EDAAndDataPrep.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CS4055 Project Group 7\n",
    "\n",
    "# Notebook 1 - EDA and Data Preparation\n",
    "\n",
    "===================================================================================================================\n",
    "\n",
    "# 1 - Introduction\n",
    "\n",
    "## a) Group Members\n",
    "\n",
    "__Enda McCarthy (19159269)__ \n",
    "\n",
    "    25% - EDA, Clustering & Feature Selection/Predictive Modelling (kNN)\n",
    "\n",
    "__Denisio Togashi (15195147)__\n",
    "\n",
    "    25% - EDA, Feature Selection/Predictive Modelling (SVC) & Evaluation of Model\n",
    "\n",
    "__Griselda Williams (19290128)__\n",
    "\n",
    "    25% - EDA, Binary Target Investigation & Feature Selection/Predictive Modelling (Random Forest)\n",
    "\n",
    "__Richard Moloney (16195434)__\n",
    "\n",
    "    25% - EDA & Feature Selection/Predictive Modelling (Linear SVC)\n",
    "    \n",
    "The Group came together regularly to consolidate their findings, analyse the results, and to finalise the documentation.\n",
    "\n",
    "\n",
    "## b) Dataset Choice\n",
    "\n",
    "The dataset contains information collected from two Portuguese secondary schools about students who are studying the Portuguese language.\n",
    "\n",
    "The information collected relates to the students personal and academic lives and was collected during the 2005-2006 school year.\n",
    "\n",
    "The target attribute if called '__G3__'. This is the students final grade for the subject.\n",
    "\n",
    "There are 649 rows in the dataset.\n",
    "\n",
    "The source of the data is: Paulo Cortez, University of Minho, Guimaraes, Portugal, http://www3.dsi.uminho.pt/pcortez\n",
    "\n",
    "__Why we chose this dataset:__\n",
    "\n",
    "We chose this dataset becuase education was a sector that we could all relate to, enabling us to better understand the relationship between features, and their potential impact on our target outcomes.\n",
    "\n",
    "__N.B.__ \n",
    "\n",
    "In this notebook we use 'students.csv' which has values artificially removed. This is so we can demonstrate how to approach dealing with missing data. In Notebooks 2 and 3 we will use 'studentPor.csv', the original dataset which has no missing values. We begin Notebooks 2 and 3 by removing the outliers, even though we have demonstrated and analysed outlier removal in detail already in Notebook 1.\n",
    "\n",
    "\n",
    "## c) Project Documentation\n",
    "\n",
    "We are sumbitting a zip file with __3 sub folders__:\n",
    "\n",
    "- EDA_AND_DATAPREP\n",
    "- PREDICTIVE_MODELS\n",
    "- CLUSTERING\n",
    "\n",
    "Each subfolder contains the Jupyter notebook, the relevant CSV files and any images linked in the notebook.\n",
    "\n",
    "\n",
    "## d) Application of Lab Work\n",
    "\n",
    "- Lab 2 - EDA (Notebook 1)\n",
    "- Lab 3 - Data preparation (Notebook 1)\n",
    "- Lab 4 - Model evaluation (Notebook 2)\n",
    "- Lab 5 - Feature selection & predictive modelling (Notebook 2)\n",
    "- Lab 6 - Clustering (Notebook 3)\n",
    "\n",
    "\n",
    "## e) Project Demonstration\n",
    "\n",
    "Group 7 gave a demonstration of our work in progress on Friday 15th November (week 10).\n",
    "\n",
    "===================================================================================================================\n",
    "\n",
    "# 2 - EDA\n",
    "\n",
    "## a) Imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset with values articicially removed\n",
    "df = pd.read_csv('./students.csv')\n",
    "# description of dataset\n",
    "df_info = pd.read_csv('./student_dataset_info.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 1 - Dataset Info__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_01e58052_12c5_11ea_a557_acde48001122 th {\n",
       "          text-align: left;\n",
       "    }    #T_01e58052_12c5_11ea_a557_acde48001122 td {\n",
       "          text-align: left;\n",
       "    }</style><table id=\"T_01e58052_12c5_11ea_a557_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Attribute</th>        <th class=\"col_heading level0 col1\" >Description</th>        <th class=\"col_heading level0 col2\" >Type</th>        <th class=\"col_heading level0 col3\" >Values</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row0_col0\" class=\"data row0 col0\" >School</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row0_col1\" class=\"data row0 col1\" >Student’s School</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row0_col2\" class=\"data row0 col2\" >Binary</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row0_col3\" class=\"data row0 col3\" >GP (Gabriel Pereira) or MS (Mousinho da Silveira)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row1_col0\" class=\"data row1 col0\" >Sex</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row1_col1\" class=\"data row1 col1\" >Student’s Sex</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row1_col2\" class=\"data row1 col2\" >Binary</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row1_col3\" class=\"data row1 col3\" >F or M</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row2_col0\" class=\"data row2 col0\" >Age</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row2_col1\" class=\"data row2 col1\" >Student’s Age</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row2_col2\" class=\"data row2 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row2_col3\" class=\"data row2 col3\" >15 to 22</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row3_col0\" class=\"data row3 col0\" >Address</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row3_col1\" class=\"data row3 col1\" >Student’s Home Address Type</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row3_col2\" class=\"data row3 col2\" >Binary</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row3_col3\" class=\"data row3 col3\" >U (urban) or R (rural)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row4_col0\" class=\"data row4 col0\" >FamSize</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row4_col1\" class=\"data row4 col1\" >Family Size</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row4_col2\" class=\"data row4 col2\" >Binary</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row4_col3\" class=\"data row4 col3\" >LE3 (less or equal to 3) or GT3 (greater than 3)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row5_col0\" class=\"data row5 col0\" >Pstatus</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row5_col1\" class=\"data row5 col1\" >Parent’s Cohabitation Status</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row5_col2\" class=\"data row5 col2\" >Binary</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row5_col3\" class=\"data row5 col3\" >T (living together) or A (apart)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row6_col0\" class=\"data row6 col0\" >Medu</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row6_col1\" class=\"data row6 col1\" >Mother’s Education</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row6_col2\" class=\"data row6 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row6_col3\" class=\"data row6 col3\" >0 (none), 1 (lower primary), 2 (higher primary), 3 (secondary education) or 4 (higher education)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row7_col0\" class=\"data row7 col0\" >Fedu</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row7_col1\" class=\"data row7 col1\" >Father’s Education</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row7_col2\" class=\"data row7 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row7_col3\" class=\"data row7 col3\" >0 (none), 1 (lower primary), 2 (higher primary), 3 (secondary education) or 4 (higher education)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row8_col0\" class=\"data row8 col0\" >Mjob</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row8_col1\" class=\"data row8 col1\" >Mother’s Job</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row8_col2\" class=\"data row8 col2\" >Nominal</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row8_col3\" class=\"data row8 col3\" >teacher, health, services, at_home, other</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row9_col0\" class=\"data row9 col0\" >Fjob</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row9_col1\" class=\"data row9 col1\" >Father’s Job</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row9_col2\" class=\"data row9 col2\" >Nominal</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row9_col3\" class=\"data row9 col3\" >teacher, health, services, at_home, other</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row10_col0\" class=\"data row10 col0\" >Reason</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row10_col1\" class=\"data row10 col1\" >Reason To Choose This School</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row10_col2\" class=\"data row10 col2\" >Nominal</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row10_col3\" class=\"data row10 col3\" >home (close to home), reputation (of school), course (preference) or other</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row11_col0\" class=\"data row11 col0\" >Guardian</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row11_col1\" class=\"data row11 col1\" >Student’s Guardian</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row11_col2\" class=\"data row11 col2\" >Nominal</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row11_col3\" class=\"data row11 col3\" >mother, father or other</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row12_col0\" class=\"data row12 col0\" >TravelTime</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row12_col1\" class=\"data row12 col1\" >Home To School Travel Time</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row12_col2\" class=\"data row12 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row12_col3\" class=\"data row12 col3\" >1 (<15 min), 2 (15 to 30 min), 3 (30 min to 1 hour), or 4 (>1 hour)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row13_col0\" class=\"data row13 col0\" >StudyTime</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row13_col1\" class=\"data row13 col1\" >Weekly Study Time</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row13_col2\" class=\"data row13 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row13_col3\" class=\"data row13 col3\" >1 (<2 hours), 2 (2 to 5 hours), 3 (5 to 10 hours), or 4 (>10 hours)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row14_col0\" class=\"data row14 col0\" >Failures</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row14_col1\" class=\"data row14 col1\" >Number Of Past Class Failures</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row14_col2\" class=\"data row14 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row14_col3\" class=\"data row14 col3\" >0-4 (anything above 4 is labelled 4)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row15_col0\" class=\"data row15 col0\" >Schoolsup</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row15_col1\" class=\"data row15 col1\" >Extra Educational Support</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row15_col2\" class=\"data row15 col2\" >Binary</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row15_col3\" class=\"data row15 col3\" >yes or no</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row16_col0\" class=\"data row16 col0\" >Famsup</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row16_col1\" class=\"data row16 col1\" >Family Educational Support</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row16_col2\" class=\"data row16 col2\" >Binary</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row16_col3\" class=\"data row16 col3\" >yes or no</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row17_col0\" class=\"data row17 col0\" >Paid</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row17_col1\" class=\"data row17 col1\" >Extra Paid Classes Within The Course Subject</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row17_col2\" class=\"data row17 col2\" >Binary</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row17_col3\" class=\"data row17 col3\" >yes or no</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row18_col0\" class=\"data row18 col0\" >Activities</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row18_col1\" class=\"data row18 col1\" >Extra-Curricular Activities</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row18_col2\" class=\"data row18 col2\" >Binary</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row18_col3\" class=\"data row18 col3\" >yes or no</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row19_col0\" class=\"data row19 col0\" >Nursery</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row19_col1\" class=\"data row19 col1\" >Attended Nursery School</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row19_col2\" class=\"data row19 col2\" >Binary</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row19_col3\" class=\"data row19 col3\" >yes or no</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row20_col0\" class=\"data row20 col0\" >Higher</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row20_col1\" class=\"data row20 col1\" >Wants To Take Higher Education</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row20_col2\" class=\"data row20 col2\" >Binary</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row20_col3\" class=\"data row20 col3\" >yes or no</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row21_col0\" class=\"data row21 col0\" >Internet</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row21_col1\" class=\"data row21 col1\" >Internet Access At Home</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row21_col2\" class=\"data row21 col2\" >Binary</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row21_col3\" class=\"data row21 col3\" >yes or no</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row22_col0\" class=\"data row22 col0\" >Romantic</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row22_col1\" class=\"data row22 col1\" >In A Romantic Relationship</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row22_col2\" class=\"data row22 col2\" >Binary</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row22_col3\" class=\"data row22 col3\" >yes or no</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row23_col0\" class=\"data row23 col0\" >Famrel</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row23_col1\" class=\"data row23 col1\" >Quality Of Family Relationships</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row23_col2\" class=\"data row23 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row23_col3\" class=\"data row23 col3\" >from 1 to 5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row24_col0\" class=\"data row24 col0\" >Freetime</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row24_col1\" class=\"data row24 col1\" >Free Time After School</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row24_col2\" class=\"data row24 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row24_col3\" class=\"data row24 col3\" >from 1 to 5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row25_col0\" class=\"data row25 col0\" >Goout</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row25_col1\" class=\"data row25 col1\" >Going Out With Friends</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row25_col2\" class=\"data row25 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row25_col3\" class=\"data row25 col3\" >from 1 to 5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row26_col0\" class=\"data row26 col0\" >Dalc</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row26_col1\" class=\"data row26 col1\" >Workday Alcohol Consumption</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row26_col2\" class=\"data row26 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row26_col3\" class=\"data row26 col3\" >from 1 to 5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row27_col0\" class=\"data row27 col0\" >Walc</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row27_col1\" class=\"data row27 col1\" >Weekend Alcohol Consumption</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row27_col2\" class=\"data row27 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row27_col3\" class=\"data row27 col3\" >from 1 to 5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row28_col0\" class=\"data row28 col0\" >Health</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row28_col1\" class=\"data row28 col1\" >Current Health Status</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row28_col2\" class=\"data row28 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row28_col3\" class=\"data row28 col3\" >from 1 to 5</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row29_col0\" class=\"data row29 col0\" >Absences</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row29_col1\" class=\"data row29 col1\" >Number Of School Absences</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row29_col2\" class=\"data row29 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row29_col3\" class=\"data row29 col3\" >from 0 to 93</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row30_col0\" class=\"data row30 col0\" >G1</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row30_col1\" class=\"data row30 col1\" >First Period Grade</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row30_col2\" class=\"data row30 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row30_col3\" class=\"data row30 col3\" >from 0 to 20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row31_col0\" class=\"data row31 col0\" >G2</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row31_col1\" class=\"data row31 col1\" >Second Period Grade</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row31_col2\" class=\"data row31 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row31_col3\" class=\"data row31 col3\" >from 0 to 20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01e58052_12c5_11ea_a557_acde48001122level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row32_col0\" class=\"data row32 col0\" >G3</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row32_col1\" class=\"data row32 col1\" >Final Grade</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row32_col2\" class=\"data row32 col2\" >Numeric</td>\n",
