH9/Jylc7aTz3Yo4ueoeAtHN00Ts0nnsxK1/tZPx4d/emNmlCdZNcwSxfk1zBLF+TXME533R1lIvVBiu6DmcdMEJVm0VkEvAEsJ+qrheRW4FVQDPwNrDbFby6upoZM2bg8/kIh8NMmTKFWbNmUVVVRUFBAdnZ2TQ2NlJWVkZ9fT0+n3LdzWVcfNl22traKCsro7m5maFDh7JlSw0iwqBBg6ipqaG4uJhwOExLSwvl5eVUVVXh9/spKSmhtraWkpISgsEgbW1tu7bn5ORQVFREXV0dAwcOpK2tjfb29l3b8/LyCAQC7Nixg9LSUpqamggGg7u2BwIBcnJyaGhoYPDgwTQ0NBAKhSgvL+eDDz5gjz322C1PqkpZWRnbt2/fNTZLd55qajKTp5qaGg444IBe89TT55TuPG3dupWCgoKUfE5O52nDhg3stddeaT33+pOn9vZ2/H6/675PsfK0YcMGBgwY4LrvU095CgaDFBQU9OlzinvhTse45yIyBrhRVSfYy1cDqOotcfb5BDhcVWuj1v8G2Kqq8yPXV1RU6KhRo/rkd+edd3L55Zf3ad9MYJKvSa5glq9JrmCWr0mu0D/fdevWrR03btzhsbalq4rpTWA/ERkpIjnAdGBFZAIRKRe7t4eIHGG71dnLQ+y/ewFTgEdTKbd8+fJUHs5xTPI1yRXM8jXJFczyNckVnPNNSxWTqnaKyGXASiAb+KOqviciM+3tC4FpwCUi0gm0AdP1s9ubZSJSCoSAWaq6I5V+nZ3ufuYQjUm+JrmCWb4muYJZvia5gnO+aaliSgcvvPBCDfTSRboH6uvrBw8aNKi295TuwCRfk1zBLF+TXMEsX5Ncod++I8aNGxdzSrovTIDw8PDw8EgtX7qhNjw8PDw8EsMLEB4eHh4eMflSBwgR2VNEXhKR9fY4T65t1yYieSLyD7sj4Xsi8stMOyWCiGSLyL9E5OlMu8RDRD4RkX/b44D9M9M+vSEiA0TkryLyvn3+jsm0UyxE5ICI8dXeEpHG/o6n5jQi8hP7O/auiDwqIq4d2jmV49TFPP6X+RmEiAwDhqnqOhEpAtYCp6vqfzKstht2E+ACuyOhH3gNuFxVY8985BJE5KfA4UCxqp6SaZ+e6KnfjVsRkT9hjV222G46nq+qOzOsFRd7TLb/Akeqap8alDiNiHwF67t1kKq2ichS4BlVfSCzZrtjj1O3BGsooyDwHHCJqn6Uqvf4Ut9BqOqnqrrO/r8JWE/sIUAyjlp0Tzzrt1+uju4iMhw4GVicaZcvEiJSDHwbuA9AVYNuDw4244ANbg0OEfiAgIj4gHxgW4Z9eiKl49TF4ksdICIRkb2BrwNvZFilR+zqmreAamCVqrrW1eYO4CrAvSMffoYCz4vIWhG5ONMyvbAPUAPcb1ffLRaRgkxLJcB0UtzJNdWo6n+B3wObgU+BBlV9PrNWPZLyceqi8QIEICKFwDLgClVtzLRPT6hqWFUPA4YDR9i3mK5ERE4BqlV1baZdEuQYVR2NNST9LBH5dqaF4uADRgMLVPXrQAuw2xwrbsKuBpsM/CXTLvEQkYFYI02PBPYACkTkB5m1io2qrge6x6l7jh7GqesPX/oAYdfnLwMeVlUj+tfb1QlrgImZNYnLMcBku25/CXC8iDyUWaWeUdVt9t9q4HGsel23shVrPLLuO8i/YgUMN3MSsE5Vt2dapBdOADapao2qhoDlwNEZduoRVb1PVUer6reBeiBlzx/gSx4g7Ae/9wHrVfUPmfaJh4iUicgA+/8A1on8fkal4qCqV6vqcFXdG6tq4UVVdeUvMREpsBspYFfVnIh1++5KVLUK2CIiB9irxgGua1gRxdm4vHrJZjNwlIjk29eHcVjPJl2J0+PUpWu4b7dyDHAO8G+7bh/gGlV9JnNKPTIM+JPdEiQLWKqqrm46ahBDgcftsSJ9wCOq+lxmlXplNvCwXXWzETg/wz49YtePjwd+lGmX3lDVN0Tkr1jTD3QC/wLuyaxVXBwdp+5L3czVw8PDw6NnvtRVTB4eHh4ePeMFCA8PDw+PmHgBwsPDw8MjJl6A8PDw8PCIiRcgPDw8PDxi4gUIDw8PD4+YeAHCw8Mh7CHET4hYni4iO0TkuEx6eXgkihcgPDzSgIicB8wDTlbVlzPt4+GRCF6A8PBwGHt02NuACar690z7eHgkiteT2sPDIeyBCtcC3wJOVNW3M2vk4ZEc3h2Eh4ezjAdeB/6daREPj2TxAoSHh7PMBPYHFtujg3p4GIMXIDw8nKUaa8joY4H5GXbx8EgKL0B4eDiMPRnR8cBEEbk90z4eHonyZZ8PwsMjLajqFhE5HnhFRNpV9epMO3l49IbXisnDw8PDIyZeFZOHh4eHR0y8AOHh4eHhERMvQHh4eHh4xMQLEB4eHh4eMfEChIeHh4dHTLwA4eHh4eEREy9AeHh4eHjExAsQHh4eHh4x8QKEh4eHh0dM/j/N/22hcRJOhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "SMOTE Classification Score: 0.707089772325115\n",
      "\n",
      "Accuracy of logistic regression classifier on test set: 0.71\n",
      "\n",
      " [[3469 1593]\n",
      " [1340 3771]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.72      0.69      0.70      5062\n",
      "         2.0       0.70      0.74      0.72      5111\n",
      "\n",
      "    accuracy                           0.71     10173\n",
      "   macro avg       0.71      0.71      0.71     10173\n",
      "weighted avg       0.71      0.71      0.71     10173\n",
      "\n",
      "\n",
      "KNN Model Score:  0.9133982109505554 \n",
      "\n",
      "\n",
      " [[4209  853]\n",
      " [  28 5083]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.99      0.83      0.91      5062\n",
      "         2.0       0.86      0.99      0.92      5111\n",
      "\n",
      "    accuracy                           0.91     10173\n",
      "   macro avg       0.92      0.91      0.91     10173\n",
      "weighted avg       0.92      0.91      0.91     10173\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_all(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "- Logistic Regression:\n",
