ML_bank_2cleaning.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# **Costs prediction of a Marketing Campaign (Data Cleaning & Feature Selection- Part II)**\n",
    "### A Data Science approach to predict the best candidates to be targeted for a marketing campaign"
   ]
  },
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   "cell_type": "markdown",
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    "id": "5kCJU90teZS2"
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   "source": [
    "### **About the project**\n",
    "\n",
    "This article is the 2nd out of 3 of a Machine Learning - binary classification - project which goal is to train the best machine learning model that should be able to predict the optimal number of candidates to be targeted on a Marketing Campaign, to reduce to the minimum costs and to maximize efficiency."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to determine the costs for the campaign to be successful, the marketing team has reached to a conclusion:\n",
    "\n",
    "- For each customer identified as a good candidate and therefore defined as a target of the campaign but doesn't subscribe the term deposit, the bank had a cost of 500 EUR.\n",
    "- For each customer who was identified as a bad candidate and therfore excluded from the target of the campaign but was actually a good candidate and would subscribe the product, the bank had a cost of 2000 EUR."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The metric used for evaluation is the total costs since the objective is to determine the minimum costs of the marketing campaign.**\n",
    "\n",
    "In this article, the focus is on the second section only, the Cleaning & Feature Selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set-up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get (download) the data, click or copy and past this URL in a browser: https://bit.ly/31a0EAL (monitorized link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jNLkrQajeZS6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/GGG/opt/anaconda3/lib/python3.7/site-packages/outdated/utils.py:18: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.3.5, the latest is 0.3.8.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  **kwargs\n"
     ]
    }
   ],
   "source": [
    "# import the libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import table\n",
    "import pingouin as pg\n",
    "\n",
    "import scipy\n",
    "from scipy.stats import chi2\n",
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import pearsonr, spearmanr\n",
    "\n",
    "from IPython.display import Image\n",
    "from category_encoders import BinaryEncoder\n",
    "import pydotplus\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n8lubR7ReZS-"
   },
   "outputs": [],
   "source": [
    "# Loading the datset\n",
    "df = pd.read_csv('bank_marketing_campaign.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   age        job  marital    education  default housing loan  pdays  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no    999   \n",
       "1   57   services  married  high.school  unknown      no   no    999   \n",
       "2   37   services  married  high.school       no     yes   no    999   \n",
       "3   40     admin.  married     basic.6y       no      no   no    999   \n",
       "4   56   services  married  high.school       no      no  yes    999   \n",
       "\n",
       "   previous     poutcome  emp.var.rate  cons.price.idx  cons.conf.idx  \\\n",
       "0         0  nonexistent           1.1          93.994          -36.4   \n",
       "1         0  nonexistent           1.1          93.994          -36.4   \n",
       "2         0  nonexistent           1.1          93.994          -36.4   \n",
       "3         0  nonexistent           1.1          93.994          -36.4   \n",
       "4         0  nonexistent           1.1          93.994          -36.4   \n",
       "\n",
       "   euribor3m  nr.employed   y  \n",
       "0      4.857       5191.0  no  \n",
       "1      4.857       5191.0  no  \n",
       "2      4.857       5191.0  no  \n",
       "3      4.857       5191.0  no  \n",
       "4      4.857       5191.0  no  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Briefly visualization of the dataset (display the first five rows by default)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the target from binary/categoric into binary/numeric\n",
    "df['target'] = df.apply(lambda row: 1 if row[\"y\"] == \"yes\" else 0, axis=1)\n",
    "df.drop([\"y\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming some columns for better typing and calling variables\n",
    "df.rename(columns={\"emp.var.rate\":\"emp_var_rate\",\n",
    "                   \"cons.price.idx\":\"cons_price_idx\",\n",
    "                   \"cons.conf.idx\":\"cons_conf_idx\",\n",
    "                   \"nr.employed\":\"nr_employed\"}\n",
    "          ,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting-up numeric (df_num) and categoric (df_cat) dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create numeric (df_num) and categoric (df_cat) dataframes\n",
    "df_num = df.copy().select_dtypes(include=[\"float64\",\"int64\"])\n",
    "df_cat = df.copy().select_dtypes(exclude=[\"float64\",\"int64\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create indexes\n",
    "cat_features = list(df.select_dtypes('object').columns)\n",
    "int_features = list(df.select_dtypes('int64').columns)\n",
    "float_features = list(df.select_dtypes('float64').columns)\n",
    "num_features = int_features+float_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the [first post](https://bit.ly/3lDzknU), we have conducted the **Exploratory Data Analysis** that has allowed us to look further and beyond the initial dataset. \n",
    "\n",
    "EDA can be a very time-consuming task and rarely is a one-time-walk-through but although we may find ourselves going back to early sections changing and trying a few different approaches quite often, the detailed analysis usually pays and gives us a ton of information about the data and the variables' behavior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic info of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clients:  41188\n",
      "Number of variables:   16 incl. target\n"
     ]
    }
   ],
   "source": [
    "# Printing number of observations and variables, including the target\n",
    "print(f\"Number of clients:  {df.shape[0]}\") # Number of observations\n",
    "print(f\"Number of variables:   {df.shape[1]} incl. target\") # Number of variables or attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0] Number of clients that haven't subscribed the term deposit: ... 36548\n",
      "[1] Number of clients that have subscribed the term deposit: ....... 4640\n",
      "\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>clients</th>\n",
       "      <th>%</th>\n",
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       "      <td>36548</td>\n",
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       "      <td>4640</td>\n",
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       "   clients      %\n",
       "0    36548  88.73\n",
       "1     4640  11.27"
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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many clients have subscribed the term deposit and how many didn't?\n",
    "absolut = df.target.value_counts().to_frame().rename(columns={\"target\":\"clients\"})\n",
    "percent = (df.target.value_counts(normalize=True)*100).to_frame().rename(columns={\"target\":\"%\"})\n",
    "df_bal = pd.concat([absolut,percent],axis=1).round(decimals=2)\n",
    "print(\"\")\n",
    "print(f\"[0] Number of clients that haven't subscribed the term deposit: ... {df.target.value_counts()[0]}\")\n",
    "print(f\"[1] Number of clients that have subscribed the term deposit: ....... {df.target.value_counts()[1]}\")\n",
    "print(\"\")\n",
    "display(df_bal)\n",
    "absolut.plot(kind='pie', subplots=True, autopct='%1.2f%%', \n",
    "             explode= (0.05, 0.05), startangle=80, \n",
    "             legend=False, fontsize=12, figsize=(14,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The dataset is highly unbalanced.\n",
    "From the above information we can clearly state that the dataset is absolutely unbalanced with the 0:'no' aproximately 8 times higher than 1:'yes'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 16)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's step into the first section and take a brief overview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What are the most common categories?\n",
    "\n",
    "n = len(cat_features)\n",
    "i=1\n",
    "plt.figure(figsize=(16,14))\n",
    "\n",
    "for feature in df[cat_features]:\n",
    "    plt.subplot(round(n/2),round(n/3), i)\n",
    "    df[feature].value_counts().plot.bar()\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.title(feature)\n",
    "    i+=1\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How these categories influence the target variable?\n",
    "\n",
    "n = len(cat_features)\n",
    "i=1\n",
    "plt.figure(figsize=(16,14))\n",
    "\n",
    "for feature in df[cat_features]:\n",
    "    plt.subplot(round(n/2),round(n/3), i)\n",
    "    df.groupby([feature])['target'].mean().multiply(100).plot.barh()\n",
    "    plt.xlabel('Subscribed [%]')\n",
    "    plt.title(feature)\n",
    "    i+=1\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "                 job\n",
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       "entrepreneur    1456\n",
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       "unemployed      1014\n",
       "student          875\n",
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       "                     education\n",
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       "basic.9y                  6045\n",
       "professional.course       5243\n",
       "basic.4y                  4176\n",
       "basic.6y                  2292\n",
       "unknown                   1731\n",
       "illiterate                  18"
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       "         default\n",
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       "         housing\n",
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       "          loan\n",
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       "             poutcome\n",
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   ],
   "source": [
    "for col in cat_features:\n",
    "    display(pd.DataFrame(df[col].value_counts()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- You can get familiarized with the EDA section of this project by clicking [here](https://bit.ly/3lDzknU).\n",
    "- You can also find the entire code of this project [here](https://bit.ly/3hmI3YS).\n",
    "- The 'bank_marketing_campaign.csv' dataset can be downloaded [here](https://bit.ly/31a0EAL)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Correlations**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Numeric variables** (df_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the context of the **dimensionality reduction**, the analysis of the correlation between features and the target variable proves to be a fundamental step in bi-variate data analysis because it helps us to compute the importance of the features. \n",
    "\n",
    "### Let's begin by remembering the concepts of Correlation Coefficient both Pearson and Spearman.\n",
    "\n",
    "By definition, the **Pearson Correlation Coefficient**, commonly referred as **Pearson's r**, is a statistical measure - and a feature selection process - that describes how much two random variables are linearly correlated, how strong their statistical relationship is, in which direction it occurs, whether casual or not in bivariate data.\n",
    "\n",
    "The values range from -1.0 and 1.0 where the first one shows the total negative linear correlation while the second one shows the total positive linear correlation. The value of 0.0 reveals there is no linear relationship.\n",
    "\n",
    "Another important inference to be aware of is that The Pearson Correlation can only evaluate the linearity between continuous variables, meaning that the change in one variable implies a proportional change in the other, at a constant rate - otherwise it's a non-linear relationship. The use of this parametric test is only recommended when the variables are normally distributed.\n",
    "\n",
    "One of the outputs is the **p-value for the correlation**, roughly showing the probability of an uncorrelated system creating a correlation value of some magnitude. Low p-values (usually ≤0.05) tell us that the correlation is, most probably, significant. In concrete, the p-value will reveal the feature's predictive power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subdivision of target\n",
    "ynum = df_num.target\n",
    "Xnum = df_num.drop([\"target\"], axis= \"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target            1.000000\n",
       "previous          0.230181\n",
       "cons_conf_idx     0.054878\n",
       "age               0.030399\n",
       "cons_price_idx   -0.136211\n",
       "emp_var_rate     -0.298334\n",
       "euribor3m        -0.307771\n",
       "pdays            -0.324914\n",
       "nr_employed      -0.354678\n",
       "Name: target, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ordered by rank in absolute values\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "target            1.000000\n",
       "nr_employed       0.354678\n",
       "pdays             0.324914\n",
       "euribor3m         0.307771\n",
       "emp_var_rate      0.298334\n",
       "previous          0.230181\n",
       "cons_price_idx    0.136211\n",
       "cons_conf_idx     0.054878\n",
       "age               0.030399\n",
       "Name: target, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The numeric most correlated with the target (Pearson)\n",