       "                        <td id=\"T_01e58052_12c5_11ea_a557_acde48001122row32_col3\" class=\"data row32 col3\" >from 0 to 20, output target</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a1c8b2d50>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_info\n",
    "df_info.style.set_table_styles([dict(selector='th', props=[('text-align', 'left')]),\n",
    "                                dict(selector='td', props=[('text-align', 'left')])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## b) Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 2 - First Half Head__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "      <th>schoolsup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  \\\n",
       "0     GP   F   18       U     GT3       A   4.0     4  at_home   teacher   \n",
       "1     GP   F   17       U     GT3       T   1.0     1  at_home     other   \n",
       "2     GP   F   15       U     LE3       T   1.0     1  at_home     other   \n",
       "3     GP   F   15       U     GT3       T   4.0     2   health  services   \n",
       "4     GP   F   16       U     GT3       T   3.0     3    other     other   \n",
       "\n",
       "   reason guardian  traveltime  studytime  failures schoolsup  \n",
       "0  course   mother           2          2       0.0       yes  \n",
       "1  course   father           1          2       0.0        no  \n",
       "2   other   mother           1          2       0.0       yes  \n",
       "3    home   mother           1          3       0.0        no  \n",
       "4    home   father           1          2       0.0        no  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show head of first half of feature columns\n",
    "df.iloc[:,0:16].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 3 - Second Half Head__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>famsup</th>\n",
       "      <th>paid</th>\n",
       "      <th>activities</th>\n",
       "      <th>nursery</th>\n",
       "      <th>higher</th>\n",
       "      <th>internet</th>\n",
       "      <th>romantic</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  famsup paid activities nursery higher internet romantic  famrel  freetime  \\\n",
       "0     no   no        NaN     yes    yes       no       no       4         3   \n",
       "1    yes   no         no      no    yes      yes       no       5         3   \n",
       "2     no   no         no     yes    yes      yes       no       4         3   \n",
       "3    yes   no        NaN     yes    yes      yes      yes       3         2   \n",
       "4    yes   no         no     yes    yes       no       no       4         3   \n",
       "\n",
       "   goout  Dalc  Walc  health  absences  G1  G2  G3  \n",
       "0      4     1     1       3         4   0  11  11  \n",
       "1      3     1     1       3         2   9  11  11  \n",
       "2      2     2     3       3         6  12  13  12  \n",
       "3      2     1     1       5         0  14  14  14  \n",
       "4      2     1     2       5         0  11  13  13  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show head of second half of feature columns\n",
    "df.iloc[:,16:].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 4 - First Half Tail__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "      <th>schoolsup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>644</td>\n",
       "      <td>MS</td>\n",
       "      <td>F</td>\n",
       "      <td>19</td>\n",
       "      <td>R</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>services</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>645</td>\n",
       "      <td>MS</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>teacher</td>\n",
       "      <td>NaN</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>646</td>\n",
       "      <td>MS</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>647</td>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>services</td>\n",
       "      <td>services</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>648</td>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>R</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>services</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
       "644     MS   F   19       R     GT3       T   NaN     3  services     other   \n",
       "645     MS   F   18       U     LE3       T   3.0     1   teacher       NaN   \n",
       "646     MS   F   18       U     GT3       T   NaN     1     other     other   \n",
       "647     MS   M   17       U     LE3       T   3.0     1  services  services   \n",
       "648     MS   M   18       R     LE3       T   3.0     2  services     other   \n",
       "\n",
       "     reason guardian  traveltime  studytime  failures schoolsup  \n",
       "644  course   mother           1          3       1.0        no  \n",
       "645  course   mother           1          2       0.0        no  \n",
       "646  course   mother           2          2       0.0        no  \n",
       "647  course   mother           2          1       0.0        no  \n",
       "648  course   mother           3          1       0.0        no  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show tail of first half of feature columns\n",
    "df.iloc[:,0:16].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 5 - Second Half Tail__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>famsup</th>\n",
       "      <th>paid</th>\n",
       "      <th>activities</th>\n",
       "      <th>nursery</th>\n",
       "      <th>higher</th>\n",
       "      <th>internet</th>\n",
       "      <th>romantic</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>644</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>645</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>646</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>647</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>648</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    famsup paid activities nursery higher internet romantic  famrel  freetime  \\\n",
       "644     no   no        yes      no    yes      yes       no       5         4   \n",
       "645    yes   no         no     yes    yes      yes       no       4         3   \n",
       "646     no   no        NaN     yes    yes       no       no       1         1   \n",
       "647     no   no         no      no    yes      yes       no       2         4   \n",
       "648     no   no         no      no    yes      yes       no       4         4   \n",
       "\n",
       "     goout  Dalc  Walc  health  absences  G1  G2  G3  \n",
       "644      2     1     2       5         4  10  11  10  \n",
       "645      4     1     1       1         4  15  15  16  \n",
       "646      1     1     1       5         6  11  12   9  \n",
       "647      5     3     4       2         6  10  10  10  \n",
       "648      1     3     4       5         4  10  11  11  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show tail of second half of feature columns\n",
    "df.iloc[:,16:].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also look the total dataset structure in more detail by apply the folowing code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((649, 33), school                                               [[GP, MS]]\n",
       " sex                                                    [[F, M]]\n",
       " age                          [[18, 17, 15, 16, 19, 22, 20, 21]]\n",
       " address                                                [[U, R]]\n",
       " famsize                                            [[GT3, LE3]]\n",
       " Pstatus                                           [[A, T, nan]]\n",
       " Medu                           [[4.0, 1.0, 3.0, 2.0, nan, 0.0]]\n",
       " Fedu                                          [[4, 1, 2, 3, 0]]\n",
       " Mjob              [[at_home, health, other, services, teacher]]\n",
       " Fjob          [[teacher, other, services, nan, health, at_ho...\n",
       " reason                 [[course, other, home, reputation, nan]]\n",
       " guardian                              [[mother, father, other]]\n",
       " traveltime                                       [[2, 1, 3, 4]]\n",
       " studytime                                        [[2, 3, 1, 4]]\n",
       " failures                            [[0.0, nan, 3.0, 1.0, 2.0]]\n",
       " schoolsup                                           [[yes, no]]\n",
       " famsup                                         [[no, yes, nan]]\n",
       " paid                                                [[no, yes]]\n",
       " activities                                     [[nan, no, yes]]\n",
       " nursery                                             [[yes, no]]\n",
       " higher                                              [[yes, no]]\n",
       " internet                                            [[no, yes]]\n",
       " romantic                                            [[no, yes]]\n",
       " famrel                                        [[4, 5, 3, 1, 2]]\n",
       " freetime                                      [[3, 2, 4, 1, 5]]\n",
       " goout                                         [[4, 3, 2, 1, 5]]\n",
       " Dalc                                          [[1, 2, 5, 3, 4]]\n",
       " Walc                                          [[1, 3, 2, 4, 5]]\n",
       " health                                        [[3, 5, 1, 2, 4]]\n",
       " absences      [[4, 2, 6, 0, 10, 8, 16, 14, 1, 12, 24, 22, 32...\n",
       " G1            [[0, 9, 12, 14, 11, 13, 10, 15, 17, 8, 16, 18,...\n",
       " G2            [[11, 13, 14, 12, 16, 17, 8, 10, 15, 9, 7, 6, ...\n",
       " G3            [[11, 12, 14, 13, 17, 15, 7, 10, 16, 9, 8, 18,...\n",
       " dtype: object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def rstr(df): return df.shape, df.apply(lambda x: [x.unique()])\n",
    "rstr(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The command .info() is also very useful because give information about column data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 649 entries, 0 to 648\n",
      "Data columns (total 33 columns):\n",
      "school        649 non-null object\n",
      "sex           649 non-null object\n",
      "age           649 non-null int64\n",
      "address       649 non-null object\n",
      "famsize       649 non-null object\n",
      "Pstatus       613 non-null object\n",
      "Medu          620 non-null float64\n",
      "Fedu          649 non-null int64\n",
      "Mjob          649 non-null object\n",
      "Fjob          582 non-null object\n",
      "reason        586 non-null object\n",
      "guardian      649 non-null object\n",
      "traveltime    649 non-null int64\n",
      "studytime     649 non-null int64\n",
      "failures      552 non-null float64\n",
      "schoolsup     649 non-null object\n",
      "famsup        597 non-null object\n",
      "paid          649 non-null object\n",
      "activities    519 non-null object\n",
      "nursery       649 non-null object\n",
      "higher        649 non-null object\n",
      "internet      649 non-null object\n",
      "romantic      649 non-null object\n",
      "famrel        649 non-null int64\n",
      "freetime      649 non-null int64\n",
      "goout         649 non-null int64\n",
      "Dalc          649 non-null int64\n",
      "Walc          649 non-null int64\n",
      "health        649 non-null int64\n",
      "absences      649 non-null int64\n",
      "G1            649 non-null int64\n",
      "G2            649 non-null int64\n",
      "G3            649 non-null int64\n",
      "dtypes: float64(2), int64(14), object(17)\n",
      "memory usage: 167.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the dataset has **649** data rows, and **33** atrributes. The data rows are typically called *observations*, *examples* or *instances*, and the columns are also called *attributes* or *features*. See the features acronynms in Figure 1 above.\n",
    "\n",
    "Ordinal features, i.e., categorical features that have an order associated to them, will be considered as numerical attributes. The remainder are the categorical with nominal features (or dtypes object, which number 17 in total). Later on, these categorial features will be processed into numbers (numerical form) to enable them to be used in some learning models. \n",
    "\n",
    "Also, note the presence of *NaN* values in some of categorical attributes in this small overview of the data set.\n",
    "\n",
    "**Note:** the *NaN* values were deliberated imputed in the dataset in order to simulate such a situation. \n",
    "\n",
    "__Figure 6 - Statistical summary for all numerical attributes__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>620.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>16.744222</td>\n",
       "      <td>2.504839</td>\n",
       "      <td>2.306626</td>\n",
       "      <td>1.568567</td>\n",
       "      <td>1.930663</td>\n",
       "      <td>0.219203</td>\n",
       "      <td>3.930663</td>\n",
       "      <td>3.180277</td>\n",
       "      <td>3.184900</td>\n",
       "      <td>1.502311</td>\n",
       "      <td>2.280431</td>\n",
       "      <td>3.536210</td>\n",
       "      <td>3.659476</td>\n",
       "      <td>11.399076</td>\n",
       "      <td>11.570108</td>\n",
       "      <td>11.906009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.218138</td>\n",
       "      <td>1.130418</td>\n",
       "      <td>1.099931</td>\n",
       "      <td>0.748660</td>\n",
       "      <td>0.829510</td>\n",
       "      <td>0.594097</td>\n",
       "      <td>0.955717</td>\n",
       "      <td>1.051093</td>\n",
       "      <td>1.175766</td>\n",
       "      <td>0.924834</td>\n",
       "      <td>1.284380</td>\n",
       "      <td>1.446259</td>\n",
       "      <td>4.640759</td>\n",
       "      <td>2.745265</td>\n",
       "      <td>2.913639</td>\n",
       "      <td>3.230656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age        Medu        Fedu  traveltime   studytime    failures  \\\n",
       "count  649.000000  620.000000  649.000000  649.000000  649.000000  552.000000   \n",
       "mean    16.744222    2.504839    2.306626    1.568567    1.930663    0.219203   \n",