    " - Random Oversampling Classification Score: 0.702\n",
    " - Random Undersampling Classification Score: 0.696\n",
    " - SMOTE Classification Score: 0.711\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "- K-Nearest-Neighbor:\n",
    " - Random Oversampling Classification Score:  0.926 \n",
    " - Random Undersampling Classification Score: 0.637\n",
    " - SMOTE Classification Score: 0.909\n",
    "\n",
    "<br>\n",
    "Conclusion:\n",
    "Our KNN scores are unrealistic, and additional transformations did not bring the classification scores back down to a realistic level, so we will be selecting a Logistic Regression, utilizing the SMOTE sampling technique (Score of 0.711) as our model of choice to provide to the bank to assess whether a client will accept or reject a credit card offer.\n",
    "\n",
    "Our model predicted 'Yes' more than 71% of the time, but was 71% correct. This does not include True Negatives, False Positives, or False Negatives.\n",
    "\n",
    "Below, we will iterate upon our model in efforts to improve it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9 Further Analysis; Changing X\n",
    "\n",
    "This section of the notebook was created for further data adjustments beyond initial data cleaning, to quickly test small changes to an X dataset of an unchanged shape against our independent Y variable.\n",
    "\n",
    "Here, we will run two additional tests:\n",
    "\n",
    "1. Removing Columns with High P-values \n",
    "2. Removing Outliers from Average Balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17974, 16)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the Shape of DF\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.1. Columns with High P-Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17974,) (17974, 4)\n"
     ]
    }
   ],
   "source": [
    "#Create new X\n",
    "x = df[['reward', 'income_level','mailer_type','credit_rating']]\n",
    "print (y.shape, x.shape)\n",
    "\n",
    "#Setting x and y prior to creating a training and test set\n",
    "y = df['offer_accepted']\n",
    "x = df.drop(['offer_accepted', 'household_size', 'overdraft_protection'], axis=1)\n",
    "\n",
    "#converting y from object to int - Python didn't want to accept the object type\n",
    "y = y.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "SMOTE Classification Score: 0.7054382446620266\n",
      "\n",
      "Accuracy of logistic regression classifier on test set: 0.71\n",
      "\n",
      " [[3486 1526]\n",
      " [1430 3731]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.71      0.70      0.70      5012\n",
      "           2       0.71      0.72      0.72      5161\n",
      "\n",
      "    accuracy                           0.71     10173\n",
      "   macro avg       0.71      0.71      0.71     10173\n",
      "weighted avg       0.71      0.71      0.71     10173\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "smote = SMOTE()\n",
    "Xsmote = x\n",
    "    \n",
    "X_sm, y_sm = smote.fit_sample(Xsmote, y)\n",
    "    \n",
    "classification = LogisticRegression(random_state=0, solver='lbfgs',\n",
    "                    multi_class='ovr').fit(X_sm, y_sm)\n",
    "print('\\n\\nSMOTE Classification Score:', classification.score(X_sm, y_sm))\n",
    "    \n",
    "#Accuracy Score and Confusion Matrix\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_sm, y_sm, test_size = 0.3)\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train,y_train)\n",
    "y_pred = logreg.predict(X_test)\n",
    "print ('\\nAccuracy of logistic regression classifier on test set: {:.2F}'.format(logreg.score(X_test,y_test)))\n",