    "pearson = df_num.corr()\n",
    "corr_target = pearson.target\n",
    "display(corr_target.sort_values(ascending=False))\n",
    "print(\"\")\n",
    "print(\"Ordered by rank in absolute values\")\n",
    "display(corr_target.abs().sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EDPV39/4R2zWAZRHby4GT8zjVRWbWEvgBuMPdl+VzbI39yVeFroiIiIgUSLio7b+bkLwqbc+1PRp41923mtl/gMHAaQU8dq+o0BUREREJiHiv0SXUhT00YrsmsDIywN3XRGy+BjwacWzrXMdO2p9k4j4bIiIiIhIYM4EjzewwMysJXAqMigwws2oRmx2AReH344C2ZlbRzCoCbcNj+0wdXREREZGAiHdH1913mNkthArURGCguy8wsweBWe4+CuhqZh2AHUAmcE342Ewze4hQsQzwoLtn7k8+KnRFREREpNC4+8fAx7nG7o94fzdwdz7HDgQGFlYuKnRFREREAiLeHd2iRrMhIiIiIoGkjq6IiIhIQKijG02FbhH15Rdf8NQTT5CdlUWH88/n6muvjdq/bds2Hrj/fhYvWkT58uV5uF8/qlevzlczZvDi88+zY/t2SiQl0fW22zixadOoY7vdcQcrVqzg3WHDDuQt5alu2zupXLc5Wdv/5Psxvdi0avEuMcdd8Roly1Yhe8dWAOa9cyPbt6ylVLlq1G/fi6QyFdnx5wYWjbyXbRvTD/Qt7LUBPXrRvllL0tdm0ujqTvFOR0REJLD+doVu+FN/U9z9s3jnkp+srCwe79eP5196iZTUVK658kpatGrF4YcfnhMzasQIDilXjvdHjuTTceN48bnneKRfPypUqMCTzzxDcnIyP//0E7fdcgtjxo7NOW7i559z0EEHxeO2dlGp7qmUqVSLr1/uyCHVG3Fku3uYM+iqPGMXjbyXTb9Hf0123TPuIG3+R6TNH02F2idxeJtbWTzqvgOR+n4Z9MloXvhgKG/e+1C8UxERkYBRRzdasZ4NM0vc22Pc/f6iXOQCLFywgJqHHkqNmjVJSkrizLZtmTJpUlTMlMmTObd9ewBOO/10Zn79Ne5O/QYNSE5OBuDwunXZum0b27ZtA2DLli2887//ce0//3lA7yc/leu1YtW8MQBsXDmfEqUPoWTZKgU+vkyVw1n721cArFsyk8r1WscizUI3de5sMjesj3caIiIigVdkC10zq2Nmi81ssJnNM7PhZlbGzH4zs/vNbBrQyczqmtlYM/vGzKaaWQMzKx+OSwifq4yZLTOzJDMbZGYXh8dPN7M5ZjbfzAaaWanw+G9mViX8/kQzmxR+38rMvg2/5pjZIbG49/T0dFJTU3O2U1JTycjIiIrJyMggJRxTokQJypYty/p166JiPp8wgfr161OyZEkAXn35ZS6/4gpKly4di7T3WqlDUti6YVXO9tYNaZQ8JCXP2Prte9Pkn0Oodeq/csY2pf1AcoPTAahS/zRKlCpLiYPKxzZpERGRIiwxISGmr+KmqGdcH+jv7scCG4CbwuN/uvup7j6E0Pct3+ruTYBuwEvuvh6YC7QKx58HjHP37TtPbGalgUFAZ3dvRGgZx417yKcbcLO7Hw+0AP7IHWBmN5jZLDObNWjgPj4Gznf9WmczyxWy+5hffv6ZF597jh733APAD99/z/Jly2h92mn7llMsWB5faZ3HfS0aeQ/fvHYJ3755HeUPbUxqo1An+5cJT1O+VhNOuP5dytdqwtYNaXh2VqyzFhERkWKiqK/RXebu08Pv/wd0Db8fCmBmZYFmwHsRRV6piJjOwERCXz/3Uq5z1wd+dfcfwtuDgZuBZ3aTz3TgKTN7G/jA3ZfnDnD3/oSKb9Zt2rRr1VYAKamppKWl5Wynp6VRpUr0r/RTUlJIT0sjNTWVHTt2sGnTJsqVD3Uz09LSuLNbN3o9+CA1Dw193fT8efNYvGgR57dvz46sLNZmZnLjDTfwcv/++5LiPqve5BKqNb4QgI0rF1CqXNWcfaXKpbJtU8Yux2zbGBrL2raF9AWfcEj1hqTNH8O2TRksfL8bAAlJB5Hc4HSytm46AHchIiJSNBXHrmssFfXZyF0o7tzeHP4zAVjn7sdHvI4K7xsFnG1mlYAmwOe5zpVHOzHHDv6am5zf87t7P+CfwEHADDNrsFd3U0BHHX00y5YtY+WKFWzfvp3xn35Ky1atomJatGrFR2NC61s/nzCBE086CTNj48aN/Pe227jplls47vjjc+Iv6tSJj8aNY8SYMfQfMIBatWsf8CIXYOU3w/jm9Uv55vVLWf3DRKoeG+rOHlK9ETu2bmLbptXRB1giJQ6qEHqbUILKR7Rkc8bPAOHx0F9jrebXsWruyAN2HyIiIlL0FfVCt5aZnRJ+3wWYFrnT3TcAv5pZJwALOS68bxPwNfAsMMbdc/9OezFQx8yOCG9fCUwOv/+NUHEMcNHOA8ysrrvPd/dHgVlATArdEiVK0O3OO+l6yy10vugizjjzTA6vW5dXX36ZKZNDKXbo2JH169ZxUceOvPv229x8660AvDd0KMuXLWPg669zRZcuXNGlC5mZ+/U10TGT+dM0/li7nKY3jaLeuffx49i+Ofua/HMIAAklkji2y4s0+edQmvxzCFs3pvP7nA8AqFD7RE66cQQn/WcEJQ+uzJLpr8flPvbWO7368uUrg6lfqzbL3h/LdeeeH++UREQkILRGN5rltdazKDCzOoS+J3kKoeUJPxIqRhcCJ7r76nDcYcDLQDUgCRji7g+G910MvAe0dvfJ4bFBhArf4WZ2OvAEoSUcM4Eb3X2rmbUABgBpwFfh67U2s+eBNkBWOI9r3H1rfvewr0sXirq5z7aIdwox03rsnmOKK586J94piIgUNbv77W6xdNeX42Naezx6ypnFas6K+hrdbHf/T66xOpEb7v4r0C6vg919OLn+Ebv7NRHvJwCN8zhuKlAvj/FbC5i3iIiIiMRZUS90RURERKSAiuPyglgqsoWuu/8GHBPvPERERESkeCqyha6IiIiI7B11dKNpNkREREQkkNTRFREREQkIdXSjaTZEREREJJDU0RUREREJiAR1dKNoNkREREQkkNTRFREREQmIRCtWX1wWc+roioiIiEggqaMrIiIiEhCJph5mJM2GiIiIiASSOroiIiIiAaE1utHU0RURERGRQFJHV0RERCQg1NGNpo6uiIiIiASSOroiIiIiAZGgpy5E0WyIiIiISCCpoysiIiISEFqjG02FroiIiEhAJCbol/WRNBsiIiIiEkjq6MZQyYRg/vog84oh8U4hZn76x4B4pxATR/Qej7VoHO80YsKnzol3CiIiRYaWLkRTR1dEREREAkkdXREREZGASFBHN4o6uiIiIiISSOroioiIiAREor4wIopmQ0REREQCSR1dERERkYDQUxeiqaMrIiIiIoGkjq6IiIhIQGiNbjTNhoiIiIgEkjq6IiIiIgGhNbrR1NEVERERkUBSR1dEREQkIPTNaNHU0RURERGRQFJHV0RERCQgEhPUw4yk2RARERGRQFJHV0RERCQg9NSFaOroioiIiEggqdAVERERCYhES4jpqyDMrJ2ZfW9mP5lZjzz2/9fMFprZPDObYGa1I/Zlmdm34deo/Z0PLV0QERERCYh4L10ws0TgReBMYDkw08xGufvCiLA5wInuvsXMbgQeAzqH9/3h7scXVj7q6IqIiIhIYWkK/OTuv7j7NmAI0DEywN0nuvuW8OYMoGasklGhKyIiIhIQCZYQ05eZ3WBmsyJeN+RKoQawLGJ7eXgsP9cDn0Rslw6fd4aZnb+/86GlCyIiIiJSIO7eH+i/m5C81k54noFmVwAnAq0ihmu5+0ozOxz43Mzmu/vP+5qvCt04c3cee+xxpk+fRunSpXnggQc46qijdolbuHAhvXr1ZuvWP2ne/FTuvLM7Zsb69eu5664erFy5kurVq/PYY49Srly5fM+7cuVKunXrRlZWNjt27ODSSy+lU6eL2bx5M9ddd33O9dLT0znnnLPp3r17zO79+5nfMPrl1/HsLE5q15bWl14ctX/q8BHMHDuehMQEDi5fnov/rysVU1P4+dt5jHllQE5cxrLldLmnOw2b/yNmue4td+fV9+Yzc0E6pZIS+e9VjTmiVoV84x94+StWrd7My/edBsCADxbw1fxVlEhMoFpyGe648gTKlkk6UOnvswE9etG+WUvS12bS6OpO8U5HRORvJ95rdAl1cA+N2K4JrMwdZGZnAPcCrdx9685xd18Z/vMXM5sENAb2udDV0oV8mNk1ZlY91teZNm06S5cuZeTIkfTs2ZM+ffrmGdenT1969ryXkSNHsnTpUqZP/wKAN954g6ZNmzJq1EiaNm3KG2+8sdvzJicnM2jQIIYOHcJbb73JG2+8QXp6BgcffDBDhw7JeVWrVpXTTjstZvednZXFyBde5dpHenHHay/y7aQppC1ZGhVT/YjDueWFp7j91edp1KIZn7w+CIC6xx/Lba88y22vPMu/HnuYpNKlOLJJ45jlui9mLUhnRfpmXu99Ol0vP44XhszNN3b6nJWULpUYNda4QTIv92zDSz3bUCOlLMPG/RDrlAvFoE9G067bzfFOQ0RE4mcmcKSZHWZmJYFLgainJ5hZY+BVoIO7p0eMVzSzUuH3VYDmQOSH2PZaIAtdC9njvYU/GZifa4CYF7qTJ0+iffv2mBnHHnssGzduJCMjIyomIyODzZs3c9xxx2FmtG/fnkmTJgIwadJkzjuvPQDnndeeiRMn7fa8SUlJlCxZEoBt27bhvutvE5YsWUpm5lpOOOGEmN33su9/pHL1alSuVpUSSUkc16oFC7/4Kiqm7vHHUrJ0KQAOPao+6zNW73Ke+VOnU//EJjlxRcWMeb9z+smHYmY0OKwSm7dsJ3P9n7vE/fHnDj78/Ge6nF0vavyEo1NITAz9E25wWEVWr9v12KJo6tzZZG5YH+80RET+thLNYvraE3ffAdwCjAMWAcPcfYGZPWhmHcJhjwNlgfdyPUbsKGCWmc0FJgL9cj2tYa/tU6FrZleY2dfh5F41s0Qz22Rmj5rZN2b2mZk1NbNJZvbLzhsLd0lHmtnY8PPVeu3mGo+a2U0R273N7P/MrGz4mWuzzWy+mXUM769jZovM7CVgNtFt88jzbgpP9lfAKWZ2v5nNNLPvzKx/uEi+mNCakbfD93iQmTUxs8nh+xtnZtX2Ze5yS09Pp2rV1Jzt1NQU0tMzcsVkkJKSkism9APQmjVrSE5OBkLd2szMzD2ed9WqVVxyySWcffY5XHPN1aSkJEddb+zYsbRt2xaL4a8/NqxeQ/nkKjnb5ZOrsGHNmnzjZ40dT72TmuwyPnfSVI5r0zImOe6P1ev+JLniQTnbVSoexOp1f+wS99aYRVx4el1Klcx/FdGnXyzlxKNT8t0vIiJSlLj7x+5ez93ruvsj4bH73X1U+P0Z7p7q7seHXx3C41+4eyN3Py7854DdXacg9rrQNbOjCD3rrHn4OWdZwOXAwcAkd28CbAQeJvQMtQuAByNO0TQcfzzQycxOzOdSQ/jrmWoAlwDvAX8CF7j7CUAb4En7qyKrD7zp7o3dfUk+5z0Y+M7dT3b3acAL7n6Sux8DHAS0d/fhwCzg8vA97gCeBy4O399A4JE9TlYB5NFQZdf6ctegPRWhuztv1apVGTZsGCNHjmT06DGsyVVgjhs3jnbtztrt+feX57UuPZ97mvPZRJb/8BOtOl0YNb5hTSZpvy2h3olFa9kCkOey+9x/Zz8vW8/K9M00Oz7/XxwM+eR7EhONNk1j9uQVEREJkMSEhJi+ipt9+TDa6UATQg8AhlBxmA5sA8aGY+YDW919u5nNB+pEHD/e3dcAmNkHwKmEisoo7j7HzFLC62STgbXuvtTMkoA+ZtYSyCb0yIqdrcsl7j5jD/lnAe9HbLcxszuBMkAlYAEwOtcx9YFjgPHhe04Efs/r5OHHbNwA8Pzzz3HdddftEjN06FA++OBDABo2bMiqVWk5+9LS0nM6tDulpPzVwc0dU7lyZTIyMkhOTiYjI4NKlSoBoQ7uns+bTN26hzN79hzOPPMMAL7//geysrI4+uij87q9QlO+SpWopQjrM1ZTLpx7pB9nf8vn777Hv5/oQ4mS0R/GmjdlGg2b/YPEEkXjM5WjJ//CuOmhn6+OrF2RjLV/dXBXr/2DyuVLR8Uv/jWTn5at45qen5KV7azfuJW7np7Go3ecCsBnM5by9Xdp9LmtWUy76yIiIkG1LxWCAYPd/e6oQbNu/r+gJ8UAACAASURBVNeCz2xgK4C7Z5tZ5HVy97ryfORE2HDgYqAqoQ4vhLrByUCTcCH9G7CzgthcgPz/dPescM6lgZcIfTvHMjPrHXGuqNsDFrj7KXs6eeRjN7Zs2ZznvXXu3JnOnUPN6qlTpzJkyFDatTuL+fPnU7Zs2V0K0uTkZMqUKcO8efNo1KgRY8aM4dJLLwWgVauWjB49huuuu5bRo8fQunWr8HirPM+blpZG+fLlKV26NBs2bODbb+dyxRVX5Fxr7NixMe/mAtSsfyRrVqwk8/dVlKtSmbmTp9KlR7eomBU//cyHz77EdX16U7birk8smDtxCu2uuyrmuRbUea0O57xWhwPw9fxVjJ78K61OrMH3v63l4IOSqJSr0D235WGc2/IwANLWbKH3SzNyitxZC9J479MfeeyOUym9m2UNIiIikRLUGImyL/8HnQCMNLOn3T3dzCoBh+zF8WeGj/kDOB/YteX5lyHAa0AV/nrGWnkgPVzktgFq53dwAeysPFabWVlCRfXw8NhG/rqv74FkMzvF3b8Md5XrufuC/bg2AKeeeirTpk2jQ4eOlC5dmt69e+fs69z5UoYODdX399xzD7169WLr1q00b96MU09tDsC1117LXXfdxYgRI6hWrSqPPfbYbs/766+/8tRTTxGq3Z2rrrqSI488Muea48eP5/nnn9vf29qjxMREOtzybwbe05vs7GxOPOsMUuvU4tPBb1Oz3hEcfcrJfPLaILb98QdvP/QoABVSkrn6wZ4AZK5KY33Gag479piY57ovTjomlZkL0ri+12eUKpnIHVf+tbzilj4TeeGeNrs9/uVh89m+PYt7nw89XaN+nUrcetlxMc25MLzTqy+tGzehSvkKLHt/LL0GvsLAj0bEOy0REfmbsrw+db/Hg8w6A3cTWuO7HbgZ+Mzdy4b39wY2ufsT4e1N7l7WzK4BziG0TvYI4B13f2AP15oPrHb3NuHtKoSWFiQB3xJ69MTZ4fAx4bW2uzvfpp15hrcfJvToi98IfZPHEnfvbWYXAX0IFeSnEFq+8ByhQrsE8Iy7v7a7a+XX0S3uxmUsj3cKMXPsT/u97r1IOqL3+HinEDM+dU68UxCR4itw7c/p6StiWns0T6lRrOZsnwrdfb5YqNA90d1vOWAXjSMVusWPCt3iR4WuiOyHYlW0FYQK3Wha/CciIiISEEXgm9GKlANa6Lr7IGBQ5JiZVSa07je303c+nWFfhJ+Tm/tbBK509/n7ek4RERERKT7i3tENF7PHx+C8Jxf2OUVERESKsoTgrcbYL8Xvyb8iIiIiIgUQ946uiIiIiBSOBDV0o6ijKyIiIiKBpI6uiIiISECY1uhGUUdXRERERAJJHV0RERGRgEjQc3SjqNAVERERCQj9qj6a5kNEREREAkkdXREREZGA0NKFaOroioiIiEggqaMrIiIiEhDqYEbTfIiIiIhIIKmjKyIiIhIQ+sKIaOroioiIiEggqaMrIiIiEhB66kI0dXRFREREJJDU0RUREREJCHUwo2k+RERERCSQ1NGNIQ/oJx9PrJAS7xRipnLDc+OdQkyUPC4z3inExLa532AtGsc7jZjxqXPinYKIFDNaoxtNHV0RERERCSR1dEVEREQCIiGgv03eV+roioiIiEggqaMrIiIiEhBaohtNHV0RERERCSR1dEVEREQCQmt0o6mjKyIiIiKBpI6uiIiISECogxlNha6IiIhIQOgLI6Kp8BcRERGRQFJHV0RERCQg9GG0aOroioiIiEggqaMrIiIiEhBaohtNHV0RERERCSR1dEVEREQCQmt0o6mjKyIiIiKBpI6uiIiISEDoObrR1NEVERERkUBSR1dEREQkINTBjKb5EBEREZFAUkdXREREJCD01IVo6uiKiIiISCCpoysiIiISEHroQjQVunE2ffp0nnj8cbKys7ng/PO59rrrovZv27aN++67j0WLFlGhfHn6Pfoo1atXB2DggAGMGDmSxIQEut95J82aNdvtOb/66iuefeYZsrOzKVOmDL0feIBatWoB8Omnn/LqK69gZtSrV48+ffvG5H6//vJLXnryabKzszm7Ywe6XH3VLvf7aO8H+HHx95QrX46ejzxM1erVmTB2LMPeejsn7peffuLltwZzRL16TBw/nnfeGER2VjYnN2/GDV1vjUnu+8rdeey5oUz/aj6lS5Xkgbuv4ah6taNi/vhzK3f2epXlKzNISEigZbPjuO3fF8Yp4/y1PaohT17chcSEBAZ+MZUnxn8Stf/xCzvTql59AMqULEly2XKk3tkVgD4dL+bsYxqRYAlMWLyQ/w5/94Dnv68G9OhF+2YtSV+bSaOrO8U7HRERKSAtXYijrKwsHu3Xj+dfeIH333+fsWPH8svPP0fFjBgxgnKHHMKoUaO4/PLLefbZZwH45eefGTduHMOHD+eFF1+kX9++ZGVl7facffv04eFHHmHI0KG0O/tsBrz+OgBLlyzhjYEDeWPQIIa//z7duneP2f0+/9gT9Hn2aQYMfZeJ4z5lyS+/RsV8MmoUhxxSjjc/GM5FXbrw2gsvAnB6u3a8+vZbvPr2W9z1QC9Sq1XjiHr1WL9uPf2fe4HHX3yBAUPfZW3mWmZ/PTMm+e+raV99x9LlaYx8+2F6druSPk+9nWfcVZ3b8uFbDzHk9fuYO/8nps2Yf4Az3b0EM5695HI6vPQMxz18H52bNKVB1WpRMd0/GErTfg/StN+DvDT5c0bMnQ3APw6ryymHH0GTPr1p/Mj9NKldh5ZH1o/HbeyTQZ+Mpl23m+OdhojIHiVgMX0VhJm1M7PvzewnM+uRx/5SZjY0vP8rM6sTse/u8Pj3ZnbW/s9HMWVm/zGzq/YcWeDzfZHP+CAzu7iwrhPpu+++o+ahh1KzZk2SkpI466yzmDRpUlTMpEmTaH/eeQCcfsYZzPz6a9ydSZMmcdZZZ1GyZElq1KhBzUMP5bvvvtvtOc2MzZs3A7Bp40aqJCcD8MGHH3LJJZdQrlw5ACpVqhSL2+X7BQupXrMm1WvUICkpidZtz2T6lClRMV9Mnkrbc88BoOVpbZgzcxbuHhUz8dPxnNb2TAB+X7mCmrUOpULFigCc0PQkpk6cGJP899Xkad/S/qxTMDOObXg4Gzf9QcaadVExB5UuxUknNAAgKakEDerVIj1jXV6ni5uT6hzGz6vT+XXNarZnZTFs9tecd+zx+cZf0qQpw775GgDHKZ2URMkSJShVIomkxETSN2w4UKnvt6lzZ5O5YX280xARKfLMLBF4ETgbOBroYmZH5wq7Hljr7kcATwOPho89GrgUaAi0A14Kn2+fFculC2ZWwt1fKcxzunuzwjxfQWSkp1M1NTVnOyU1le+++27XmKpVAShRogRly5Zl3bp1pGdk0KhRo5y41JQUMtLTAfI9533330/XW2+lVKlSHHzwwQx+800g1NEFuPaaa8jKzubf//43zZs3L/T7XZ2RQUpqSs52ckoKixcsiIpZk5FBcjj/xBIlOLhsWTasX0/5ChVyYiaN/4wHn3gMgBo1a7JsyRJWrVxJckoK0ydPZsf27YWe+/5IX72OqikVc7ZTkyuSnrGO5MoV8ozfuHELU76Yx2UXn36gUiyQ6uUrsmzt2pztFWvX0rTO4XnG1qpYiTqVqzDx+0UAfPXrL0z+cTFLHnkSM3h5ykQWp/1+QPIWEfk7KQLfjNYU+MndfwEwsyFAR2BhRExHoHf4/XDgBTOz8PgQd98K/GpmP4XP9+W+JlOgjq6ZXWVm88xsrpm9ZWa1zWxCeGyCmdUKxw0ys+fM7Asz+2VnJ9TMqpnZFDP71sy+M7MWu7nWJjN70sxmh8+dHB6fZGZ9zGwycJuZ9TazbuF9R5jZZ+H8ZptZ3fB4dzObGc7zgT3c46bwn2ZmL5jZQjP7CEgJj5cPt9Hrh7ffNbN/5XGeG8xslpnNGjhw4G7n1fMYy/3PM3c3M3yN/Md3c863336b555/nrHjxtGhY0eeevJJAHZkZbFs6VL6v/Yaffv25aEHH2Tjxo27zX1f5JVzwWL+mpVF331HqdKlOaxuXQAOKVeO2+66k4fv7cntN/yHqtWqkZhYtH5+y+/vKi87dmTR48HX6HLRadSsnhzr1PZKXil7nv/ioFOTpnz47Tdkh++9bpUUGqRW4/Ce3Tns3u60rteAU+seGct0RUQkBiLrnPDrhlwhNYBlEdvLw2N5xrj7DmA9ULmAx+6VPVYEZtYQuBdo7u6rzawSMBh4090Hm9l1wHPA+eFDqgGnAg2AUYQq9cuAce7+SLgFXWY3lzwYmO3u/2dm9wO9gFvC+yq4e6twXr0jjnkb6OfuH5pZaSDBzNoCRxL6ScCAUWbW0t2jf1e+qwuA+kAjIJXQTyAD3X29md0CDDKzZ4GK7v5a7oPdvT/QH2Dzli27rexSUlJYlZaWs52elkZycnRxk5KayqpVq0hNTWXHjh1s2rSJ8uXLk5qSQtqqVTlxaenpOUsR8jrn2sxMfvzhh5wucNu2bbnl5tCaw9SUFBodeyxJSUnUqFGD2nXqsHTpUho2bLiHqdo7ySkppKel52xnpKdTOdf9VklJISMtjeTUFLJ27GDzpk2UK18uZ//ETz/LWbaw0yktWnBKi9DPTmM+HEFCQvxX5Az9cCIfjJkKQMP6dViV/lcnNC1jLclVyud53MNPvEWtmqlc3umMA5Ln3lixbi2HVvyrM12jYkVWrs97ecUlTZpy27C/1iJ3PK4xX/32C5u3bQVg3IL5nHxYXab9/GNskxYR+ZvJu+VVeCLrnHxTyOOwAsYU5Ni9UpCK4DRguLuvBnD3TOAU4J3w/rcIFbY7jXD3bHdfSKhQBJgJXBsuThu5++7ahdnA0PD7/+U699DcwWZ2CFDD3T8M5/enu28B2oZfc4DZhArvgrSQWgLvunuWu68EPt+5w93HA/MJrT35ZwHOtVsNGzZk2dKlrFixgu3btzNu3DhatW4dFdOqVSvGjB4NwITPPuOkk07CzGjVujXjxo1j27ZtrFixgmVLl3LMMcfke85DypVj06ZNLAkvU/hqxgwOO+wwAFq3acOsmaEPcK1du5alS5ZQo8Z+/QCVp/pHH8WKZcv4fcVKtm/fzqRPx9OsRXRzv1nLFnz60ccATPl8IsefeGJO9zM7O5spn0+gda5Cd21mJgAbN2xg9PD3Oadjx0LPfW91vqANQwfcz9AB99OmxfGMGfcl7s68Bb9Q9uCD8ly28OLrI9i4+Q+633pJHDLes1lLfuOI5FTqVK5CUmIil5zQlDHz5u4SVy8llQplyjDj178+WLl0bSYtj6hHYkICJRISaXlkfRav0tIFEZFC59mxfe3ZcuDQiO2awMr8YsysBFAeyCzgsXulIL/jNfZcTUfu35rrWNx9ipm1BM4F3jKzx939zQLmGHnuzfnklxcD+rr7qwW8Tn7X/OuEZgnAUcAfQCVCfyH7rESJEtx1113cfNNNZGdn06FjR+rWrcvLL73E0UcfTavWrTn//PO5r2dPOnToQPly5ejbrx8AdevW5cy2bbn4ootITEykR48eJCaG1mvndU6AnvfdR/du3TAzypUrR6/evQFo1qwZM778kosuvJDExERuv/12KlTIe/3o/kgsUYJbu3ejR9fbyM7Opt157alT93AGvdqfekc1oFnLlpzd4Tz69XqAqy68mEPKlePeRx7KOX7enDlUSUmheq4i/KWnnubnH0OdwSuvv56atWsVeu7749R/NGLajO/ocNm9lC5Vkt49rsnZ1/n6Bxk64H7S0tfy+lsfc1itqnT518OhfRe04cL2+a7yOeCysrO5fdg7jLn5dhItgUEzprNo1UruP7cjs5f+xpj5oaL3khNP5r1vop988cGcWbSp14DZ9zyAu/Ppou/46Ltdi+Si6p1efWnduAlVyldg2ftj6TXwFQZ+NCLeaYmIFEUzgSPN7DBgBaEPl12WK2YUcDWhtbcXA5+7u5vZKOAdM3sKqE6oQfn1/iRje1o3GV668CFwiruvCS9dGAS85+5vmdk1QEd3v8DMBgFj3H14+NhN7l7WzGoDK9x9h5ndDtRx99vzuZ4DXdx9iJn1BFLd/VYzmwR0c/dZ4bjewCZ3f8LMZhBaujDCzEoBiYQ6wQ8Bp7v7JjOrAWx39/R8rrsz1wuBfwPnEFqfuxD4l7sPN7P/I7Ss4S1CnxI8xd3z/eTTnpYuFFeZ27fuOaiYqvzHvHinEBMVH34r3inExLa538Q7hZjyqXPinYJI0MX9k1uFbcvm9TGtPcocXH6Pc2Zm5wDPEKrHBoaXrj4IzHL3UeFlpm8BjQl1ci+N+PDavcB1wA7gdnf/JM+LFNAeO7ruvsDMHgEmm1kWoaUAXYGBZtYdyACu3cNpWgPdzWw7sAnY3WPBNgMNzewbQouTO+/xLuBK4NXwJG4HOrn7p2Z2FPBl+Fffm4ArgDwL3QgfElquMR/4AZgMYGb1CC1XaOruG81sCtCT0BpiEREREQHc/WPg41xj90e8/xPI89t33P0R4JHCymWPHd0DbWdnNd55FAZ1dIsfdXSLF3V0RWQ/Ba+ju2ltbDu6ZSsWqzmL/8fTRURERERiIG4PHDWzr4BSuYavjGU318wqAxPy2HW6u6+J1XVFREREDowCPRnhbyNuha67nxyHa64B8v/OUhEREREJjKL1FVIiIiIisu8K9qzbvw2t0RURERGRQFJHV0RERCQw1NGNpI6uiIiIiASSOroiIiIiQaE1ulHU0RURERGRQFJHV0RERCQw1NGNpI6uiIiIiASSOroiIiIiQaE1ulHU0RURERGRQFJHV0RERCQw1NGNpI6uiIiIiASSOroiIiIiQaE1ulFU6IqIiIgEhgrdSFq6ICIiIiKBpI6uiIiISECYe7xTKFLU0RURERGRQDJX5R8zi9euDuTk1rKN8U4hdhLLxDuDmJj3Z1a8U4iJOqUPjncKMVPtnNbxTiFmfOqceKcgspPFO4HC9sfaH2JaexxUsV6xmjN1dEVEREQkkLRGV0RERCQo9HixKOroioiIiEggqaMrIiIiEhjq6EZSR1dEREREAkkdXREREZGg0BrdKOroioiIiEggqaMrIiIiEhjq6EZSR1dEREREAkkdXREREZGg0BrdKOroioiIiEggqaMrIiIiEhjq6EZSR1dEREREAkkdXREREZGg0BrdKOroioiIiEggqaMrIiIiEhjq6EZSR1dEREREAkkdXREREZGg0BrdKCp0RURERAJDhW4kLV0QERERkUBSR1dEREQkKLR0IYo6uiIiIiISSOroFgOzv5zBa08/Q3Z2Nmd2OI+Lr7oyav+COd/y+tPP8tvPP9PtoQdoflobANJ/X0W/HveQnZ3Fjh07OLfTxZx94QXxuIUc7s5jT73M9C9mUrp0KR647/84qsGRu8QtXPQjvR56kq1bt9K82Unc+d8bMTO+/+EXHnn0Of7440+qV0vlkQfupGzZg1m3fgPdezzMgkU/0OHcM+nR/eb43NsTzzF9+ozQvfW+m6Ma1M/j3r6nV+8+bN26jebN/8Gd3bpiZrzy6kA+GDGGihUrAHDLTf+ixamnsG7derrfdT8LFi6mQ/t29LjrjgN9a3maN+Nr3n72BbKzs2jV/lzaX3lZ1P6xQ4YxeczHJCQmUq5Cea6/+06qVK0ap2x39dUXX/L8E0+SnZ3Nued35PJrro7av23bNvr06s0PixZTrnx5evV9hGrVq/P7ypVc1akztWrXAuDoY47h/+65G4DPPx3PWwPfIDs7i380b86Nt3U94Pe1Nwb06EX7Zi1JX5tJo6s7xTsdESkE7lnxTqFIUUe3iMvKyuLVJ56k19NP8sK7bzP1089Y+uuvUTFVUlO57b57adn2zKjxilUq8+hrr/DMW4N5fMBrfPDm/1iTkXEg09/FtC9msnTZSkYOH0jPHrfR57EX8ozr89jz9Ly7KyOHD2TpspVM/3IWAA/2eZquN1/He++8QptWzRj8v+EAlCpZkpv+fRV3dP3XAbuX3KZNn8HSZcsZ+eE79Ly3O336PpVnXJ++T9Lz3u6M/PAdli5bzvQvvsrZd8VlnRj6zkCGvjOQFqeeAkCpUiW56cbrueO2mw7IfRREdlYWbz71LP/3RD/6/m8QMz6bwIpff4uKqV3vSHq//gqPDB7Aia1bMfSlV+OTbB6ysrJ45tHHeOy5Zxn83lAmjBvHb7/8EhXz0chRHHLIIbwz4gM6XdaFV5//699qjRo1GPDO2wx45+2cInf9unW8/OxzPP3yiwweNpS1mZl88/XXB/S+9tagT0bTrtuB/6FQRORA+VsUumbW1cwWmdnb+ezvYGY98tm3KbbZ7d6PCxdRtWZNqtaoQVJSEi3OPJ2vp0yNikmtXo06Rx5BglnUeFJSEkklSwKwfft2st0PWN75mTzlS9qffTpmxrGNjmLjxk1krF4TFZOxeg2bN2/huEZHY2a0P/t0Jk3+AoAlS1bQpHEjAP5x8glMmDgdgIMOKk3j44+hVMmkA3tDESZPnkb7c84K31vD8L2tjorJWL06dG/HHhO6t3POYtKkqfmcMeSggw6i8fHHUqpUyVimv1d+WbSY1JrVSalRnRJJSZx8xmnMnjY9KuaoExpTqnRpAI5oeDSZcf4hK9KiBQuocWhNqtcM/Xd1Wtu2TJs8JSpm+uTJnNX+XABanX4as7+eie/mv6GVK1ZyaO1aVKhYEYAmTZsy+fOJsbuJQjB17mwyN6yPdxoiUog8Ozumr+Lmb1HoAjcB57j75XntdPdR7t7vAOdUIGsyMqiSkpKzXTklZa+6shlpaXS9/Cqu73ABF155OZWTk2ORZoGlZ6yhaupfOaSmJJOesWaXmJSUKnnG1K1bm0lTZgAwfsIU0tKLTvGUnrGaqlX/+rtKTU0mPT260E1PX01K5P2nJpOe8VfMkGEfcsml19D7gX5s2LAx9knvo7UZq6kU8e+yUnIyazNW5xs/eczHHHvyyQcitQJZnZ5BSmpqznZySgqrc/1biowpUaIEB5cty/r1oaLw95Uruf6yK+h6w7+ZO2cOADUPrcnS35bw+8qV7Nixg2mTJpOelnaA7khERPKy14WumV1lZvPMbK6ZvWVmtc1sQnhsgpnVCscNMrPnzOwLM/vFzC4Oj1czsylm9q2ZfWdmLXZzrXZmNjt8rQnhsUpmNiJ8vRlmdmx4vLeZDTSzSeHrdQ2PvwIcDowyszwXN5rZNWb2Qvj9YWb2pZnNNLOHImIuMLPPLKSamf1gZrssODSzG8xslpnNGjbozb2d3l3l0UEyLI/AvCWnpvLc22/yyvChTPz4E9atydz/nPZDXh2xXe4nr5hwt7p3z/8ybPhoLrvqFrZs+YOkEkVnmXme92YFv7dOF5/P6BHvMuSdgVSpUpmnnn4xJnkWhgLda9j0ceP5bfH3nHNZ51inVWBOHp3Z3H9VecQYULlKFYaNGcWAd/7HzXfczkM972Pzpk0cUq4cd/S4iwfuvpdb/3UDVatXIzExMTY3ICKSD/esmL6Km72qEsysIXAv0NzdV5tZJWAw8Ka7Dzaz64DngPPDh1QDTgUaAKOA4cBlwDh3f8T+n737Do+i7Po4/j0pEGqANHrvCoIU6SB2RUBRUR8LlgcrPooNK1gQsBdUREUUGyrYUJogCIJK7yK9BtKQXpLd+/0jS8iShL7ZsO/vc117sTtzZubc2WVy5+zZWbNwoGgex4oD3gfaOefW+I4F8AwwzznX1cw6Ap8AjXzr6gLnAiWA5Wb2rnPuTjO7GDjXOZd3yemQN4B3nXOfmFlW85pz7lsz6wbcA1wM9HXObTl8Y+fcUGAowN/bUk66VyAmPp6UpKSsx6lJSZSJiz3CFnnsJy6OStWqsWTBgqwPq+WXkV//wOjvxwFwRv3abNl6qHK2NSmZuLgyfvHx8bF+ldCtScnExWbGVKtaiXffegGAdes3Mu334PZAjvxqNKO/GwPAGfXrsmXLoedq69Zk4uJi/OLjE+JIyj7+rcnExWbGxMQc+jlceUUn7rs/126aAqFMfBxp2V6XacnJlIqNyRG3ZNYcfvzkUx4f/HpWG01BEBcf71dtTU5KIvawdzsOxsQnJJCRkcHuXbsoGR2NmVHIN5Y69epRoUJFNqxfT9369Wndri2t22X+7f7D6G8JC/v/8qaZiEjBdLxn4Y7ANwcnjM65NKAl8Llv/QgyJ7YHfeec8zrnlgIH3yecBdxiZv2ABs65vN6fbQH85pxbk+1Y+PY/wrdsMhBjZtG+dT855/b78kvKdszj0Rr4Itt4susFPAbsd859QT6oVa8uiRs2snXzZtLT05k2cRLN27Y5+oZASlIS+/ftB2DXjh38vXARFSpXDmS6uep+dWdGfvoOIz99h3PbtWTM2Ek451i4aBnFixfLmugdFBcbQ9GiRVi4aBnOOcaMnUT7dpkfzEpL+xcAr9fL+8O+4KorLsv38WTX/Zorsz48dm6Htoz5ebxvbEt8Y/P/oyQuNpaixYqycNGSzLH9PJ727TOfz+z9vJN/nUaNGtXydSzHo1rdumzdsInkzYlkpKfz5y+Tady6lV/Mun9W8NFLr3L/wP6U9PWtFhR169dn44YNJG7aRHp6OpMnTMiaoB7Uul07xo/5CYCpkybTuFlTzIx/t23D48msamzeuImNGzZQvkIFALalZZ6mdu7YwffffEOnrl3ycVQiIuC8noDeTjfH+76vQW7v+fnJvn7/YdvinPvNzNoBlwEjzOwl51xu7/Hndazc3h89GJf9eB5O/PJpeY2xApnfrZdgZmHOBf6qzOEREfR86AH6/a83Xq+H8zp1onL16nw29H1q1q3LOe3asmLpMgY8+hi7du5k1vTf+eL9Dxj8xWdsXLOWYW8OxsxwztH1P9dRtWaNQKd8RG1aN2f6jFl07nYrUVGF6fdU76x13W+4m5GfvgPA3qgToAAAIABJREFU44/2ou+zr2RegqtlU9q0agbAuAlTGPnNjwB0PLc1XS6/MGv7S7vexO7de0hPz+DXqTN5583+1KheJR/H1oLpv8+kc9frMsfW97FDY7v+VkZ+PixzbH1607ffAN+l086hTesWALzxxhCW/7MCM6NcubI8+cRDh8Z2+TXs3r3bN7bpvDP4FWpUr5pvYztceEQ4N/a+j5d6P4LX66XdZZdQsXo1Rn8wjKp163B2m9Z8+fYQ9u/dy9tP9QOgTEICDwzqH7Scs4uIiOD+hx/moV734fV4ubTz5VSrUYMPh7xH3Xr1aN2+HZd26Uz/p/tyfdcrKVGyJH1fyMx9wdx5DHvvPcLDwwkLC6f3Y30oGZ35t/abL7/KqhUrALj59tuoVCX/Xn8n4vO+A+jQuAmx0aXYMGocfYcNYdhP3wU7LRGRU8aO9CniHMGZrQvfAi2dc6m+doLhwNfOuRFm1gPo4py7wsyGA2Occ9/4tt3lnCtuZlWATc65DDO7H6jqnLs/l2PFAXPJ1rrgnEszszeBZOfcc2bWAXjNOdfYVyHe5Zx72bf9YqCTc26tma0FmubVuuDLu6lz7l4z+wH4yjn3qZndBbzkyzsCmAn0Bm4Clh88Vl5ORetCQVTZCu6HpE5aeK6dNKe9hftOv7/Cj0XVqGLBTiFgyl3aIdgpBIybNi/YKYgcdOwfejlN7Fj/c0DnHiUrX3rCPzPfvHEkUBVYC1zjnNt2WEwj4F2gJJlFy/7OuZG+dcOB9sDBy8X0cM7NP9Ixj6vi6ZxbYmb9galm5gHmAfcBw8zsYSAZuOUou+kAPGxm6cAuMieNuR0r2cx6AqPNLIzMVoQLgH7AR2a2ENgD3Jzb9ifhf8DnZvY/YFS25Y8D05xz08xsPjDLzH5yzi07xccXERERCUV9gEnOuYG+y7r2AR49LGYPcJNzboWZlQfmmNl459y/vvUPHyyiHovjqujK8VFF9zSkiu5pRRXd05MqulKAhF5Fd+2YwFZ0q3Y6mYrucqCDcy7RzMoBU5xzOb9C1H+bBcBVvonvcLJ1CxwLfSRYRERERI5J9suo+m49j2PzBOdcIoDv3/gjBZtZc6AQsCrb4v6+S8y+ZmaFj3bAAnERUjP7Ezg82Rudc4tO8XFuIbM1IbvfnXP6DkwRERE57QX6WrfZL6OaGzP7BcjxPQNkXp72mPkqviOAm7N9+P8xYAuZk9+hZLY9PHuk/RSIia5zLl++Msk59xHwUX4cS0REROT/G+fc+XmtM7OtZlYuW+tCUh5xJYGfgCedc39k23ei7+5+M/sIeCi37bNT64KIiIhIiCjg19H9gUMXEbgZ+P7wADMrROYVvj5xzn192Lpyvn+NzC8nW3y0A2qiKyIiIiL5YSBwgZmtIPNKWgMBzKypmX3gi7kGaAf0MLP5vtvBb8D9zMwWAYuAWOD5ox2wQLQuiIiIiMjJC3SP7slwzqUC5+WyfDZwu+/+p8CneWzf8XiPqYmuiIiISIhw3oB/aetpRa0LIiIiIhKSVNEVERERCREFuXUhGFTRFREREZGQpIquiIiISIg4BZcACymq6IqIiIhISFJFV0RERCREqEfXnyq6IiIiIhKSVNEVERERCRG6jq4/VXRFREREJCSpoisiIiISItSj608VXREREREJSaroioiIiIQKXUfXjyq6IiIiIhKSVNENoAqFigQ7hYAYnbgl2CkETJPFA4KdQkC0fHN+sFMIiErtzwt2CgHz9Ygvgp1CQFx943VY28bBTiMg3LR5wU5BRD26h1FFV0RERERCkiq6IiIiIiFC19H1p4quiIiIiIQkVXRFREREQoR6dP2poisiIiIiIUkVXREREZEQ4XQdXT+a6IqIiIiECLUu+FPrgoiIiIiEJFV0RUREREKELi/mTxVdEREREQlJquiKiIiIhAj16PpTRVdEREREQpIquiIiIiIhQpcX86eKroiIiIiEJFV0RUREREKEenT9qaIrIiIiIiFJFV0RERGREKEeXX+q6IqIiIhISFJFV0RERCREOI8qutmpoisiIiIiIUkVXREREZEQoR5df6roioiIiEhIUkVXREREJESoR9efJroF1Izff+fll1/G6/HQ9Yor6HHLLX7rDxw4QN+nnmLZsmVElyrFgIEDKV++PP/++y+PPvIIS5csodPll/Nonz5Z27w9eDA///QTO3bsYNrvv+f3kI5q5ex5jB86DOf10vjC82h9zZV+6+f8PJ5ZY8YRFhZGoSJRXNbrTuIqVwpStkfmnOODH1YxZ3kqhSPDue+aOtSoUCLP+P7DF7M1bS9v9m4GwBcT1zLxr0RKFosE4IaLq9G0bky+5H4kFzVrwRv3Pkh4WBgf/Pw9g774xG995YSyDHv4KeKiS5G2cwc3vNCXTSlJdGjUhNfufiArrm7lKlz73JN8//vU/B5CrtrXqE2/izsRHhbGl3Nn8U4ueXWq34AHOpyHc7B0ayL3jR4JwGPnX0zHWnUAePO3yfy4ZFG+5n40/8yay09D3sfr8dL0kgto3/0qv/XTR33P7HETCAsPp1h0NFf27kXphHgAxn0wnOV/zsY5R82zz+Kyu/6LmQVjGMftwz596dSqHUnb0mhw89XBTkdEguS0nOiaWWegvnNuoJkNB8Y45745wX1VAUYD4UAk8JZzbsgpS/YEeDweBg0axNvvvENCQgI33XAD7dq3p3r16lkx33/3HSVKluS7H35g/PjxvPXGGwwYNIjChQtz1113sXLVKlatXOm333bt2tG9e3eu6No1v4d0VF6Ph3Hvvs9/nn+akrExfPDAo9Ru0cxvIntmh7Y0ufQiAJb/MYuJ7w/n+ueeClbKRzRneRqJKXt49+Hm/LN+J0O+XcFL956da+zMxclEFQ7Psbxzm4p0bV9wJvJhYWG8/b9HuODhe9mYnMSsdz/mhxnTWLZuTVbMy3f+j08m/MwnE37i3MZNGfDfu7lpQD+mzJ9D4543AFC6RElWjhjFhNl/BGsofsLMeP7SzvxnxIck7tjBj/+9h4nLl7EiJSkrpmqZGO5u04Erhw1h+759xBQtBkDHWnU4s2x5Lh7yFoUiwvm6R09+XfEPuw7sD9Zw/Hg9Hn58+z1uGfAMJWNjeLfXQ9Rr0Zz4KpWzYsrXqMbdb71KoajC/PnjWMZ/MJxrn3iEdUuWsW7JMnoNeQOAoQ8+xpqFi6l+VoNgDee4DB/7I4NHj+STJ54Ldioi+cqrHl0/p12PrplFOOd+cM4NPBX7AhKBVs65RsA5QB8zK3+y+z4ZSxYvplLFilSsWJHIyEguvOgipk6Z4hczdcoUOnXqBMB5553HX7Nm4ZyjSJEiNGrcmMKFCuXYb4OGDYmNi8uPIRy3zf+spHT5spQuV5bwyEjOaNeG5X/M8ospXLRo1v30ffugAFeW/lqSSocmZTEz6lQpye69GaTtyDn52bvfww/TNnJNx8q57KVgaV73DFZu2siaxM2kZ2Tw5eQJdGnVzi+mfpVqTJqb+bz9Om92jvUAV7XryNi/ZrJ3f8GYDDaqUIm1aams/3cb6V4PPy5ZwIV16/nFXH92Mz6ZNZPt+/YBkLpnNwC14uL5Y90aPM7L3vR0lm5NpEPN2vk+hrxsXL6CMuXLUqZcWSIiI2nYoS3LZv7lF1O9UUMKRRUGoFK9OmxPSQXAzMg4kI4nI4OM9Aw8GRkUL10q38dwoqYtmEvaju3BTkNEgixoE10zu8HM/jKz+Wb2npmFm9mubOuv8lVrMbPhZvaqmf0KDDKzHmY2ONvuzjezaWb2j5l18m0TZWYfmdkiM5tnZuf6lvcws6/N7EdggnPugHPu4G/cwmT7mZjZLjMbZGZzzOwXM2tuZlPMbLWvqhwQScnJJJQtm/U4Pj6epKSkPGMiIiIoXrw42//9N1ApBdyO1DRKxsZmPS4ZW4adqak54maNGcvg2+5m0kcjuOiOW/MzxeOStmM/sdGFsx7HRBcmbceBHHGfT1hDl7aVKBSZs6L708xN/O+12bz19XJ27UkPaL7HokJsHBuStmY93piSRIXD/nBasGoF3dqdC8AVbTtQslhxypSM9ou5tuOFfDF5QuATPkZlS5Rkc7YJUeKOHSSU8M+5Wkws1WNiGX3LHXx32120r5E5mV26ZQvn1qxNVEQkpYsUpVXVGpSL9t82mHakphIdl/3/VUzWRDY3s8dNpHazJgBUrl+X6mc1YOB1tzDwuh7UatKY+ALaKiQihziPJ6C3001QJrpmVg/oDrT2VVI9wH+Osllt4Hzn3IO5rKsKtAcuA4aYWRRwD4BzrgFwHfCxbzlAS+Bm51xHXz6VzGwhsAEY5Jzb7IsrBkxxzjUBdgLPAxcAVwDP5jG2nmY228xmfzRs2FGGlAfnctvvUWMKcoXzqHIbMznH06zTJdz74Tt0vOVGpo8clR+ZnZBcnp0cVm/eRWLqXlqcGZtj3SUtyjPkkXN47X9NKF2iEB/9tPrUJ3mccuvNPPxpe2jIG7Q/62zmvjeC9g3PZmPyVjI8GVnry5aJoUG1GoyfNTPQ6R6z3P7buMOewYiwcKqWieWaj9+n16gvebHzlZQsHMW01SuYvHI53952J4O7XcucDevxeL35lPnR5X6ayP08MX/SFDavWEnbq64AIHVTIkkbNvDIZx/y6OfDWL1gEWsWLQlkuiJyCmii6y9YPbrnAU2AWb6TbhEg6YhbwNfOubx+wl8557zACjNbDdQF2gBvATjn/jazdWROlgEmOufSDm7snNsANPS1LHxnZt8457YCB4BxvrBFwH7nXLqZLSJzcp2Dc24oMBRg5+7dxzLfySE+Pp6tW7ZkPU5KSiLusMrZwZiEhAQyMjLYtWsX0QWoknS8SsbGsCMlJevxjpQ0iseUyTP+zHatGfv20PxI7Zj9PGMTE/5KBKBWxRKkbD/01nzq9v2UKenfTrJ83Q5WbdzFfwf+gdfj2L47nSfem0//OxpRqsSh2Aual6P/8OB/wGljchKV4hOyHleMjWdzSrJfTGJqCt36PgpAsagidGt3Ljt2785af02H8/l2+hQyCtDJMnHHDspnqzqXK1mSpJ07DovZztyN68nwetnw7zZWpyRTNSaWhZs3MnjaFAZPmwLAm1d2Z00u70QES3RsDNuTs/+/SqVkLv+vVs6dz5Qvvub2l/sTUSjzA5BLZ8ykUt06FC5SBIDaTc9mw7LlVGtwRv4kLyJyCgSrdcGAj51zjXy3Os65fvgXwqIO22Y3eTt8Qul8x8hLrvvyVXKXAG19i9Kdy6qJeIH9vjgvAfwjof4ZZ7BhwwY2bdpEeno6E8aPp1379n4x7dq3Z8yYMQBMmjSJZs2anTafhs5N+do1SduUyLYtW/Gkp7Pkt+nUPqepX0zqps1Z91fMmkOZ8uXyO80jurRVBV6/vymv39+Uc86IZcqcLTjnWL5uB8WiIihTsrBf/CUty/PRky15v08LXrirMeVji9D/jkYAfv28fy5JoXJCsXwdS25m/b2UWhUqUbVseSIjIri244X8MHOaX0xMyeis1+Fj1/dg2Ngf/dZfV8DaFgAWbNpItZhYKpUqTWRYOJefcRYTly/zixn/91JaVa0BQOkiRakWE8v6bWmEmVGqSGbveN34stRLKMtvq1bk+xjyUqFOLVI3JZK2ZSsZ6eksnDKNui2a+8VsXrma7998lxueeYLipQ714EbHxbF24WI8Hg+ejAzWLFpCXOWK+T0EETlOzpsR0NvpJlgV3UnA92b2mnMuyczKACWArb62huVktgfsPMb9XW1mHwPVgOq+7X8jsx1ispnVBir7lvt99N3MKgKpzrm9ZlYaaA28etIjPAkRERE8/Oij9LrnHjxeL507d6ZGjRoMefdd6tWvT/v27enStStPP/UUXTt3pmR0NC8MGJC1/eWXXcbu3btJT09n6pQpDH7nHapXr84br7/O+HHj2LdvH5defDFdunbljjvvDOJIDwkLD+fiu27n86eew3m9nHVBR+KrVGbKiC8oV6smdVo0Y/aYsayev5Dw8Aiiihejc+97g512nprULcOc5Wnc+eJfFC4Uzn1X18lad//rs3n9/qZH2Bo+/nk1axJ3Y0B86SjuurJWgDM+Oo/Xw71vvcT4QW8SHh7GsLE/snTtap7p0ZPZ/yzjxxnT6NCoCQNuvxvn4LeF87jnzReztq+SUI5K8QlMXTA3iKPIyeO8PPXzD4y44VbCzRg5fzb/JCfRu8P5LNq8iYn/LGPqqn9oV6MWk+6+H4/X0X/iWP7du4fC4RGMuqUnADv37+d/o7/C4wpO60J4eDiX39OT4Y/3w3m9nH3heSRUrcwvH39Ghdo1qdfyHMa9/xH79+7li+czn6tS8bHc+MyTnNm2FasXLOKtO+4Dy6zo1jtsklyQfd53AB0aNyE2uhQbRo2j77AhDPvpu2CnJSL5zFxuTVz5cWCz7sBjZFaV08nsqa0IDCKzV3YxUNw51+PwS4iZWQ+gqXPuXt+6bUBTIAHo7Zwb4+vHHUJmi0SGb/mv2bf17esC4BUOVYEH+9oPMLNdzrnivvv9gF3OuZcPX5eXE21dKOi+T1xz9KDTVJPFbwY7hYCo/+b8YKcQEJXanxfsFALm1ZtvDnYKAXH1jdcFO4WAcdPmBTsFOX6n71uheVgy6raAzj3O6PbhafUzC9p1dJ1zI4GRuazKcT1c51yPwx4PB4bnti5bzD4gx7rs2/oeTwQa5rGP4tnu98trnYiIiIgUPKflF0aIiIiISE5OXxjh57T7wggRERERkWOhiq6IiIhIiDgdr3UbSKroioiIiEhIUkVXREREJEQU5B5d3+VkR5L5pVtrgWucc9tyifOQ+UVdAOudc519y6sBXwJlgLnAjc65A0c6piq6IiIiIpIf+gCTnHO1yPxOhT55xO3N9qVinbMtHwS85tt+G3Db0Q6oia6IiIhIiPB6PAG9naQuwMe++x8DXY91Q8v82s2OHLoM7TFtr4muiIiIiBwTM+tpZrOz3Xoex+YJzrlEAN+/8XnERfn2/YeZHZzMxgD/OucOfg/xRqDC0Q6oHl0RERGREBHoHl3ft8cOzWu9mf0ClM1l1RPHcZjKzrnNZlYdmGxmi4AduaVztB1poisiIiIip4Rz7vy81pnZVjMr55xLNLNyQFIe+9js+3e1mU0BGgOjgFJmFuGr6lYENh8tH7UuiIiIiIQI5/EE9HaSfgBu9t2/Gfj+8AAzK21mhX33Y4HWwFLnnAN+Ba460vaH00RXRERERPLDQOACM1sBXOB7jJk1NbMPfDH1gNlmtoDMie1A59xS37pHgd5mtpLMnt0Pj3ZAtS6IiIiIhAjnyTh6UJA451KB83JZPhu43Xd/BtAgj+1XA82P55iq6IqIiIhISFJFV0RERCREeAvwN6MFgyq6IiIiIhKSVNEVERERCRGn4MoIIUUTXREREZEQEegvjDjdqHVBREREREKSKroiIiIiIUKtC/400Q2gcArutexORuKencFOIWD2bU8LdgqBUSI62BkERLMzzwh2CgHTpHR8sFMIjOIlgp1BYOzaibVtHOwsAsZNmxfsFEROiCa6IiIiIiFClxfzpx5dEREREQlJquiKiIiIhAj16PpTRVdEREREQpIquiIiIiIhwnlD84PwJ0oVXREREREJSaroioiIiIQI9ej6U0VXREREREKSKroiIiIiIULX0fWniq6IiIiIhCRVdEVERERChHp0/amiKyIiIiIhSRVdERERkRDh9bpgp1CgqKIrIiIiIiFJFV0RERGREOH1eoOdQoGiiq6IiIiIhCRVdEVERERChHp0/WmiKyIiIhIiNNH1p9YFEREREQlJquiKiIiIhAiv04fRslNFV0RERERCkiq6IiIiIiFCPbr+VNEVERERkZCkim4B5ZzjxZde4ffpM4iKiuKZZ56mXr26OeKWLl1G337Psn/fflq3acUjDz+ImTFx4i8Mee991qxZy4gRH3FG/foApKdn8Oxzz/P338vxZHi4rNOl3HZrj3weXaYN8xbyx0ef47xe6pzXjrOu6OS3ftmEySwdNxkLMyKjomhzRw9KV6rAymkzWPj92Ky4tPUbuWJQP2KqVcnvIRyzCs1vJbpCY7wZB1j3+2D2pq3JEVPzomeILFIKr+cAAKsmPkfGvh35neoRXdS4KW/8927Cw8L4YOJYBo0a6be+clw8w3o9RFx0NGk7d3LDawPZlJoCwNi+L9Cidj2mL1vM5c8/FYz089QooRy3ntWMMDMmrVnJt/8s8Vt/bpXq3NjgbNL27gFg7Kp/mLR2JVWjS9OzcXOKRkbidY5v/l7MjI3rgjGEYzJ75kzefeV1vF4PF3fpTPebb/Jbv2juPIa89jprVq7iseefpe15HYOU6dFd1KQ5b9x5X+ZrcdxPDPr6M7/1leMTGPZAH+KiS5G2cwc3vPQ8m1KSAcgY8yuL1q4GYH1yEl2eeSzf8z9RH/bpS6dW7UjalkaDm68OdjpSAOkLI/xpoltATf99BuvXb+D770exaNFiXhgwiBGffJQj7oUBg3jyicdo2LAB9/a6n99nzKRN61bUqFGDV15+kef7D/CL/+WXXzhwIJ2vv/qCvXv30e2q7lxy8YWUL18+v4YGgNfjZcaHI7jkqYcpVqYM3z/2DJWbNqZ0pQpZMTXatKTehZm/aNfNmsefH3/BxU8+RM22rajZthUAaes2MPHFNwv0JLdkhcZElSjH0m97UTS2FpVa9OSfn3P/xbp22pvsTV2Vzxkem7CwMN6+oxcX9H2UjakpzHp5MD/8NZNlG9Znxbx8yx188utEPvl1Iuc2aMSAG2/jptcHAfDSt19TtHBh7rjosmANIVdhGP9t1Jxnp08idc8eBnW8hFmJG9m4c7tf3IyN6/hg/iy/Zfs9Gbw1ewaJu3ZSOqoIL3W8lPlbN7MnPT0/h3BMPB4Pb7/4Ci8MfoPY+Hjuu/lWWrRtS5Xq1bJi4sqW5cGnn2LUp58dYU/BFxYWxtv3PMAFj/dmY0oys94Yyg9/TmfZ+kN/ZLx8+918Mmk8n/wyjnPPOpsBPXpy08v9Adh7YD+N770tWOmflOFjf2Tw6JF88sRzwU5F5LQQkq0LZtbPzB4K4P6rmtniQO0fYOqU3+jU6VLMjIYNG7Bz506Sk1P8YpKTU9i9ezdnndUQM6NTp0uZ8utUAKpXr0bVqrlM/szYt3cvGRkZ7N+/j8jICIoVKxbIoeQqeeVqSpZNoGRCPOGREVRvfQ7rZs/ziylUtEjW/Yz9+8Esx35W/f4n1VufE/B8T0Z0pWakrZ4CwJ6UFYQXKkpEkVLBTeoENK9Vh5VbNrNm6xbSMzL4ctoUujRv5RdTv1JlJi3MfB5/XTSfLue0zFo3eeE8dvoqogVJzTIxbNm9k627d5HhvEzfuJZm5Sse07aJu3aSuGsnANv27WX7/n1EF44KZLonbPmSpZSrWJFyFSoQGRlJ+wvPZ+Zvv/nFlC1fjuq1amJhBftXQ/Pa9Vi5eRNrtiRmvhanTqJLizZ+MfUrV2XS/DkA/LpgLl1atsltV6edaQvmkrZj+9ED5f8tr9cF9Ha6yZezmZmpcnyckpKSKJuQkPU4IT6epOQk/5jkJOLj4/1jkvxjDnf+eecRVaQIF1x4KZdc2pmbbryB6OjoU5v8MdiTto1iMWWyHhcrU5o9qdtyxC0d9wsj732Yvz79ipa3/ifH+tUz/qRGmxYBzfVkRRaN4cDu1KzH6XvSiCwak2tsldZ3U+fyl0hoeFV+pXfMKsTEssH31i/AxtQUKsTE+sUsWLOabi3bAnBFizaULFqMMiVK5Guex6tMkaKk7Dk0AU/bu4eYIkVzxLUoX5lXz7+Mh85pm+v6mqVjiAgLY4tv4lvQpCYnE5dw6HwRGx9PanLyEbYouCrExrIh2/lwY0oyFWLi/GIWrF5Jt9btAbiiVTvfa7EkAFGFCjHrjaHMfO3dkJkAi0juTnqi66tuLjOz981siZlNMLMiZjbFzF4ws6nA//LYNs7MRpnZLN+ttW95PzP72LevtWZ2pZm9aGaLzGycmUX64taa2SAz+8t3q5nLMRqZ2R9mttDMvjWz0mZWw8zmZoupZWZzfPebmNlUM5tjZuPNrFy25QvMbCZwzxF+Hj3NbLaZzR42bPgJ/1xz+5vJsKMGWS5Vz+yWLFlCeHgYE8b/zE9jvmPEp5+xceOmE87zRLlck8+5qP7F59N98Es0+8/VzB/1o9+6pBWriChUmDKVj636FjS5PSUu5/jXTXuDv394kBVjn6J4Qj3KVG8f+NyOQ47XH5m95Nk9NHwo7c9syNzX3qX9mQ3ZmJJMhseTXymekNyfHv9xzUrcyJ3jvqX3Lz+xMGkLvZr6V7JLRRXhvmatGTxnZq7/dwuCw8cEuT+np4NcX4uH/eQf+uAd2jdoxNzBH9C+QSM2piRlvRYr33Q1zf7Xk+sHPcvrd/Siern8bd0SCSRVdP2dqkprLeA659x/zewroJtveSnn3JF+W78BvOacm25mlYHxQD3fuhrAuUB9YCbQzTn3iJl9C1wGfOeL2+Gca25mNwGvA/6faIJPgF7Oualm9izQ1zl3v5ltN7NGzrn5wC3AcN8E+i2gi3Mu2cy6A/2BW4GPsu3npbwG5JwbCgwF2LN7+3G9IkaO/JrR32YO64wz6rNl69asdVuTkoiL869YxB9Wwc0t5nBjx46nVcuWREZGUKZMGRqddRZLly6lYsUKR9zuVCtWpgy7U9OyHu9O20bRMqXzjK/R+hx+f/8Tsr+YVv/+JzXaFMy2hdg6FxNT+zwA9qSsolCxGHb71kUWLUP63rQc26TvyVzmzdjHttXTKBpXk7TVU/Mr5aPamJpMpdhDr6+KMbFsTkv1i0lMS6XbwGcAKBYVRbeWbdixp+C1K2SXuncPsUUPVWjLFClK2r69fjG7DhzIuv/LmpXc0KBx1uMiEZEEDtMrAAAgAElEQVQ80epcvlgynxVp/u1FBUlsfDzJWw+dL1KSkigTF3uELQqujSnJVIo7VJ2uGBvH5lT/n31iWirdnn8SgGJRRejWph079uzOWgewZksiUxbOp3GNWqxO3JxP2YtIfjpVrQtrfBNGgDlAVd/9kbmHZzkfGGxm84EfgJJmdvB9zrHOuXRgERAOjPMtX5Rt/wBfZPu3ZbblmFk0mZPtg7OFj4F2vvsfALeYWTjQHfgcqAOcCUz05fQkUDGX/Yw4yrhOSPfuVzPyy88Y+eVnnNuhPWPG/IxzjoULF1G8eHHiDvulFBcXS9GiRVm4cBHOOcaM+Zn2HdrlsfdMZcslMGvWbJxz7N27l4WLFlO1atVADOeI4mpWY0fiVnZuTcaTnsHq3/+kStPGfjHbE7dk3V8/dwHR5Q61cjivl9UzZxXY/tyU5eNY/uPDLP/xYbav/4sy1TsAUDS2Fp70PWTs/dd/AwsjvLDvpW/hlKzYhL3bNuRrzkcza8VyapWrQNX4skRGRHBt2w788NdMv5iYEiWz3lV47KrrGDZpfDBSPS4rt6VSrngJ4osWI8LCaFOxKrM3b/SLKRV1qF+8afmKbPL1SEZYGI+0bMeU9auZuWk9BVmd+vXYvGEDWzZtJj09nakTfqFF27bBTuuEzPrnb2qVr0jVhHKZr8X25/HDH7/7xcSUjD70Wuz+H4ZN+BmAUsWLUygyMiumdf0GLF2/Nl/zFwkkr9cb0Nvp5lRVdPdnu+8BDv5W2J1LbHZhQEvnnF/5xHdy2g/gnPOaWbo79L6bF/+8XR73j2YU0BeYDMxxzqWaWXlgiXPu8AlzqePc90lr06Y106fPoHOXK4mKiqJfv0OXY+p+7X8Y+WXmp6Iff/xR+vZ9lv3799O6VSvatM58S3Xy5F8Z9OIrbNu2jfvu602d2rV455236H7N1fTt9yxXXX0tzkGXzp2oXbtWfg4NgLDwcFrddgNj+7+M83qpfW5bSleqwJwvRxNboxpVmjVm6dhJbFq0hLDwcAoXL0b7e/+btX3isuUUiylNyWw9hwXVjk1zKVnxbOpfORhvxn7W/f5O1ro6l7/E8h8fJiw8kpoXPIlZBISFsXPzQlJX/BLErHPyeL3cO3Qw4/sNIDwsjGGTxrN0wzqeuf5mZq/8hx//mkmHBmcx4MbbcM7x29JF3DPkraztf3vhVepWrETxqCJs+PBzbhv8KhPmzQ7iiDJ5neOD+bN4qs15hJkxee0qNuzczrX1G7JyWxqzEzdyWY06NCtfEY/XsevAfgbPyZzgt6pYhfqxCZQoVJhzq1QHYPDsmazdnrPfPNjCIyK4++EHeeK++/F6vVx4eSeq1qjOJ+8NpVa9erRs15blS5fy3CN92LljJ39Om86IoR8wdOTnwU49B4/Xw73vvs74518mPDyMYRN+Zun6tTxz463M/mc5P/75Ox0aNmJAjzsyX4uLF3DPO68BUK9SVd7r9RBe5yXMwhj41Wd+V2so6D7vO4AOjZsQG12KDaPG0XfYEIb99N3RNxT5f8py69s6rh2YVQXGOOfO9D1+CCgOdAAecs7l+ZvMzD4H5jnnXvI9buScm29m/YBdzrmXfct3OeeK++5nrTOztcAQ59xAM7sB6O6cu/ywmAXAvc65ab7l0c65B3z7eovMNovbnHNjzawQsBS40Tk309fKUNs5t8TMFgJ3+9osBgGXHRxzXo63deF08faqpcFOIWDOn/dKsFMIiLNHh+antK+86eZgpxAwL59/abBTCIjq13YNdgqBUUA/hHiquGnzjh50ejo9G9WP4KteVQI697jmrXWn1c8s2FdDuA942zeJjAB+A+48zn0UNrM/yawOX5fL+puBIWZWFFhNZj/uQZ8BVwITAJxzB8zsKuBNX7tCBJl9v0t82w0zsz1k9hKLiIiISAF20hNd59xaMvtaDz5++Ti2TSGzP/bw5f0Oe1w8r3XA2865Z/La3tc7nNf1p9oAw5xznsPiczS6OufmAGdlW3R4HiIiIiJBdTpeGSGQgl3RDRrf1RtqAAX3Oy5FRERE5ITly0TXzJ4ADv9S7q+dc/1PZr/Ouaonse0VJ3NsERERkYLG606/KyMEUr5MdH0T2pOa1IqIiIiIHI//t60LIiIiIqFGPbr+NNEVERERCRGn45c6BNKp+mY0EREREZECRRVdERERkRCh1gV/quiKiIiISEhSRVdEREQkRKii608VXREREREJSZroioiIiIQIr9cb0NvJMLMyZjbRzFb4/i2dS8y5ZjY/222fmXX1rRtuZmuyrWt0tGNqoisiIiIi+aEPMMk5VwuY5Hvsxzn3q3OukXOuEdAR2ANMyBby8MH1zrn5RzugenRFREREQkQB79HtAnTw3f8YmAI8eoT4q4Cxzrk9J3pAVXRFREREJD8kOOcSAXz/xh8l/lrgi8OW9TezhWb2mpkVPtoBVdEVERERCRGBruiaWU+gZ7ZFQ51zQ7Ot/wUom8umTxznccoBDYDx2RY/BmwBCgFDyawGP3uk/WiiKyIiIiLHxDepHXqE9efntc7MtppZOedcom8im3SEQ10DfOucS8+270Tf3f1m9hHw0NHyVeuCiIiISIgoyFddAH4Abvbdvxn4/gix13FY24JvcoyZGdAVWHy0A2qiKyIiIiL5YSBwgZmtAC7wPcbMmprZBweDzKwqUAmYetj2n5nZImAREAs8f7QDqnVBREREJER4XcG96oJzLhU4L5fls4Hbsz1eC1TIJa7j8R5TFV0RERERCUmq6AbQARce7BQC4ia3INgpBMziC14OdgoBkdRsSrBTCIjiCUf9UpzT1vaw0Dx/bH2pR7BTCIj0vduCnULAVOz9Kda2cbDTCAg3bV6wUzjlTkEfbUhRRVdEREREQpIquiIiIiIhooB/M1q+U0VXREREREKSKroiIiIiIUIVXX+a6IqIiIiECE10/al1QURERERCkiq6IiIiIiFCBV1/quiKiIiISEhSRVdEREQkRKii608VXREREREJSaroioiIiIQIj0q6flTRFREREZGQpIquiIiISIhQQdefKroiIiIiEpJU0RUREREJEaro+lNFV0RERERCkiq6IiIiIiFCFV1/quiKiIiISEhSRVdEREQkRHi9wc6gYFFFV0RERERCkiq6IiIiIiHC49Skm50quiIiIiISklTRPQ3MnDGDV19+Ga/HQ+euXbn5llv81h84cIBnnn6av5ctIzo6mucHDqR8+fIsWbyYAf37A+Cc4789e9KhY8dgDCFXzjne/GgKf8xbQ+HCkTx294XUqZ6QI+6h/qNJ/Xc3Ho+XhnUr8MDtHQkPC+OdEb8xY85qIiLCqZAQTZ+7L6REsaggjCSnJX/N4uvBQ3AeD60uu4SLru/ut37SV6P4/edxhIWHUyI6mhse6U1M2QRSt2xl6NPP4rxePBkZtL+yC+06dwrSKI7MOccbH05k5pxVRBWO5PFenahTo2yOuN7Pfknqtszn76x6lejd80LCwwve39jOOV58bRjTZ84lKqoQzz7Zi3p1queIe2vIZ4wZN5UdO3czc9JnOdZPnDyTh598mc8+HMQZ9WrmR+o5/DljJm+9/Aper5fLunbhPz1u9lt/4MABXujbj3+W/U3J6Gj6DuhPufLlSdy8mZuu7k7lKpUBqH/mmTz4+GMA/K/nnaSmpFA4qjAALw9+i9JlyuTvwLJxzvHGsEn8MXcVhQtF8nivS6lTPefr78HnviJ1267M11/9Sjxw+wWEh4fx64y/GTZyOus2pTJ04E3UrVkuCKPInXOOwSNm8Of8DUQVjuCRnh2oXS02R9yjg34mdfsePB5Hwzplua9Ha8LDwhg+ajY/TfmbUiWKAHDbNc1o0ahyfg/juHzYpy+dWrUjaVsaDW6+OtjpnNZ01QV/ITPRNbNSwPXOuXcCfJwOwAHn3IxAHucgj8fDSwMH8tY77xCfkECPG2+kbfv2VK9+6BfwD999R4mSJRn1/fdMGD+et998k/4DB1KjRg2GjxhBREQEKcnJ3HDddbRp146IiILxtP8xby0bt/zL52/ewtIVW3j1g8m898J1OeKeeeAyihUtjHOOp14Zw5SZKzivdR2aNqxCz+vbEBEexrufTuPTb2dx1w1tgzASf16Ph5FvvM19Lw2gVFwsg+7sRcNWLShXtUpWTMVaNegz5C0KRUXx2/c/8u17H3B73yeIjinDQ4NfI7JQIfbt3cvzt9xBw1YtKRUbE8QR5e6PuavYsHkbX75zJ0v+2czL743j/Rd75Ih77qErsp6/J1/8ll9n/M35bevnf8JHMX3mXNZvTOSHrwazaMkK+r80lE8/GJgjrn2bZlx71aV07n5vjnW7d+/li69/osEZtfIj5Vx5PB5eH/Qir7w9mLiEeO646WZat2tL1WznjJ++/4ESJUrw+XejmTR+Au+9NZh+A14AoEKFCnz4ec4JPMCTzz9L3foF47n7Y+5qNiam8cXgnixdsZlXhk5g6MCbcsQ9+2CXQ+ePl77j15l/c36b+lSrHEv/R67gpffGByH7I/tzwQY2bdnBiFe6s2xVEq8Pn8Y7z1yRI+7pXudTrGghnHP0e3MiU/9cTceWmX9cXXVxA7pfdlZ+p37Cho/9kcGjR/LJE88FOxUJMQWvrHLiSgF3H2uwZTqR8XcAWp3Adidk6ZIlVKxUiQoVKxIZGckFF17Ib1Om+MX8NnUql3XKrPp1PO88Zv31F845oooUyZrUHjhwAMzyK+1jMn32Ki5qVw8z44za5di1ez8p23bliCtWNLOC5PF4ycjwgG8Yzc+qQoSvMnhG7XIkp+XcNhjW/r2cuPLliS1fjojISJp07MCC32f6xdRp3IhCUZnV52r16/FvcgoAEZGRRBYqBEDGgXScK7gfn5321wouPvdMzIwz61TIfP5yeQ6yP3/pGZ6C9jLMMmXaLDpd3B4zo+GZtdm5azfJKdtyxDU8szZxsaVz3cfb739Bjxu6Usj3HAbDsiVLqFCpIuUrViAyMpKOF17I9Km/+cX8PnUqF3W6DID253Vk7l+zcKdZX9/0WSu4uP2ZvvNHhWM6f2S+/jJfgFUrxlK5QsH7AxJgxpy1XNCmFmZG/ZoJ7Np9gNRte3LEFSua+TrzeBzpGd6ssZ2Opi2YS9qO7cFOIyR4vYG9nW5CaaI7EKhhZvPN7DUzm2Rmc81skZl1ATCzqma2zMzeAeYClczsNjP7x8ymmNn7ZjbYFxtnZqPMbJbv1trMqgJ3Ag/4jhPw8mFSUhIJCYfezo9PSCA5OdkvJjk5mXhfTEREBMWLF2f7v/8CsHjRIq69+mqu796dPo89VmCquQApabuIjy2R9TgupniuEyWAB/uPpvN/36NokUJ0aJGzWvbz5MW0aFQ1UKkel39TUikdH5f1uHRcLNtTUvKMn/HzOM44p1nW47SkJJ6/7U6e6H4DF157TYGs5gKkpO4kPqZk1uP4mBKkpO3MNbb3M1/Sqcebmc9fy7r5leJxSUpOo2zCobeHE+JiSEpOPebt/16+mq1JKbRr3TQQ6R2zlKRD5wOAuPh4UpKS84yJiIigWPHibN+eOclI3LyZ266/gft63sGCefP8thv4zHPcdv1/+PiDD4M+MU5O20V87KHXX1xMCVJS83j9PTuSy299y3f+qJNfKZ6wlG17iI8pnvU4rkwxUrbtzjX2kUE/c+Xdn1A0KpJ2zatlLf9u4hJuf+wbXhw6hZ279wc8Z5GCKpQmun2AVc65RsDDwBXOubOBc4FX7NCfunWAT5xzjYF04CmgBXABkP038BvAa865ZkA34APn3FpgiG95I+fctMOTMLOeZjbbzGYPHzbs5EeVyy+Tw/9qz+0XzsGYMxs04Muvv+ajESP4ePhw9u8vOCe83H5P5lWReOWJK/n2vZ4cSPcwd/EGv3WfjP6T8PAwLmhbQCZQuQ8s19A/J05i3fIVnN/9qqxlZeLjefLDITzz6Uf8MWEiO9JyVhULglynOXkUlF7tey3fD+tFerqHuYvWBTKtE3ak/0dH4/V6eenN4fTu1eMUZ3X8XG7PjB09xoCY2Fi+GvMDH37+Kfc8cD/PPfkUu3dl/vH55PPPMnzkF7z1/lAWzpvP+J9+DkD2x+54nq9Xn+7Odx/cm/n6W1wwX3/Z5T623GNffPRSvhl8A+kZHuYt2QxA5/Pr8+mr1zK0fzdiShXl3c9m5r6xhCSvC+ztdFNwynunlgEvmFk7wAtUAA6WONY55/7w3W8OTHXOpQGY2ddAbd+684H62U6cJc3sUPkxD865ocBQgH937Trpl0R8QgJbt27Nepy0dSuxsf4fSoiPjydp61YSEhLIyMhg165dlIyO9oupVq0aUVFRrF61inpB7LEbPW4+YyYtBqBujQSSUg5VYJJTdxFTulie2xYuFEHrptWZPmsVzRpm9ruOnbKEmXPW8NrT3QrM23al4mLZlq2Cti05heiYnFXZv+fMZdynX9D79Zez2hX89hMbQ7mqVVi5aDFntw9+7zHAqJ/n8OPE+QDUq1mOpNQdWeuSUncSWzrv/yKFC0XQpllNpv31D80aVcszLj99OWoso3/4BYAz6tZky9ZDlfetyanExR7bh61279nLqtXruf2epwFITfuX+x8dyOuD+uT7B9LifOeDg5KTkoiNi8s1Jt53ztjtO2eYWVbbRZ169ahQoSIb1q+nbv36xMXHA1C0WDHOv/gi/l6ylIt97Q/5ZfTYufz4ywIA6tYsS1LKoddfcupOYsoUz2vTzPNHs5pM/2slzc4qGK+/7L6buISffv0bgDrV40hKPfTuVnLabmJK5X1uLFQoglaNq/L73LU0bVCRMtFFs9Zddm49Hn9lXOASlwLndJyMBlIoVXSz+w8QBzTxVXi3Agc/jp/9/Z8jzYzCgJa+ym0j51wF51zu74sFUL369dmwYQObN20iPT2diRMm0K59e7+Ytu3b89OYMQBMnjSJps2aYWZs3rSJjIwMABITE1m/bh3lygX3k8VXXtyIYS/dwLCXbqBt8xqM/20ZzjmW/JNIsaKFiC3t/4tqz74DWX13GR4vf8xbS+UKmZOPP+ev5fPvZzPg0c5EFY7M97HkpUrdOiRt2kRK4hYy0tOZM3kKDVu18IvZsGIln7/6Jnf1f4YSpUtlLd+WnMwBX9V9z86drF68lIRKFfM1/yPpdmkThr92G8Nfu42259Rm3K+Lcc6xePkmihctTOxhE409ew9ktaNkeLzMnLuaKhULTivGtd0u4auPX+Grj1/h3HbNGTNuKs45Fi7+h+LFiubZi3u4EsWLMWXscMaOHsLY0UNocEbtoExyAerWr8/GDRtI9J0zJk+YQOt2/n8otW7XjvFjfgJg6qTJNG7WFDPj323b8Hg8AGzeuImNGzZQvkIFMjIy+NfXDpWRkcHMadOpViPnFSkC7cpLzuajV27ho1duoW3z2oybuth3/vC9/g4/f+w97Pwxd3XW+aOg6XrBGbz/Qjfef6EbbZpUZeL0FTjnWLpyK8WKFiKmdFG/+L370rP6dj0eL38uWE/lcpnnkuz9vNNmr6FaxWN7HYuEolCq6O4EDpaTooEk51y6mZ0LVMljm7+A18ystG/7bsAi37oJwL3ASwBm1sg5N98XVzKXfQVEREQEDz3yCPfdey9ej4fLu3Sheo0avPfuu9SrX5927dvTuUsX+j31FN26dKFkdDTPv5D56en58+fzyfDhREREEGbGI336UKp0wTnhtWhcjZlz13LdfR9RuFAEj919Yda6Wx/+lGEv3cC+fek8/uIPHEj34PV6OfvMynS5oCEAr384mQMZHno/NxqA+rXK8lDP84MyluzCw8Ppft89DH7kcbxeLy0vuZDy1ary47CPqVKnNg1bt2T0kPfZv3cvH/R7HoDSCfHc1f8Ztqxbz6h338fIbA04/5qrqFC94FWfAFo2qcHMOavoftcQ3+XFDlX3ejzwIcNfu419+9PpM+Br0tM9eLyOJg2q0OWis4OYdd7atjqb6TPncvnV9xAVVZhnnrgna901Nz/IVx+/AsBrb3/C2AnT2LdvPxd2+S9XXH4+d93ePa/d5ruIiAjuf/hhHup1H16Pl0s7X061GjX4cMh71K1Xj9bt23Fpl870f7ov13e9khIlS9L3hczLEC6YO49h771HeHg4YWHh9H6sDyWjo9m7dy8P33sfGRkZeL0emjRvTqcrugZ1nC3Prs4fc1dx7T1DiSocwWP3XJq17pYHP+KjV25h3/50Hhsw6tD5o0EVulzUGIDf/vyH1z+YyL879vLIC99Qs2o8rz5dMJ7HcxpV4s8F67nhwS+JKpR5ebGD/vv4KN5/oRt796fz5KvjSc/I/L/VuH55Op+X+W7de1/+wap1qZgZCbHF6X1ruyCN5Nh93ncAHRo3ITa6FBtGjaPvsCEM++m7YKd1WtIXRvizYH+g4FQys8+BhsAsMvttI4H5QGvgEl/YGOfcmdm26Qk8BGwGlgFpzrknzCwWeBuoR+YfBL855+40s9rAN2S2RPTKrU/3oFPRulAQ7V/1abBTCJjFcRcHO4WAaPjvlGCnEBDFE4L7wa9A2l6oUrBTCIiwdaOCnUJApO8tmL30p0LF3qF7znfT5hWMnrdT6H/nRgV07vHGr/tOq59ZKFV0cc5dfwxhZx72+HPn3FAziwC+JbOSi3MuBcjx571z7h8yJ9MiIiIiBcrpeAmwQArVHt3j0c/M5gOLgTWA3isRERERCQEhVdE9Ec65h4Kdg4iIiMipoKsu+FNFV0RERERC0v/7iq6IiIhIqFBF158quiIiIiISklTRFREREQkRuuqCP1V0RURERCQkqaIrIiIiEiL0zWj+VNEVERERkZCkiq6IiIhIiNBVF/ypoisiIiIiIUkVXREREZEQoasu+FNFV0RERERCkiq6IiIiIiFCPbr+VNEVERERkZCkiq6IiIhIiFBF158muiIiIiIhQl8Y4U+tCyIiIiISkjTRFREREQkRXm9gbyfDzK42syVm5jWzpkeIu9jMlpvZSjPrk215NTP708xWmNlIMyt0tGNqoisiIiIi+WExcCXwW14BZhYOvA1cAtQHrjOz+r7Vg4DXnHO1gG3AbUc7oCa6IiIiIiHC6wJ7OxnOuWXOueVHCWsOrHTOrXbOHQC+BLqYmQEdgW98cR8DXY92TE10Rf6vvTsPs6uq0z3+fROZJITJAA2IIg3I8BCDcgmDCgGxlaHbAAICCtgNIn2ZbLnS2NLiVVuuQwPaKN0x0IAKNkOjgAwSpqBXQhIMky2CDA3KZZIIhCnv/WOtQ07Kqkrg7Dqr1s7v8zz1VPY+VdS7n1OcWmfttX6/EEIIIYwW6wEPdR0/nM+tCTxt++UB54cVVRdG0GrjxqmfP0/S4bbPGvEfNPETI/4juvXtuoC1+/FDsn5eF+se0pcf09HXa+ujfl7XSv34IVlfn68tD+vLj+mI38Xe+aZP9ePHAO19vvrph3NeHNGxh6TDgcO7Tp3V/ZxJuhZYZ5BvPcn2fy7NjxjknIc5P6yY0W2Xw5f8JVWK66pPW68trqs+bb22uK5QhO2zbL+r6+OsAY/vanvLQT6WZpALaab2zV3H6wOPAI8Dq0l6w4Dzw4qBbgghhBBCGC1uBTbOFRaWB/YHLrNtYAawT/66jwFLHDzHQDeEEEIIIYw4SR+S9DCwHXC5pKvy+XUlXQGQ1+D+LXAVcDdwoe0783/ifwHHS7qXtGZ32pJ+ZqzRbZe2rmuK66pPW68trqs+bb22uK5QHduXAJcMcv4R4INdx1cAVwzydfeRqjIsNTlaxYUQQgghhBaKpQshhBBCCKGVYqAbQgghhBBaKQa6IYQQQgihlWKg2wKSVi6doWmSVpY0Jv97E0l7SVqudK5etfW6Qn0krTXIuU1LZAnLNkk7LM25EF6PGOhWTNL2ku4ild9A0kRJ/1I4VlNuBFaUtB7wU+BQ4OyiiZrRuuuStLakaZKuzMebS/p46VxNGXgtksZKOrlUngbdJOnDnQNJn2KQ3dC1kbSHpDmSnpT0jKT5kp4pnasXkuZJ+uVQH6XzNeCMpTwXwmsW5cXq9g3g/cBlALZvl/SespEaI9vP5UHGGbZPlTSndKgGtPG6zgamAyfl4/8CLmAp6htWYhdJewMfJ9VtnA7cUDZSI3YCzpK0L6n79d28xrI9o9Q/A1OBeW5PWaE98uej8udz8+cDgef6H6cZkrYDtgcmSDq+66HxwNgyqULbxEC3crYfkhZr//xKqSwNU34RPJA0wIB2/L628breZPtCSSdCKvYtqS2/h9j+iKT9gHmkQcUBtmcWjtUz249K+glwIrAQONH2HwvHasJDwB0tGuRi+wFIt/Ntd9/S/4ykmcApZZL1bHlgHOk1cJWu88+wqPtVCD2p/Q/ssu4hSdsDzm3yjiYvY2iBY0h/gC+xfaekt5Fa/9XuWNp3Xc9KWhMwgKTJwB/KRmqOpI1Jv48XAZsBB0uaY7vamTQASdcAjwJbknrGf1fSjbb/rmyynp0AXCHpBuCFzknbXy8XqTErS9rR9s2Qlq8B1e7RsH0DcIOks20/IGll28+WzhXaJRpGVEzSm4DTgF0BAVcDx9h+omiwBkja0vYdpXOEJZO0NWk93ZbAHcAEYF/btxcN1hBJ9wBH2f6p0u2T44HDbG9ROFpPJP2V7Uu7jt9AmtX9QsFYPZN0NfBH0gz8ws55258vFqohkt4JfBdYlfTG8g+k38XZRYP1KN/lmgaMs72BpInAEbY/WThaaIEY6IZRSdLNpNtaZwPfs/102UTNkDSDPPPZzfaUAnEaIWkF0pKZTUlvuH4FjLH9wrDfWAlJ420/M+DcxrZ/XSpTGJqkWbbfVTrHSJI0nvT3uxV3TiT9X9JShctsT8rn7rC9ZdlkoQ1i6ULFJJ0+yOk/ALNs/2e/8zTJ9o6SNiFVJZgl6RfA2clx6jUAABOXSURBVLavLhytV923hVcE9gZeLpSlKT+zvTVwZ+eEpNnA1uUi9U7S1K5/D/YlVQ50Jc1nkDdbHbbH9zHOSLhW0m4teK34E5LWBr4ErGv7A5I2B7azXf3GzxbvNwmFxUC3bisCbwd+mI/3Jg02Pi5pZ9vHFkvWANv/JemzwCzgdGBSvnX897YvLpvu9bF924BTM/NawupIWgdYD1hJ0iTSbC6kHdNvLBasOXvmz2uRdoZfl493Bq4Hav0dXAVA0inA70g7+EXaILnKMN9ai6OAEyS9ALxEuja3YAAP7a1w0ub9JqGwGOjW7c+BKbZfBpB0Jmmd7vtI69OqJWkr0mzu7sA1wJ62Z0taF/gZlQ4yJK3RdTgGeCewTqE4vXo/cAhpI1P3Rp/5wN+XCNQk24cCSPoxsLntR/PxnwHfKpmtIe+3vW3X8Zn5FvKppQI1oTOQb6m2Vjj5BGm/yXrAw6S/Y0cN+x0hLKUY6NZtPdKO2846rZVJt7ReybMZNfsm8K+k2dvnOydtP5JneWt1G+m2sUhLFu5nUZmxqtg+BzhH0t62LyqdZwS9tTPIzX4PbFIqTINekXQg8APS7+QBVH67ON9lwPbvJE0A3g3cY/uusska08oKJ7YfJ91RCKFxMdCt26nAXEnXkwZO7wG+pNQS+NqSwXple8jGF7bPHeqx0c72hqUzNM32RZJ2B7YgLafpnK+1tudA10u6Cvg+aYCxP/WXhAP4CGkW7TTSdc3M56ok6QjgM+mf+grpbsOdwJclndqGdazAp0gNgjbK9XMn0IJ6s23ebxLKi6oLlcu38g8G7iHN6D5s+8ayqXqXa5d+GdicxQdPbysWqgGSlgOOJL0pgbTW8zu2XyoWqkeSvk1ak7sz8G+kP7y/sF3lTPVg8sa0d+fDG21X3yq3bSTNA7YFVgIeAP48z+yuDsyw/Y6iARuSy8C9WuGk5teODklnMfh+kzcD99W+3ySUFQPdikn6a1Ih+/WBucBk0g74aktVdeTyYieT2hzvSVqvK9snFw3WI0n/BiwHnJNPHQy8Yvuvy6XqjaRf2t6q6/M44GLbu5XOFv6UpBNy6+kzGLzU3dEFYvVM0uxc/QNJt9ue2PXYnE7ZqppJmkWqo/t920+VztMUSdcBu3XtN3kDXftNbG9eMl+oWyxdqNsxwDbAz23vLOntQPVF0bOVOgX6c/vLf5R0E2nwW7Ntuv8AA9dJqr2xwoL8+bl8h+EJoPolGpJuzmXuBpbjqn0Xf2c3+6yiKZq3UNJyeYZz985JSSuSNn62wf6kN/235kHvdODqFrQ7bvN+k1BYDHTrtsD2AklIWsH2PZI2LR2qIQskjQF+Lelvgf8mlXmq3SuSNrL9G4DcArjqDUDAjyStBvwfYDZpUPivZSP1zvaO+fOwu/glrV7T7JrtH+XP5wz3dZLOsP0/+5OqEVPJb0hsP9x1fk3S2tbq2b4XOEnSPwB7kGZ3F0r6LnCa7SeLBnz9WrvfJJQXSxcqJukS0rv7Y4EpwFPAcrY/WDRYAyRtQ5p5Wg34Aqnl5am2f140WI8k7UKahbmP9IL+FuBQ21VubspvRibbviUfrwCs2JaOTUuj+5Z5m9R4XZLGAlfZ3rV0lpHSVXrxg8BVwPnAjsDBNa5DzrXR1ydVofkfpNfFX9h+pGiw0Box0G0JSe8lDQZ/YvvF0nnC0PJgsLOZ5J7aW+VK+pnt7UrnKKUt6z8HqnGgCyDpMtKgr3VvtiTdBjxNahBxUfdrh6SLbU8d8ptHMUm32X5n6RyhnWLpQkvYrrK71kCSfsTw7Un36mOcxkiaYvu67ray2UaSqLXTW3a1pL1JG9CWxXfOy+I1j2YLgHmSrgGe7ZysdZPdAPvavm+wB2od5GY/l7SN7VtLBwntEwPdMNp8NX+eSuoYdl4+PgD4bYlADXkvqYXsnoM8Zirt9JYdT9o88rKkBdS/WSskWvKXjEqX5482ekLS11lUnvAG4JQWzF7vDBwh6QHSm5POa8hWZWOFNoilC2FUknTjwKYRg52rjaSxtmvffPaaSNrC9p2lc4yU2pYuSDrX9sGSjrF92jBfd4jts/sYrTGSlmdR97pW1JoFkHQRcAeLlyecWPlsLpLeMtj5XHEnhJ7EQDeMSpLuBnbv3KaTtCFwhe3NyibrjaQHgZ8AFwDXLQu3+mtd69lN0o7Axran59ay42zfnx9bo6bd7pLuAj5A6rC1EwNmbmu6lsFI2ok0EPwt6dreDHysJY105g7ccDbYuVpJWovFGwQ9WDBOaIlYuhBGq+NIrVfvI93a3xA4omykRmxKWr5wFDBN0o+BH9i+uWysEVXrLXAAJJ0MvIv03E0nNfw4D9gBqhwYfpv0ZuttwG0s/vw4n6/Z10jNB34FIGkTUvvmNmx2el7Sjp3XC0k7AM8XztQzSXuRnrd1gcdI1WjuJrUVD6EnMaMbRqVc5P1TpAHGqsA1wDdsLxj2GyuSW5OeBhxoe2zpPCOl9hldSXOBScDszhKFThe4ssl6I+lM20eWztG0wZ6bNjxfAJLeQZqtXpX0BuVJ4BDbVTedyU1zpgDX2p4kaWfgANuHF44WWiBmdMNo9e/AM8Dp+fgA4Fxg32KJGpJLwe1Hun18K/DhsonCErxo25IMkIvYV8/2kbnu7Np0/S1owe3iWZKmkV4vAA4kzVxXz/ZcYKKk8fn4mcKRmvKS7SckjZE0xvYMSV8pHSq0Qwx0w2i16YBWuTNa0CoXSfcDc4ELgU/bfnYJ3zKqdYq9235omC+rva7zhZK+A6wm6W+Aw2hB57fccfAfgd8DC/NpA7XPfB5JWhp0NGnW80bgX4om6pGk44c4D4Dtr/c1UPOeljSO9FydL+kxoBUbCEN5MdANo9UcSZM7ndAkbQvMLJypCRNbNAtDnum8lGHWP9qe3MdIjbP9VUnvI91h2BT4nO1rCsdqwrGkN5RPlA7SJNsvSPom8FPSAP5XLWiiM2wb6ha4HXiOtDfjQNLSjHFFE4XWiDW6YVTKVRc2BTq3UTcgbU5YSMX1FfPGmDOBtW1vmdt57mX7fxeO9rpJ+hZwdluLveeKH4921odLWon0/P22aLAeSZoBvM/2y6WzNEnS7qQNd78hzehuCBxh+8qiwcKQBlvH35Z11aG8GOiGUWmouoodtdZXlHQD8GngO10bm+6