       "std      1.218138    1.130418    1.099931    0.748660    0.829510    0.594097   \n",
       "min     15.000000    0.000000    0.000000    1.000000    1.000000    0.000000   \n",
       "25%     16.000000    2.000000    1.000000    1.000000    1.000000    0.000000   \n",
       "50%     17.000000    2.000000    2.000000    1.000000    2.000000    0.000000   \n",
       "75%     18.000000    4.000000    3.000000    2.000000    2.000000    0.000000   \n",
       "max     22.000000    4.000000    4.000000    4.000000    4.000000    3.000000   \n",
       "\n",
       "           famrel    freetime       goout        Dalc        Walc      health  \\\n",
       "count  649.000000  649.000000  649.000000  649.000000  649.000000  649.000000   \n",
       "mean     3.930663    3.180277    3.184900    1.502311    2.280431    3.536210   \n",
       "std      0.955717    1.051093    1.175766    0.924834    1.284380    1.446259   \n",
       "min      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "25%      4.000000    3.000000    2.000000    1.000000    1.000000    2.000000   \n",
       "50%      4.000000    3.000000    3.000000    1.000000    2.000000    4.000000   \n",
       "75%      5.000000    4.000000    4.000000    2.000000    3.000000    5.000000   \n",
       "max      5.000000    5.000000    5.000000    5.000000    5.000000    5.000000   \n",
       "\n",
       "         absences          G1          G2          G3  \n",
       "count  649.000000  649.000000  649.000000  649.000000  \n",
       "mean     3.659476   11.399076   11.570108   11.906009  \n",
       "std      4.640759    2.745265    2.913639    3.230656  \n",
       "min      0.000000    0.000000    0.000000    0.000000  \n",
       "25%      0.000000   10.000000   10.000000   10.000000  \n",
       "50%      2.000000   11.000000   11.000000   12.000000  \n",
       "75%      6.000000   13.000000   13.000000   14.000000  \n",
       "max     32.000000   19.000000   19.000000   19.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few observations from the output of `describe()`:\n",
    "\n",
    "- Missing data appears in 'Medu' (29) and 'failures' (97). \n",
    "- Note that we do not know about non-numerical data atributes.\n",
    "- all numeric data are positive numbers\n",
    "\n",
    "__Missing Values:__\n",
    "\n",
    "A more effective way to find the total complement of missing values is to view the sum of each NULL of NaaN data instance for each feature using `isnull()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school          0\n",
       "sex             0\n",
       "age             0\n",
       "address         0\n",
       "famsize         0\n",
       "Pstatus        36\n",
       "Medu           29\n",
       "Fedu            0\n",
       "Mjob            0\n",
       "Fjob           67\n",
       "reason         63\n",
       "guardian        0\n",
       "traveltime      0\n",
       "studytime       0\n",
       "failures       97\n",
       "schoolsup       0\n",
       "famsup         52\n",
       "paid            0\n",
       "activities    130\n",
       "nursery         0\n",
       "higher          0\n",
       "internet        0\n",
       "romantic        0\n",
       "famrel          0\n",
       "freetime        0\n",
       "goout           0\n",
       "Dalc            0\n",
       "Walc            0\n",
       "health          0\n",
       "absences        0\n",
       "G1              0\n",
       "G2              0\n",
       "G3              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features, both numeric and categorical, that have missing data are: 'Pstatus'(36), 'Medu'(29), 'Fjob'(67), 'reason'(63), 'failures'(97), 'famsup'(52), 'activities'(130).\n",
    "\n",
    "The impact of such missing data within each feature can be determined by their percentage distribution.\n",
    "\n",
    "__Percentage of Missing Values:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school         0.0\n",
       "sex            0.0\n",
       "age            0.0\n",
       "address        0.0\n",
       "famsize        0.0\n",
       "Pstatus        6.0\n",
       "Medu           4.0\n",
       "Fedu           0.0\n",
       "Mjob           0.0\n",
       "Fjob          10.0\n",
       "reason        10.0\n",
       "guardian       0.0\n",
       "traveltime     0.0\n",
       "studytime      0.0\n",
       "failures      15.0\n",
       "schoolsup      0.0\n",
       "famsup         8.0\n",
       "paid           0.0\n",
       "activities    20.0\n",
       "nursery        0.0\n",
       "higher         0.0\n",
       "internet       0.0\n",
       "romantic       0.0\n",
       "famrel         0.0\n",
       "freetime       0.0\n",
       "goout          0.0\n",
       "Dalc           0.0\n",
       "Walc           0.0\n",
       "health         0.0\n",
       "absences       0.0\n",
       "G1             0.0\n",
       "G2             0.0\n",
       "G3             0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df.apply(lambda x: x.isnull().sum()/len(x)*100, axis=0),)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the missing values for 'Fjob', 'reason' 'failures', and 'activities' represent approximately more than 10% of their own data. Also, note except for 'Medu' and 'failures', all the other missing data are presented in nominal features.\n",
    "\n",
    "## c) Distribution Analysis for Numerical Attributes\n",
    "\n",
    "__Figure 7 - Histogram for all numeric attributes__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w4pYkdZ9n4DWuquo7wNP3KHvtkMKRNED7TAhVVQG72pv7tz8FvBT4xbZ8E/BOmoTQie11gI8B/yNJ2ueRpGVTVafOUXzBY9T/beC3BxeRJEmSJI2HvtYQSrIfcCPwbOAPgK8C91fV7JYtvbv1HArcBVBVu5PspMkwf3OP51zULj797hQzDruVdG1XFdsz2rrWHkkaV0kOBD4EPI/mJNsbgK8AHwXW0IxufM2eWzpLkiQtp74SQu20iiPbA5jLgR+bq1p72ddOPovdxaffnWLcJWbl2Z7R1rX2SNIYOw/4VFW9ul2D8UnA24Frqmpjkg3ABuCcYQYpSZK6bZ+LSveqqvuBGeDFwIFJZjMzvbv1/GAnn/b+pwL3LkewkiRJ4yzJDwM/Qzu9taq+3x5fnUgzBZ/28qThRChJkibFPofaJHkG8E9VdX+SA4CX0SwU/Wng1cClwOnAFe1Drmxvf7a9/69dP0iSJAmAfw58A/jjJM+nmZL/FmBqdkvnqtqe5JC5HrzYKfddnTY8yu3qZ4mDx9LvMgmjZl/vxyi/Z5I0afqZMrYa2NSuI/Q44LKq+vMkXwIuTfJfgL/nkYVcLwD+JMntNCODThlA3JIkTazNd+/kjD52MNqy8YQViEYL9HjghcB/qKrrk5xHMz2sL4udct/VacOj3K5+/kcfy/q1u/taJmHU7GvZhlF+zyRp0vSzy9gXgBfMUX4HcNQc5f8InLws0UmSpLG0kC23Lzx21QAjGTlbga1VdX17+2M0CaF7kqxuRwetBnYMLUJJkjQRFrSGkCRJkhavqv4vcFeS57RFxwBf4pEp9/DoqfiSJEkDMX7jUCVJksbbfwAubncYuwN4Pe20/CRnAnfiaGtJkjRgJoQkSZJWUFXdDKyb465jVjoWSZI0uZwyJkmSJEmSNGFMCEmSJEmSJE0YE0KSJEmSJEkTxoSQJEmSJEnShDEhJEmSJEmSNGFMCEmSJEmSJE0Yt52XJEmSJEljac2GT/ZVb8vGEwYcyfgxISRJkiRJAiDJFuAB4CFgd1WtS3IQ8FFgDbAFeE1V3TesGCUtD6eMSRprST6cZEeSW3rKDkpydZLb2sunteVJ8vtJbk/yhSQvHF7kkiRJI+slVXVkVa1rb28ArqmqI4Br2tuSxpwJIUnj7kLg2D3K5jtoOQ44ov05C3j/CsUoSZI0zk4ENrXXNwEnDTEWScvEKWOSxlpVXZtkzR7FJwLT7fVNwAxwTlt+UVUVcF2SA5OsrqrtKxOtJEnSyCvgr5IU8EdVdT4wNXu8VFXbkxyy54OSnEVzwo2pqSlmZmb6erFdu3b1XXecjHK71q/dvaTHTx2w9OcYhn29H6P8ng3KPhNCSQ4HLgKeCTwMnF9V5803jzRJgPOA44HvAGdU1U2DCV+S5jTfQcuhwF099ba2ZY9KCHlAs7AP+nFo86S2Z5htWciBYpf+dySpA46uqm3t8dPVSb7cz4PaxNH5AOvWravp6em+XmxmZoZ+646TUW7XGX0uwjyf9Wt3c+7m8RtbsuW06ce8f5Tfs0Hp513cDayvqpuSPAW4McnVwBk0UzI2JtlAMyXjHB49JeNFNFMyXjSI4CVpgTJHWe1V4AEN77v4ir4/6Pf14ToKJrU9w2zLQg42Lzx2VWf+dyRp3FXVtvZyR5LLgaOAe2ZHVSdZDewYapCSlsU+1xCqqu2zI3yq6gHgVpoz6vPNI/3BlIyqug44sO00JGml3DPb7+xx0LIVOLyn3mHAthWOTZIkaSQlWdUOAiDJKuBngVuAK4HT22qnA1cMJ0JJy2lB47zadTpeAFzPkKZkjMMw+X51bYi87RltXWvPPswetGzk0QctVwJnJ7mUZuTiTtcPkjQMSfYDbgDurqpXJHkWcClwEHAT8Nqq+v4wY9Tc1ixxqoU04qaAy5tVQHg88JGq+lSSzwOXJTkTuBM4eYgxSlomfSeEkjwZ+Djw1qr6dttJzFl1jrJlm5IxDsPk+9Wl6SVge0Zd19ozK8klNAtIH5xkK/AOmkTQXActV9Gsb3Y7zRpnr1/xgCWp8RaaUdc/3N7+HeC9VXVpkg8AZ+JOiJJWWFXdATx/jvJvAcesfESSBqmvhFCS/WmSQRdX1Sfa4vnmkTolQ9KKqapT57lrr4OWdnexNw82Ikl6bEkOA04Afhv4tXZDjpcCv9hW2QS8ExNCkiRpgPrZZSzABcCtVfWenruckiFJkrRwvwf8BvCU9vbTgfuranZO/Ox0+724C+KjDaNdK7XVsts6S5IGrZ8RQkcDrwU2J7m5LXs7TsmQJElakCSvAHZU1Y1JpmeL56i613R7cBfEPQ2jXUvdrrlfbussSRq0fX7KVNVnmPtABZySIUmStBBHA69McjzwRJo1hH6PZlfWx7ejhJxuL0mSBm78TjtIkiSNqap6G/A2gHaE0K9X1WlJ/gx4Nc1OY27pLElaMHdB1EI9btgBSJIkiXNoFpi+nWZNoQuGHI8kSeo4RwhJkiQNQVXNADPt9TuAo4YZj7Qc9jVCYf3a3Zyx4ZNs2XjCCkUkSQ37p705QkiSJEmSJGnCOEJIkiRJE811NyRJk8gRQpIkSZIkSRPGhJAkSZIkSdKEMSEkSZIkSZI0YUwISZIkSZIkTRgTQpIkSZIkSRPGhJAkSZIkSdKEMSEkSZIkSZI0YUwISZIkSdKES3J4kk8nuTXJF5O8pS1/Z5K7k9zc/hw/7FglLY/HDzuASbb57p2cseGTfdXdsvGEAUcjdUuSLcADwEPA7qpal+Qg4KPAGmAL8Jqqum9YMUqSJI2Q3cD6qropyVOAG5Nc3d733qr63SHGJmkAHCEkqcteUlVHVtW69vYG4JqqOgK4pr0tSZI08apqe1Xd1F5/ALgVOHS4UUkapH2OEEryYeAVwI6qel5bNudZ9iQBzgOOB74DnDHbqUjSCDgRmG6vbwJmgHOGFYwkSdIoSrIGeAFwPXA0cHaS1wE30Iwi2muEdZKzgLMApqammJmZ6eu1du3a1XfdcTKMdq1fu3tFXmfqgJV7rZU02673XXxF349Ze+hTBxjR4PUzZexC4H8AF/WUzZ5l35hkQ3v7HOA44Ij250XA+9tLSVppBfxVkgL+qKrOB6aqajs0Z8GSHDLXAz2gWdgH/Ti0eVLbM8y2LORAsUv/O/uS5HCaY6pnAg8D51fVeU5plTQqkjwZ+Djw1qr6dpL3A++iObZ6F3Au8IY9H9cea50PsG7dupqenu7r9WZmZui37jgZRrv6XY5kqdav3c25m7u3+sxi2rXltOnBBLNC9tnaqrq2zRD3mu8s+4nARVVVwHVJDkyyevYLmCStoKOralub9Lk6yZf7faAHNPC+i6/o+wNxHD4IJ7U9w2zLQg5KLzx2VWf+d/ow3xodZzD3yTapk9YsoI9wLc2Vk2R/mmTQxVX1CYCquqfn/g8Cfz6k8CQts8Wm9eY7y34ocFdPva1t2V4JocWegR+Hs6L96toZ666d4bU9462qtrWXO5JcDhwF3DObpE6yGtgx1CAlTZz2+Gn2GOqBJLNrdDilVdJQtct/XADcWlXv6SnvPcH/KuCWYcQnafkt9zivzFFWc1Vc7Bn4cTgr2q+unbHu0ugIsD3jLMkq4HHtl61VwM8CvwVcCZwObGwv+58gLEnLbI81OpzSugjL1a5RXAuj62t0LEQX/3ZH1NHAa4HNSW5uy94OnJrkSJrvdVuANw4nPEnLbbEJofnOsm8FDu+pdxiwbSkBStIiTAGXNye6eDzwkar6VJLPA5clORO4Ezh5iDFKmmBzrNHR1+Oc0vpoy9WulVp3YyFco+MR43BitAuq6jPMfYL/qpWORdLKWOynzHxn2a+kWYH+UprFpHe6fpCklVZVdwDPn6P8W8AxKx+RJD1irjU6cEqrJEljp9/10EZ1LbTH7atCkkuAzwLPSbK1PbO+EXh5ktuAl7e3ocke3wHcDnwQeNNAopYkSRpD863RwSMn28AprZIkaQX0s8vYqfPctddZ9nZ3sTcvNShJkqSOmm+Njo04pVWa07ifgZekUdW9icmSJEkj6jHW6ACntEqSpBVkQkiSJEmSpBHU7wg5aTH2uYaQJEmSJEmSusURQpIkSZIkSQMyqmuhOUJIkiRJkiRpwpgQkiRJkiRJmjAmhCRJkiRJkiaMawhp2Wy+eydn9DE3cqXnRUqSpMnk7jySJM3PEUKSJEmSJEkTxhFCkiRJksbeqO7iI0mjyoSQJEmSJGkoJnXZCae0ahQ4ZUySJEmSJGnCOEJIkiRJY6P3rPr6tbv7GlkgSZL2ZkJImsc4DF9dyFDTC49dNcBIJEmSJEnjZGAJoSTHAucB+wEfqqqNg3otSVqIQfVP45BElDTaPH6SNKrsn/oze8LWEYwaBwNJCCXZD/gD4OXAVuDzSa6sqi8N4vUkqV/2T5JG1aT3Ty6wKo2uSe+fpK4a1Aiho4Dbq+oOgCSXAicCdhiShs3+SdKoGlj/5AhG6RELST76P/EDHj9JK2Cl+6dU1ZKfZK8nTV4NHFtVv9Tefi3woqo6u6fOWcBZ7c3nAF/p8+kPBr65jOEOU5faArZn1PXbnh+tqmcMOphhsX/qW5faArZn1Nk/Yf+0SF1tF3S3bV1tl/2T/dOeutou6G7butquefunQY0Qyhxlj8o8VdX5wPkLfuLkhqpat9jARkmX2gK2Z9R1rT1LYP/Uhy61BWzPqOtae5bA/mmButou6G7butquCWD/tEBdbRd0t21dbddjedyAnncrcHjP7cOAbQN6LUlaCPsnSaPK/knSqLJ/kjpoUAmhzwNHJHlWkicApwBXDui1JGkh7J8kjSr7J0mjyv5J6qCBTBmrqt1Jzgb+kmZbwg9X1ReX6ekXPAxxhHWpLWB7Rl3X2rMo9k9961JbwPaMuq61Z1Hsnxalq+2C7ratq+3qNPunRelqu6C7betqu+Y1kEWlJUmSJEmSNLoGNWVMkiRJkiRJI8qEkCRJkiRJ0oQZi4RQkg8n2ZHklmHHshySHJ7k00luTfLFJG8ZdkxLkeSJST6X5B/a9vzmsGNaqiT7Jfn7JH8+7FiWKsmWJJuT3JzkhmHH00VJjk3ylSS3J9kw7HiWwv52tNnfaqG61D/tqUufb3P1vUkOSnJ1ktvay6cNM8bFmKdd70xyd/u+3Zzk+GHGqOGxfxoP9k/dNhYJIeBC4NhhB7GMdgPrq+rHgBcDb07y3CHHtBTfA15aVc8HjgSOTfLiIce0VG8Bbh12EMvoJVV1ZFWtG3YgXZNkP+APgOOA5wKnjvn/84XY344y+1v1rYP901y68vl2IXv3vRuAa6rqCOCa9va4uZC5P1Pe275vR1bVVSsck0aA/dNYuRD7p84ai4RQVV0L3DvsOJZLVW2vqpva6w/QHAgfOtyoFq8au9qb+7c/Y7taeZLDgBOADw07Fo2Fo4Dbq+qOqvo+cClw4pBjWjT729Fmf6sF6lT/1GXz9L0nApva65uAk1Y0qGXQtc8ULSv7pzFh/9RtY5EQ6rIka4AXANcPN5KlaYf83wzsAK6uqnFuz+8BvwE8POxAlkkBf5XkxiRnDTuYDjoUuKvn9lbGOOHQZfa3I6lr/e2o6Xr/1PXPt6mq2g5Nchs4ZMjxLKezk3yhnbIxdlNNtCzsn8ab/VNHmBAaoiRPBj4OvLWqvj3seJaiqh6qqiOBw4Cjkjxv2DEtRpJXADuq6sZhx7KMjq6qF9IMyX1zkp8ZdkAdkznKxnbERlfZ346ejva3o6br/ZOfb+Pp/cC/oJn2uh04d7jhaEjsnzSKJq5/MiE0JEn2p/lycnFVfWLY8SyXqrofmGF81yA5Gnhlki00Q1dfmuRPhxvS0lTVtvZyB3A5zRBdLZ+twOE9tw8Dtg0pFs3B/nZkda6/HUGd7p8m4PPtniSrAdrLHUOOZ1lU1T1tYvth4IN0731Tf+yfxpv9U0eYEBqCJAEuAG6tqvcMO56lSvKMJAe21w8AXgZ8ebhRLU5Vva2qDquqNcApwF9X1b8bcliLlmRVkqfMXgd+FujE7lEj5PPAEUmeleQJNH83Vw45JrXsb0dX1/rbEdXZ/mlCPt+uBE5vr58OXDHEWJbN7JfI1qvo3vum/tg/jTf7p454/LAD6FSHtNMAACAASURBVEeSS4Bp4OAkW4F3VNUFw41qSY4GXgtsbteBAHj7GK9ivhrY1O4W8Djgsqpy++DRMAVc3nwn5vHAR6rqU8MNqVuqaneSs4G/BPYDPlxVXxxyWItmfzvy7G/Vt671T3vo1OfbXH0vsBG4LMmZwJ3AycOLcHHmadd0kiNppgdtAd44tAA1NPZP48P+qdtS1aWpmpIkSZIkSdoXp4xJkiRJkiRNGBNCkqSJlaSSPHvYcUhSryTvdJF1SQuV5Iwknxl2HBofJoS0YElOSXJ9kgeT7GivvymNtya5I8m3k2xL8t4kY7FWlaTRlmRLku8nOXiP8pvbxM6a4UQmSZDkbUmu2qPstnnKTlnZ6CRJ2psJIS1IkvXAecB/B55Js2jar9As3PoE4H8BL6yqHwaeBzwf+NXhRCupg74GnDp7I8la4IDhhSNJP3AtcHS76DtJngnsD7xwj7Jnt3UlSRoqE0LqW5KnAr8FvKmqPlZVD1Tj76vqtKr6XlV9tarun30I8DDNgY8kLYc/AV7Xc/t04KLZG0l+KMnvJrkzyT1JPtBuzz57/39Ksr0dwfiG3idOMpPkl3puO+xa0kJ8niYBdGR7+2eATwNf2aPsq1W1Lcl5Se5qR1XfmOSn53viJD+V5O+S3N8+5owBtkPSiEuyIclXkzyQ5EtJXvXou/O+JDuTfDnJMT13nNHO5nggydeSnNZz3xuS3JrkviR/meRHe+6rJL/SjnC8L8kfpN1Grb3/l9vHzsbzwrb8R5J8PMk32tf71Z7HHJXkhrYPvCfJewb2C9O8TAhpIf418EPAFY9VKckvJvk28E2aEUJ/tAKxSZoM1wE/nOTH2jPuvwD0rrPxO8C/pPny9WzgUOA/AyQ5Fvh14OXAEcDLVjBuSR1XVd8HrqdJ+tBe/i3wmT3KZkcHfZ6mrzoI+AjwZ0meuOfzJvlnwF8A7wOe0T7m5sG0QtKY+Crw08BTgd8E/jTJ6va+FwF3AAfTbKX+iSQHJVkF/D5wXFU9BfhJ2r4kyUnA24F/S9PP/C1wyR6v+QrgJ2i+370G+Ln2sScD76Q5YffDwCuBbyV5HM3skX+gOR47Bnhrkp9rn+884Lx2Zsm/AC5bjl+MFsaEkBbiYOCbVbV7tqDnbNV3k/wMQFV9pP3H/pfAB4B7hhOupI6aHSX0cuDLwN1teYBfBv5jVd1bVQ8A/xWYXavjNcAfV9UtVfUgzcGLJC2nv+GR5M9P03yp+ts9yv4GoKr+tKq+VVW7q+pcmpNuz5njOU8D/ndVXVJV/9Q+xoSQNMGq6s+qaltVPVxVHwVuA45q794B/F7bX3yUZpTiCe19DwPPS3JAVW2vqi+25W8E/ltV3dp+1/uvwJG9o4SAjVV1f1XdSTP6cXbk4y8B766qz7ezR26vqq/TJI+eUVW/VVXfr6o7gA/yyHHZPwHPTnJwVe2qquuW+dekPpgQ0kJ8Czi4d5HoqvrJqjqwve9Rf09VdRvwReAPVzRKSV33J8AvAmfQM12M5ozWk4Ab20T1/cCn2nKAHwHu6qn/9cGHKmnCXAv8VJKn0XwRug34O+An27LntXVIsr6dYrGz7a+eSnPybU+H04wGkCQAkryu3VRj9njneTzSf9xdVdVT/evAj7Qnw36BZv3X7Uk+meT/aev8KHBez/PdS3Oi7dCe5/m/Pde/Azy5vT5fH/WjwI/MPmf7vG+nWYMW4EyaAQRfTvL5JK9Y8C9CS2ZCSAvxWeB7wIkLeMzjaYYAStKyaM86fQ04HvhEz13fBL4L/HhVHdj+PLWqZg9YttMctMz6Z3s89YM0CaVZz1zeyCVNgM/SJHbOAv4/gKr6NrCtLdtWVV9r1ws6h2bk4tPak2s7ab6A7ekuPJaS1GpH7XwQOBt4ett/3MIj/cehvev70BzvbAOoqr+sqpcDq2lGWX+wrXMX8Mae46cDq+qAqvq7PkKar4+6C/jaHs/5lKo6vo3ltqo6FTiEZsr/x9ppbVpBJoTUt3ax6N8E/jDJq5M8OcnjkhwJrAJI8ktJDmmvPxd4G3DN0IKW1FVnAi9tz3bNepjmwOa9Pf3QoT1z1S8Dzkjy3CRPoplX3+tm4N8meVKSZ7evIUl9q6rvAjcAv0YzVWzWZ9qy2fWDngLsBr4BPD7Jf6ZZe2MuFwMvS/KaJI9P8vT22EvSZFoFFE3/QZLX04wQmnUI8KtJ9m/X9/kx4KokU0le2SZdvgfsAh5qH/MB4G1Jfrx9zqe2j+3Hh4BfT/Kv0nh2m7T6HPDtJOckOSDJfkmel+Qn2tf4d0meUVUPA7ObEj00z2toQEwIaUGq6t00BzS/QTM/9R6aRaPPoRkSfTSwOcmDwFXtz9uHE62krmp3NLxhjrvOAW4HrmsXt//ftGtyVNVfAL8H/HVb56/3eOx7ge/T9GubaL6ESdJC/Q3NF7LeXQr/ti2bTQj9Jc1C0f+HZjrHP/LoKa0/0K7XcTywnmYax800i7pKmkBV9SXgXJoRifcAa2lHJLaup9k845vAbwOvrqrZ5T3W04wWuhf4N8Cb2ue8nGaUzqXt8dMtwHF9xvNn7et8BHgA+J/AQVX1EPDzNGsNfa2N50M0oygBjgW+mGQXzQLTp1TVPy7w16ElyqOnF0qSJEmSJKnrHCEkSZIkSZI0YUwISZIkSZIkTRgTQpIkSZIkSRPGhJAkSZIkSdKEefywAwA4+OCDa82aNX3VffDBB1m1atVgAxqCrrYLutu2rrbrxhtv/GZVPWPYcYyKSe2futQWsD2jrt/22D89mv1TN9ie0Wb/tDiT2j8t1qT/Dia9/TDY38Fj9U8jkRBas2YNN9ww1+7Be5uZmWF6enqwAQ1BV9sF3W1bV9uV5OvDjmGUTGr/1KW2gO0Zdf22x/7p0eyfusH2jDb7p8WZ1P5psSb9dzDp7YfB/g4eq39yypgkSZIkSdKEMSEkSZIkSZI0YUwISZIkSZIkTRgTQpIkSZIkSRNmJBaV1mRZs+GTfdfdsvGEAUYiaVJsvnsnZ/TZ99jvqIv6/R/w71/SSrN/koZnnyOEknw4yY4kt/SUHZTk6iS3tZdPa8uT5PeT3J7kC0leOMjgJUmSJEmStHD9TBm7EDh2j7INwDVVdQRwTXsb4DjgiPbnLOD9yxOmJEmSJEmSlss+E0JVdS1w7x7FJwKb2uubgJN6yi+qxnXAgUlWL1ewkiRJkiRJWrrFriE0VVXbAapqe5JD2vJDgbt66m1ty7bv+QRJzqIZRcTU1BQzMzN9vfCuXbv6rjtOutou2Ltt69fu7vuxo/w76fJ7JkmSJEnqtuVeVDpzlNVcFavqfOB8gHXr1tX09HRfLzAzM0O/dcdJV9sFe7et34VdAbacNr3POsPS5fdMkiRJktRti912/p7ZqWDt5Y62fCtweE+9w4Btiw9PkiRJkiRJy22xI4SuBE4HNraXV/SUn53kUuBFwM7ZqWXqvvm2k1+/dveCRgVJkiRJkqTB2mdCKMklwDRwcJKtwDtoEkGXJTkTuBM4ua1+FXA8cDvwHeD1A4hZkiRJkiRJS7DPhFBVnTrPXcfMUbeANy81KEmSNN7mGzU6lwuPXTXASCRJkjSXxa4hJEmSJEmSpDFlQkiSJEmSJGnCmBCSJEmSJEmaMCaEJI2tJB9OsiPJLT1l70xyd5Kb25/je+57W5Lbk3wlyc8NJ2pJkiRJGj4TQpLG2YXAsXOUv7eqjmx/rgJI8lzgFODH28f8YZL9VixSSZIkSRohJoQkja2quha4t8/qJwKXVtX3quprwO3AUQMLTpIkSZJG2D63nZekMXR2ktcBNwDrq+o+4FDgup46W9uyvSQ5CzgLYGpqipmZmb5edNeuXX3XHXVdagvA1AGwfu3uvuqOQ7vH4f3p9/cN49EeSZKkrjEhJKlr3g+8C6j28lzgDUDmqFtzPUFVnQ+cD7Bu3bqanp7u64VnZmbot+6o61JbAN538RWcu7m/j7wtp00PNphlMA7vzxkbPtl33QuPXTXy7VmoJIcDFwHPBB4Gzq+q85IcBHwUWANsAV5TVfclCXAecDzwHeCMqrppGLFLkqTJ4JQxSZ1SVfdU1UNV9TDwQR6ZFrYVOLyn6mHAtpWOT9LE2E0zQvHHgBcDb27XMtsAXFNVRwDXtLcBjgOOaH/OokluS5IkDYwJIUmdkmR1z81XAbM7kF0JnJLkh5I8i+ZL1+dWOj5Jk6Gqts+O8KmqB4BbaaapnghsaqttAk5qr58IXFSN64AD9+jPJEmSlpVTxiSNrSSXANPAwUm2Au8AppMcSTMdbAvwRoCq+mKSy4Av0Zy5f3NVPTSMuCVNliRrgBcA1wNTVbUdmqRRkkPaaocCd/U8bHads+17PNei1jjrdx2tcVjLqWtrTtme0da19oBTWiU9woSQpLFVVafOUXzBY9T/beC3BxeRJD1akicDHwfeWlXfbr5XzV11jrK91jlb7Bpn/a6j5RpaK8/2jLautac1O6X1piRPAW5McjVwBs2U1o1JNtBMaT2HR09pfRHNlNYXDSVyScvKhJA6YU2fi5du2XjCgCORJKmRZH+aZNDFVfWJtvieJKvb0UGrgR1tueucSVoR7SjF2ZGKDyTpndI63VbbBMzQJIR+MKUVuC7JgbP92ErHLml5mRCSJElaZu0UiwuAW6vqPT13XQmcDmxsL6/oKT87yaU0Z953+mVL0qA5pXU0dHFq4kJMevtheL8DE0KSpL1svntn39uGO/JOmtPRwGuBzUlubsveTpMIuizJmcCdwMntfVfRrM9xO80aHa9f2XAlTRqntI6Ojk5N7Nuktx+G9zswISRJ0pjpN2Fnsm54quozzP0lCuCYOeoX8OaBBiVJLae0SoIlbDuf5DlJbu75+XaStyZ5Z5K7e8qPX86AJUmSJEmL08eUVth7Suvr0ngxTmmVOmPRI4Sq6ivAkQBJ9gPuBi6nGeL83qr63WWJUJIkSZK0XJzSKglYviljxwBfraqvP8bcU0mSJEnSEDmlVdKs5UoInQJc0nP77CSvA24A1lfVfXs+YLGr0Hd1BfIutGu+3QH63TlgLv3+Tvp9/uX8HXfhPZMkSZIkTaYlJ4SSPAF4JfC2tuj9wLtoVp5/F3Au8IY9H7fYVei7ugJ5F9o13wKn69fu7mvngLn0u5tA37shLePuBF14zyRJkiRJk2nRi0r3OA64qaruAaiqe6rqoap6GPggcNQyvIYkSZIkSZKWyXJMGTuVnulis1sVtjdfBdyyDK+hCbWmz5E/kiRJkiSpf0tKCCV5EvBy4I09xe9OciTNlLEte9wnSZIkSZKkIVtSQqiqvgM8fY+y1y4pIkmSJEmSJA3UcqwhJEmSJEmSpDFiQkiSJEmSJGnCmBCSJEmSJEmaMCaEJEmSJEmSJowJIUmSJEmSpAljQkiSJEmSJGnCLGnbeWncrNnwyb7rbtl4wgAjkSRJi7H57p2c0efnuZ/lkiTNzxFCkiRJkiRJE8aEkKSxluTDSXYkuaWn7KAkVye5rb18WlueJL+f5PYkX0jywuFFLkmSJEnDY0JI0ri7EDh2j7INwDVVdQRwTXsb4DjgiPbnLOD9KxSjJEmSJI0UE0KSxlpVXQvcu0fxicCm9vom4KSe8ouqcR1wYJLVKxOpJEmSJI0OF5WW1EVTVbUdoKq2JzmkLT8UuKun3ta2bHvvg5OcRTOCiKmpKWZmZvp60V27dvVdd9RNHQDr1+7uq+44tHlS2zPMtvT7+4Zu/e9IkiSNCxNCkiZJ5iirvQqqzgfOB1i3bl1NT0/39eQzMzP0W3fUve/iKzh3c38fEVtOmx5sMMtgUtszzLb0uwsUwIXHrurM/44kSdK4cMqYpC66Z3YqWHu5oy3fChzeU+8wYNsKxyZJkiRJQ2dCSFIXXQmc3l4/Hbiip/x17W5jLwZ2zk4tkyRJkqRJ4pQxSWMtySXANHBwkq3AO4CNwGVJzgTuBE5uq18FHA/cDnwHeP2KByxJkiRJI8CEkKSxVlWnznPXMXPULeDNg41IkiRJkkbfkhJCSbYADwAPAbural2Sg4CPAmuALcBrquq+pYUpSZIkSZKk5bIcawi9pKqOrKp17e0NwDVVdQRwTXtbkiRJkiRJI2IQU8ZOpFnPA2ATMAOcM4DXkSRJksba5rt3csaGT/ZVd8vGEwYcjSRpkiw1IVTAXyUp4I+q6nxganbXnqranuSQuR6Y5CzgLICpqSlmZmb6esFdu3b1XXecdKFd69funrN86oD57xtl+3o/uvCeSZIkSZIm01ITQkdX1bY26XN1ki/3+8A2eXQ+wLp162p6erqvx83MzNBv3XHShXbNd3Zr/drdnLt5DNcv3/zgY969fu1DnPuZBz1bJ0mSJEkaO0v6ll5V29rLHUkuB44C7kmyuh0dtBrYsQxxSpIkSZIm1Jo+p1aC0yulfi16Uekkq5I8ZfY68LPALcCVwOlttdOBK5YapCRJkiRJkpbPUkYITQGXJ5l9no9U1aeSfB64LMmZwJ3AyUsPU5IkSZIkSctl0QmhqroDeP4c5d8CjllKUJIkSeMuyYeBVwA7qup5bdlBwEeBNcAW4DVVdV+aM2znAccD3wHOqKqbhhG3JEmaDIueMiZJkqTHdCFw7B5lG4BrquoI4Jr2NsBxwBHtz1nA+1coRkkTKMmHk+xIcktP2UFJrk5yW3v5tLY8SX4/ye1JvpDkhcOLXNJyMiEkSZI0AFV1LXDvHsUnApva65uAk3rKL6rGdcCB7eYckjQIF2LCWpp4Y7gXuCRJ0tiaqqrtAO2OrIe05YcCd/XU29qWbe99cJKzaL6QMTU1xczMTH8vegCsX7t7n/X6fb5h6rctYHuGYdeuXWMRZ7+61p5ZVXVtkjV7FJ8ITLfXNwEzwDn0JKyB65IcOLur9MpEK2lQTAhJkiQNX+Yoq70Kqs4HzgdYt25dTU9P9/Xk77v4Cs7dvO/Dvi2n9fd8w9RvW8D2DMPMzAz9/l2Og661Zx9GOmG9EOOWxOtq4rFfk95+GN7vwISQJEnSyrln9sx6OyVsR1u+FTi8p95hwLYVj06S9jYSCeuFGIfkaa8JSzzuZdLbD8P7HbiGkCRJ0sq5Eji9vX46cEVP+evaxVtfDOx0OoakFXbP7NplJqylyeAIIUmSpAFIcgnNehwHJ9kKvAPYCFyW5EzgTuDktvpVNFvO306z7fzrVzxgSZNuNmG9kb0T1mcnuRR4EWOQsF6z4ZN91duy8YQBRyKNNhNCkiRJA1BVp85z1zFz1C3gzYONSBq8zXfv5Iw+voz7RXy4TFhLAhNC0pL1ewYCPPiRJEnS8JmwlgQmhCbWQpIYkiRJkiSpW1xUWpIkSZIkacKYEJIkSZIkSZowThmT1ElJtgAPAA8Bu6tqXZKDgI8Ca4AtwGuq6r5hxShJkiRJw+IIIUld9pKqOrKq1rW3NwDXVNURwDXtbUmSJEmaOCaEJE2SE4FN7fVNwElDjEWSJEmShsYpY9IK6nd3N7enXxYF/FWSAv6oqs4HpqpqO0BVbU9yyFwPTHIWcBbA1NQUMzMzfb3grl27+q476qYOgPVrd/dVdxzaPKntGWZb+v19Q7f+dyRJksbFohNCSQ4HLgKeCTwMnF9V5yV5J/DLwDfaqm+vqquWGqgkLdDRVbWtTfpcneTL/T6wTR6dD7Bu3bqanp7u63EzMzP0W3fUve/iKzh3c38fEVtOmx5sMMtgUtszzLac0WcCHODCY1d15n9HkiRpXCxlhNBuYH1V3ZTkKcCNSa5u73tvVf3u0sOTpMWpqm3t5Y4klwNHAfckWd2ODloN7BhqkJIkSZI0JIteQ6iqtlfVTe31B4BbgUOXKzBJWqwkq9pENUlWAT8L3AJcCZzeVjsduGI4EUqSJEnScC3LGkJJ1gAvAK4HjgbOTvI64AaaUUR7bevsGh2PttLtWsjaDku1kLU7xskg29XFv/EVNgVcngSafu4jVfWpJJ8HLktyJnAncPIQY5QkSdIQub6nJt2SE0JJngx8HHhrVX07yfuBd9Es6Pou4FzgDXs+zjU6Hm2l27WQtR2Wav3a3X2v3TFOBtmucVjDZJRV1R3A8+co/xZwzMpHJEmSJEmjZUnbzifZnyYZdHFVfQKgqu6pqoeq6mHggzTrdkiSJEmSJGlELDohlGYuxgXArVX1np7y1T3VXkWzbockSZIkSZJGxFLmuxwNvBbYnOTmtuztwKlJjqSZMrYFeOOSIpQkSZIkSdKyWnRCqKo+A2SOu65afDiSJEmSJI0nF6rWOOneSr+SJEmSNI9+v7ADXHjsqgFGIknDtaRFpSVJkiRJkjR+TAhJkiRJkiRNGBNCkiRJkiRJE2bs1hDafPdOzuhj3q+LdGmcuRidJEmSJGmQHCEkSZIkSZI0YUwISZIkSZIkTRgTQpIkSZIkSRPGhJAkSZIkSdKEGbtFpfXY+l2MWJIkSZI0HL3f29av3T3vxkluIqNBcoSQJEmSJEnShDEhJEmSJEmSNGFMCEmSJEmSJE0YE0KSJEn/P3v3Hy9XVd/7//WW8EugRAgcQxKJlugFTQ0YIb20egR/hB812C8glgKh0dQWbqHmfiVqW/EHbehtRAQvGgwlaIAgyE0ErCJwytUrQUAgQvQSMJJDYiKQBAKCHvzcP/YaMpzMOWefc+bHnj3v5+Mxj5lZe83enzU5+5M9a++1tpmZmVmH8aTSZmZmZmZmZgU0nJsGeQJqGy53CJmZmZmZmZnZK7gzqvwa1iEkaSZwMbAT8LWIWNCobXUC307erH4alZ9WPbF1wFuGVvN/mGY2EB8/mVlROT8VX97fjD4WtYqGdAhJ2gn4MvAeoBf4saQVEfFwI7Zn1qncaz98zk9mVlTOT2ZWVM5PZuXUqCuEDgfWRMRjAJKuBWYBTUsY7dA7Wh3jvKl9ua4sMLNRa3l+MjMbgPOTmRVVR+cnj9YYWr1/f/vEd3MoIuq/UulEYGZEfDi9Pw04IiLOrqozF5ib3r4J+HnO1Y8DnqxjuEVR1nZBedtW1nYdGBH7tTqIRnF+yq1MbQG3p+jytsf5yfkJytUWcHuKzvkJ56cm6PTvoNPbD439DgbMT426Qkg1yl7R8xQRi4BFw16xdE9ETB9pYEVV1nZBedtW1nZ1AOenHMrUFnB7iq5s7RkF56ccytQWcHuKrmztGQXnpwbq9O+g09sPrfsOXtWg9fYCk6reTwTWN2hbZmbD4fxkZkXl/GRmReX8ZFZCjeoQ+jEwRdLrJe0CnAKsaNC2zMyGw/nJzIrK+cnMisr5yayEGjJkLCL6JJ0NfJfstoRXRMRDdVr9sC9DbBNlbReUt21lbVepOT/lVqa2gNtTdGVrz4g4P+VWpraA21N0ZWvPiDg/NVynfwed3n5o0XfQkEmlzczMzMzMzMysuBo1ZMzMzMzMzMzMzArKHUJmZmZmZmZmZh2mbTqEJM2U9HNJayTNb3U89SRpraRVku6XdE+r4xkpSVdI2iTpp1Vl+0i6VdIj6fk1rYxxpAZo2/mSnkj/bvdLOraVMVrrlCk/1fpbb2eSJkm6Q9JqSQ9JOqfVMY2GpN0k3S3pgdSez7Q6ptGStJOkn0i6qdWxlFGZ9mnvz8VXpv25LMfnRVam46e8yvx7Ka+BcnmnfA8D5f40YfvK1P5lafL2hmuLDiFJOwFfBo4BDgE+JOmQ1kZVd++KiGkRMb3VgYzClcDMfmXzgdsiYgpwW3rfjq5kx7YBXJT+3aZFxC1NjskKoIT56Upq/623qz5gXkQcDMwAzmrzf58XgaMi4q3ANGCmpBktjmm0zgFWtzqIEruS8uzT3p+Lr2z7cxmOzwuphMdPeV1JeX8v5TVQLu+U72Gg3H8h2W/LKcBmYE4zgmmLDiHgcGBNRDwWEb8FrgVmtTgm6yci7gSe7lc8C1iSXi8BTmhqUHUyQNvMoGT5qWx/6xGxISLuS6+fJfuhMqG1UY1cZLaltzunR9veHULSROA44GutjqWsyrRPe38uNu/PNkylOn7Kq8y/l/IaJJd3xPcwSO4/Crg+lTet/e3SITQBWFf1vpc2PgCoIYDvSbpX0txWB1NnXRGxAbKdH9i/xfHU29mSHkyXf5byskYbUtnzU2lImgwcCqxsbSSjk4Zk3A9sAm6NiHZuzxeBjwO/b3Ug1l68PxdS2fbnMh+fF4GPn7Yr+++lAfXL5R3zPfTP/cCjwJaI6EtVmrY/tEuHkGqUte0ZlBqOjIjDyC6ZPEvSO1odkOVyGfCHZJf6bQAWtjYca5Gy56dSkLQncANwbkQ80+p4RiMiXoqIacBE4HBJb2l1TCMh6XhgU0Tc2+pYrL14fy6eku7PPj5vLB8/dbgy5fLh6p/7gYNrVWtGLO3SIdQLTKp6PxFY36JY6i4i1qfnTcCNZH8UZbFR0niA9LypxfHUTURsTDvz74HLKde/m+VX6vxUBpJ2JjvgWBoR32p1PPUSEVuAHtp3fpgjgfdLWks2VOAoSd9obUhWdN6fC6t0+3PJj8+LwMdP25X299JABsjlHfc9VOX+GcBYSWPSoqbtD+3SIfRjYEqaeXsX4BRgRYtjqgtJe0jaq/IaeC/Q9ncCqbICOCO9PgNY3sJY6qqSsJIPUK5/N8uvtPmpDCQJWAysjogvtDqe0ZK0n6Sx6fXuwLuBn7U2qpGJiE9ExMSImEy239weEX/Z4rCswLw/F1fZ9ucOOD4vAh8/bVfa30u1DJLLO+J7GCD3rwbuAE5M1ZrW/jFDV2m9iOiTdDbwXWAn4IqIeKjFYdVLF3Bjtl8wBrg6Iv6jtSGNjKRrgG5gnKRe4NPAAuA6SXOAx4GTWhfhyA3Qtm5J08gu51sL/HXLArSWKVt+qvW3HhGLWxvVqBwJnAasSmO1AT7ZxncFHA8sSXdneRVwXUS0/e2drXFKtk97f7ZmKc3xeVGV7fgprzL/XhqGmrmczvkeauZ+SQ8D10r6miSt9gAAIABJREFUPPATsk6zhlOEh2qamZmZmZmZmXWSdhkyZmZmZmZmZmZmdeIOITMzMzMzMzOzDuMOITMzKxRJb5L0E0nPSvq7Qeq9TtK2NAYbST2SPty8SM2sk+XNVU2KJSQd1MoYzKz5mpmH+h93WTm0xaTSZmbWUT4O9ETEoYNViojHgT2bE5KZ2Q5y5SozswZqWB6StBb4cER8H3zcVVa+QsjMzIrmQKBpdxpRxv8fmtlwNSVXSfIJXDMbyIB5yFfyWB4+ALZcJM2X9Gi6HPFhSR9I5TtJWijpSUm/kHR2umx5TFq+t6TFkjZIekLS552czGwgkm4H3gVcmi5LPiddCv2MpHWSzq+qO7k63/Rbz/mSvjFQ3TS87AJJPwSeB94wWL6SdJCk/5S0NeW7ZQ3+KsyswEaYq85MyzZL+qikt0t6UNIWSZdW1Z8t6YeSLpL0NHB+Kv8rSavT578r6cAmN9vMCqRGHrpa0mWSbpH0HPAuSbtK+jdJj0vaKOkrknavWsfxku5Peej/SPqjVP514HXAt9O6Pz7AsdTn0+e2Sfq2pH0lLU258MeSJldt679IulXS05J+LunkJn5dNgB3CFlejwJ/CuwNfAb4hqTxwEeAY4BpwGHACf0+twToAw4CDgXeC3iODzOrKSKOAv43cHZE7Ak8AJwOjAWOA/5GUv88M1KnAXOBvYBfMni++hzwPeA1wETgkjrFYGZtaIS56ghgCvBB4IvAp4B3A28GTpb0zn51HwP2By5I6/ok8OfAfmnb1zSmdWbWDmrkod8CfwFcQHZs8wPgQuCNZL/VDgImAP8EIOkw4Argr4F9ga8CKyTtGhGnAY8DfxYRe0bEvw4Qxilkx1MTgD8EfgT8O7APsBr4dNrWHsCtwNVkee1DwP+U9OZ6fR82Mu4Qslwi4psRsT4ifh8Ry4BHgMOBk4GLI6I3IjYDCyqfkdRF1ll0bkQ8FxGbgIvIEoeZ2ZAioiciVqXc8yDZD6B3DvW5nK6MiIcioo/swGWwfPU7ssuyD4iIFyLiB3WKwcxKIGeu+lzKH98DngOuiYhNEfEE2Y+66jlA1kfEJRHRFxG/IfvB9i8RsTrlrH8GpvkqITPrZ3lE/DAifg+8SHby/u8j4umIeJYsd1SObT4CfDUiVkbESxGxJH1mxjC29+8R8WhEbAW+AzwaEd9PeeqbbM9rxwNrI+LfU167D7gBOHG0DbbR8Zhky0XS6cDHgMmpaE9gHHAAsK6qavXrA4GdgQ2SKmWv6lfHzGxAko4g62h+C7ALsCvZAUY9DCdffZzsKqG7JW0GFkbEFXWKw8zaXM5ctbHq9W9qvK+erLX/sdKBwMWSFlZvluys/C9HHrmZlUx17tgPeDVwb9WxjYDK9B0HAmdI+m9Vn9mF7PddXnnz2oHAEZK2VC0fA3x9GNuyBnCHkA0pnX26HDga+FFEvCTpfrKEsoFs+ETFpKrX68h6mcelXmIzs+G6GrgUOCYiXpD0RbLO6KE8R3YQVPHaGnWi6vWg+SoifkV2Jg1JfwJ8X9KdEbEmXzPMrORGmqsGEv3erwMuiIilo1inmZVfde54kqxT5s3pSsT+KnnlghzrGq11wH9GxHvquE6rAw8Zszz2IEsIvwaQdCbZGTCA64BzJE2QNBY4r/KhiNhANufGQkl/IOlVkv6w3xh5M7PB7AU8nX5gHU42Nj6P+4F3SHqdpL2BTwxWeah8JekkSZXO781kOfGlkTTIzEpppLkqr68An6jMt5EmwT+pztswsxJJw8YuBy6StD9A+s32vlTlcuCjko5QZg9Jx0naKy3fCLyhTuHcBLxR0mmSdk6Pt0s6uE7rtxFyh5ANKSIeBhaSTRK2EZgK/DAtvpzsR9SDwE+AW8gmZa38UDqd7NLDh8l+RF0PjG9W7GbW9v4W+KykZ8kmQbwuz4ci4lZgGVluupfsQGQog+WrtwMrJW0DVgDnRMQvhtEOMyu3EeWqvCLiRrLJYa+V9AzwU7J5z8zMBnMesAa4K+WO7wNvAoiIe8iufr6U7LhnDTC76rP/AvxDugPZfx9NEGn+oveSzV+0HvgVWU7bdTTrtdFTRD2vBLNOJ+kY4CsR4UkOzczMzMzMzArKVwjZqEjaXdKxksZImkB2a8EbWx2XmZmZmZmZmQ3MVwjZqEh6NfCfwH8hm7TsZrKhFM+0NDAzMzMzMzMzG5A7hMzMzMzMzMzMOoyHjJmZmZmZmZmZdZgxrQ4AYNy4cTF58uRWhzGg5557jj322KPVYTSF21peedt77733PhkR+zUhpLYwnPxUlL+posQBxYmlKHFAcWIpShzg/DRS7Zif6qFMbQG3p+icn0amU/MTlKs9ZWoLdG57Bs1PEdHyx9ve9rYosjvuuKPVITSN21peedsL3BMFyAtFeQwnPxXlb6oocUQUJ5aixBFRnFiKEkeE89NIH+2Yn+qhTG2JcHuKzvnJ+Wm4ytSeMrUlonPbM1h+8pAxMzMzMzMzM7MO4w4hMzMzMzMzM7MO4w4hMzMzMzMzM7MOU4hJpTvV5Pk35667dsFxDYzEzOph1RNbmZ1jv/b+bGZmZeX/C83KI+/+DN6n21XHdwjl6ZSZN7WP2fNv9h+5mZmZmZmZmZWCh4yZmZmZmZmZmXUYdwiZmZmZ1Zmk3STdLekBSQ9J+kwqf72klZIekbRM0i6pfNf0fk1aPrmV8ZuZmVn5uUPIzMzMrP5eBI6KiLcC04CZkmYAFwIXRcQUYDMwJ9WfA2yOiIOAi1I9MzMzs4Zxh5CZmZlZnUVmW3q7c3oEcBRwfSpfApyQXs9K70nLj5akJoVrZmZmHajjJ5U2MzMzawRJOwH3AgcBXwYeBbZERF+q0gtMSK8nAOsAIqJP0lZgX+DJfuucC8wF6OrqoqenJ1cs27Zty1236MrUFihfe7p2z27IMpR2aXPZ/n3MzKq5Q8jMzMysASLiJWCapLHAjcDBtaql51pXA8UOBRGLgEUA06dPj+7u7lyx9PT0kLdu0ZWpLVC+9lyydDkLVw39E2Ptqd2ND6YOyvbvY2ZWzUPGzMzMzBooIrYAPcAMYKykyq/licD69LoXmASQlu8NPN3cSM3MzKyTuEPIzMzMrM4k7ZeuDELS7sC7gdXAHcCJqdoZwPL0ekV6T1p+e0TscIWQmZmZWb14yJiZmZlZ/Y0HlqR5hF4FXBcRN0l6GLhW0ueBnwCLU/3FwNclrSG7MuiUVgRtZmZmncMdQmZmZmZ1FhEPAofWKH8MOLxG+QvASU0IzczMzAzwkDEzKwFJO0n6iaSb0vvXS1op6RFJyyTtksp3Te/XpOWTWxm3mZmZmZlZq7hDyMzK4ByyuTkqLgQuiogpwGZgTiqfA2yOiIOAi1I9MzMzMzOzjjNkh5CkKyRtkvTTqrLzJT0h6f70OLZq2SfS2fefS3pfowI3MwOQNBE4Dvhaei/gKOD6VGUJcEJ6PSu9Jy0/OtU3MzMzMzPrKHnmELoSuBS4ql/5RRHxb9UFkg4hmwTxzcABwPclvTEiXqpDrGZmtXwR+DiwV3q/L7AlIvrS+15gQno9AVgHEBF9kram+k9Wr1DSXGAuQFdXFz09PbkC6dod5k3tG7Je3vWN1LZt2xq+jbyKEktR4oDixFKUOKBYsZiZmZl1iiE7hCLizmHMszELuDYiXgR+ke6UcTjwoxFHaGY2AEnHA5si4l5J3ZXiGlUjx7LtBRGLgEUA06dPj+7u7v5Varpk6XIWrhq6n33tqfnWN1I9PT3kjbnRihJLUeKA4sRSlDigWLGYmZmZdYrR3GXsbEmnA/cA8yJiM9nZ97uq6lSfmX+FkZ6Br7c8Z/MrZ/3rHWOebVc06/vppLO0ndRWKG17jwTen4at7gb8AdkVQ2MljUlXCU0E1qf6vcAkoFfSGGBvsts7m5mZmZmZdZSRdghdBnyO7Mz654CFwF+R8+w7jPwMfL3Nnn/zkHXmTe1j4aoxdT+rn2fbFY2+oqCik87SdlJboZztjYhPAJ8ASFcI/feIOFXSN4ETgWuBM4Dl6SMr0vsfpeW3R0TNHGVmZmZWRpImkU0H8lrg98CiiLhY0j7AMmAysBY4OSI2p/kWLwaOBZ4HZkfEfa2I3czqa0R3GYuIjRHxUkT8HricbFgYbD/7XlF9Zt7MrFnOAz6Whq3uCyxO5YuBfVP5x4D5LYrPzMzMrFX6yEZ4HAzMAM5Kc8HOB25Ld2m9je3HSccAU9JjLtnFAWZWAiO6QkjS+IjYkN5+AKjcgWwFcLWkL5BNKj0FuHvUUZqZDSEieoCe9PoxtndUV9d5ATipqYGZmZmZFUj6HbchvX5W0mqyaT5mAd2p2hKy46rzUvlV6arquySN7fd70Mza1JAdQpKuIUsM4yT1Ap8GuiVNIxsOthb4a4CIeEjSdcDDZD3PZ/kOY2ZmZmZmZsWTbh50KLAS6Kp08kTEBkn7p2ov36U1qcwT+4oOoZHOEVu2eS7L1J68d9CF5s15Oxpl+reB+rQnz13GPlSjeHGNskr9C4ALRhOUmZmZmZmZNY6kPYEbgHMj4plsqqDaVWuU1e0urWWb57JM7cl7B11o3py3o1GmfxuoT3tGc5cxa2OTB5jQet7Uvh0mu1674LhmhGRmZmZmZk0gaWeyzqClEfGtVLyxMhRM0nhgUyr3PLFmJTWiSaXNzMzMzMys/aS7hi0GVkfEF6oWVe7GCjvepfV0ZWYAWz1/kFk5+AohMzMzMzOzznEkcBqwStL9qeyTwALgOklzgMfZfiOOW8huOb+G7LbzZzY3XDNrFHcImZmZmZmZdYiI+AG15wUCOLpG/QDOamhQZtYSHjJmZmZmZmZmZtZh3CFkZmZmZmZmZtZh3CFkZmZmZmZmZtZh3CFkZmZmZmZmZtZhPKm0mZmZmZmZmZXaqie2Mnv+zUPWW7vguCZEUwy+QsjMzMzMzMzMrMO4Q8jMzMzMzMzMrMO4Q8jMzMzMzMzMrMO4Q8jM2pak3STdLekBSQ9J+kwqf72klZIekbRM0i6pfNf0fk1aPrmV8ZuZmZmZmbWKO4TMrJ29CBwVEW8FpgEzJc0ALgQuiogpwGZgTqo/B9gcEQcBF6V6ZmZ1J2mSpDskrU4d1uek8n0k3Zo6rG+V9JpULklfSh3WD0o6rLUtMDMzs7Jzh5CZta3IbEtvd06PAI4Crk/lS4AT0utZ6T1p+dGS1KRwzayz9AHzIuJgYAZwlqRDgPnAbanD+rb0HuAYYEp6zAUua37IZmZm1kl823kza2uSdgLuBQ4Cvgw8CmyJiL5UpReYkF5PANYBRESfpK3AvsCT/dY5l+wHGV1dXfT09OSKpWt3mDe1b8h6edc3Utu2bWv4NvIqSixFiQOKE0tR4oBixVIvEbEB2JBePytpNVkOmgV0p2pLgB7gvFR+VUQEcJeksZLGp/WYmZmZ1V3bdQhNnn9zrnprFxzX4EjMrAgi4iVgmqSxwI3AwbWqpedaVwPFDgURi4BFANOnT4/u7u5csVyydDkLVw2dVteemm99I9XT00PemButKLEUJQ4oTixFiQOKFUsjpPnKDgVWAl2VTp6I2CBp/1Tt5Q7rpNKZ/YoOoZF2WJep061MbYHytacoJ0fqpWz/PmZm1dquQ8jMrJaI2CKph2xoxlhJY9JVQhOB9alaLzAJ6JU0BtgbeLoV8ZpZZ5C0J3ADcG5EPDPIKNWGdliXqdOtTG2B8rWnKCdH6qVs/z5mZtU8h5CZtS1J+6Urg5C0O/BuYDVwB3BiqnYGsDy9XpHek5bfnoZnmJnVnaSdyTqDlkbEt1LxRknj0/LxwKZUXumwrqjuzDYzMzOrO3cImVk7Gw/cIelB4MfArRFxE9l8HB+TtIZsjqDFqf5iYN9U/jG2T+ZqZlZXacL6xcDqiPhC1aLqjun+Hdanp7uNzQC2ev4gMzMzayQPGTOzthURD5LNy9G//DHg8BrlLwAnNSE0M7MjgdOAVZLuT2WfBBYA10maAzzO9px0C3AssAZ4HjizueGamZlZpxmyQ0jSFcDxwKaIeEsq2wdYBkwG1gInR8TmdDbsYrIDmueB2RFxX2NCNzMzMyumiPgBtecFAji6Rv0AzmpoUGZmZmZV8gwZuxKY2a9sPnBbREwBbmP7sItjgCnpMRe4rD5hmpmZmZmZmZlZvQx5hVBE3Jlul1ptFtCdXi8Besjm7JgFXJXOct0laayk8R4Db2ZmZmZmRTB5/s256145c48GRmJm1lojnUOoq9LJExEbJO2fyicA66rq9aayHTqEJM0lu4qIrq4uenp6cm143tS+XPXqub6u3bN6edeZV962QP72jHbblbY2cttFsW3bttK2rZZOa6+ZmZmZmZkNrN6TStcaK1/zls4RsQhYBDB9+vTo7u7OtYHZOXv0155av/XNm9rHwlVjcq8zr7xtgfztGe22K21t5LaLoqenh7x/d2XQae01MzMzMzOzgY30tvMbJY0HSM+bUnkvMKmq3kRg/cjDMzMzMzMzMzOzehtph9AK4Iz0+gxgeVX56crMALZ6/iAzMzMzMzMzs2LJc9v5a8gmkB4nqRf4NLAAuE7SHOBx4KRU/RayW86vIbvt/JkNiNnMzMzMzMzMzEYhz13GPjTAoqNr1A3grNEGZWZmZmZmZmZmjTPSIWNmZmZmZmZmZtam3CFkZmZmZmZmZtZh3CFkZmZmZmZmZtZh3CFkZmZmZmZmZtZh3CFkZm1L0iRJd0haLekhSeek8n0k3SrpkfT8mlQuSV+StEbSg5IOa20LzMzMzMzMWsMdQmbWzvqAeRFxMDADOEvSIcB84LaImALclt4DHANMSY+5wGXND9nMzMzMzKz13CFkZm0rIjZExH3p9bPAamACMAtYkqotAU5Ir2cBV0XmLmCspPFNDtvMzMzMzKzlxrQ6ADOzepA0GTgUWAl0RcQGyDqNJO2fqk0A1lV9rDeVbei3rrlkVxDR1dVFT09Prhi6dod5U/uGrJd3fSO1bdu2hm8jr6LEUpQ4oDixFCUOKFYsZmadQNIVwPHApoh4SyrbB1gGTAbWAidHxGZJAi4GjgWeB2ZXTsiZWXtzh5CZtT1JewI3AOdGxDPZcUvtqjXKYoeCiEXAIoDp06dHd3d3rjguWbqchauGTqtrT823vpHq6ekhb8yNVpRYihIHFCeWosQBxYrFzKxDXAlcClxVVVYZcr9A0vz0/jxeOeT+CLIh90c0Ndo2suqJrcyef/OQ9dYuOK4J0ZgNzkPGzKytSdqZrDNoaUR8KxVvrAwFS8+bUnkvMKnq4xOB9c2K1czMzKwIIuJO4Ol+xR5yb9ZhfIWQmbWtdAnzYmB1RHyhatEK4AxgQXpeXlV+tqRryc5sba0MLTMzMzPrcC0Zcl+2YcNFmUKgHvK2BcrVnnZoC9Rn33GHkJm1syOB04BVku5PZZ8k6wi6TtIc4HHgpLTsFrLx72vIxsCf2dxwzczMzNpOQ4fcl23YcFGmEKiHvG2BcrWnHdoC9dl33CFkZm0rIn5A7YMUgKNr1A/grIYGZWZmZtaeNkoan64O8pB7sw7gOYTMzMzMzMysMuQedhxyf7oyM/CQe7PS8BVCZmZmZmZmHUTSNUA3ME5SL/BpPOTerOO4Q8jMzMzMzKyDRMSHBljkIfdmHcRDxszMzMwaQNIVkjZJ+mlV2T6SbpX0SHp+TSqXpC9JWiPpQUmHtS5yMzMz6wTuEDIzMzNrjCuBmf3K5gO3RcQU4Lb0HuAYYEp6zAUua1KMZmZm1qHcIWRmZmbWABFxJ/B0v+JZwJL0eglwQlX5VZG5Cxib7vJjZmZm1hCeQ8jMzMyseboqd+dJt3beP5VPANZV1etNZa+4k4+kuWRXENHV1UVPT0+ujW7bti133aIrU1sANj29lUuWLh+6IjB1wt4Njmb0unaHeVP7hqzXyn/DPPFVlO3vzcys2qg6hCStBZ4FXgL6ImK6pH2AZcBkYC1wckRsHl2YZmZmZqWmGmWxQ0HEImARwPTp06O7uzvXynt6eshbt+jK1BaAS5YuZ+GqfIfka0/tbmwwdZC3Pa1sy+z5N+eue+XMPUr192ZmVq0eQ8beFRHTImJ6ej/Q2HgzMzOzTrexMhQsPW9K5b3ApKp6E4H1TY7NzMzMOkgj5hAaaGy8mZmZWadbAZyRXp8BLK8qPz3dbWwGsLUytMzMzMysEUY7h1AA35MUwFfTZcwDjY1/hZGOgc875ree66uMha73+OHhjF9u1rZrjfsu67jpThsT3mntNTNrNUnXAN3AOEm9wKeBBcB1kuYAjwMnpeq3AMcCa4DngTObHrCZmZl1lNF2CB0ZEetTp8+tkn6W94MjHQOfd8xv3nHJedY3b2ofC1eNqftY5+GMX27WtittbeS2i6JscxAMpdPaa2bWahHxoQEWHV2jbgBnNTYiMzMzK7LJw5zjbLRGNWQsItan503AjcDhDDw23szMzMzMzMzMCmDEHUKS9pC0V+U18F7gpww8Nt7MzMzMzMzMzApgNFcIdQE/kPQAcDdwc0T8B9nY+PdIegR4T3pvZtYQkq6QtEnST6vK9pF0q6RH0vNrUrkkfUnSGkkPSjqsdZGbmZmZmZm1zog7hCLisYh4a3q8OSIuSOVPRcTRETElPT9dv3DNzHZwJTCzX9l84LaImALclt4DHANMSY+5wGVNitHMzMzMzKxQGnHbeTOzpomIO4H+Hc+zgCXp9RLghKryqyJzFzC2MueZmZmZmZlZJxntXcbMzIqoKyI2AETEhnQnRIAJwLqqer2pbEP1hyXNJbuCiK6uLnp6evJtdPfsTn1Dybu+kdq2bVvDt5FXUWIpShxQnFiKEgcUKxYzMzOzTuEOITPrJKpRFjsURCwCFgFMnz49uru7c638kqXLWbhq6LS69tR86xupnp4e8sbcaEWJpShxQHFiKUocUKxYzMzMzDqFh4yZWRltrAwFS8+bUnkvMKmq3kRgfZNjMzMzMzMzazl3CJlZGa0AzkivzwCWV5Wfnu42NgPYWhlaZmZmZmZm1kk8ZMyabvL8m3PXXbvguAZGYmUg6RqgGxgnqRf4NLAAuE7SHOBx4KRU/RbgWGAN8DxwZtMDNjMzM7OXrXpiK7Nz/D7w7wKz+nOHkJm1tYj40ACLjq5RN4CzGhuRmZmZmZlZ8XnImJmZmZmZmZlZh3GHkJmZmZmZmZlZh3GHkJmZmZmZmZlZh/EcQmZmNiqDTRQ/b2rfyxNFejJIMzMzM7Pi8BVCZmZmZmZmZmYdxh1CZmZmZmZmZmYdxh1CZmZmZmZmZmYdxnMImZmZmVnbWPXE1pfnJhuK5y4zMzMbmK8QMjMzMzMzMzPrML5CyMzMzKzk8l5V4ytqzMzMOoevEDIzMzMzMzMz6zDuEDIzMzMzMzMz6zDuEDIzMzMzMzMz6zCeQ8g6yuSq+RPmTe0bdD4Fz6NgZmZmZmZmZdWwDiFJM4GLgZ2Ar0XEgkZty8xsOJyfrJkm9+t4Hqwzukwd0f3bPZgrZ+7RwEjai/OTmRWV85NZ+TSkQ0jSTsCXgfcAvcCPJa2IiIcbsT2zdpH3B1KZfhQWjfOTmRWV85OZFZXzk1k5NeoKocOBNRHxGICka4FZgBOGWR35DPyIOD+ZWVE5P5lZUTk/mZWQIqL+K5VOBGZGxIfT+9OAIyLi7Ko6c4G56e2bgJ/XPZD6GQc82eogmsRtLa+87T0wIvZrdDCt0uD8VJS/qaLEAcWJpShxQHFiKUoc4PwEdEx+qocytQXcnqJzfsL5aZjK1J4ytQU6tz0D5qdGXSGkGmWv6HmKiEXAogZtv64k3RMR01sdRzO4reXVae0dRMPyU1G+46LEAcWJpShxQHFiKUocUKxYWqz0+akeytQWcHuKrmztGQXnp5zK1J4ytQXcnloaddv5XmBS1fuJwPoGbcvMbDicn8ysqJyfzKyonJ/MSqhRHUI/BqZIer2kXYBTgBUN2paZ2XA4P5lZUTk/mVlROT+ZlVBDhoxFRJ+ks4Hvkt2W8IqIeKgR22qSthjaVidua3l1WntranB+Ksp3XJQ4oDixFCUOKE4sRYkDihVLy3RIfqqHMrUF3J6iK1t7RsT5aVjK1J4ytQXcnh00ZFJpMzMzMzMzMzMrrkYNGTMzMzMzMzMzs4Jyh5CZmZmZmZmZWYdxh1A/kq6QtEnST/uV/zdJP5f0kKR/bVV89VSrrZKmSbpL0v2S7pF0eCtjrBdJkyTdIWl1+jc8J5XvI+lWSY+k59e0OtbRGqSt/0PSzyQ9KOlGSWNbHWs7kjQz5YI1kubXWL6rpGVp+UpJk1sYy2xJv0778/2SPtygOGrmzarlkvSlFOeDkg5rURzdkrZWfR//1KA4au6D/eo06zvJE0uzvpfdJN0t6YEUy2dq1Gna/tMphtov2kmev+d2kmefaDeSdpL0E0k3tTqW0ZK0VtKqyjFxq+MpI+en4nJ+Kra65qeI8KPqAbwDOAz4aVXZu4DvA7um9/u3Os4GtvV7wDHp9bFAT6vjrFNbxwOHpdd7Af8XOAT4V2B+Kp8PXNjqWBvY1vcCY1L5hWVoawu+252AR4E3ALsADwCH9Kvzt8BX0utTgGUtjGU2cGkTvpcdckm/5ccC3wEEzABWtiiObuCmJnwfNffBFn0neWJp1vciYM/0emdgJTCjX52m7D+d9Bhqv2inR56/53Z65Nkn2u0BfAy4uhk5pQltWQuMa3UcZX44PxX34fxU7Ec985OvEOonIu4Enu5X/DfAgoh4MdXZ1PTAGmCAtgbwB+n13sD6pgbVIBGxISLuS6+fBVYDE4BZwJJUbQlwQmsirJ+B2hoR34uIvlTtLmBiq2JsY4cDayLisYj4LXAt2d9Qteq/qeuBoyWpRbE0xQC5pNos4KrI3AWMlTS+BXE0xSD5plqzvpM8sTRFauu29Hbn9Oh/Z4tm7T8doyj7RT0U6e+5HnLuE21D0kTgOODCvV7QAAAgAElEQVRrrY7F2oPzU3E5P3UOdwjl80bgT9Pl6/8p6e2tDqiBzgX+h6R1wL8Bn2hxPHWXhiAcStbT3RURGyBL5MD+rYus/vq1tdpfkV2dYMMzAVhX9b6XHf+zf7lO6oDbCuzbolgA/r80JOl6SZMaEEceeWNthj9Olz9/R9KbG72xQfbBpn8ng8QCTfpe0uXa9wObgFsjYsDvpcH7j7W5If6e20aOfaKdfBH4OPD7VgdSJwF8T9K9kua2OhhrH85PheT8NAB3COUzBngN2WX9/z9wXYnPWP4N8PcRMQn4e2Bxi+OpK0l7AjcA50bEM62Op5EGaqukTwF9wNJWxdbGau33/c+W5KnTrFi+DUyOiD8iG/a6ZMePNEWzvpOh3AccGBFvBS4B/lcjNzZEvmnqdzJELE37XiLipYiYRnaF4uGS3tI/1Fofa1Q81p7K9H95jn2iLUg6HtgUEfe2OpY6OjIiDgOOAc6S9I5WB2TF5/xUPM5Pg3OHUD69wLfSpXN3k/UsjmtxTI1yBvCt9PqbZMNSSkHSzmQJemlEVNq4sTJMIz2XYjjgAG1F0hnA8cCpkQag2rD0AtVX2Uxkx2GVL9eRNIZs6GUjLoceMpaIeKoy1BW4HHhbA+LII8/31nAR8Uzl8ueIuAXYWVJDcvlA+2CVpn0nQ8XSzO+laptbgB5gZr9Fzdp/rE3l2Lfa0iD7RLs4Eni/pLVkQ5iPkvSN1oY0OhGxPj1vAm6kRMfE1hjOT4Xl/DQIdwjl87+AowAkvZFsAtcnWxpR46wH3pleHwU80sJY6iZd0bUYWB0RX6hatIKsE4z0vLzZsdXbQG2VNBM4D3h/RDzfqvja3I+BKZJeL2kXsklvV/SrU/03dSJwe4M634aMpd+cNO8nG8/eCiuA05WZAWytDNVsJkmvrVzdqewOiq8CnmrAdgbKN9Wa8p3kiaWJ38t+Snc3lLQ78G7gZ/2qNWv/sTaUc99qGzn3ibYQEZ+IiIkRMZns/6PbI+IvWxzWiEnaQ9JelddkN+Zo+zthWeM4PxWX89PgxtQrsLKQdA3ZHVfGSeoFPg1cAVyh7JaIvwXOKMMB6gBt/QhwcToz+wJQljHTRwKnAavSWFiATwILyIYAzgEeB05qUXz1NFBbvwTsCtyafvvdFREfbU2I7Ski+iSdDXyX7C5fV0TEQ5I+C9wTESvIDga+LmkN2ZUNp7Qwlr+T9H6yIYJPk911rO4GyCU7pzi/AtxCdletNcDzwJktiuNE4G8k9QG/AU5pUC4faB98XVUsTflOcsbSrO9lPLBE0k5knU7XRcRNrdh/Okmt/SIi2nU4eM2/53RlWzuquU+0OCbLdAE3puOlMcDVEfEfrQ2pfJyfCs35qbjqmp9Ugn4NMzMzMzMzMzMbBg8ZMzMzMzMzMzPrMO4QMjOzwpK0VtK767zO2ZJ+MMjy7nTpuplZ3Ywmn0n6iqR/rHdMZmaDkTRZUqTpRAaqs03SG5oZl9WPO4SsZfIkGDOzRkt56KBWx2FmxSXp/GbdlaZWp3VEfDQiPteM7ZtZ8TTiBNkI4+iR9OHqsojYMyIea1VMNjruEDIzMzMzMzNrQz65bqPhDiF7maTDJP1E0rOSvilpmaTPp2UfkbRG0tOSVkg6oOpz/1XSjyVtTc//tWrZK3qz+51huzM9b0mXGv5xM9ppZm1nmqQHU45ZJmk3AEnHS7pf0hZJ/0fSH1U+IGm+pEdTPntY0gdqrVhSJQ89kPLQB6uWzZO0SdIGSY26C5iZFYyk8yQ9kfLHzyUdR3Z3vg+mPPFAqjfYMQ6STpP0S0lPSfpUVflrJT0vad+qsrdJ+rWkqcBXgD9O29qSll9ZdUzWLalX0serctQJko6V9H/Tsdonq9b9qqqc+JSk6yTt07hv0MzqSdLXye4O+u2UFz6erm6eI+lx4PZU75uSfpWOl+6U9OZUPiOV71S1zg9IejC9zpUjJF0A/ClwaYrj0lT+8pXWKVf9T0nfSXV+mHLeFyVtlvQzSYdWrfMASTek/PcLSX/XsC/SanKHkAEgaRfgRuBKYB/gGuADadlRwL8AJ5PdgvCXwLVp2T7AzWS3NN8X+AJwc/VBziDekZ7HpksNf1Sv9phZqZwMzAReD/wRMFvSYcAVwF+T5Z6vAisk7Zo+8yjZQcvewGeAb0ga33/FEVHJQ29NeWhZev/a9NkJwBzgy5Je04jGmVlxSHoTcDbw9ojYC3gf8DPgn4FlKU+8Ncd6DgEuI7sN9QFkeWoiQET8Cughy20VfwlcGxGrgI8CP0rbGjvAJl4L7EaWo/4JuDyt421kue+ftH1Oj78DTgDemWLZDHx5yC/DzAohIk4DHgf+LCL2BK5Li94JHEyWpwC+A0wB9gfuA5amz98FPAccVbXavwCuTq9z5YiI+BTwv4GzU346e4CQTwb+ARgHvAj8KMUzDrie7Pcikl4FfBt4gCyXHQ2cK+l9NdZpDeIOIauYAYwBvhQRv4uIbwF3p2WnAldExH0R8SLwCbIzV5OB44BHIuLrEdEXEdeQHTj9WdNbYGZl9aWIWB8RT5MdOEwDPgJ8NSJWRsRLEbGE7KBjBkBEfDN95vepk+cR4PBhbPN3wGdTPrwF2Aa8qZ6NMrNCegnYFThE0s4RsTYiHh3Bek4EboqIO9Ox0z8Cv69avoSsA4d01v5DwNeHsf7fARdExO/ITtKNAy6OiGcj4iHgIbIOdMg6zj8VEb0plvOBE+VhJmbt7vyIeC4ifgMQEVekHFDZz98qae9U9xqyPIOkvYBjUxnUP0fcGBH3RsQLZBccvBARV0XES8AyoHKF0NuB/SLisxHx2zQP0eXAKSPcro2AO4Ss4gDgiYiIqrJ1Vct+WSmMiG3AU2Q9ua9YlvwyLTMzq4dfVb1+HtgTOBCYl4aLbUnDKiaR5SQknV41nGwL8BayH0x5PRURfTW2a2YlFhFrgHPJfhBtknStqobJD8MBbD+OIiKeIzt2qlhO1un0BuA9wNaIuJv8nko/rgB+k543Vi3/Ddtz1oHAjVX5cDVZx1fXMLZnZsXzco6RtJOkBWnY1zPA2rSocuxzNfDn6UrqPwfui4jKb7h654j+uWiw3HRAv2O5T45iuzYC7hCyig3ABEmqKpuUnteT7bAASNqD7NLnJ/ovS16XlkF2eeKrq5a9tup1deeTmdlwrCM7Oz626vHqiLhG0oFkZ5jOBvZNQy5+CmiwFZqZAUTE1RHxJ2THNwFcSO1jlsGOcTaw/TgKSa8mO3aqbOMFsmEfp5INK6u+Oqjex0frgGP65cvdIuKJIT9pZkVRKy9Ul/0FMAt4N9mQ98mpXAAR8TDZSftjeOVwMRhejqhnfloH/KLfdveKiGPruA0bgjuErOJHZD3BZ0saI2kW24dXXA2cKWla6lX+Z2BlRKwFbgHeKOkv0uc+CBwC3JQ+ez9wiqSdJU0nu4S64tdkl0+/ATOz4bkc+KikI5TZQ9Jx6TLoPcgOWH4NoGxC6LcMsq6NOA+ZGdkcQpKOSsc7L5CdzX6JLE9MTnNeVAx2jHM9cLykP0nzNH6WHY+7rwJmA+8Hqm9pvxGYmD5XD18BLkid5UjaLx3nmVn7GOpYZS+yofNPkXVU/3ONOleTzRf0DuCbVeXDyRH1PGa6G3hG2UT+u6ernN4i6e11Wr/l4A4hAyAifkt2+eAcYAvZuPabgBcj4jayse83kJ3x+kPS2M6IeAo4HphHloA+DhwfEU+mVf9jqr+ZbGLXl3ujI+J54ALgh+kywRkNbqaZlURE3EM2j9ClZPllDdkPq8pZsIVkHd0bganADwdZ3fnAkpSHTh6knpmV367AAuBJsuGq+5MNYaj8eHpK0n3p9WDHOA8BZ6WyDalOb/WGIuKHZCfG7ksn2SpuJ5sD6FeSnmT0LgZWAN+T9CxwF3BEHdZrZs3zL8A/pGFVJ9ZYfhXZFUBPAA+T7ef9XQN0A7dX/VaD4eWIi8nmF9os6UsjaUhFGvb6Z2RzQ/6CLO9+jewKJ2sSvXLKGLPtJK0EvhIR/97qWMzMzMzKRtLtwNUR8bVWx2JmZp3HVwjZyyS9U9Jr09CvM8juTvEfrY7LzMzMrGzSsIjDyO66Y2Zm1nS+3aRVexPZBId7Ao8CJ0bEhtaGZGZmZlYukpYAJwDnRMSzrY7HzMw6k4eMmZmZmZmZmZl1GA8ZMzMzMzMzMzPrMIUYMjZu3LiYPHlyrrrPPfcce+yxR2MDapIytQXcnqLL25577733yYjYrwkhtQXnp3Jwe4rN+Wlk2jE/FSUOKE4sRYkDihNLUeIA56eRcn4anaLEUpQ4oDixFCUOqFN+ioiWP972trdFXnfccUfuukVXprZEuD1Fl7c9wD1RgLxQlIfzUzm4PcXm/NQ5+akocUQUJ5aixBFRnFiKEkeE89NIH85Po1OUWIoSR0RxYilKHBH1yU8eMmZmZmZmZmZm1mHcIWRmZmZmZmZm1mHcIWRmZmZWZ5J2k3S3pAckPSTpM6n89ZJWSnpE0jJJu6TyXdP7NWn55FbGb2ZmZuU35KTSkiYBVwGvBX4PLIqIiyXtAywDJgNrgZMjYrMkARcDxwLPA7Mj4r7GhG9FsuqJrcyef/OQ9dYuOK4J0ZiZWStNzvH/QcWVM4sxOWOdvQgcFRHbJO0M/EDSd4CPARdFxLWSvgLMAS5Lz5sj4iBJpwAXAh9sVfBlMdjf4bypfS8ft/jYxKx1/BvCrHXyXCHUB8yLiIOBGcBZkg4B5gO3RcQU4Lb0HuAYYEp6zCU7yDEzMzPrGGkex23p7c7pEcBRwPWpfAlwQno9K70nLT86nWQzMzMza4ghrxCKiA3AhvT6WUmrgQlkBy7dqdoSoAc4L5VflWazvkvSWEnj03rMzMzMOoKknYB7gYOALwOPAlsioi9V6SU7piI9rwOIiD5JW4F9gSf7rXMu2Qk3urq66OnpyRXLtm3bctdtpGbHMW9q34DLunbfvryV301R/m2gOLEUJQ4oVixmZvU2ZIdQtTSe/VBgJdBV6eSJiA2S9k/VXj6gSSoHO6/oEGr3A5p6KFNb4JUHVoNplzaX7d+nbO0xMyu6iHgJmCZpLHAjcHCtaum51tVAsUNBxCJgEcD06dOju7s7Vyw9PT3krdtIzY5jsGEo86b2sXBVdii89tTuJkW0o6L820BxYilKHFCsWMzM6i13h5CkPYEbgHMj4plBrmLuiAOaeihTWwAuWbr85QOrwbTyoGs4yvbvU7b2mJm1i4jYIqmHbOj9WElj0lVCE4H1qVovMAnolTQG2Bt4uhXxmpmZWWfIdZexNBniDcDSiPhWKt4oaXxaPh7YlMorBzQV1Qc7ZmZmZqUnab90ZRCSdgfeDawG7gBOTNXOAJan1yvSe9Ly29PwezMzM7OGGLJDKE1ouBhYHRFfqFpUfeDS/4DmdGVmAFs9f5CZmZl1mPHAHZIeBH4M3BoRN5HNt/gxSWvI5ghanOovBvZN5R9j+806zMzMzBoiz5CxI4HTgFWS7k9lnwQWANdJmgM8DpyUlt1Cdsv5NWS3nT+zrhGbmZmZFVxEPEg272L/8seAw2uUv8D2YykzMzOzhstzl7EfUHteIICja9QP4KxRxmVmZmZmZmZmZg2Saw4hMzMzMzMzMzMrD3cImZmZmZmZmZl1GHcImZmZmZmZmZl1GHcImZmZmZmZmZl1GHcImZmZmZmZmZl1GHcImZmZmZmZmZl1GHcImZmZmZmZmZl1GHcImZmZmZmZdQhJu0m6W9IDkh6S9JlU/npJKyU9ImmZpF1S+a7p/Zq0fHIr4zez+nGHkJmZmZmZWed4ETgqIt4KTANmSpoBXAhcFBFTgM3AnFR/DrA5Ig4CLkr1zKwE3CFkZm1L0iRJd0hanc5wnZPK95F0azrDdauk16RySfpSOsP1oKTDWtsCMzMzs+aKzLb0duf0COAo4PpUvgQ4Ib2eld6Tlh8tSU0K18waaEyrAzAzG4U+YF5E3CdpL+BeSbcCs4HbImKBpPnAfOA84BhgSnocAVyWns3MzMw6hqSdgHuBg4AvA48CWyKiL1XpBSak1xOAdQAR0SdpK7Av8GS/dc4F5gJ0dXXR09OTK5au3WHe1L4h6+Vd30ht27at4dvIqyixFCUOKE4sRYkD6hOLO4TMrG1FxAZgQ3r9rKTVZActs4DuVG0J0EPWITQLuCoiArhL0lhJ49N6zMzMzDpCRLwETJM0FrgROLhWtfRc62qg2KEgYhGwCGD69OnR3d2dK5ZLli5n4aqhf5auPTXf+kaqp6eHvDE3WlFiKUocUJxYihIH1CcWdwiZWSmkCQ4PBVYCXZVOnojYIGn/VO3lM1xJ5ezXKzqERnqGq0hnDEarTG0Bt6cV8pztrWiH9piZlVFEbJHUA8wAxkoak64SmgisT9V6gUlAr6QxwN7A062I18zqyx1CZtb2JO0J3ACcGxHPDDKsvaFnuIp0xmC0ytQWcHtaYfb8m3PXvXLmHoVvj5lZWUjaD/hd6gzaHXg32UTRdwAnAtcCZwDL00dWpPc/SstvT1dbm1mbc4eQmbU1STuTdQYtjYhvpeKNlaFgksYDm1J55QxXRfXZLzMzM7NOMB5YkuYRehVwXUTcJOlh4FpJnwd+AixO9RcDX5e0huzKoFNaEbSZ1Z87hMysbaU7XCwGVkfEF6oWVc5kLWDHM1xnS7qWbDLprZ4/yMzMzDpJRDxINsy+f/ljwOE1yl8ATmpCaGbWZEPedl7SFZI2SfppVdn5kp6QdH96HFu17BPpls4/l/S+RgVuZgYcCZwGHNUvHy0A3iPpEeA96T3ALcBjwBrgcuBvWxCzmZmZmZlZy+W5QuhK4FLgqn7lF0XEv1UXSDqE7BLCNwMHAN+X9MY0i72ZWV1FxA+oPS8QwNE16gdwVkODMjMzMzMzawNDXiEUEXeSfxb5WcC1EfFiRPyC7Cz8DpcdmpmZmZmZmZlZ64xmDqGzJZ0O3APMi4jNZLdvvquqTuWWzjvwbZ3L1RaArt3z3Wa4Xdpctn+fsrXHzMzMzMzMRm6kHUKXAZ8ju13z54CFwF+R85bO4Ns6Q7naAnDJ0uUsXDX0n9TaU7sbH0wdlO3fp2ztMTMzMzMzs5EbcsjY/2vvfmPkqs47jn8fEdoiE9WhDlvLduu+sKLQuEncFXGFFG1DiQyOYqqGCETBRkSuGkdNVEuNmxdFrRTJb0ANtErqFGTTGgJKQu0mThrqZoWQavKHUgx1Uiy0gsUWbv7U4FC1WvL0xdwl4/V693pn7p+Z+X6k1d45ez33dw+7D/bZc8+ZT2a+nJmvZ+ZP6SzMOvtYmFs6S5IkSZIktdySZghFxMqurZp/F5jdgewg8EBE3EVnUel1wLd6Tjmkjr50mm27vlrq3KndmytOI0mSJEmSRsWiA0IR8SAwAayIiGngDmAiIt5F53GwKeAPADLz2Yh4GPgPYAbY4Q5jkiRJzSr7Syh/ASVJ0uhYdEAoM2+ap/neBc7/NPDpXkJJbeBfniVJkiRJw2pJawhJkiTp/CJiTUR8MyKORcSzEfHxov2yiHg0Ip4rPr+laI+IuDsijkfE0xGxodk7kCRJw84BIUmSpP6bAXZm5tuBjcCOiLgC2AUczsx1wOHiNcC1dNZeXAdsp7OjqyRJUmUcEJIkSeqzzDyZmU8Wx68Cx4BVwBZgX3HaPuD64ngLcH92HAGWR8TKmmNLkqQRsqRdxiRJklRORKwF3g08AYzN7tSamScj4vLitFXAi11/bLpoO9nVRkRspzODiLGxMSYnJ0tlGLsEdq6fWfS8su+3VGfOnKn8Gt0WuufuPqkz01x198lC2pKlLTmgXVkkqd8cEJIkSapIRFwKfAn4RGa+EhHnPXWetjynIXMPsAdgfHw8JyYmSuW4Z/8B7jy6+F/7pm4u935LNTk5SdnM/bDQ5hA718+80SdV3/dC6u6ThbQlS1tyQLuySFK/+ciYJElSBSLiYjqDQfsz88tF88uzj4IVn08V7dPAmq4/vho4UVdWSZI0ehwQkiRJ6rPoTAW6FziWmXd1fekgsLU43goc6Gq/tdhtbCNwevbRMkmSpCr4yJgkSVL/XQXcAhyNiKeKtk8Bu4GHI+J24AXghuJrh4DrgOPAa8Bt9caVJEmjxgEhSZKkPsvMx5l/XSCAq+c5P4EdlYaSJEnq4iNjkiRJkiRJI8YZQpIkSZIk1WjtIrsgzu6SOLV7c12RNIKcISRJkiRJIyIi1kTENyPiWEQ8GxEfL9ovi4hHI+K54vNbivaIiLsj4nhEPB0RG5q9A0n94oCQJEmSJI2OGWBnZr4d2AjsiIgrgF3A4cxcBxwuXgNcC6wrPrYDn60/sqQqOCAkSZIkSSMiM09m5pPF8avAMWAVsAXYV5y2D7i+ON4C3J8dR4DlEbGy5tiSKuAaQpIkSZI0giJiLfBu4AlgLDNPQmfQKCIuL05bBbzY9cemi7aTc95rO50ZRIyNjTE5OVkqw9glnTVzFlP2/ZbqzJkzlV+j20L33N0ndWaaq+4+WUhbsrQlB/QniwNC0gBbaDG6ufZuWlZhEkmSJA2SiLgU+BLwicx8JSLOe+o8bXlOQ+YeYA/A+Ph4TkxMlMpxz/4D3Hl08X+WTt1c7v2WanJykrKZ+2HbIotKz/ZJ1fe9kLr7ZCFtydKWHNCfLD4yJkmSJEkjJCIupjMYtD8zv1w0vzz7KFjx+VTRPg2s6frjq4ETdWWVVJ1FB4Qi4r6IOBURz3S1uQK9JEmSJA2Y6EwFuhc4lpl3dX3pILC1ON4KHOhqv7X4t95G4PTso2WSBluZGUJ7gU1z2lyBXpIkSZIGz1XALcD7IuKp4uM6YDdwTUQ8B1xTvAY4BDwPHAc+D3y0gcySKrDow5qZ+Vix2Fi3LcBEcbwPmAQ+SdcK9MCRiFgeESsdQZYkNenoS6cXfFa/29TuzRWnkSSpOZn5OPOvCwRw9TznJ7Cj0lCSGrHURaV7WoEelr4KfZtW9e5V2RX1odnV5ctqyw4B/TII91P2+weG62dHkiRJktSbfu8yVmoFelj6KvRtWtW7V2VX1IdmV5cvqy07BPTLINxP2RkP0NllbFh+diRJkiRJvVnqLmOuQC9JkiRJkjSgljog5Ar0kiRJkiRJA2rR52Ei4kE6C0iviIhp4A46K84/HBG3Ay8ANxSnHwKuo7MC/WvAbRVkliRJkiRJUg/K7DJ203m+5Ar0kiRJkiRJA6jfi0pLUq0i4j7gA8CpzHxH0XYZ8BCwFpgCPpyZP46IAD5DZybja8C2zHyyidxSL46+dLrUovJTuzfXkEaSJEmDaKlrCElSW+wFNs1p2wUczsx1wOHiNcC1wLriYzvw2ZoySpIkSVKrOCAkaaBl5mPAj+Y0bwH2Fcf7gOu72u/PjiPA8tkdEyVJkiRplPjImKRhNDa7w2FmnoyIy4v2VcCLXedNF21n7YYYEdvpzCBibGyMycnJUhc9c+ZM6XPbbpjuBWDsEti5fqbUuYNw32Xvp8l7KdvfMHzfb5IkSYPAASFJoyTmactzGjL3AHsAxsfHc2JiotSbT05OUvbcthumewG4Z/8B7jxa7n95UzdPVBumD8reT5P3UmaNo1l7Ny0bqu83SZKkQeAjY5KG0cuzj4IVn08V7dPAmq7zVgMnas4mSZIkSY1zQEjSMDoIbC2OtwIHutpvjY6NwOnZR8skSZIkaZT4yJikgRYRDwITwIqImAbuAHYDD0fE7cALwA3F6YfobDl/nM6287fVHliSJEmSWsABIUkDLTNvOs+Xrp7n3AR2VJXl6EunS62bMrV7c1URJLVIRNwHfAA4lZnvKNouAx4C1gJTwIcz88cREcBn6AxavwZsy8wnm8gtSZJGg4+MSZIkVWMvsGlO2y7gcGauAw4XrwGuBdYVH9uBz9aUUZIkjSgHhCRJkiqQmY8BP5rTvAXYVxzvA67var8/O44Ay2cXx5ckSaqCj4xJkiTVZ2x2MfvMPBkRlxftq4AXu86bLtrOWvg+IrbTmUHE2NgYk5OT5S56CexcP7PoeWXfb6nOnDlT+TW6LXTP3X1SZ6a56u6ThbQlS1tyQLuySFK/OSAkSZLUvJinLc9pyNwD7AEYHx/PiYmJUm9+z/4D3Hl08b/2Td1c7v2WanJykrKZ+2Ghdd12rp95o0+qvu+F1N0n3dbO6Z+d61/nzsd/Mu+5da5/12SfzNWmLJLUbw4ISZLOUXaBbHCRbOkCvRwRK4vZQSuBU0X7NLCm67zVwIna00mSpJHhGkKSJEn1OQhsLY63Age62m+Njo3A6dlHyySp3yLivog4FRHPdLVdFhGPRsRzxee3FO0REXdHxPGIeDoiNjSXXFI/OSAkSZJUgYh4EPhX4G0RMR0RtwO7gWsi4jngmuI1wCHgeeA48Hngow1EljQ69uIuiNLI85ExSZKkCmTmTef50tXznJvAjmoTSVJHZj4WEWvnNG8BJorjfcAk8Em6dkEEjkTE8tlHX+tJK6kqPQ0IRcQU8CrwOjCTmeMRcRnwELAWmAI+nJk/7i2mJEmSJKlC7oLoLohnadMue23J0pYc0J8s/Zgh9NuZ+YOu17NTDXdHxK7i9Sf7cB1JkiRJUr3cBbEC7oJ4YdqSpS05oD9ZqlhDaAudKYYUn6+v4BqSJEmSpP55udj9EHdBlEZDrzOEEvhGRCTwN8Wo8PmmGp5lqVMK2zRFq1dlp0dCs1MFy2rLdM9+GYT7Kfv9A8P1syNJkqS+m90FcTfn7oL4sYj4AvAe3AVRGhq9DghdlZknikGfRyPie2X/YE9TCh//yaLnTe3eXDZKY8pOj4RmpwqW1Zbpnv0yCPez0FTTufZuWtaa6Y2SJElqTrEL4gSwIiKmgTvoDAQ9XOyI+AJwQ3H6IeA6OrsgvgbcVntgSZXoaUAoM08Un09FxCPAlRRTDYvZQd1TDSVJkiRJDXMXREnQwxpCEei9m4cAAAg3SURBVLEsIt48ewy8H3iGn001hLOnGkqSJEmSJKkFepkhNAY8EhGz7/NAZn49Ir7N/FMNJUmSJEmS1AJLHhDKzOeBd87T/kPmmWooSZIkSZKkdqhi23lJkiRJkiS1mANCkiRJkiRJI8YBIUmSJEmSpBHjgJAkSZIkSdKI6WWXMUmSJEmSpL5au+urZ73euX6GbXPaZk3t3lxHpKHkDCFJkiRJkqQR44CQJEmSJEnSiHFASJIkSZIkacS4hpAkSZKkkTF3bZKF7N20rMIkktQsZwhJkiRJkiSNGAeEJEmSJEmSRowDQpIkSZIkSSPGASFJkiRJkqQR44CQJEmSJEnSiHFASJIkSZIkacS47bwkSZIkSVLD1u76aulz925a1vP1nCEkSZIkSZI0YiobEIqITRHx/Yg4HhG7qrqOJF0o65OktrI+SWor65M0fCoZEIqIi4C/Bq4FrgBuiogrqriWJF0I65OktrI+SWor65M0nKqaIXQlcDwzn8/M/wO+AGyp6FqSdCGsT5Layvokqa2sT9IQiszs/5tGfAjYlJkfKV7fArwnMz/Wdc52YHvx8m3A90u+/QrgB32M26Rhuhfwftqu7P38ama+teowTbE+lTZM9wLeT9tZnxiZ+tSWHNCeLG3JAe3J0pYcYH0CrE8NaEuWtuSA9mRpSw7oQ32qapexmKftrJGnzNwD7LngN474TmaOLzVYmwzTvYD303bDdj89sD6VMEz3At5P2w3b/fRg6OtTW3JAe7K0JQe0J0tbckC7sjTM+lSjtmRpSw5oT5a25ID+ZKnqkbFpYE3X69XAiYquJUkXwvokqa2sT5LayvokDaGqBoS+DayLiF+LiJ8DbgQOVnQtSboQ1idJbWV9ktRW1idpCFXyyFhmzkTEx4B/Ai4C7svMZ/v09hc8DbHFhulewPtpu2G7nyWxPpU2TPcC3k/bDdv9LMmI1Ke25ID2ZGlLDmhPlrbkgHZlaYz1qXZtydKWHNCeLG3JAX3IUsmi0pIkSZIkSWqvqh4ZkyRJkiRJUks5ICRJkiRJkjRiBmJAKCLui4hTEfFM01n6ISLWRMQ3I+JYRDwbER9vOlMvIuIXIuJbEfHvxf38edOZehURF0XEv0XEV5rO0quImIqIoxHxVER8p+k8w2iYapT1qf2sTzqfxWpRdNwdEccj4umI2NBQjomIOF38d38qIv6sihzFtRataXX0S8kctfRLmboYET8fEQ8VffJERKxtKMe2iPivrj75SL9zdF3rvLW1jv4Ydtanea9lfTr3Otan+fNUV58ys/UfwHuBDcAzTWfp0/2sBDYUx28G/hO4oulcPdxPAJcWxxcDTwAbm87V4z39MfAA8JWms/ThXqaAFU3nGOaPYapR1qf2f1if/FigPxesRcB1wNeKn4uNwBMN5Zio6/u3TE2ro19K5qilX8rUReCjwOeK4xuBhxrKsQ34q5q+V85bW+voj2H/sD7Ney3r07lZrE/z56msPg3EDKHMfAz4UdM5+iUzT2bmk8Xxq8AxYFWzqZYuO84ULy8uPgZ2tfKIWA1sBv626SwaDMNUo6xP7WZ90kJK1KItwP3Fz8URYHlErGwgR21K1rTK+6VNtbVkXdwC7CuOvwhcHRHRQI5alKitlffHsLM+ncv6NG8W69McVdengRgQGmbFlK530xl1HFjFNLangFPAo5k5yPfzl8CfAD9tOkifJPCNiPhuRGxvOowGh/WplaxP6sUq4MWu19M0N+D7W8VU/K9FxK/XccEFalqt/bJIba2lX0rUxTf6JDNngNPALzWQA+D3ikdlvhgRa/qdobBYba2lP0ac9cn6NJvB+nS2SuuTA0INiohLgS8Bn8jMV5rO04vMfD0z3wWsBq6MiHc0nWkpIuIDwKnM/G7TWfroqszcAFwL7IiI9zYdSO1nfWof65P6YL7fGDbxG88ngV/NzHcC9wD/UPUFF6lptfXLIjlq65cSdbGWPimR4x+BtZn5G8A/87PfgvdNydralp+dYdaWPrY+WZ/K5hiK+uSAUEMi4mI6P3D7M/PLTefpl8z8b2AS2NRwlKW6CvhgREwBXwDeFxF/32yk3mTmieLzKeAR4MpmE6ntrE+tZX1Sr6aB7t9grgZO1B0iM1+ZnYqfmYeAiyNiRVXXK1HTaumXxXLU3S/Fdc5XF9/ok4h4E/CLVPiYzflyZOYPM/N/i5efB36zgsuXqa219seIsj5Zn+Ze0/pUQ31yQKgBxTN99wLHMvOupvP0KiLeGhHLi+NLgN8BvtdsqqXJzD/NzNWZuZbOolz/kpm/33CsJYuIZRHx5tlj4P3AwO+EpepYn9rL+qQ+OAjcGh0bgdOZebLuEBHxy7PrG0TElXT+PvrDiq5VpqZV3i9lctTVLyXr4kFga3H8ITr1pq+/gS+TI85eK+WDdNY26auStbXy/pD16TynWZ+sT5XWpzf1JWnFIuJBOiubr4iIaeCOzLy32VQ9uQq4BThaPJcI8KlitHUQrQT2RcRFdIrDw5k58NshD4kx4JGifr8JeCAzv95spOEzZDXK+qS6WJ/6bL5aRGchTDLzc8AhOjvWHAdeA25rKMeHgD+MiBngf4AbK/zH9bw1DfiVrjx19EuZHHX1y7x1MSL+AvhOZh6k84/Dv4uI43R+03xjQzn+KCI+CMwUObZVkGNeDfTHULM+zcv6dC7rUwn97I9wcFuSJEmSJGm0+MiYJEmSJEnSiHFASJIkSZIkacQ4ICRJkiRJkjRiHBCSJEmSJEkaMQ4ISZIkSZIkjRgHhCRJkiRJkkaMA0KSJEmSJEkj5v8BvI+cm3vUFyMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a histogram for all numeric attributes:\n",
    "\n",
    "df.hist(bins=20,figsize=(20,10))\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the histograms above, we can highlight that:\n",
    "\n",
    "* G1, G2 and G3 has normal distribution with G3 slightly right skewed.  \n",
    "* 'absence' presents an exponential distribution.\n",
    "* Not much difference between parents education indicated by 'Fedu' and 'Medu' attributes.\n",
    "* Similar trends appear between alcohol consumption on workdays (Dalc) and on weekends (Walc).\n",
    "* A slightly right skewed normal distribution for 'age' with a expected mean of 17 years-old.\n",
    "* Majority of students have not failed classes in the past, as indicated by 'failures'\n",
    "* The family quality (famrel) relationship presented a left skewed histogram, i.e., towards excelent.\n",
    "* 'health' also slightly left skewed histogram\n",
    "* 'freetime', 'studytime' and 'goout' (indicating frequency students socialise with friends) seems to follow a normal distribution.\n",
    "* Majority of the students live near the school, as illustrated by 'traveltime'.\n",
    "\n",
    "\n",
    "We can also perform boxplots to better understand the distribution of the numerical data:\n",
    "\n",
    "__Figure 8 - Boxplots of Distribution__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e535390>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Next, we look at boxplots to understand the distribution better.\n",
    "\n",
    "df.boxplot(figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boxplot above confirms the presence of outliers on the majority of features. The outiliers are more accentuated in the 'absences' attribute.\n",
    "\n",
    "Let us concentrate on 'G1', 'G2' and 'G3'.\n",
    "\n",