    "print('\\n',confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test, y_pred))\n",
    "    \n",
    "logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1], pos_label=2)\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logreg Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "- Logistic Regression:\n",
    " - SMOTE Classification Score: 0.72\n",
    " \n",
    "<br>\n",
    "Conclusion:\n",
    "Our model is, despite the number of removed columns, unchanged in terms of accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.2. Outliers Removed from Average Balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = df\n",
    "\n",
    "#Removing IQR from average balance\n",
    "\n",
    "iqr = np.percentile(temp['average_balance'],75) - np.percentile(temp['average_balance'],25)\n",
    "upper_limit = np.percentile(temp['average_balance'],75) + 1.5*iqr\n",
    "lower_limit = np.percentile(temp['average_balance'],25) - 1.5*iqr\n",
    "temp = temp[(temp['average_balance']>lower_limit) & (temp['average_balance']<upper_limit)]\n",
    "\n",
    "#Setting x and y prior to creating a training and test set\n",
    "y = temp['offer_accepted']\n",
    "x = temp.drop(['offer_accepted', 'household_size', 'overdraft_protection'], axis=1)\n",
    "\n",
    "#converting y from object to int - Python didn't want to accept the object type\n",
    "y = y.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15436,) (15436, 13)\n"
     ]
    }
   ],
   "source": [
    "#checking shape\n",
    "print (y.shape, x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "SMOTE Classification Score: 0.7132852729145212\n",
      "\n",
      "Accuracy of logistic regression classifier on test set: 0.71\n",
      "\n",
      " [[3002 1356]\n",
      " [1143 3238]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.72      0.69      0.71      4358\n",
      "           2       0.70      0.74      0.72      4381\n",
      "\n",
      "    accuracy                           0.71      8739\n",
      "   macro avg       0.71      0.71      0.71      8739\n",
      "weighted avg       0.71      0.71      0.71      8739\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "    smote = SMOTE()\n",
    "    Xsmote = x\n",
    "    \n",
    "    X_sm, y_sm = smote.fit_sample(Xsmote, y)\n",
    "    \n",
    "    classification = LogisticRegression(random_state=0, solver='lbfgs',\n",
    "                        multi_class='ovr').fit(X_sm, y_sm)\n",
    "    print('\\n\\nSMOTE Classification Score:', classification.score(X_sm, y_sm))\n",
    "    \n",
    "    #Accuracy Score and Confusion Matrix\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_sm, y_sm, test_size = 0.3)\n",
    "    logreg = LogisticRegression()\n",
    "    logreg.fit(X_train,y_train)\n",
    "    y_pred = logreg.predict(X_test)\n",
    "    print ('\\nAccuracy of logistic regression classifier on test set: {:.2F}'.format(logreg.score(X_test,y_test)))\n",
    "    print('\\n',confusion_matrix(y_test,y_pred))\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1], pos_label=2)\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, label='Logreg Regression (area = %0.2f)' % logit_roc_auc)\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.savefig('Log_ROC')\n",
    "    plt.show()\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "- Logistic Regression:\n",
    " - SMOTE Classification Score: 0.72\n",
    " \n",
    "<br>\n",
    "Conclusion:\n",
    "Our model has improved, but very slightly. It predicted 'Yes' more than 72% of the time, but was 72% correct."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
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}