wvWXZZK9fLle1CdDKYu+SZgHbd2YFc43Wmba3KZusN3kd66ak5gqvtpKt/Ta4pHuAPWzfm483Ai63/fayyXon6W2kDayTSctMfgYcN1S3tNFO0pHAJ4GNgHu7HlqF9P/YQUWChVaJpQthVKp1ILsU3mj7F521dVntM2ofKB1ghL2h+9a37RfzYLd2D+aP5fNHWzzWGeRm95FKVrXB94BvAR/Kx/uTSqdtWyxRb74HXAl8GfhM1/n5FZbtC6NUDHRD6K/H8wxTZwf/PsCjZSP1pvOmZGCx9xb5f5L2sn0ZgKS/BB4vnKlntj8PIGmVdOg/Fo7UE0md7mB3SrqCtOHTpEotbVlWI9vndh2flzcVVim3Lv4DqapOCCMili6E0Ef51uNZwPbAU8D9pDq61c5gD1Xs3XYrir3nNybnk65PwEPARwfMGlZH0pakElxr5FOPk66rynbNkqYP87BtH9a3MCNE0j8BTwM/IA3i9wNWIM3y1ti8JIQRFwPdEPpE0hhgH9sX5lqsY2zPL52rV8tKsfdc/khteM4AJN0CnGR7Rj7eCfiS7e2LBgtDyuUJh2LbtXe1C6FxsXQhhD6xvTDfZryw9vq5A7Sy2Lukg2yfN7CGaYtql67cGeQC2L6+5mYYkk6wfaqkM8hLg7rZPrpArEbZ3rB0hhBqEwPdEPrrGkl/B1xAqlAAVH/LcbBi77VvsAPoDPraWsP0Pkn/wKIOYgeRltLU6u78eVbRFCMod7LbHXgri3ezq/1NVwgjJpYuhNBH+dbjYLNN1d5yzLOAzwNjWFTs/fw2NCLIA4ujbX+jdJamSVod+DywYz51I/B520+VS9Wb/Hz9k+1Pl84yEvImuwXAPBZ1s3t1Y2EI4U/FQDeEPsrNBj5JGlwYuAn4tu3niwbrgaTjgB/afrh0lpEgaYbtnUvnCEtH0nW2p5TOMRKiiUIIr92Y0gFCWMacA2wGnA6ckf99TtFEvRsPXCXpJklHSVq7dKCG3SLpm5LeLWnrzkfpUL2SdI2k1bqOV5d0VclMDZkj6TJJB0ua2vkoHaohV0rarXSIEGoSM7oh9JGk221PXNK5GuV2xvsBewMP2961cKRG5Fa5A7n2WUNJczrd+YY7V5shyoy1pbzYh4DzSJNUL7GoC+H4osFCGMViM1oI/TVH0mTbPweQtC0ws3CmpjwG/A54AlircJbGtHjZwkJJG9h+EF5tu139zIftQ0tnGEFfA7YD5jlmqUJYKjHQDaG/tgU+KunBfLwBcLekeaSZmerW3+V+9fsBE4D/AP7G9l1lUzVH0prAySxaV30zcEoLNtudBNws6YZ8/B6g+trHeUZ3sA2f1c/oAr8G7ohBbghLLwa6IfTXX5QOMALeAhxre+5gD0pavead/KQuVDeSlmRAqixxAVD10gzbP8lrjSeTboEfZ/vV1saStqi0S9qPu/69IvAh4JFCWZr2KHC9pCuBFzono7xYCEOLNbohhBElabbtajdvSbrN9jsHnJtl+12lMvVD7c9bR+5IeG3ta6oBJJ082PkoLxbC0GJGN4Qw0lQ6QI9mSNofuDAf7wNcXjBPv9T+vHVsTFoiVL3OgFbSyi3rrhjCiIkZ3RDCiKp9ZlDSfFKXtFfyqbEs6mrX2h3vtT5v+fnq/sP2O+Azti8uFKkxkrYDpgHjbG8gaSJwhO1PFo4WwqgVdXRDCGEYtlexPcb2cvljTD63iu3xkrYonTEsZlXgKOCr+U3IrkBbmpn8M/B+UmUTbN9O2kQYQhhCDHRDCCOtLbfAh3Ju6QAj5MXSAV6nb5E22B2Qj+fnc61g+6EBp14Z9AtDCECs0Q0hNCDv3u+U35ppe3bXw7uUSdU3VQ7kJe0AzLX9rKSDgK2B02w/AGB7ctGAr9+2treWNAfA9lOSli8dqiEPSdoecL6mo4G7C2cKYVSLGd0QQk8kfY7UxnhN4E3AdEmf7Txu+8lS2fqk1o0OZwLP5XWeJwAPAP9eNlIjXpI0lvy8SJoALCwbqTGfIC3LWI+0HOMd+TiEMITYjBZC6Imku4FJthfk45WA2bY3K5usPyretDU7z3x+Dvhv29NqvZZukg4kNTDZmvQGbB/gs7Z/WDRYH0g60faXS+cIYTSJpQshhF79llSYf0E+XgH4TbE0/VfrWtb5kk4EDgLek2dBlyucqWe2z5d0G2nJjIC/sr2s3N7fF4iBbghdYkY3hNATSZcC2wDXkG4Xv4/UJvcxANtHl0vXuyWtZa2VpHWAjwC32r5J0gbATrbbsHxhmSRpju1JpXOEMJrEQDeE0BNJHxvucdvn9CvLSJD0S2AisBWpwsI0YKrt9xYNFsIAbVh6EkLTYulCCKEntQ9kl8LLti3pL0kzudOWNLivgaSpwFeAtUi3+EWLG2AsI6qsABLCSIqqCyGEnkjaQ9IcSU9KekbSfEnPlM7VoO61rJe3ZS0rcCqwl+1VbY/vNMAoHSoMTtJYScct4ctav+EuhNcqli6EEHoi6V5gKjDPLXxBaetaVkkzbe9QOkdYepKut71T6Rwh1CQGuiGEnkiaAexiuy21SpcJkk4D1gEuBV7onLd9cbFQYViSvkhqcXwB8Gzn/IAGLSGELjHQDSH0RNI2wBeAG1h8wPT1YqEa1Na1rJKmD3Latg/re5iwVPKbShjQpMT2lAJxQqhCbEYLIfTqi8AfSbV029JqtdupwJ5tq8Vq+9DSGcJr9gFgb+CtLPr7HbNVIQwjBrohhF6tYXu30iFG0O/bNsgFkLQ+cAawA2mwdDNwjO2HiwYLw7kUeBqYzaIGLTHQDWEYMdANIfTqWkm72b66dJARMkvSBbRvLet04HukblqQqkpMJzX8CKPT+rb/onSIEGoSa3RDCD2RNB94I6kV7ku0ZA1rR1vXskqaa/sdSzoXRg9JZwFn2J5XOksItYgZ3RBCr1YFDgQ2tH1KLr/1Z4UzNabFa1kfzy2Nv5+PDwCeKJgnLNmOwCGS7ifdXei8qdyqbKwQRq+Y0Q0h9ETSmcBCYIrtzSStDlxte5vC0RrR1rWs+Q3JN4HtSNd1C3C07QeLBgtDkvSWwc7bfqDfWUKoRczohhB6ta3trSXNAbD9lKQ2VV9o61rWLwAfs/0UgKQ1gK8CVS/JaLMY0Ibw2kUL4BBCr17KbXENIGkCaYa3LSbYnm775fxxNjChdKgGbNUZ5ALYfhKYVDBPCCE0Lga6IYRenQ5cAqyVOzfdDHypbKRGPS7pIElj88dBtGMt65i8zAR4dUY37vKFEFol1uiGEHom6e3ALqTNMT9tU93Ztq5llfRR4ETgP0jX9WHgi7bPLRoshBAaFAPdEEIYhqRzgGMHrmWtvbwYgKTNgSkseoNyV+FIIYTQqBjohhDCMCTNsT1pSedCCCGMPrFGN4QQhhdrWUMIoVLxYh1CCMP7GnCLpMXWspaNFEIIYWnE0oUQQliCWMsaQgh1ioFuCCGEEEJopVijG0IIIYQQWikGuiGEEEIIoZVioBtCCCGEEFopBrohhBBCCKGV/j+BbkPw345MSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1368x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap visualization: Pearson\n",
    "mask = np.triu(df_num.corr(method=\"pearson\"), 1)\n",
    "plt.figure(figsize=(19, 9))\n",
    "sns.heatmap(df_num.corr(method=\"pearson\"), annot=True, \n",
    "            vmax=1, vmin = -1, square=True, cmap='BrBG', mask=mask);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Nr_employed' is the most correlated with the target followed by 'pdays', 'euribor3m', and 'emp_avr_rate' and at the same time, the strength of their relationships with the target is low. \n",
    "\n",
    "The table below leads us to conclude that all features have predictive power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Pearson Corr.</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>-0.32</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_var_rate</th>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons_price_idx</th>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euribor3m</th>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nr_employed</th>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Pearson Corr.  p-value\n",
       "age                      0.03      0.0\n",
       "pdays                   -0.32      0.0\n",
       "previous                 0.23      0.0\n",
       "emp_var_rate            -0.30      0.0\n",
       "cons_price_idx          -0.14      0.0\n",
       "cons_conf_idx            0.05      0.0\n",
       "euribor3m               -0.31      0.0\n",
       "nr_employed             -0.35      0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying variables with predictive power (Pearson Correlation p-value)\n",
    "pd.DataFrame(\n",
    "    [scipy.stats.pearsonr(Xnum[col], ynum) for col in Xnum.columns],\n",
    "    columns=[\"Pearson Corr.\", \"p-value\"],\n",
    "    index=Xnum.columns,\n",
    ").round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All variables have predictive power.\n",
    "##### **Strategy:** No transformation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand, the **Spearman Correlation Coefficient**, or **Spearman's r**, is a nonparametric measure of rank-order correlation that evaluates the monotonic relationship between two continuous or ordinal variables.\n",
    "\n",
    "A monotonic relationship happens when one of the following occurs: \n",
    "- a) when the values of one variable increase so do the others' AND \n",
    "- b) when the values of one variable increase, there is a decrease in the others' BUT \n",
    "- a)+b) not at a constant rate. \n",
    "\n",
    "This means that all linear variable's relationships are monotonic at the same time but the inverse is not always true, simply because we can have both monotonic non-linear correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numeric variables with higher monotonicity (spearman)\n",
    "df_spearman = df_num.copy()\n",
    "df_spearman.drop([\"target\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The numeric 10 most correlated pairs, Spearman method:\n"
     ]
    },
    {
     "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emp_var_rate</td>\n",
       "      <td>nr_employed</td>\n",
       "      <td>0.944702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>emp_var_rate</td>\n",
       "      <td>euribor3m</td>\n",
       "      <td>0.939915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>euribor3m</td>\n",
       "      <td>nr_employed</td>\n",
       "      <td>0.928857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>emp_var_rate</td>\n",
       "      <td>cons_price_idx</td>\n",
       "      <td>0.664890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cons_price_idx</td>\n",
       "      <td>euribor3m</td>\n",
       "      <td>0.490970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>previous</td>\n",
       "      <td>cons_price_idx</td>\n",
       "      <td>-0.282720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>previous</td>\n",
       "      <td>emp_var_rate</td>\n",
       "      <td>-0.435293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>previous</td>\n",
       "      <td>nr_employed</td>\n",
       "      <td>-0.438721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>previous</td>\n",
       "      <td>euribor3m</td>\n",
       "      <td>-0.454724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pdays</td>\n",
       "      <td>previous</td>\n",
       "      <td>-0.509583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                X               Y         r\n",
       "0    emp_var_rate     nr_employed  0.944702\n",
       "1    emp_var_rate       euribor3m  0.939915\n",
       "2       euribor3m     nr_employed  0.928857\n",
       "3    emp_var_rate  cons_price_idx  0.664890\n",
       "4  cons_price_idx       euribor3m  0.490970\n",
       "5        previous  cons_price_idx -0.282720\n",
       "6        previous    emp_var_rate -0.435293\n",
       "7        previous     nr_employed -0.438721\n",
       "8        previous       euribor3m -0.454724\n",
       "9           pdays        previous -0.509583"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"The numeric 10 most correlated pairs, Spearman method:\")\n",
    "spearman_rank = pg.pairwise_corr(df_spearman, method='spearman').loc[:,['X','Y','r']]\n",
    "pos = spearman_rank.sort_values(kind=\"quicksort\", by=['r'], ascending=False).iloc[:5,:]\n",
    "neg = spearman_rank.sort_values(kind=\"quicksort\", by=['r'], ascending=False).iloc[-5:,:]\n",
    "con = pd.concat([pos,neg], axis=0)\n",
    "display(con.reset_index(drop=True))\n",
    "\n",
    "mask = np.triu(df_spearman.corr(method='spearman'), 1)\n",
    "plt.figure(figsize=(19, 9))\n",
    "sns.heatmap(df_spearman.corr(method='spearman'), annot=True, \n",
    "            vmax=1, vmin = -1, square=True, cmap='BrBG', mask=mask);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Variables 'emp_var_rate', 'nr_employed', 'euribor3m' are very redundant. 'nr_employed' is the most correlated with the target.\n",
    "##### **Strategy:** Keep all variables for the time being."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Categoric variables** (df_cat_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **Chi-square test for independence** is used when we want to check if there is a relationship between two categorical variables (besides, we're dealing with a binary classification problem and chi-square is a very suitable test to perform here). Firstly, I'll specify the null and alternative hypotheses to run the test.\n",
    "\n",
    "The alternative hypothesis is always what we are trying to prove and the null is the hypothesis that we are trying to demonstrate evidence against.\n",
    "- (Null hypothesis) H0: The feature and the target are independent\n",
    "- (Alternative hypothesis) Ha: The feature and the target are not independent\n",
    "\n",
    "So, for the feature to be considered relevant, we want to reject the H0 (null hypothesis) with the lowest p-value (p-value ≤0.05)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating categoric subset in order to see correlations results\n",
    "Xcat = df_cat.select_dtypes(exclude=['int64','float64']).copy()\n",
    "ycat = df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Study chi² for independence:\n",
    "chi2 = []\n",
    "p_val = []\n",
    "for feature in cat_features:\n",
    "    table = pd.crosstab(df_cat[feature], ycat)\n",
    "    chi2.append(round(chi2_contingency(table)[0], 2))\n",
    "    p_val.append(round(chi2_contingency(table)[1], 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>chi2</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>961.24</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <td>122.66</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>193.11</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default</th>\n",
       "      <td>406.58</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>5.68</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>1.09</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poutcome</th>\n",
       "      <td>4230.52</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              chi2  p_value\n",
       "job         961.24     0.00\n",
       "marital     122.66     0.00\n",
       "education   193.11     0.00\n",
       "default     406.58     0.00\n",
       "housing       5.68     0.06\n",
       "loan          1.09     0.58\n",
       "poutcome   4230.52     0.00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([chi2,p_val], columns = cat_features, index = ['chi2', 'p_value']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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       "      <th>admin.</th>\n",
       "      <td>9070</td>\n",
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       "      <th>blue-collar</th>\n",
       "      <td>8616</td>\n",
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       "      <th>entrepreneur</th>\n",
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       "      <th>self-employed</th>\n",
       "      <td>1272</td>\n",
       "      <td>149</td>\n",
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       "      <th>services</th>\n",
       "      <td>3646</td>\n",
       "      <td>323</td>\n",
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       "      <td>6013</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
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       "      <th>unemployed</th>\n",
       "      <td>870</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
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       "      <th>unknown</th>\n",
       "      <td>293</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target            0     1\n",
       "job                      \n",
       "admin.         9070  1352\n",
       "blue-collar    8616   638\n",
       "entrepreneur   1332   124\n",
       "housemaid       954   106\n",
       "management     2596   328\n",
       "retired        1286   434\n",
       "self-employed  1272   149\n",
       "services       3646   323\n",
       "student         600   275\n",
       "technician     6013   730\n",
       "unemployed      870   144\n",
       "unknown         293    37"
      ]
     },
     "metadata": {},
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      "p-value: 0.000\n",
      "\n"
     ]
    },
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      "p-value: 0.000\n",
      "\n"