    "__Figure 9 - Boxplots of G1, G2 & G3__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e941d50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(figsize=(15,5),column=['G1','G2','G3'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that the median for 'G3' is higher thant 'G1' and 'G2'. However, 'G3' seems also to have higher variance.\n",
    "\n",
    "Now let's examine if we can find some relationship between the attributes with a scatter plot, by using `scatter_matrix` from pandas.\n",
    "\n",
    "__Figure 10 - Scatter Matrix of ALL Features__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 256 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "scatter_matrix(df, figsize = (20, 20))\n",
    "plt.suptitle(\"\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All possible combinations are on 'off-diagonal'. The diagonal plots are the respective histograms of the attributes. Note that the grades are high in each instance.\n",
    "\n",
    "Whilst this grid gives us an insight into paired combinations of features, it is only 2 dimensional. Once we want to start exploring which combinations of features have the most significant inpact on our target result, we will need to use Machine Learnign Feature Selection Algorithms (section 3).\n",
    "\n",
    "## d) Distribution Analysis for Categorical Nominal Attributes\n",
    "\n",
    "Here we are analysing the categorical features that are not ordered, or nominal attributes. \n",
    "Let's extract them from the dataset by creating a new dataset called cat_df. \n",
    "To do this we use the method .copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_df = df.select_dtypes(include=['object']).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 11 - Head of cat_df dataframe__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "      <th>schoolsup</th>\n",
       "      <th>famsup</th>\n",
       "      <th>paid</th>\n",
       "      <th>activities</th>\n",
       "      <th>nursery</th>\n",
       "      <th>higher</th>\n",
       "      <th>internet</th>\n",
       "      <th>romantic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex address famsize Pstatus     Mjob      Fjob  reason guardian  \\\n",
       "0     GP   F       U     GT3       A  at_home   teacher  course   mother   \n",
       "1     GP   F       U     GT3       T  at_home     other  course   father   \n",
       "2     GP   F       U     LE3       T  at_home     other   other   mother   \n",
       "3     GP   F       U     GT3       T   health  services    home   mother   \n",
       "4     GP   F       U     GT3       T    other     other    home   father   \n",
       "\n",
       "  schoolsup famsup paid activities nursery higher internet romantic  \n",
       "0       yes     no   no        NaN     yes    yes       no       no  \n",
       "1        no    yes   no         no      no    yes      yes       no  \n",
       "2       yes     no   no         no     yes    yes      yes       no  \n",
       "3        no    yes   no        NaN     yes    yes      yes      yes  \n",
       "4        no    yes   no         no     yes    yes       no       no  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Missing Values in cat_df dataframe:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school          0\n",
       "sex             0\n",
       "address         0\n",
       "famsize         0\n",
       "Pstatus        36\n",
       "Mjob            0\n",
       "Fjob           67\n",
       "reason         63\n",
       "guardian        0\n",
       "schoolsup       0\n",
       "famsup         52\n",
       "paid            0\n",
       "activities    130\n",
       "nursery         0\n",
       "higher          0\n",
       "internet        0\n",
       "romantic        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 12 - Categorical Data in cat_df dataframe__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <td>T</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
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       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
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       "      <td>no</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>2</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
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       "      <td>3</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex address famsize Pstatus     Mjob      Fjob  reason guardian  \\\n",
       "0     GP   F       U     GT3       A  at_home   teacher  course   mother   \n",
       "1     GP   F       U     GT3       T  at_home     other  course   father   \n",
       "2     GP   F       U     LE3       T  at_home     other   other   mother   \n",
       "3     GP   F       U     GT3       T   health  services    home   mother   \n",
       "4     GP   F       U     GT3       T    other     other    home   father   \n",
       "\n",
       "  schoolsup famsup paid activities nursery higher internet romantic  \n",
       "0       yes     no   no        NaN     yes    yes       no       no  \n",
       "1        no    yes   no         no      no    yes      yes       no  \n",
       "2       yes     no   no         no     yes    yes      yes       no  \n",
       "3        no    yes   no        NaN     yes    yes      yes      yes  \n",
       "4        no    yes   no         no     yes    yes       no       no  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 13 - Histogram of Categorical Data in cat_df dataframe__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 35 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "categoricalList = cat_df.columns.tolist()\n",
    "\n",
    "fig, axs = plt.subplots(3,6, sharex='row', figsize=(15, 10))\n",
    "fig.subplots_adjust(hspace = 0.7, wspace=0.7)\n",
    "\n",
    "num=1\n",
    "for attribute in categoricalList:\n",
    "    ax = fig.add_subplot(3,6,num)\n",
    "   \n",
    "    values = cat_df[attribute].value_counts()/cat_df[attribute].count()\n",
    "    values.plot.bar( color=['red', 'blue', 'green', 'yellow', 'orange', 'purple'] )\n",
    "    num+=1\n",
    "\n",
    "    ax.set_title(attribute)\n",
    "    plt.ylim(0,1)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By analysing the bar chart from the categorical data we can observe that, the student profile in the dataset is:\n",
    "\n",
    "<pre>\n",
    "~60% is GP school\n",
    "~60% is Female\n",
    "~70% live in Urban areas\n",
    "~70% have a family size > 3\n",
    "~80% have parents living together\n",
    "~70% have a Mother as a guardian\n",
    "~90% do not get extra education support from the school\n",
    "~60% get support from their family\n",
    "~95% do not pay for extra classes within the course subject\n",
    "~45% do participate in extra-curricular activities\n",
    "~80% attended nursery school\n",
    "~90% want to go into higher education\n",
    "~80% have internet access at home\n",
    "~60% are not in a romantic relationship\n",
    "+40% of the Father and Mother jobs belong to the 'other' classification \n",
    "</pre>\n",
    "\n",
    "===================================================================================================================\n",
    "\n",
    "\n",
    "# 3 - Data Preparation\n",
    "\n",
    "## a) Missing Data\n",
    "\n",
    "In this section we will deal with the missing data. \n",
    "\n",
    "There are different methods that we can apply which include:\n",
    "- removing the instances (rows) that contains the missing data\n",
    "- filling with the most frequent data or with the average value\n",
    "- or using prediction methods to predict the missing values based on the other attributes\n",
    "\n",
    "We used 2 approaches to fill the missing data, and we will examine both in this section.\n",
    "\n",
    "__APPROACH 1__\n",
    "\n",
    "In our first approach, we are going to use the replacement by the most_frequent value since the majority of our categorical data is nominal, i.e., only 'Medu' and 'failures' are numerical.\n",
    "\n",
    "The new dataframe, with all missing values filled, will be called 'newDf'.\n",
    "\n",
    "\n",
    "__APPROACH 2__\n",
    "\n",
    "In our second approach, we are going to look to see if any trends exist between the features with missing data, and any other features, to see if they can be used to determine the missing values.\n",
    "\n",
    "The new dataframe with these misisng values filled, will be called 'new2Df'.\n",
    "\n",
    "### APPROACH 1\n",
    "\n",
    "New Database with Missing Values Filled:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling missing data with the mean for numerical attribute\n",
    "newDf = df\n",
    "newDf['Medu'].fillna(np.trunc(df['Medu'].mean()), inplace=True)\n",
    "newDf['failures'].fillna(np.trunc(df['failures'].mean()), inplace=True)\n",
    "\n",
    "# getting the values only\n",
    "temp = newDf.loc[:,:].values \n",
    "\n",
    "# filling the matrix with the most frequent value\n",
    "imputer_most_frequent = SimpleImputer(strategy = \"most_frequent\")\n",
    "imputer_most_frequent.fit(temp)\n",
    "temp = imputer_most_frequent.transform(temp)\n",
    "\n",
    "# converting to dataframe\n",
    "pd.DataFrame(temp)\n",
    "newDf = pd.DataFrame(temp, columns=df.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking if all the values have filled:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school        0\n",
       "sex           0\n",
       "age           0\n",
       "address       0\n",
       "famsize       0\n",
       "Pstatus       0\n",
       "Medu          0\n",
       "Fedu          0\n",
       "Mjob          0\n",
       "Fjob          0\n",
       "reason        0\n",
       "guardian      0\n",
       "traveltime    0\n",
       "studytime     0\n",
       "failures      0\n",
       "schoolsup     0\n",
       "famsup        0\n",
       "paid          0\n",
       "activities    0\n",
       "nursery       0\n",
       "higher        0\n",
       "internet      0\n",
       "romantic      0\n",
       "famrel        0\n",
       "freetime      0\n",
       "goout         0\n",
       "Dalc          0\n",
       "Walc          0\n",
       "health        0\n",
       "absences      0\n",
       "G1            0\n",
       "G2            0\n",
       "G3            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newDf.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since, we used the most frequent values to fill the missing data, we need to check if we have any major changes in the numerical attributes for 'Medu' and 'failures' by Mean and Variance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.48 +/- 1.23\n"
     ]
    }
   ],
   "source": [
    "print(round(df['Medu'].mean(),2),'+/-',round(df['Medu'].var(),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.48 +/- 1.23\n"
     ]
    }
   ],
   "source": [
    "print(round(newDf['Medu'].mean(),2),'+/-',round(newDf['Medu'].var(),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.19 +/- 0.31\n"
     ]
    }
   ],
   "source": [
    "print(round(df['failures'].mean(),2),'+/-',round(df['failures'].var(),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.19 +/- 0.31\n"
     ]
    }
   ],
   "source": [
    "print(round(newDf['failures'].mean(),2),'+/-',round(newDf['failures'].var(),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 14 - Checking New Database against Original Database  - 'Medu' feature__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([df['Medu'],newDf['Medu']], bins=20, label=['df','newDf'], stacked=False)\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 15 - Checking New Database against Original Database - 'failure' feature__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([df['failures'],newDf['failures']], bins=20, label=['df','newDf'])\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that filling the missing values with the most frequent values does not have great impact on the final results.\n",
    "\n",
    "### APPROACH 2\n",
    "\n",
    "We will try to fill in what looks to be the easier attributes first. \n",
    "\n",
    "__`failures`__\n",
    "\n",
    "There is a total of 97 missing values for past class failures. The attribute contains a numeric value between 0-4. Let's look at the counts of each value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    565\n",
       "1.0     60\n",
       "3.0     13\n",
       "2.0     11\n",
       "Name: failures, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2Df = df\n",
    "new2Df.failures.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells us that the vast majority of students have 0 failures. As the dataset is relatively small we could just fill in all our missing values with 0, but let's try going one step further to get a little bit more accuracy.\n",
    "\n",
    "One thing we can do for failures is to compare it to the final grade. We would expect students with a low final grade to have at least 1 failure.\n",
    "\n",
    "__Figure 16 - G3 v failures__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24560390>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"G3\", y=\"failures\", data=new2Df, ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this we can see that the majority of students who have at least 1 failure seem to have a grade less than 5. We can double check this by getting the counts for a grade less than 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    7\n",
       "1.0    7\n",
       "3.0    2\n",
       "2.0    1\n",
       "Name: failures, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2Df.failures[df['G3'] <= 5].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of students who have a grade less than 5 have 1 failure (at least). We can now determine that students with grades less than 5 have 1 failure and students with grade greater than 5 will have 0 failures.\n",
    "\n",
    "We can fill in the missing values as per this conclusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "new2Df.loc[(df['G3'] > 5) & (new2Df['failures'].isnull()), 'failures'] = 0\n",
    "new2Df.loc[(df['G3'] <= 5) & (new2Df['failures'].isnull()), 'failures'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__`Medu`__\n",
    "\n",
    "There is a total of 29 missing values for mother's education. Again this a numeric attribute between 0-4. Let's get the counts for each value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    207\n",
       "4.0    163\n",
       "1.0    137\n",
       "3.0    136\n",
       "0.0      6\n",
       "Name: Medu, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2Df.Medu.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is a relatively even distibution across all the values bar 0.\n",
    "\n",
    "The first thing we can do for Medu is to look at it in terms of the mother's job.\n",
    "\n",
    "__Figure 17 - Medu v Mjob__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Mjob</th>\n",
       "      <th>at_home</th>\n",
       "      <th>health</th>\n",
       "      <th>other</th>\n",
       "      <th>services</th>\n",
       "      <th>teacher</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medu</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2.0</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "      <td>103</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>56</td>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>31</td>\n",
       "      <td>29</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Mjob  at_home  health  other  services  teacher\n",
       "Medu                                           \n",
       "0.0         3       0      3         0        0\n",
       "1.0        64       2     65         6        0\n",
       "2.0        43       8    103        49        4\n",
       "3.0        20       5     56        52        3\n",
       "4.0         5      33     31        29       65"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2Df_Medu = pd.crosstab(new2Df.Medu, new2Df.Mjob)\n",
    "new2Df_Medu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells us the following about a mothers job and her likely education value:\n",
    "- at home - 1\n",
    "- health - 4\n",
    "- other - 2\n",
    "- services - 3\n",
    "- teacher - 4\n",
    "\n",
    "Now we can fill in the missing values based on the above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "new2Df.loc[(new2Df['Mjob'] == 'at_home') & (new2Df['Medu'].isnull()), 'Medu'] = 1\n",
    "new2Df.loc[(new2Df['Mjob'] == 'other') & (new2Df['Medu'].isnull()), 'Medu'] = 2\n",
    "new2Df.loc[(new2Df['Mjob'] == 'services') & (new2Df['Medu'].isnull()), 'Medu'] = 3\n",
    "new2Df.loc[(new2Df['Mjob'] == 'health') & (new2Df['Medu'].isnull()), 'Medu'] = 4\n",
    "new2Df.loc[(new2Df['Mjob'] == 'teacher') & (new2Df['Medu'].isnull()), 'Medu'] = 4 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__`activities`__\n",
    "\n",
    "There is a total of 130 missing values for extra curricular activities. This is a binary attribute with 'yes' and 'no' values. Let's look at the counts for both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     272\n",
       "yes    247\n",
       "Name: activities, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2Df.activities.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again the distribution is close for both values. Let's check how the students free time correalates with activities.\n",
    "\n",
    "__Figure 18 - freetime v activities__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>activities</th>\n",
       "      <th>no</th>\n",
       "      <th>yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>96</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>81</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "activities  no  yes\n",
       "freetime           \n",
       "1           29    9\n",
       "2           48   38\n",
       "3           96   93\n",
       "4           81   71\n",
       "5           18   36"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2Df_activities = pd.crosstab(new2Df.freetime, new2Df.activities)\n",
    "new2Df_activities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells us that a student with a free time value of 1 is __not likely__ do any activities, while a student with a freetime value of 5 __is likely__ to do activities. We cannot make a judgement on values in between as they are too close to call.\n",
    "\n",
    "We can fill partially fill in missing values based on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "new2Df.loc[(new2Df['freetime'] == 1) & (new2Df['activities'].isnull()), 'activities'] = \"no\"\n",
    "new2Df.loc[(new2Df['freetime'] == 5) & (new2Df['activities'].isnull()), 'activities'] = \"yes\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to fill in the remaining missing values for activities. Let's look at the students final grades compared to their activities.\n",
    "\n",
    "__Figure 19 - G3 v activities__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24912950>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9zsyqzGyrmc2Itxeb2Sozq45vuzQWS0REsifJFv8J4O/cfQAwErjNzC4GZgFr3L0/sCZeFxGRHEms8Lv7Xnd/NV7+EKgCzgfGAkvjhy0FxiWVg4iInC0nffxmVkr05ewvAT3cfS9EHw5A93r2mW5mG81s44EDB3KRpohIEBIv/GbWAXga+IG7/zHV/dx9kbuXuXtZSUlJcgmKiAQm0cJvZkVERf9xd/9VvHmfmfWM7+8J7E8yBxER+bwkR/UY8BBQ5e7/u9ZdzwBT4+WpwMqkchARkbMlOWXDV4HvAm+Y2aZ42z8A9wLLzWwa8A5wfYI5SDOgS/sLQ66njH7n7oEN3t9n7hvZPaCclljhd/f/AKyeu0cldVwREWmYrtwVEQmMCr+ISGA0LbOkTX3yIs2bWvwiIoFR4RcRCYwKv4hIYFT4RUQCo8IvIhIYFX4RkcCo8IuIBEaFX0QkMCr8IiKBUeEXEQmMpmwQkRZB0zynTi1+EZHAqPCLiARGhV9EJDDq45esU1+rNHct/TWsFr+ISGBU+EVEAqPCLyISGBV+EZHAqPCLiARGhV9EJDAq/CIigdE4fhFpFobNfKTB+1d0zFEiLYBa/CIigVHhFxEJjAq/iEhgEiv8ZrbYzPab2ZZa24rNbJWZVce3XZI6voiI1C3JFv8SYPQZ22YBa9y9P7AmXhcRkRxKrPC7+zrg0BmbxwJL4+WlwLikji8iInXL9XDOHu6+F8Dd95pZ9/oeaGbTgekAffr0yVF6Uoha+hS5kh8NDQ9t6UNDC/bkrrsvcvcydy8rKSnJdzoiIi1Grgv/PjPrCRDf7s/x8UVEgpfrwv8MMDVengqszPHxRUSCl+Rwzl8ALwIXmVmNmU0D7gX+ysyqgb+K10VEJIcSO7nr7t+p565RSR1TREQaV7And0VEJBkq/CIigVHhFxEJjAq/iEhgVPhFRAKjb+ASEclQY98OVjn/hhxlkhq1+EVEAqPCLyISGBV+EZHAtKg+fk3fW7+G+iALrf9RRJKlFr+ISGBU+EVEAqPCLyISmGbVx9/YWNmW/nVpIiLZoBa/iEhgVPhFRAKjwi8iEphm1ccvItIcFdo1Rmrxi4gERoVfRCQwKvwiIoFRH78UXP+jiCRLLX4RkcCo8IuIBEZdPQWquX2Vm4g0H2rxi4gERoVfRCQwKvwiIoEJto8/233oue6T1xBMkZar8Sno5zd4f2Pv/7y0+M1stJm9aWY7zGxWPnIQEQlVzgu/mbUGfg58HbgY+I6ZXZzrPEREQpWPFv8IYIe773T3T4AngbF5yENEJEjm7rk9oNl1wGh3vyle/y7wFXf//hmPmw5Mj1cvAt5MIXw34A9ZSjWbsQo9XiHnVujxCjm3bMcr5NyyHa+Qc0sn3hfdveTMjfk4uWt1bDvr08fdFwGL0gpsttHdy5qaWFKxCj1eIedW6PEKObdsxyvk3LIdr5Bzy0a8fHT11AAX1FrvDezJQx4iIkHKR+F/BehvZn3NrC3wbeCZPOQhIhKknHf1uPsJM/s+8G9Aa2Cxu2/NUvi0uoZyGKvQ4xVyboUer5Bzy3a8Qs4t2/EKObeM4+X85K6IiOSXpmwQEQmMCr+ISGBaROHP5hQQZrbYzPab2ZYs5rfLzN4ws01mtjHDWJ3N7Ckz225mVWZ2WQaxLopzOvXzRzP7QYb5/dDMtprZFjP7hZm1yyDWjDjO1qbkVdff0syKzWyVmVXHt10yjHd9nN9JM0treF098ebHf9vXzWyFmXXOINY9cZxNZlZhZr0yya3WfbebmZtZt0zimdk8M9td6/X3jUxyM7O/jevAVjP7pwxzW1Yrr11mtinDeEPMbMOpGmBmIzKMN9jMXozrym/M7LxU4wHg7s36h+gE8VtAP6AtsBm4OIN4VwBDgS1ZzHEX0C1LsZYCN8XLbYHOWfw9vkd0wUdTY5wPvA18IV5fDvzXJsb6MrAFaE80CGE10D/TvyXwT8CseHkWcF+G8QYQXWC4FijLQn7lQJt4+b5U86sn1nm1lv87sDCT3OLtFxANzPh9Oq/pevKbB9zehNdGXbGujl8j58Tr3TN9rrXu/wkwN8P8KoCvx8vfANZmGO8V4Mp4+UbgnnR+hy2hxZ/VKSDcfR1wKFvJZVP8qX4F8BCAu3/i7oezFH4U8Ja7/z7DOG2AL5hZG6Ki3dRrNAYAG9z9I3c/ATwPjE8nQD1/y7FEH57Et+MyiefuVe6eylXlqcariJ8vwAai61yaGuuPtVbPpY4LJdOJF3sA+Pt0YjUSL231xPoecK+7H4sfsz8buZmZAROBX2QYz4FTrfJOpPG+qCfeRcC6eHkV8F9SjQcto6vnfODdWus18bZC4kCFmVVaNBVFU/UDDgAPm9lrZvYvZnZudlLk26Tx4q6Lu+8G7gfeAfYCH7h7RRPDbQGuMLOuZtaeqJV0QSP7pKKHu++N890LdM9CzKTcCPy/TAKY2T+a2bvAZGBuhrHGALvdfXMmcc7w/bg7anE63W51+HPgL83sJTN73syGZym/vwT2uXt1hnF+AMyP/xb3A7MzjLcFGBMvX0+a742WUPhTmgIiz77q7kOJZiS9zcyuaGKcNkT/8i1w90uBI0TdFRmx6EK6McAvM4zThahF3RfoBZxrZlOaEsvdq4i6OlYBzxF14Z1ocKcWxMzuJHq+j2cSx93vdPcL4jjfb+zxDeTTHriTDD88zrAAuBAYQtRQ+EkGsdoAXYCRwExgedxaz9R3yLBBFPse8MP4b/FD4v/aM3AjUS2pBDoCn6Szc0so/AU/BYS774lv9wMriLqnmqIGqHH3l+L1p4g+CDL1deBVd9+XYZyvAW+7+wF3Pw78Cri8qcHc/SF3H+ruVxD9q5tpqwtgn5n1BIhvU+4SyBUzmwr8NTDZ407cLHiCNLsDznAh0Qf6ZjPbRfQ+e9XM/qypAd19n7t/6u4ngf9L098XEL03fuWRl4GTRBOZNVncXTkBWJZJnNhUovcDRA2sTJ4r7r7d3cvdfRjRB9Nb6ezfEgp/QU8BYWbnmlnHU8tEJ++aNGLI3d8D3jWzi+JNo4BtWUgzW62ad4CRZtY+bm2NAqqaGszMuse3fYjegNnI8RmiNyHx7cosxMwaMxsN3AGMcfePMozVv9bqGGB7U2O5+xvu3t3dS929lKjQDo1fk03Nr2et1fE08X0R+zVwTRz3z4kGPmQ6G+bXgO3uXpNhHIgao1fGy9eQYSOm1nujFfA/gYVpBUjnTHCh/hD1//4n0afenRnG+gXRv53HiV7c0zKM14+om2IzsDUL+Q0BNgKvE73Yu2QYrz1wEOiUpb/FXUQFZgvwKPEoiybGWk/0wbYZGJWNvyXQFVhD9MZbAxRnGG98vHwM2Af8W4bxdhCds9oU/6Q0EqeeWE/Hf4fXgd8A52frfUCaI9Xqye9R4I04v2eAnhnEags8Fj/fV4FrMn2uwBLg1iy97v4CqIxfyy8BwzKMN4Oo5v0ncC/xLAyp/mjKBhGRwLSErh4REUmDCr+ISGBU+EVEAqPCLyISGBV+EZHAqPCLpMjMepjZE2a2M55+40UzG29mI2rN5LjZzNKaU0gk1zScUyQF8QVpLwBL3X1hvO2LRBdGPQR84tHXivYkGqvdyz+bbE2koOT8O3dFmqlriIr76SskPZrJ9MEzHteOwpsrSuRz1NUjkppLiK4IrZOZfcXMthJdiXqrWvtSyFT4RZrAzH4e9+e/AuDuL7n7JcBwYLZl8M1jIklT4RdJzVZqzYTq7rcRTUJXUvtBHk0nfYToG8RECpIKv0hq/h1oZ2bfq7WtPUA8M2ybePmLRN+OtCvnGYqkSKN6RFIUj9h5APgK0TehHSGaDrct0RfiHCeaB/5ud/91vvIUaYwKv4hIYNTVIyISGBV+EZHAqPCLiARGhV9EJDAq/CIigVHhFxEJjAq/iEhg/j9E9xiZ/8t4/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"G3\", hue=\"activities\", data=new2Df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There would appear to be a trend whereby students with a final grade of 11 or less do not partake in activities while students with grades above 11 do partake. Let's double check this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     152\n",
       "yes    106\n",
       "Name: activities, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2Df.activities[df['G3'] <= 11].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yes    155\n",
       "no     127\n",
       "Name: activities, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2Df.activities[df['G3'] > 11].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can conclude that the above assumption is correct and fill in the missing values accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "new2Df.loc[(new2Df['G3'] <= 11) & (new2Df['activities'].isnull()), 'activities'] = \"no\"\n",
    "new2Df.loc[(new2Df['G3'] > 11) & (new2Df['activities'].isnull()), 'activities'] = \"yes\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__`Fjob`__\n",
    "\n",
    "There is a total of 67 missing values for father's job. This is a nominal attribute with 5 differant values. Let's look at the counts for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "other       332\n",
       "services    161\n",
       "at_home      35\n",
       "teacher      33\n",
       "health       21\n",
       "Name: Fjob, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2Df.Fjob.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of entries belong to 'other'. Now we can look at it in terms of the father's education. This is a similar approach to mother's education above except the opposite way around.\n",
    "\n",
    "__Figure 20 - Fedu v Fjob__"
   ]
  },
  {
   "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>Fjob</th>\n",
       "      <th>at_home</th>\n",
       "      <th>health</th>\n",
       "      <th>other</th>\n",
       "      <th>services</th>\n",
       "      <th>teacher</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fedu</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>63</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>41</td>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Fjob  at_home  health  other  services  teacher\n",
       "Fedu                                           \n",
       "0           1       0      4         0        0\n",
       "1          13       0    102        41        1\n",
       "2          14       3    122        54        0\n",
       "3           6       3     63        39        1\n",
       "4           1      15     41        27       31"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2Df_Fjob = pd.crosstab(new2Df.Fedu, new2Df.Fjob)\n",
    "new2Df_Fjob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells us that no matter what education value the father has, the most common job is 'other'.\n",
    "\n",
    "We can fill in the missing values based on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "new2Df.loc[(new2Df['Fjob'].isnull()), 'Fjob'] = 'other'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__`reason`__\n",
    "\n",
    "There is a total of 63 missing values for the students reason for choosing the school. This is a nominal attribute with 4 differant values. Let's look at the counts for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "course        258\n",
       "home          136\n",
       "reputation    130\n",
       "other          62\n",
       "Name: reason, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2Df.reason.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is very difficult to try make an educated guess for why the student choose the school. In this case we are better off to pick the most popular option ('course') and fill in accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "new2Df.loc[(new2Df['reason'].isnull()), 'reason'] = 'course'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__`famsup`__\n",
    "\n",
    "There is a total of 52 missing values for whether the students family receives support. This is a binary attribute with 'yes' and 'no' values. Let's look at the counts for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yes    364\n",
       "no     233\n",
       "Name: famsup, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2Df.famsup.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again this is a difficult attribute to guess. We are better off going with the most popular option ('yes')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "new2Df.loc[(new2Df['famsup'].isnull()), 'famsup'] = 'yes'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__`Pstatus`__\n",
    "\n",
    "There is a total of 36 missing values for parents living situation. This is a binary attribute with 'T' (living together) and 'A' (living apart) values. Let's look at the counts for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T    536\n",
       "A     77\n",
       "Name: Pstatus, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new2Df.Pstatus.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The vast majority (87%) of parents live together. We can fill in the filling values with 'T'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "new2Df.loc[(new2Df['Pstatus'].isnull()), 'Pstatus'] = 'T'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## b) Comparison of Missing Data Approaches\n",
    "\n",
    "We will measure the differences between the original data (without missing values) and the data with imputed missing values for both approaches.\n",
    "\n",
    "__Function used to measure differences:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pass in 3 dataframes\n",
    "    # 1. the original without any missing values\n",
    "    # 2. the one with the values removed\n",
    "    # 3. the one with the values filled back in again\n",
    "    \n",
    "def get_accuracy_filled_in_data(df_original_no_missing_values, df_with_values_missing, df_with_values_filled_in):\n",
    "    counter = 0\n",
    "    for row in range(df_with_values_missing.shape[0]):\n",
    "        for col in range (df_with_values_missing.shape[1]):\n",
    "            if df_original_no_missing_values.iloc[row][col] != df_with_values_filled_in.iloc[row][col]:\n",
    "                counter += 1\n",
    "    return (100 * (1 - counter/df_with_values_missing.isnull().sum().sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Reload untouched datasets:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the original dataset without any missing values\n",
    "df_original_no_missing_values = pd.read_csv('./studentPor.csv')\n",
    "# the dataset with the values removed\n",
    "df_with_values_missing = pd.read_csv('./students.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Accuracy of APPROACH 1:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59.07172995780591"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_accuracy_filled_in_data(df_original_no_missing_values, df_with_values_missing, newDf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Accuracy of APPROACH 2:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.60337552742617"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_accuracy_filled_in_data(df_original_no_missing_values, df_with_values_missing, new2Df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion:\n",
    "\n",
    "Both approaches used to fill the missing values resulted in approximately 60% accuracy relative to the original values.\n",
    "\n",
    "Therefore we can conclude that APPROACH 1 is the better option is it takes less work.\n",
    "\n",
    "If a higher accuracy is required then an option would be to actually train a machine learning algorithm to fill in missing data.\n",
    "\n",
    "## c) Outliers\n",
    "\n",
    "As observed in the EDA, the main attribute that presented a large number of outliers was'absences'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# need to convert to numerical\n",
    "newDf['absences']=newDf['absences'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 21 - Absences Boxplot__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Absences Boxplot')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(newDf['absences'])\n",
    "plt.title('Absences Boxplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.0\n"
     ]
    }
   ],
   "source": [
    "Q1 = newDf['absences'].quantile(0.25)\n",
    "Q3 = newDf['absences'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "print(IQR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba\n",
    "\n",
    "The interquartile range (IQR) is considered as statistcal spread of the values or variance, which is calculated by the difference between 1/4 and 3/4 percentiles, or IQR = Q3 - Q1.\n",
    "\n",
    "The whiskers in the boxplot are, in general, 1.5 times the inter-quartile range, and any value out of this range is consered as an outlier.\n",
    "\n",
    "Thus, we can remove the outliers by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "newDf['iqr']=(newDf['absences'] < (Q1 - 1.5 * IQR)) |(newDf['absences'] > (Q3 + 1.5 * IQR))\n",
    "\n",
    "newDfNoOutliers=newDf[newDf.iqr != True]\n",
    "\n",
    "newDfNoOutliers=newDfNoOutliers.drop('iqr', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 22 - Absences With Outliers Removed Boxplot__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'absences')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(newDfNoOutliers['absences'])\n",
    "plt.title('absences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['iqr']=(df['absences'] < (Q1 - 1.5 * IQR)) |(df['absences'] > (Q3 + 1.5 * IQR))\n",