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       "unknown               1480   251"
      ]
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       "target       0     1\n",
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      ]
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      "p-value: 0.000\n",
      "\n"
     ]
    },
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     "data": {
      "text/html": [
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       "target       0     1\n",
       "housing             \n",
       "no       16596  2026\n",
       "unknown    883   107\n",
       "yes      19069  2507"
      ]
     },
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      "p-value: 0.058\n",
      "\n"
     ]
    },
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       "target       0     1\n",
       "loan                \n",
       "no       30100  3850\n",
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      ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.579\n",
      "\n"
     ]
    },
    {
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      "text/plain": [
       "target           0     1\n",
       "poutcome                \n",
       "failure       3647   605\n",
       "nonexistent  32422  3141\n",
       "success        479   894"
      ]
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.000\n"
     ]
    }
   ],
   "source": [
    "# Statistic values and meaning results for each categoric variable\n",
    "for col in Xcat.columns:\n",
    "    table = pd.crosstab(Xcat[col], ycat)\n",
    "    print()\n",
    "    display(table)\n",
    "    _, pval, _, expected_table = scipy.stats.chi2_contingency(table)\n",
    "    print(f\"p-value: {pval:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Strategy: Remove variables 'housing', 'loan'** (p-value > 0.05) **and 'default'** (totaly unbalanced)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comments:** The 'yes' class of the **'default'** variable is meaningless and any imputation to the missing values would deeply compromise its meaning, the more effective it will be to sacrifice the variable. The **'education'** variable's classes have an order which means that it will be encoded by that same order. To the variables 'job', 'marital' and 'poutcome' will be applied a Binary Encoding and One Hot Encoding and will be evaluated concerning the best trade-off between dimensionality and information gain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Strategy2: Remove variables 'loan'** (p-value > 0.05) **and 'default'** (totaly unbalanced). **'housing'** it's beyond limits but i'm still going to keep it for now, I will encode it and test it again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outliers, Missing Values, Anomalies e Transformations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Numeric variables** (df_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing Target and making a copy\n",
    "df_drop = df_num.drop([\"target\"], axis=1).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.02406</td>\n",
       "      <td>962.475454</td>\n",
       "      <td>0.172963</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>93.575664</td>\n",
       "      <td>-40.502600</td>\n",
       "      <td>3.621291</td>\n",
       "      <td>5167.035911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.00000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.749000</td>\n",
       "      <td>-41.800000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>5191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.42125</td>\n",
       "      <td>186.910907</td>\n",
       "      <td>0.494901</td>\n",
       "      <td>1.570960</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>4.628198</td>\n",
       "      <td>1.734447</td>\n",
       "      <td>72.251528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age       pdays  previous  emp_var_rate  cons_price_idx  \\\n",
       "mean  40.02406  962.475454  0.172963      0.081886       93.575664   \n",
       "50%   38.00000  999.000000  0.000000      1.100000       93.749000   \n",
       "std   10.42125  186.910907  0.494901      1.570960        0.578840   \n",
       "\n",
       "      cons_conf_idx  euribor3m  nr_employed  \n",
       "mean     -40.502600   3.621291  5167.035911  \n",
       "50%      -41.800000   4.857000  5191.000000  \n",
       "std        4.628198   1.734447    72.251528  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Any missing values: False\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of numerical data dispersion (Boxplot)\n",
    "fig, axs = plt.subplots(ncols=2, nrows=4, figsize=(16, 8))\n",
    "index = 0\n",
    "axs = axs.flatten()\n",
    "for k,v in df_drop.items():\n",
    "    sns.boxplot(y=k, data=df_drop, ax=axs[index], orient=\"h\")\n",
    "    index += 1\n",
    "plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=5.0)\n",
    "\n",
    "display(df_drop.describe().loc[[\"mean\",\"50%\",\"std\"]])\n",
    "print(f\"Any missing values: {df_drop.isnull().values.any()}\")\n",
    "print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The technique used identifies the values ​​that are at +1.5 IQR and -1.5 IQR of the median, a non-parametric methodology generalizable for the profile of the distributions that we find."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'age' = 910 (2.2%) outliers\n",
      "'pdays' = 1515 (3.7%) outliers\n",
      "'previous' = 5625 (13.7%) outliers\n",
      "'emp_var_rate' = 0 (0.0%) outliers\n",
      "'cons_price_idx' = 770 (1.9%) outliers\n",
      "'cons_conf_idx' = 1841 (4.5%) outliers\n",
      "'euribor3m' = 0 (0.0%) outliers\n",
      "'nr_employed' = 1408 (3.4%) outliers\n"
     ]
    }
   ],
   "source": [
    "for column in df_drop.columns:\n",
    "    median = df_drop[column].quantile()\n",
    "    iqr_1_5 = (df_drop[column].quantile(q = 0.75) - df_drop[column].quantile(q = 0.25)) * 1.5\n",
    "    outliers = df_drop[(df_drop[column]< median - iqr_1_5) | (df_drop[column] > median + iqr_1_5)][column].count()\n",
    "    outliers_pct = round(outliers / df_drop[column].count() * 100, 1)\n",
    "    print(\"'{}' = {} ({}%) outliers\".format(column, outliers, outliers_pct))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax  = plt.subplots(figsize = (18,10))\n",
    "ax.axis(\"off\")\n",
    "v = 1\n",
    "for column in df_drop.columns:\n",
    "    ax1 = fig.add_subplot(2,4,v)\n",
    "    plt.hist(df_drop[column])\n",
    "    ax1.set_title(column)\n",
    "    v+=1\n",
    "    median = df_drop[column].quantile()\n",
    "    iqr_1_5 = (df_drop[column].quantile(q = 0.75) - df_drop[column].quantile(q = 0.25)) * 1.5\n",
    "    outliers = df_drop[(df_drop[column]< median - iqr_1_5) | (df_drop[column] > median + iqr_1_5)][column].count()\n",
    "    ax1.axvline(median - iqr_1_5, color='red', linewidth=2)  \n",
    "    ax1.axvline(median + iqr_1_5, color='red', linewidth=2)  \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I realized that the method used to identify outliers selects many records, the most severe case being the previous column with almost 14% of the selected data. So, I decided to keep records for all categories mostly, as far as I can tell, because the data seems to have been measured correctly and reflects reality.\n",
    "\n",
    "To emphasize that the model is not affected by the extension of the number of outliers, we will only guarantee that we will use a standardization technique that does not neglect to detail the distances between the central values.\n",
    "\n",
    "From the analysis of the dispersion of the numeric data, it seems to be no outliers. The plotted data of 'age' has a normal behavior, and I assume that the extreme data points in the variable 'pdays' are 'out of range' values, as for the rest, every variable seems to have a balanced body.\n",
    "\n",
    "Regarding missing values and other anomalies, I have analyzed the unique values and respective counts of all the numerical variables, and there seem to be no missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Categoric veriables** (df_cat_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving on to the categoric variables, time to check if there are any missing values or other anomalies that need to be dealt with so it can be resumed by transforming these into numerical features through various techniques that will be analyzed case by case, concerning the best trade-off between dimensionality reduction and information gain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>nonexistent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>10422</td>\n",
       "      <td>24928</td>\n",
       "      <td>12168</td>\n",
       "      <td>32588</td>\n",
       "      <td>21576</td>\n",
       "      <td>33950</td>\n",
       "      <td>35563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           job  marital          education default housing   loan     poutcome\n",
       "count    41188    41188              41188   41188   41188  41188        41188\n",
       "unique      12        4                  8       3       3      3            3\n",
       "top     admin.  married  university.degree      no     yes     no  nonexistent\n",
       "freq     10422    24928              12168   32588   21576  33950        35563"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a copy\n",
    "df_cat_t = df_cat.copy()\n",
    "df_cat.describe() # Describing the categorical dataframe "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable: **job**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: 12\n",
      "\n"
     ]
    },
    {
     "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>Freq abs</th>\n",
       "      <th>percent %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>admin.</th>\n",
       "      <td>10422</td>\n",
       "      <td>25.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue-collar</th>\n",
       "      <td>9254</td>\n",
       "      <td>22.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>6743</td>\n",
       "      <td>16.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>services</th>\n",
       "      <td>3969</td>\n",
       "      <td>9.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>2924</td>\n",
       "      <td>7.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>1720</td>\n",
       "      <td>4.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entrepreneur</th>\n",
       "      <td>1456</td>\n",
       "      <td>3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>self-employed</th>\n",
       "      <td>1421</td>\n",
       "      <td>3.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housemaid</th>\n",
       "      <td>1060</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployed</th>\n",
       "      <td>1014</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>875</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>330</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Freq abs  percent %\n",
       "admin.            10422      25.30\n",
       "blue-collar        9254      22.47\n",
       "technician         6743      16.37\n",
       "services           3969       9.64\n",
       "management         2924       7.10\n",
       "retired            1720       4.18\n",
       "entrepreneur       1456       3.54\n",
       "self-employed      1421       3.45\n",
       "housemaid          1060       2.57\n",
       "unemployed         1014       2.46\n",
       "student             875       2.12\n",
       "unknown             330       0.80"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Unique values: {df_cat.job.nunique()}\")\n",
    "print(\"\")\n",
    "\n",
    "# Observations by class\n",
    "num_obs = df_cat.job.value_counts()\n",
    "num_o = pd.DataFrame(num_obs)\n",
    "num_o.rename(columns={\"job\":\"Freq abs\"}, inplace=True)\n",
    "num_o_pc = (df_cat.job.value_counts(normalize=True) * 100).round(decimals=2)\n",
    "num_obs_pc = pd.DataFrame(num_o_pc)\n",
    "num_obs_pc.rename(columns={\"job\":\"percent %\"}, inplace=True)\n",
    "n_obs = pd.concat([num_o,num_obs_pc], axis=1)\n",
    "display(n_obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, 'unknown' is our missing value. The strategy here is to impute the 'unknowns' by the most frequent value, the modal value. Let's start by converting it into a np.nan and then perform the imputation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: 11\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replacing 'unknown' by NaN\n",
    "df_cat_t.job.replace(to_replace=\"unknown\", value=np.nan, inplace=True)\n",
    "\n",
    "# Imputation missing values by the modal value\n",
    "df_cat_t['job'] = df_cat_t.job.fillna(df_cat_t.job.value_counts().index[0])\n",
    "\n",
    "# Confirming and visualizing of \"job\"\n",
    "df_cat_t.job.value_counts(dropna=False).plot(kind='pie', figsize=(10,6), \n",
    "                                           explode=(0.02, 0.02, 0.02, 0.02, \n",
    "                                                    0.02, 0.02, 0.02, 0.02,\n",
    "                                                    0.02, 0.02, 0.02));\n",
    "print(f\"Unique values: {df_cat_t.job.nunique()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll also be displaying the results of how many extra columns both One Hot Encoding and Binary Encoding would create under the encoding procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*Results*\n",
      "job OHE: 11 columns\n",
      "job BIN: 5 columns\n"
     ]
    }
   ],
   "source": [
    "# Encoding 'job' OHE or BIN\n",
    "df_job = df_cat_t.job\n",
    "job_ohe = pd.get_dummies(df_job)\n",
    "binary_encoder_job = BinaryEncoder()\n",
    "job_bin = binary_encoder_job.fit_transform(df_job)\n",
    "print(f'''\n",
    "*Results*\n",
    "job OHE: {job_ohe.shape[1]} columns\n",
    "job BIN: {job_bin.shape[1]} columns''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Binary Encoding to be applied here. Although there is some loss of information in comparison with OHE, it is a better trade-off in terms of dimensionality reduction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>job_0</th>\n",
       "      <th>job_1</th>\n",
       "      <th>job_2</th>\n",
       "      <th>job_3</th>\n",
       "      <th>job_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   marital    education  default housing loan     poutcome  job_0  job_1  \\\n",
       "0  married     basic.4y       no      no   no  nonexistent      0      0   \n",
       "1  married  high.school  unknown      no   no  nonexistent      0      0   \n",
       "\n",
       "   job_2  job_3  job_4  \n",
       "0      0      0      1  \n",
       "1      0      1      0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(41188, 11)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Removing 'job' and concatenation job_bin\n",
    "df_cat_t.drop([\"job\"],axis=1,inplace=True)\n",
    "df_cat_t = pd.concat([df_cat_t,job_bin],axis=1)\n",
    "display(df_cat_t.head(2))\n",
    "display(df_cat_t.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable: **marital**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: 4\n",
      "\n"
     ]
    },
    {
     "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>Freq abs</th>\n",
       "      <th>percent %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>24928</td>\n",
       "      <td>60.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>single</th>\n",
       "      <td>11568</td>\n",
       "      <td>28.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>4612</td>\n",
       "      <td>11.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>80</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Freq abs  percent %\n",
       "married      24928      60.52\n",
       "single       11568      28.09\n",
       "divorced      4612      11.20\n",
       "unknown         80       0.19"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Unique values: {df_cat.marital.nunique()}\")\n",
    "print(\"\")\n",
    "\n",
    "# Observations by class\n",
    "marital_obs = df_cat.marital.value_counts()\n",
    "marital_o = pd.DataFrame(marital_obs)\n",
    "marital_o.rename(columns={\"marital\":\"Freq abs\"}, inplace=True)\n",
    "marital_o_pc = (df_cat.marital.value_counts(normalize=True) * 100).round(decimals=2)\n",
    "marital_obs_pc = pd.DataFrame(marital_o_pc)\n",
    "marital_obs_pc.rename(columns={\"marital\":\"percent %\"}, inplace=True)\n",
    "marital_obs = pd.concat([marital_o,marital_obs_pc], axis=1)\n",
    "display(marital_obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "‘Unknown’ is interpreted as a missing value. It is the same situation as with the ‘job’ variable. The strategy is to impute the missing values by the most frequent value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: 3\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replacing 'unknown' by NaN\n",
    "df_cat_t.marital.replace(to_replace=\"unknown\", value=np.nan, inplace=True)\n",
    "\n",
    "# Imputation of missing values by modal value\n",
    "df_cat_t['marital'] = df_cat_t.marital.fillna(df_cat_t.marital.value_counts().index[0])\n",
    "\n",
    "# Graph \"pie\"\n",
    "df_cat_t['marital'].value_counts(dropna=False).plot(kind='pie', figsize=(10,6),\n",
    "                                              explode = (0.02, 0.02, 0.02),\n",
    "                                              autopct='%1.1f%%',\n",
    "                                              startangle=120);\n",
    "print(f\"Unique values: {df_cat_t.marital.nunique()}\")\n",
    "print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, displaying the results of how many extra columns both One Hot Encoding and Binary Encoding would create with the encoding procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*Results*\n",
      "marital OHE: 3 columns\n",
      "marital BIN: 3 columns\n"
     ]
    }
   ],
   "source": [
    "# marital\n",
    "df_marital = df_cat_t.marital\n",
    "marital_ohe = pd.get_dummies(df_marital)\n",
    "binary_encoder_marital = BinaryEncoder()\n",
    "marital_bin = binary_encoder_marital.fit_transform(df_marital)\n",
    "print(f'''\n",
    "*Results*\n",
    "marital OHE: {marital_ohe.shape[1]} columns\n",
    "marital BIN: {marital_bin.shape[1]} columns''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One Hot Encoding to be applied here as it preserves more information in comparison with Binary Encoding."
   ]
  },
  {
   "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>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>job_0</th>\n",
       "      <th>job_1</th>\n",
       "      <th>job_2</th>\n",
       "      <th>job_3</th>\n",
       "      <th>job_4</th>\n",
       "      <th>divorced</th>\n",
       "      <th>married</th>\n",
       "      <th>single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     education  default housing loan     poutcome  job_0  job_1  job_2  job_3  \\\n",
       "0     basic.4y       no      no   no  nonexistent      0      0      0      0   \n",
       "1  high.school  unknown      no   no  nonexistent      0      0      0      1   \n",
       "\n",
       "   job_4  divorced  married  single  \n",
       "0      1         0        1       0  \n",
       "1      0         0        1       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(41188, 13)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cat_t.drop([\"marital\"],axis=1,inplace=True)\n",
    "df_cat_t = pd.concat([df_cat_t,marital_ohe],axis=1)\n",
    "display(df_cat_t.head(2))\n",
    "display(df_cat_t.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable: **education**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: 8\n",
      "\n"
     ]
    },
    {
     "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>Freq abs</th>\n",
       "      <th>percent %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>university.degree</th>\n",
       "      <td>12168</td>\n",
       "      <td>29.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high.school</th>\n",
       "      <td>9515</td>\n",
       "      <td>23.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basic.9y</th>\n",
       "      <td>6045</td>\n",
       "      <td>14.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>professional.course</th>\n",
       "      <td>5243</td>\n",
       "      <td>12.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basic.4y</th>\n",
       "      <td>4176</td>\n",
       "      <td>10.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basic.6y</th>\n",
       "      <td>2292</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>1731</td>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>illiterate</th>\n",
       "      <td>18</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Freq abs  percent %\n",
       "university.degree       12168      29.54\n",
       "high.school              9515      23.10\n",
       "basic.9y                 6045      14.68\n",
       "professional.course      5243      12.73\n",
       "basic.4y                 4176      10.14\n",
       "basic.6y                 2292       5.56\n",
       "unknown                  1731       4.20\n",
       "illiterate                 18       0.04"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Unique values: {df_cat.education.nunique()}\")\n",
    "print(\"\")\n",
    "\n",
    "# Observations by class\n",
    "edu_obs = df_cat.education.value_counts()\n",
    "edu_o = pd.DataFrame(edu_obs)\n",
    "edu_o.rename(columns={\"education\":\"Freq abs\"}, inplace=True)\n",
    "edu_o_pc = (df_cat.education.value_counts(normalize=True) * 100).round(decimals=2)\n",
    "edu_obs_pc = pd.DataFrame(edu_o_pc)\n",
    "edu_obs_pc.rename(columns={\"education\":\"percent %\"}, inplace=True)\n",
    "edu_obs = pd.concat([edu_o,edu_obs_pc], axis=1)\n",
    "display(edu_obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're looking at the same case where ‘unknown’ is interpreted as missing values. The strategy adopted is to impute by the most frequent value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: 7\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replacing 'unknown' by NaN\n",
    "df_cat_t.education.replace(to_replace=\"unknown\", value=np.nan, inplace=True)\n",
    "\n",
    "# Imputation of missing values by modal value\n",
    "df_cat_t['education'] = df_cat_t.education.fillna(df_cat_t.education.value_counts().index[0])\n",
    "\n",
    "# Graph \"pie\"\n",
    "df_cat_t['education'].value_counts(dropna=False).plot(kind='pie', \n",
    "                                                figsize=(10,6), \n",
    "                                                explode = (0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01),\n",
    "                                                autopct='%1.1f%%',\n",
    "                                                startangle=0);\n",
    "print(f\"Unique values: {df_cat_t.education.nunique()}\")\n",
    "print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "university.degree      13899\n",
       "high.school             9515\n",
       "basic.9y                6045\n",
       "professional.course     5243\n",
       "basic.4y                4176\n",
       "basic.6y                2292\n",
       "illiterate                18\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat_t.education.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Education'' has 8 classes that obey a specific order meaning it will be encoded considering that same rank. The ascending order is: 'Illiterate', ' basic.4y', 'nasic.6y', 'basic.9y', 'high.school', 'professional.course', and 'university.degree'.\n",
    "\n",
    "For this purpose, I will apply a map function. Data mapping is used for replacing a specific set of values in a Series by another set of values, that may be derived from a function, a dict or a Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    13899\n",
       "5     9515\n",
       "4     6045\n",
       "6     5243\n",
       "2     4176\n",
       "3     2292\n",
       "1       18\n",
       "Name: education, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>job_0</th>\n",
       "      <th>job_1</th>\n",
       "      <th>job_2</th>\n",
       "      <th>job_3</th>\n",
       "      <th>job_4</th>\n",
       "      <th>divorced</th>\n",
       "      <th>married</th>\n",
       "      <th>single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   education  default housing loan     poutcome  job_0  job_1  job_2  job_3  \\\n",
       "0          2       no      no   no  nonexistent      0      0      0      0   \n",
       "1          5  unknown      no   no  nonexistent      0      0      0      1   \n",
       "\n",
       "   job_4  divorced  married  single  \n",
       "0      1         0        1       0  \n",
       "1      0         0        1       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(41188, 13)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cat_t[\"education\"] = df_cat_t.education.map({\"university.degree\":7, \n",
    "                                                \"professional.course\":6,\n",
    "                                                \"high.school\":5,\"basic.9y\":4,\n",
    "                                                \"basic.6y\":3, \"basic.4y\":2,\n",
    "                                                \"illiterate\":1 })\n",
    "display(df_cat_t.education.value_counts())\n",
    "display(df_cat_t.head(2))\n",
    "display(df_cat_t.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable: **default**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: 3\n",
      "\n"
     ]
    },
    {
     "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>Freq abs</th>\n",
       "      <th>percent %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>32588</td>\n",
       "      <td>79.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>8597</td>\n",
       "      <td>20.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>3</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Freq abs  percent %\n",
       "no          32588      79.12\n",
       "unknown      8597      20.87\n",
       "yes             3       0.01"
      ]
     },
     "metadata": {},
     "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": [
    "print(f\"Unique values: {df.default.nunique()}\")\n",
    "print(\"\")\n",
    "\n",
    "# Visualizing graph \"bar\"\n",
    "df['default'].value_counts(dropna=False).plot(kind='bar', figsize=(6,4));\n",
    "\n",
    "# Number of observations by class\n",
    "default_obs = df.default.value_counts()\n",
    "default_o = pd.DataFrame(default_obs)\n",
    "default_o.rename(columns={\"default\":\"Freq abs\"}, inplace=True)\n",
    "default_o_pc = (df.default.value_counts(normalize=True) * 100).round(decimals=2)\n",
    "default_obs_pc = pd.DataFrame(default_o_pc)\n",
    "default_obs_pc.rename(columns={\"default\":\"percent %\"}, inplace=True)\n",
    "default_obs = pd.concat([default_o,default_obs_pc], axis=1)\n",
    "display(default_obs)\n",
    "\n",
    "\n",
    "# Graph 'default' vs. 'target'\n",
    "default_0 = df[df.target == 0].iloc[:,4:5]\n",
    "default_1 = df[df.target == 1].iloc[:,4:5]\n",
    "\n",
    "a = np.array(default_0)\n",
    "b = np.array(default_1)\n",
    "\n",
    "np.warnings.filterwarnings('ignore')\n",
    "\n",
    "plt.hist(a, bins=40, color=\"r\", alpha = 1, label='bad client', align=\"left\")\n",
    "plt.hist(b, bins=40, color=\"y\", alpha = 1, label='good client', align=\"right\")\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('default', fontsize=12)\n",
    "plt.xlabel('Credit in default')\n",
    "plt.xticks(rotation='horizontal')\n",
    "plt.ylabel('Absolute frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, 'unknown' values are interpreted as missing values. The 'yes' class is meaningless and any imputation of the 'unknown' values could deeply compromise all the results. Therefore, it's better to remove this variable."