    "\n",
    "dfNoOutliers=df[df.iqr != True]\n",
    "\n",
    "dfNoOutliers=dfNoOutliers.drop('iqr', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Dataset with outliers ==>  newDf\n",
    "+ Dataset without outliers ==>  newDfNoOutliers\n",
    "+ Dataset with outliers without missing values (original) ==> df\n",
    "+ Dataset (original) without outliers ==> dfNoOutliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whilst it is good practice to remove outliers from a database during the preparation phase, it is interesting to explore whether or not the outliers in this particular feature actually had any impact on our G3 target result, and to see which students were absent the most.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 23 - Absences With Outliers vs G3__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a25079910>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9g6Os2biTzbv6w+6acU0Z+EVkUESOA6uB7+KUbBjDGfq5uf7dM8a0miikQ7a6Ka/cVdXZufsiMg84B+iod6eMMa0r6NSRpv78lmz4FHATsBBnQpb341Tn/GD9umaMaUVBp4409ed3jP8m4H3Aa6r6W8B7gXfq1itjTMuKQjpkq/M7A9eoqo6KCCLSrqq7ROTdde2ZCVXYWRlB1x/28kGKrEHwInNhvn+rlvaxYsf+otdvWT3Nw+83/v0iMhf4F+AJEXkEeKt+3TJhCjsrI+j6w14+V2QtV2Ihk1Ue3nGAmx/Y7mv5XJG5kVSGRMwZJrnryT2s27S7If0Pat2m3Wx84W1iAu0JISZOnSG//Tf157dkw9WqekxVbwX+N3AvTsE2MwOFnZURdP1hLx+0yFrQInNhv39hF8kzlfkd6pmgqj+tR0dM8wg7KyPo+sNePmiRtaFx55t+vmqKzIX9/gXtv6k/v0M9JkLCLlIVdP1hLx+0yFrQInNhv39hF8kzlVngN0XCzsoIuv6wlw9aZC1okbmw37+wi+SZyizwmyJhF6kKuv6wl7/z2uVctLinoO2ixT2+s3o+e8W5XHjGXFIZZSytpDLKhWfM9Z3Vs2ppHytOn8O+w8O8+NZx9h0eZsXpcxr2/oVdJM9U5qtIW9isSJtpJWFPHRn21JOmeQQt0maM8SnsqSMtq8ZUYoHfmBoLe+rIsKeeNM3PAr8xNRb21JGWVWMqscBvTI2FPXWkZdWYSqq+gMsvEbkP+B2gX1XPd9vm4UzZeCawD/ikqh6tVx9MdIVZ62bV0j66Hv8luw4OTbQtnd9d1dSRd295hRPj2Ym2WW0x3/3PPS/I6w8qaK2isGstBV0+6Oev3uqW1SMilwEngG/lBf6vAEdU9XYRuQXoUdXPV/pbltVjqhE0qyVoVs5165/mmVeLv89ctLiH+1dfXHH5S2/bxP6BsaL2hXPaeeoLV1RcPmy5WkVefucNDrr9w16+mbKqGp7Vo6pbgCOe5o8DG9z7G7B6P6YOwq51UyroT9XuVSroT9XebILWKgq71lLYWVmN0Ogx/vmqegDAvS27+xSRG0Vkq4hsPXToUMM6aFpf0KyWoFk5URe0VlHQ7R/28q2QVdW0J3dV9W5VXamqK3t7e8PujmkhrV7rptUFrVUUdq2lsLOyGqHRgf+giCwAcG8bUyDcRErYtW685RoqtXstnNNeVXuzCVqrKOxaS2FnZTVCowP/RuAG9/4NwCMNXr9pEZt39XPd3c9y6Zef5Lq7n61qEpFa1LoJUqvn/tUXc3JXYcLcyV0JXyd2AZ76whVFQb5VTuyCU6vo6mULJr7hx2Pi+8QuONv/muWncWhwjJfeHuTQ4BjXLD+tZWo1tUKtonpm9dwPrAJOAQ4Cf4Uzg9dDwOnA68B/VlXvCeAiltUTLa2eVRE0qyXqgr7/ZlIYWT3XqeoCVU2q6kJVvVdVD6vqB1X1HPe2YtA30dPqWRVBs1qiLuwZxKKgaU/umuhq9ayKoFktUWdZVfVngd80nVbPqgia1RJ1llVVfxb4TdNp9ayKoFktURf2DGJRULdaPcZM16qlfazYsb+o1ks1WRXPvPJOwZWyFy3uqerE7kfu3FxUa+eHN6/yteyd1y7nsV88yljel9b2OFWd2D1/zeNFtXpeXPtR38t7yz5UmxUUtFZNkFo9q5b2MfTQv3N4OD3RdnJXomHrjwL7xm+azrpNu9n4wtvEBNoTQkycE6PrNu32vfzPXjtGMi60J4RkXPjZa8d8L+8N+gC7Dg7xkTs3+1r+0ts2FQR9gLGM0+6HN+gDnBjPcv6ax32v31veYf/AmO/157Jq+gdHmduZpH9wlDUbd/pOqc1lNeXOaWSyysM7DnDzA9t99z8/6AMcHk777n/Q9UeBBX7TdMKegcob9Cu1ewWtteMN+pXaa73+oFk1QbOagvbfsqoqs8Bvmo7NQBWuoFk1YWc1hb3+VmCB3zQdm4EqXEGzasLOagp7/a3AAr9pOmHPQLV0fndV7V5Ba+3Maiv9b1muvdbrD5pVEzSrKWj/LauqMgv8pukErXUSdPkf3ryqKMhXk9UTtNbOi2s/WhTkq8nqCbr+oLVqgtbqCdr/oOuPgrrV6qklq9VjqhU0HdGEK+ypE2eKhtfqMSYsQdMRTbiCvn/2/lfWEoF/LJ1l3ztDvHFkmAMDI/QfH+XwiTEGhlOcGEszmsqQymRphaMXU39W5Ku1hT11YhS0zJW7WVWyGSVVISMvHhPiMSERi03ejwtxybULiXhL7O/MNL1xdJi5ncmCNivy1TqCvn/2/lfWMoHfr0xWyWSVccpf7CLi7gjizo4gt0PI7TBiMSZ2HKb1LOrpon9wlK62yY+3FflqHUHfP3v/K4vkV19VJZ3NMpbKMDSW5vhIiiND4xwaHOPAwAhvHh3htcNDvOoOL715bISDx0c5NDjG0aFxjo+mGLIhpqZlRb5aW9hTJ0bBjPvGX0uqzrR9ZKDSxeIxKR5W8v7YUURjrFrax2k/3VNUpK2RRb5WrP1RUZGxbWs+7Hv5d3+xuMjbr770Md/LBykyB3D2Fx4lnfd9JiGw5zb/679u/dNF29/v1JNB37+gRf5g5hd5i+Q3/nrIqpLKZBkZz3BiLM1A3lHE2wOjE0cRew+d4PXDw+w/OszbA6P0D45yZGicgZHJE9Xj6SxZu7x82m5+YHtB0AB45tWjvot0BS3y5Q364BQZW7H2R76W9wZ9cIq8vfuLj/paPmiROW/QB0ir0+6HN+iDs/2vW/+0r+WDvn9Bi/xFocibBf4QpLNZxtNZhsfTnBhNc2x4nMMnxug/Pspbx0bYf3SYfYeHJjKZ3jpWmMk0OJpiZNx2EOUELdIVdHlv0K/U7uUN+pXavYIWmfMG/UrtXt6gXandK+j2D1qkLwpF3myop4n5zWQqNcyUiMWKTl6LRGOYKWiRLivyFa6g239oPEPC85W2miJ9UXj/LfDPAEFSXfMzmmbKDiIek5L/pH7PrwRd3gQTdPt3t8UZSRVWaK2mSF8U3v+WGOoZGU+z++AgbxwZ5vCJMUbGM5ZJMw2ZrE4MMQ2OpiaGmA66Q0xvHBnm1XeGeO1w3sVyeecgWiWTKWiRrqDLn9xV+vtUuXav9jLxqVy7V9Aic4ky8a1cu9dFi3uqavcKuv2DFumLQpG3UGr1iMg+YBDIAOlStSTytS84Rxfc8PeFfwPobIvT2RanKxmnqy3h/J6M09Xm/HRO3Cbozm9LJgoez13Z1+rfdBst5sleiolzxBDLteVdNBdr8LelIFkltVg+aFbOmbcUn0jdd7v/5YNOvbjklkcLroSJAXurWH/QrKKg23/dpt3c89SrDI1n6G6L86lLF1c19WbQ9TeLcrV6whzq+S1VfWe6CyswPJ5heDzD4Rp0Jh4TZ2dQsONITLRN7iTidLo7Du/OY3InFI/E1cF+h5igeJgpEXMuoMvtHPJ3GkFt3tXPmwNjnNXbTWfSOex/c2CMzbv6faX0BV1+3abdpFVIxp2x5aw6J0bXbdrtK/iUyx65+YHtvlIKN+/qJxaPF/Q/ldGqXv/CeV0k4zLt5YdTWrD+4VR1ywfZ/uBUaK0m0Nd6/c2uJcb4z+2bzXc+cwkj4xmGx9MMj2fc+xmGUxlG3LZc+0gqdz+d9xz3+e7FHF6ZrDI4mmZw1F/mRSW5WiFdBUcleTuT3A7D3al0tiXynlO4M+lMxlt+fNHPFdXgXFWd2wGUuqrazxFEfq0WgK62BMPjadZv2evrHzfo8vlZJeAE/3Q2yz1PveorGOVnleSoOu13Xltx8dBff9jLBxX2+hshrMCvwI9FRIH1qnq39wkiciNwI8C7Fi5iVnuCWe0JwN9kDFNJZ7KeHYJnZzKeYSRVuDOZ2JGkMp7npotmewJIZZSBkRQDI6nA/QXoSMTcHULCc/QRL9zBlDkqmTySSdCRjDXtsJbfi+amymR6/cgQczqTqOrE62xkrZews0rCrnUT9vJBhb3+Rggr8F+iqm+JSB/whIjsUtUt+U9wdwZ3A/zasuU1PRGRiMc4qTPGSZ43dzpygar4SCRduCPJ7WBSmdLtEzuc0sFhNJ1lNJ3l6HDwHUm58yMFw1dl2xN0tRfudMI4PzLVMFPvrA4OD40588YKCMJIKk3v7Hb6B0edHYTIRE0mb22moLVews4qCbvWTdjLBxX2+hshlMCvqm+5t/0i8jBwIbBl6qWak4jQlhDaEm3MrcHnIqvKaN7OITdsNTxeYkeSKnGk4tnpjKWLh1YaeX6ky3O00Vlh59LVlgg8rHXt+xZx15MvM5LK0JGMMZLKkM4qn1yxiBNTDOXlhpmuWXEaX31iN+ms0pmMM5bKkMoqf3TJmROX8E/lU5cu5q4n95DOZifG+KvNKnl4xwG8eRd+s0pWX7aENRt3MjyeLhijr6bWTSsvH1TY62+Ehgd+EekGYqo66N7/ELC20f1oVjHJnRtIcHIN/l4mqyWPNkbKnB/xHq3kD2nl/gFKraOW50faErG8cx+Vzo8Un2g/dU4HSYHXj45M/M0lJ3dy4ZJ5U643d/T23tN7GB5NcWx08nBibkecs/pm8dphJ1Mld6SQn70UE+fcwx//5hLu2PQy3oMRvycb77x2OQ/vKM7q8VsrZtXSPg5++/mirKJqat28/s3nS7b7Xf6tDc8X1fpp1PIQLKuoFrV+gqr3DGINT+cUkSXAw+6vCeC7qvqlqZb5tWXL9ZEnWvKAYMZJuedHCs6NpPKPONLsPniCf3vlHQTnW3Q6kyWjMH92O7GYVDw/Ui/tiRinz+vK20kkShypxLlny16Ol6iP0Ned4P7VF1cc1rr8qz8t+9gzX7i8cGeR22EI7q1w7l8+XnZ5Pymd5695nBPjxUd6fuft9aZy5vhN6SxVawj8p7QGXb5UrSLwH/zXbdrNXU/ucd+TySO2auZtDiI3g5g3q6qaeY9zmiadU1X3Ar/e6PWa2kjGY8zpjDFnivMjf/bgz5nX1eaMsbtGUhlO7m7njt+bfOtV3QvKUsXnO0plbXlPvo+MZwuOZEbGM0y1HxlLZ3m5/8S0X3v/UJrfvnNLXnpvoig7K39cuJQX9w8Eun7kyND4xPmJ3FFGbkeSy3QqFfSnavcq9yx/SwevNRR2raKgWVlBNSKrqCXSOU1rOXB8hJM6Cj9aHckYbx8fKWgTEdqTcdqTcWpx3iyrylgqy8e+9lTZ56y+bEnFlOADA6NTrMPJznEydMar7uN/+/a2gt9LnR+ZykPPv140vJVbtrs9QbLC9SMDwykkRsFRhuQdbTRnrldjBc3KCqoRWUUtEfjb4jFO6+kEKDrhVa4NQKf4/pdbRid+V8/vFDwh/2/lr8+7fOH6Sz3fs+KS/Z5cTlH31lnHRL89/Sr3esKw4KTOyawa12gqy6knddZ1vTEROisEzt9736KKf2eqoZq/veaCwmtESpyA3/Ky/+sSqz0/cvsPfzXl423eiOWx9gc7K558n8rbA6OTVStxdh7i7kTESaGa0qibhpU7yBHE/Vvu702QZhw0KyuoRmQVtUTgF4H2RGM2+kwzsUMrsb/J3zlMtZPy7mwK/r5nD6YKn/7AEv7msZdIZbJ0JOOMpjKowo2XLebkWe3g2aEV9dXTF+/OsuzjbtvyRXPY/sZAUV/fu/CkiW/EpXaqOfNnJTl4ojhtdv6sJCvOqFxvZqodx8N/cnFBNpb3/MjweIb7/m1f2eV7Z7VPnHwvdX5kvEQWV77vb3+zYv+nsvrbW6c4P+K0xQVK5ACQEHjr2EjxAx7JGKRKvIy2GLx+eBjvviEWc45UckcxZ5/SyZ53itdzdm8XR4bG3XNPTlv+zirXdv37T+frP92LaqZgjP+Gi85geDxN7rhI8pbL34HljqKmuxNrRFZRKLV6qrVy5UrdunVr2N0wVchlJew/OszCaWQlBM1qmE6tmvwjqt+8fRNvHp8cyjntpDZ+8r8+6DwPLdxBenYiWVUuuPXHRX9/219e4e5kmPg75C2TvyO85PYni5Z/8s8/UNDXsbTnRHte1tZXfvgS+SMTiRh84Ny+8hcn+qm7EVBMKFkra3K4arJ9w9P7CnZscYFvXL/CPZ/iHJlMdXTzqW/+jAvAdgAAAA58SURBVL2HC7O67vmDC3339f88+kue/NUhsur0+/J39/IXH3vPNF6zFOwYciZ3Gu5OhMKd0TOvvMM/Pfs6BwZGWDCnk+vffzoXn31KwRFV/g4o/4gpdz8RF07qbCt5ctcCv2k6QbMaws7KqNf6VZWsujsonB1I1v3/zd9xTNz3tOXas+qUz8jdzypksllG3aOP5145wjef2eec4IwLY6ks6axyyVmncMrsthLnR/xdP1Jridz5kdwwVVGBxvJHJd76W/nXj/xs7xHuevJlEjGhIxlj1H39N11+TsWU4GbSkYxzWk9Xc2T1GFNJ2LVygqrX+kWEuEDFgfRpUHdnkFX46o92c1Jngs6ks/0V51qQ/sExbv34ee7zlGx2cofiVe76kaFx7zUipVOCc22556ZLjGuls8rx0TTHR9NUnhW7stz1I8PjGRR1akK5J72zqnz1id189PxTi69mL7Ez6UjGiTXB+YpyLPCbphN2rZygwl7/dIg4dY8A3hwYYW5nMm+MWpjVLvQPjjL/pI6Sy2ezStrdIWSySkaVbHbyfv7RRa691A6jnPF0doqr1UvX0MpdpJi/k8k9XmrN4+lswTkS78WKQ+MZvvXsa7773JmMFwxplavsO1WBxlyqcFuitvW1LPCbphN2rZygwl5/UNPZ/rGY0DaNUhuZrDoTpWQhMzEEVbjDyGSVZNwpUjgnW5v6WqO5HUmJo4sN/7aPwbEU8ViMrLuDSmWURFxYfEp3USbXaKkz0TjbbCSVAX+XD0wpJhQMZ3V6jzi8pePbEkVfnvJZ4DdNJ2hWQ9BaOUGFvf6gGlmrxpmnobodYibviCGdu59/lKFKOjN59OEl4pw76kzGmdfdVvT4rLZEVWP8maxOnB+Z6qhkxHtUUqpkSipTMjMrq3BiLM2JsdqURbHAb5rOqqV9rIVpZwXlxtGDzMAURNjrDyro9q+33KQ+fuQfPWTc4ahMxjMs5Rl6unDJPG7iHB54/g3ePj7CqSd1cu37FpU9sRuPCd3tCbrbaxNO05ms55xHmVpbZc6PjKSyE8Na5VjgN3URNB3zhf3H2PnWAEPjGQZGUryw/1hVyz/0/OvuST84Pprmoedfryrwrlj7Iw4PT367OrkrwbY1H/a9/B2bXp64f3w0zR2bXq5q/UGnXgy6/B/kFWl74+gIz+w9XNXyQadu9NYb8ltnKGc6Uydq3s7hKz/8JUdGnMD59vExXjs8yGOf+8CURxL5vv30Ph7atp+RVIbOZJxPrljI9Ref6avviXiM2fEYszuCDWt1JOOc9telH5v58wOahsulY/YPjjK3M0n/4ChrNu5k865+X8vn0iFHUs5J0pFUhrue3MO6Tbt9Le/N4QfYPzDGpbdt8rW8N+gDHB5Os2Ltj3wtXyroTtU+05YvVeQt67b7UarI3InxLOevKV+8Lp836AM88+pRrlv/9JTLOSe4Y1xy26aJoJ9zdCTD79y1hUXzujjj5G6W9M7i9HldvGtuJ/NP6uDkWe3M7WpjVnuC+597jQ3PvsZYOkM8BmPpDBuefY1vP73PV/8bwQK/qbn8dExxy0wn48L6LXt9LZ+fDhmTmHvrtPvhDfqV2r28Qb9SuykUtMhb0CJz3qBfqd3L7/ufiMfoSDo1kuZ0JpnX3UbfSR08uHU/8ZiQjMeJ531+v7d9v7Nz6HAydpLxWGgpnzbUY2qu1dMxjQki//ObS8GMx4ThVLbkyeRs3jmIifMRmcmT1ekS5yGCssBvaq7V0zGNCaLaz28sJsQQkj4+3qqFO4L8+7nU2Nz9qdhQj6m51ZctmZiHWNW5rTYdM6vO1a5Zzbq3/tMhF85pr6rd6+Su0t+HyrWbQuWCit9gM6ut9DPLtXtdtLh0Ib1y7V5B3/+gn9+piAjJEkNMvbPbOXVOBwt7nHMQi0/pLnuxHVjgN3Wwamkfa686j77ZHQyMpOib3VHV7EGfveJcbrr8bDqTcdJZZ5iomjo3T33hiqIg76dIW862NR8u+ievJqunXPaL36yYVl++XPaO36yeF9d+tCjIV5PVc//qi1k6v7ugben87opZPTlB3/+gn99aEJk65dWKtBlTY0GLtN38wHYe3nGgqP3qZQt8zbsbdpG7oP0PqpZTF7a6clMv2jd+Y2osaFbSxhfeBpic8EQK2ysJO6sqaP+DCvr6o8ACvzE1NjReeGIPqstKKndirtIJu5w3jg4XzH4G1WdVhdn/oIK+/iiwwG9MjXW3xYtmx6omK6nc2KzfMgWLerqKJlapNqsqzP4HFfT1R4EFfmNqLGhWx1UXnAq4M3vp5IxdufZKws6qCtr/oIK+/igI5eSuiHwEuAuIA/eo6u1TPX86J3eD1ioJWmvGBLNu0+5ARc6mM/VivrO/8CjpvH+NhMCe2xpXK8eWb+3lg9YqqlX8aZqTuyISB/4B+CjwHuA6Eal+MsspBK01ErTWjAkm7Fo93qAPkFan3Y+wa+XY8q1dq6gR8SeMoZ4LgT2quldVx4EHgI+H0I+yLCsgXGHX6vEG/UrtxuQLWquoEfEnjMB/GvBG3u/73bYCInKjiGwVka2HDh1qWOfAsgLCFjSrxJhW1oj4E0bgL3Vqv+i7lKreraorVXVlb29vA7o1ybICwhU0q8SYVtaI+BNG4N8PLMr7fSHwVgj9KMuyAsIVdq2eRJmsw3LtxuQLWquoEfEnjMD/PHCOiCwWkTbgWmBjLVcQtNZI0FozJpiwa/Xsue1jRUG+mqyesGvl2PLh1yryBtZqsnoaEX/CSue8Evh7nHTO+1T1S1M932r1GGNM9cqlc4ZSZ1ZVHwMeC2PdxhgTdXblrjHGRIwFfmOMiRgL/MYYEzEW+I0xJmJaYgYuETkEvDbNxU8B3qlhd2rN+heM9S8Y618wzd6/M1S16ArYlgj8QYjI1lLpTM3C+heM9S8Y618wzd6/cmyoxxhjIsYCvzHGREwUAv/dYXegAutfMNa/YKx/wTR7/0qa8WP8xhhjCkXhG78xxpg8FviNMSZiZkzgF5GPiMivRGSPiNxS4vF2EXnQffw5ETmzgX1bJCI/EZGXRGSniNxU4jmrRGRARHa4P2sa1T93/ftE5BfuuotKoYpjnbv9XhCR5Q3s27vztssOETkuIp/zPKeh209E7hORfhF5Ma9tnog8ISIvu7c9ZZa9wX3OyyJyQwP797cisst9/x4Wkblllp3ys1DH/t0qIm/mvYdXlll2yv/1Ovbvwby+7RORHWWWrfv2C0xVW/4Hp7zzK8ASoA34OfAez3P+O/AN9/61wIMN7N8CYLl7fzawu0T/VgE/CHEb7gNOmeLxK4HHcWZQez/wXIjv9ds4F6aEtv2Ay4DlwIt5bV8BbnHv3wJ8ucRy84C97m2Pe7+nQf37EJBw73+5VP/8fBbq2L9bgf/p4/2f8n+9Xv3zPP5VYE1Y2y/oz0z5xu9nAvePAxvc+98DPigiDZlTSVUPqOp29/4g8BIl5hluch8HvqWOZ4G5IrIghH58EHhFVad7JXdNqOoW4IinOf8ztgH4RIlFPww8oapHVPUo8ATwkUb0T1V/rKpp99dncWa/C0WZ7eeHn//1wKbqnxs3PgncX+v1NspMCfx+JnCfeI774R8ATm5I7/K4Q0zvBZ4r8fBFIvJzEXlcRM5raMeceY9/LCLbROTGEo/72caNcC3l/+HC3H4A81X1ADg7e6DUlEnNsh3/COcIrpRKn4V6+ow7FHVfmaGyZth+vwkcVNWXyzwe5vbzZaYEfj8TuPua5L2eRGQW8H3gc6p63PPwdpzhi18Hvgb8SyP7BlyiqsuBjwJ/KiKXeR5vhu3XBlwF/L8SD4e9/fxqhu34RSANfKfMUyp9Furl68BZwDLgAM5wilfo2w+4jqm/7Ye1/XybKYHfzwTuE88RkQQwh+kdak6LiCRxgv53VPWfvY+r6nFVPeHefwxIisgpjeqfqr7l3vYDD+McUufzs43r7aPAdlU96H0g7O3nOpgb/nJv+0s8J9Tt6J5M/h3g99UdkPby8VmoC1U9qKoZVc0C/1hmvWFvvwTwH4EHyz0nrO1XjZkS+P1M4L4RyGVQXAM8We6DX2vumOC9wEuqekeZ55yaO+cgIhfivDeHG9S/bhGZnbuPcxLwRc/TNgL/1c3ueT8wkBvWaKCy37TC3H558j9jNwCPlHjOj4APiUiPO5TxIbet7kTkI8DngatUdbjMc/x8FurVv/xzRleXWa+f//V6ugLYpar7Sz0Y5varSthnl2v1g5N1shvnjP8X3ba1OB9ygA6cIYI9wM+AJQ3s26U4h6MvADvcnyuBTwOfdp/zGWAnTpbCs8DFDezfEne9P3f7kNt++f0T4B/c7fsLYGWD398unEA+J68ttO2HswM6AKRwvoX+Mc45o38FXnZv57nPXQnck7fsH7mfwz3AHzawf3twxsdzn8Fcltu7gMem+iw0qH/fdj9bL+AE8wXe/rm/F/2vN6J/bvs3c5+5vOc2fPsF/bGSDcYYEzEzZajHGGOMTxb4jTEmYizwG2NMxFjgN8aYiLHAb4wxEWOB3xifRGS+iHxXRPa6l+M/IyJXi8iFeVUbfy4iV4fdV2OmYumcxvjgXhz2NLBBVb/htp2BU0LiXmBcVdPuRUg/B96lkwXRjGkqibA7YEyLuBwnuH8j16BOhdCveZ7XQeNrxxhTFRvqMcaf83AKwZUkIr8hIjtxrjz9tH3bN83MAr8x0yAi/+CO5z8PoKrPqep5wPuAL4hIR7g9NKY8C/zG+LMTZ0YmAFT1T3EmhenNf5KqvgQMAec3tHfGVMECvzH+PAl0iMif5LV1AbiVIhPu/TOAd+NMv2dMU7KsHmN8cjN27gR+AziE883+Gzhzv96CU8kxC6xV1WadCMYYC/zGGBM1NtRjjDERY4HfGGMixgK/McZEjAV+Y4yJGAv8xhgTMRb4jTEmYizwG2NMxPx/df5AcHrr3lUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='G3', y='absences', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is evident from this scatter chart that infact, the students who took the most time out of school were among the highest performing students in the class, and that students who never took any absence from school were among those who performed very badly.\n",
    "\n",
    "Analysis of the following figure shows us that the students who belonged the the outlier group in 'absences' were primarily female.\n",
    "\n",
    "However in addition to the scatter plot above, the violin plot in figure 25 below indicates that female students on average performed better than the male students, despite taking long absences from school.\n",
    "\n",
    "__Figure 24 - Boxplot of absences grouped by sex__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='absences', by='sex')\n",
    "plt.title('Boxplot of absences grouped by sex')\n",
    "plt.suptitle(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 25 - G3 v sex__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e162ed0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='sex', y='G3', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## d) Possible New Features\n",
    "\n",
    "When we try to explore multi-feature combinations to understand how combinations such as `sex` and `absence` affect the `G3` target, even after reducing the categories within `G3` down to 6 groups A, B, C, D, E & F, it is very hard to visualise this simply in a graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the actual values for G3 and save to a numpy array\n",
    "G3 = df[\"G3\"].values\n",
    "\n",
    "grade = []\n",
    "\n",
    "#iterate through every G3 value and save the corresponding grade category to the grade array\n",
    "for number in G3:\n",
    "    if number >=17:\n",
    "        grade.append('A') # grade = A\n",
    "    elif number >= 15:\n",
    "        grade.append('B') # grade = B\n",
    "    elif number >= 13:\n",
    "        grade.append('C') # grade = C\n",
    "    elif number >= 11:\n",
    "        grade.append('D') # grade = D\n",
    "    elif number>= 8:\n",
    "        grade.append('E') # grade = E\n",
    "    else:\n",
    "        grade.append('F') # grade = F\n",
    "    \n",
    "#convert the grade array to a pandas dataframe\n",
    "grade = pd.DataFrame(data=grade, columns = [\"grade\"])\n",
    "#create a new dataframe called data by joining grade with df_dummies\n",
    "data = pd.concat([df, grade], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency Table for grade:\n",
      "F     30\n",
      "A     46\n",
      "B     85\n",
      "C    145\n",
      "E    167\n",
      "D    176\n",
      "Name: grade, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "frequency_table = data['grade'].value_counts(ascending=True)\n",
    "print('Frequency Table for grade:') \n",
    "print(frequency_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 26 - Final grade accross males and females__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Students')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stacked_chart_gender = pd.crosstab([data['grade'], data['sex']], data['absences'])\n",
    "stacked_chart_gender.plot(kind='bar', stacked=True, color=['red', 'blue', 'green', 'yellow', 'orange', 'purple'], grid=False)\n",
    "plt.ylabel('Number of Students')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example above, we have attempted to manage the data in order to try and introduce multiple features.\n",
    "As you can see from the bar chart, because our second feature also contains too many categories, there is too much data here to present a clean and easy to read visual graphic. \n",
    "\n",
    "Also, it is difficult for us to manually explore every combination of features to discover which feature combinations have the greatest impact on our predictor model, in an efficient way.\n",
    "\n",
    "This is when we turn to feature selection algorithms to analyse combinations of features to find out which ones have the most significant impact on our predictor models.\n",
    "\n",
    "This will be explored in Notebook 2.\n",
    "\n",
    "==================================================================================================================="
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}