   ]
  },
  {
   "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>education</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>job_0</th>\n",
       "      <th>job_1</th>\n",
       "      <th>job_2</th>\n",
       "      <th>job_3</th>\n",
       "      <th>job_4</th>\n",
       "      <th>divorced</th>\n",
       "      <th>married</th>\n",
       "      <th>single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   education housing loan     poutcome  job_0  job_1  job_2  job_3  job_4  \\\n",
       "0          2      no   no  nonexistent      0      0      0      0      1   \n",
       "1          5      no   no  nonexistent      0      0      0      1      0   \n",
       "\n",
       "   divorced  married  single  \n",
       "0         0        1       0  \n",
       "1         0        1       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(41188, 12)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cat_t.drop([\"default\"], axis=1, inplace=True)\n",
    "display(df_cat_t.head(2))\n",
    "display(df_cat_t.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable: **housing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: 3\n",
      "\n"
     ]
    },
    {
     "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>Freq abs</th>\n",
       "      <th>percent %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>21576</td>\n",
       "      <td>52.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>18622</td>\n",
       "      <td>45.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>990</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Freq abs  percent %\n",
       "yes         21576      52.38\n",
       "no          18622      45.21\n",
       "unknown       990       2.40"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Unique values: {df_cat.housing.nunique()}\")\n",
    "print(\"\")\n",
    "\n",
    "# Observations by class\n",
    "hous_obs = df_cat.housing.value_counts()\n",
    "hous_o = pd.DataFrame(hous_obs)\n",
    "hous_o.rename(columns={\"housing\":\"Freq abs\"}, inplace=True)\n",
    "hous_o_pc = (df_cat.housing.value_counts(normalize=True) * 100).round(decimals=2)\n",
    "hous_obs_pc = pd.DataFrame(hous_o_pc)\n",
    "hous_obs_pc.rename(columns={\"housing\":\"percent %\"}, inplace=True)\n",
    "hous_obs = pd.concat([hous_o,hous_obs_pc], axis=1)\n",
    "display(hous_obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, 'unknown' are interpreted as missing values.\n",
    "##### **Strategy:** Replacing by the most frequent value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: 2\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replacing 'unknown' by NaN\n",
    "df_cat_t.housing.replace(to_replace=\"unknown\", value=np.nan, inplace=True)\n",
    "\n",
    "# Imputation of missing values by modal value\n",
    "df_cat_t['housing'] = df_cat_t.housing.fillna(df_cat_t.housing.value_counts().index[0])\n",
    "\n",
    "# Visualizing \"pie\" graph\n",
    "df_cat_t['housing'].value_counts(dropna=False).plot(kind='pie', \n",
    "                                              explode = (0.01, 0.01), \n",
    "                                              figsize=(10,6), \n",
    "                                              autopct='%1.1f%%',\n",
    "                                              startangle=90);\n",
    "print(f\"Unique values: {df_cat_t.housing.nunique()}\")\n",
    "print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the Chi-square test, this variable has revealed has with little statistic importance.\n",
    "##### **Strategy:** Remove variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>education</th>\n",
       "      <th>loan</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>job_0</th>\n",
       "      <th>job_1</th>\n",
       "      <th>job_2</th>\n",
       "      <th>job_3</th>\n",
       "      <th>job_4</th>\n",
       "      <th>divorced</th>\n",
       "      <th>married</th>\n",
       "      <th>single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   education loan     poutcome  job_0  job_1  job_2  job_3  job_4  divorced  \\\n",
       "0          2   no  nonexistent      0      0      0      0      1         0   \n",
       "1          5   no  nonexistent      0      0      0      1      0         0   \n",
       "\n",
       "   married  single  \n",
       "0        1       0  \n",
       "1        1       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(41188, 11)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cat_t.drop([\"housing\"], axis=1, inplace=True)\n",
    "display(df_cat_t.head(2))\n",
    "display(df_cat_t.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable: **loan**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: 3\n",
      "\n"
     ]
    },
    {
     "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>Freq abs</th>\n",
       "      <th>percent %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>33950</td>\n",
       "      <td>82.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>6248</td>\n",
       "      <td>15.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>990</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Freq abs  percent %\n",
       "no          33950      82.43\n",
       "yes          6248      15.17\n",
       "unknown       990       2.40"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Unique values: {df_cat.loan.nunique()}\")\n",
    "print(\"\")\n",
    "\n",
    "# Observations by class\n",
    "loan_obs = df_cat.loan.value_counts()\n",
    "loan_o = pd.DataFrame(loan_obs)\n",
    "loan_o.rename(columns={\"loan\":\"Freq abs\"}, inplace=True)\n",
    "loan_o_pc = (df_cat.loan.value_counts(normalize=True) * 100).round(decimals=2)\n",
    "loan_obs_pc = pd.DataFrame(loan_o_pc)\n",
    "loan_obs_pc.rename(columns={\"loan\":\"percent %\"}, inplace=True)\n",
    "loan_obs = pd.concat([loan_o,loan_obs_pc], axis=1)\n",
    "display(loan_obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, 'unknown' are interpreted as missing values.\n",
    "##### **Strategy:** Replacing by the most frequent value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: 2\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replacing 'unknown' by NaN\n",
    "df_cat_t.loan.replace(to_replace=\"unknown\", value=np.nan, inplace=True)\n",
    "\n",
    "# Imputation of missing values by modal value\n",
    "df_cat_t['loan'] = df_cat_t.loan.fillna(df_cat_t.loan.value_counts().index[0])\n",
    "\n",
    "# Visualizing \"pie\" graph\n",
    "df_cat_t['loan'].value_counts(dropna=False).plot(kind='pie', \n",
    "                                                figsize=(10,6), \n",
    "                                                explode = (0.01, 0.1),\n",
    "                                                autopct='%1.1f%%',\n",
    "                                                startangle=115);\n",
    "print(f\"Unique values: {df_cat_t.loan.nunique()}\")\n",
    "print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the Chi-square test, this variable has revealed has with little statistic importance.\n",
    "##### **Strategy:** Remove variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>education</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>job_0</th>\n",
       "      <th>job_1</th>\n",
       "      <th>job_2</th>\n",
       "      <th>job_3</th>\n",
       "      <th>job_4</th>\n",
       "      <th>divorced</th>\n",
       "      <th>married</th>\n",
       "      <th>single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   education     poutcome  job_0  job_1  job_2  job_3  job_4  divorced  \\\n",
       "0          2  nonexistent      0      0      0      0      1         0   \n",
       "1          5  nonexistent      0      0      0      1      0         0   \n",
       "\n",
       "   married  single  \n",
       "0        1       0  \n",
       "1        1       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(41188, 10)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cat_t.drop([\"loan\"], axis=1, inplace=True)\n",
    "display(df_cat_t.head(2))\n",
    "display(df_cat_t.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variável: **poutcome**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values: 3\n",
      "\n"
     ]
    },
    {
     "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>Freq abs</th>\n",
       "      <th>percent %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>nonexistent</th>\n",
       "      <td>35563</td>\n",
       "      <td>86.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>failure</th>\n",
       "      <td>4252</td>\n",
       "      <td>10.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>success</th>\n",
       "      <td>1373</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Freq abs  percent %\n",
       "nonexistent     35563      86.34\n",
       "failure          4252      10.32\n",
       "success          1373       3.33"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Unique values: {df_cat.poutcome.nunique()}\")\n",
    "print(\"\")\n",
    "\n",
    "# Graph \"pie\"\n",
    "df['poutcome'].value_counts(dropna=False).plot(kind='pie', \n",
    "                                                figsize=(10,6), \n",
    "                                                explode = (0.1, 0.01, 0.01),\n",
    "                                                autopct='%1.1f%%',\n",
    "                                                startangle=115);\n",
    "\n",
    "# Number of observation by class\n",
    "pout_obs = df.poutcome.value_counts()\n",
    "pout_o = pd.DataFrame(pout_obs)\n",
    "pout_o.rename(columns={\"poutcome\":\"Freq abs\"}, inplace=True)\n",
    "pout_o_pc = (df.poutcome.value_counts(normalize=True) * 100).round(decimals=2)\n",
    "pout_obs_pc = pd.DataFrame(pout_o_pc)\n",
    "pout_obs_pc.rename(columns={\"poutcome\":\"percent %\"}, inplace=True)\n",
    "pout_obs = pd.concat([pout_o,pout_obs_pc], axis=1)\n",
    "pout_obs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, 'nonexistent' are interpreted as relevant values. The strategy to follow is to do no transformation to this variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*Results*\n",
      "poutcome OHE: 3 columns\n",
      "poutcome BIN: 3 columns\n"
     ]
    }
   ],
   "source": [
    "# Encoding 'poutcome' OHE\n",
    "df_poutcome = df_cat_t.poutcome\n",
    "poutcome_ohe = pd.get_dummies(df_poutcome)\n",
    "binary_encoder_poutcome = BinaryEncoder()\n",
    "poutcome_bin = binary_encoder_poutcome.fit_transform(df_poutcome)\n",
    "print(f'''\n",
    "*Results*\n",
    "poutcome OHE: {poutcome_ohe.shape[1]} columns\n",
    "poutcome BIN: {poutcome_bin.shape[1]} columns''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>education</th>\n",
       "      <th>job_0</th>\n",
       "      <th>job_1</th>\n",
       "      <th>job_2</th>\n",
       "      <th>job_3</th>\n",
       "      <th>job_4</th>\n",
       "      <th>divorced</th>\n",
       "      <th>married</th>\n",
       "      <th>single</th>\n",
       "      <th>failure</th>\n",
       "      <th>nonexistent</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   education  job_0  job_1  job_2  job_3  job_4  divorced  married  single  \\\n",
       "0          2      0      0      0      0      1         0        1       0   \n",
       "1          5      0      0      0      1      0         0        1       0   \n",
       "\n",
       "   failure  nonexistent  success  \n",
       "0        0            1        0  \n",
       "1        0            1        0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(41188, 12)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove 'poutcome' and concatenation poutcome_ohe\n",
    "df_cat_t.drop([\"poutcome\"],axis=1,inplace=True)\n",
    "df_cat_t = pd.concat([df_cat_t,poutcome_ohe],axis=1)\n",
    "display(df_cat_t.head(2))\n",
    "display(df_cat_t.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education      int64\n",
       "job_0          int64\n",
       "job_1          int64\n",
       "job_2          int64\n",
       "job_3          int64\n",
       "job_4          int64\n",
       "divorced       uint8\n",
       "married        uint8\n",
       "single         uint8\n",
       "failure        uint8\n",
       "nonexistent    uint8\n",
       "success        uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat_t.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education      int64\n",
       "job_0          int64\n",
       "job_1          int64\n",
       "job_2          int64\n",
       "job_3          int64\n",
       "job_4          int64\n",
       "divorced       uint8\n",
       "married        uint8\n",
       "single         uint8\n",
       "failure        uint8\n",
       "nonexistent    uint8\n",
       "success        uint8\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "df_cat (original): ........ (41188, 7)\n",
      "df_cat_t (transformed): ... (41188, 12)\n"
     ]
    }
   ],
   "source": [
    "# Analysis of the transformed dataset\n",
    "display(df_cat_t.dtypes)\n",
    "print(\"\")\n",
    "print(f\"df_cat (original): ........ {df_cat.shape}\")\n",
    "print(f\"df_cat_t (transformed): ... {df_cat_t.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41188 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       education  job_0  job_1  job_2  job_3  job_4  divorced  married  \\\n",
       "0              2      0      0      0      0      1         0        1   \n",
       "1              5      0      0      0      1      0         0        1   \n",
       "2              5      0      0      0      1      0         0        1   \n",
       "3              3      0      0      0      1      1         0        1   \n",
       "4              5      0      0      0      1      0         0        1   \n",
       "...          ...    ...    ...    ...    ...    ...       ...      ...   \n",
       "41183          6      0      0      1      1      0         0        1   \n",
       "41184          6      0      0      1      0      0         0        1   \n",
       "41185          7      0      0      1      1      0         0        1   \n",
       "41186          6      0      0      1      0      1         0        1   \n",
       "41187          6      0      0      1      1      0         0        1   \n",
       "\n",
       "       single  failure  ...  success  age  pdays  previous  emp_var_rate  \\\n",
       "0           0        0  ...        0   56    999         0           1.1   \n",
       "1           0        0  ...        0   57    999         0           1.1   \n",
       "2           0        0  ...        0   37    999         0           1.1   \n",
       "3           0        0  ...        0   40    999         0           1.1   \n",
       "4           0        0  ...        0   56    999         0           1.1   \n",
       "...       ...      ...  ...      ...  ...    ...       ...           ...   \n",
       "41183       0        0  ...        0   73    999         0          -1.1   \n",
       "41184       0        0  ...        0   46    999         0          -1.1   \n",
       "41185       0        0  ...        0   56    999         0          -1.1   \n",
       "41186       0        0  ...        0   44    999         0          -1.1   \n",
       "41187       0        1  ...        0   74    999         1          -1.1   \n",
       "\n",
       "       cons_price_idx  cons_conf_idx  euribor3m  nr_employed  target  \n",
       "0              93.994          -36.4      4.857       5191.0       0  \n",
       "1              93.994          -36.4      4.857       5191.0       0  \n",
       "2              93.994          -36.4      4.857       5191.0       0  \n",
       "3              93.994          -36.4      4.857       5191.0       0  \n",
       "4              93.994          -36.4      4.857       5191.0       0  \n",
       "...               ...            ...        ...          ...     ...  \n",
       "41183          94.767          -50.8      1.028       4963.6       1  \n",
       "41184          94.767          -50.8      1.028       4963.6       0  \n",
       "41185          94.767          -50.8      1.028       4963.6       0  \n",
       "41186          94.767          -50.8      1.028       4963.6       1  \n",
       "41187          94.767          -50.8      1.028       4963.6       0  \n",
       "\n",
       "[41188 rows x 21 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenation of both numeric and categorical datasets\n",
    "df_numcat = pd.concat([df_cat_t,df_num], axis=1)\n",
    "\n",
    "# Removing any NaNs or anomalies just to make sure\n",
    "df_numcat.dropna(how=\"any\", axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this stage, after dealing with all the missing values and imputations and transforming all categorical features into numerical ones, let us check for the correlations of the features, identifying and selecting the variables with no predictive power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subdivision of the target\n",
    "y_numcat = df_numcat.target\n",
    "X_numcat = df_numcat.drop([\"target\"], axis= \"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pearson Corr.</th>\n",
       "      <th>p-value</th>\n",
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       "  </thead>\n",
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       "      <th>education</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>success</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euribor3m</th>\n",
       "      <td>-0.31</td>\n",
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       "    <tr>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <td>0.05</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons_price_idx</th>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_var_rate</th>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>-0.32</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.03</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>nonexistent</th>\n",
       "      <td>-0.19</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_1</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>failure</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>single</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_4</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_3</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_2</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nr_employed</th>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Pearson Corr.  p-value\n",
       "divorced                -0.01     0.03\n",
       "education                0.06     0.00\n",
       "success                  0.32     0.00\n",
       "euribor3m               -0.31     0.00\n",
       "cons_conf_idx            0.05     0.00\n",
       "cons_price_idx          -0.14     0.00\n",
       "emp_var_rate            -0.30     0.00\n",
       "previous                 0.23     0.00\n",
       "pdays                   -0.32     0.00\n",
       "age                      0.03     0.00\n",
       "nonexistent             -0.19     0.00\n",
       "job_1                    0.04     0.00\n",
       "failure                  0.03     0.00\n",
       "single                   0.05     0.00\n",
       "married                 -0.04     0.00\n",
       "job_4                    0.05     0.00\n",
       "job_3                    0.07     0.00\n",
       "job_2                   -0.03     0.00\n",
       "nr_employed             -0.35     0.00\n",
       "job_0                     NaN      NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Identifying all variables with great predictive power (Pearson Correlation p-value)\n",
    "dataframe = pd.DataFrame(\n",
    "    [scipy.stats.pearsonr(X_numcat[col], y_numcat) for col in X_numcat.columns],\n",
    "    columns=[\"Pearson Corr.\", \"p-value\"],\n",
    "    index=X_numcat.columns,\n",
    ").round(2).sort_values(by=\"p-value\", ascending=False)\n",
    "\n",
    "display(dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identifying columns in which p-value > 0.05\n",
    "column = []\n",
    "for item in dataframe.index:\n",
    "    if dataframe['p-value'][item] >= 0.05:\n",
    "        column.append(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing statistically non significant columns\n",
    "df_numcat.drop(column, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 21)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numcat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>education</th>\n",
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       "      <th>euribor3m</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41188 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       education  job_0  job_1  job_2  job_3  job_4  divorced  married  \\\n",
       "0              2      0      0      0      0      1         0        1   \n",
       "1              5      0      0      0      1      0         0        1   \n",
       "2              5      0      0      0      1      0         0        1   \n",
       "3              3      0      0      0      1      1         0        1   \n",
       "4              5      0      0      0      1      0         0        1   \n",
       "...          ...    ...    ...    ...    ...    ...       ...      ...   \n",
       "41183          6      0      0      1      1      0         0        1   \n",
       "41184          6      0      0      1      0      0         0        1   \n",
       "41185          7      0      0      1      1      0         0        1   \n",
       "41186          6      0      0      1      0      1         0        1   \n",
       "41187          6      0      0      1      1      0         0        1   \n",
       "\n",
       "       single  failure  ...  success  age  pdays  previous  emp_var_rate  \\\n",
       "0           0        0  ...        0   56    999         0           1.1   \n",
       "1           0        0  ...        0   57    999         0           1.1   \n",
       "2           0        0  ...        0   37    999         0           1.1   \n",
       "3           0        0  ...        0   40    999         0           1.1   \n",
       "4           0        0  ...        0   56    999         0           1.1   \n",
       "...       ...      ...  ...      ...  ...    ...       ...           ...   \n",
       "41183       0        0  ...        0   73    999         0          -1.1   \n",
       "41184       0        0  ...        0   46    999         0          -1.1   \n",
       "41185       0        0  ...        0   56    999         0          -1.1   \n",
       "41186       0        0  ...        0   44    999         0          -1.1   \n",
       "41187       0        1  ...        0   74    999         1          -1.1   \n",
       "\n",
       "       cons_price_idx  cons_conf_idx  euribor3m  nr_employed  target  \n",
       "0              93.994          -36.4      4.857       5191.0       0  \n",
       "1              93.994          -36.4      4.857       5191.0       0  \n",
       "2              93.994          -36.4      4.857       5191.0       0  \n",
       "3              93.994          -36.4      4.857       5191.0       0  \n",
       "4              93.994          -36.4      4.857       5191.0       0  \n",
       "...               ...            ...        ...          ...     ...  \n",
       "41183          94.767          -50.8      1.028       4963.6       1  \n",
       "41184          94.767          -50.8      1.028       4963.6       0  \n",
       "41185          94.767          -50.8      1.028       4963.6       0  \n",
       "41186          94.767          -50.8      1.028       4963.6       1  \n",
       "41187          94.767          -50.8      1.028       4963.6       0  \n",
       "\n",
       "[41188 rows x 21 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numcat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Et voilà. We are all set to go for the third and last part, definitely the most exciting phase of our project, the modeling part. See you there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author:** Gonçalo Guimarães Gomes. Portuguese Data Analyst and Digital Brand Marketer. Postgraduate in Data Science and in Digital Marketing. 5 yrs Degree and Executive Master in Marketing Management."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Contacts**\n",
    "\n",
    "Feel free to contact me:\n",
    "\n",
    "- [Linkedin](http://bit.ly/2ybRqYT)\n",
    "- [Twitter](https://bit.ly/3gAwMTP)\n",
    "- [Medium](https://bit.ly/3gDC5Sp)\n",
    "- [GitHub](https://bit.ly/3hHvuHR)\n",
    "- [Kaggle](https://bit.ly/31Co038)\n",
    "- [Email](mailto:goncaloggomes@gmail.com)"
   ]
  }
 ],
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