08_Machine_Learning_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Space X  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment:  Machine Learning Prediction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **60** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Space X advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against space X for a rocket launch.   In this lab, you will create a machine learning pipeline  to predict if the first stage will land given the data from the preceding labs.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform exploratory  Data Analysis and determine Training Labels \n",
    "\n",
    "- create a column for the class\n",
    "- Standardize the data\n",
    "- Split into training data and test data\n",
    "-Find best Hyperparameter for SVM, Classification Trees and Logistic Regression\n",
    "- Find the method performs best using test data \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries for the lab \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas is a software library written for the Python programming language for data manipulation and analysis.\n",
    "import pandas as pd\n",
    "# NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
    "import numpy as np\n",
    "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
    "import seaborn as sns\n",
    "# Preprocessing allows us to standarsize our data\n",
    "from sklearn import preprocessing\n",
    "# Allows us to split our data into training and testing data\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Allows us to test parameters of classification algorithms and find the best one\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# Logistic Regression classification algorithm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# Support Vector Machine classification algorithm\n",
    "from sklearn.svm import SVC\n",
    "# Decision Tree classification algorithm\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "# K Nearest Neighbors classification algorithm\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function is to plot the confusion matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y,y_predict):\n",
    "    \"this function plots the confusion matrix\"\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "\n",
    "    cm = confusion_matrix(y, y_predict)\n",
    "    ax= plt.subplot()\n",
    "    sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n",
    "    ax.set_xlabel('Predicted labels')\n",
    "    ax.set_ylabel('True labels')\n",
    "    ax.set_title('Confusion Matrix'); \n",
    "    ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataframe  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>Date</th>\n",
       "      <th>BoosterVersion</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
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       "      <th>0</th>\n",
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       "      <td>2010-06-04</td>\n",
       "      <td>Falcon 9</td>\n",
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       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>Falcon 9</td>\n",
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       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
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       "      <td>-80.577366</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-09-29</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>False Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "      <td>-120.610829</td>\n",
       "      <td>34.632093</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013-12-03</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-80.577366</td>\n",
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      "text/plain": [
       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n",
    "\n",
    "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
    "\n",
    "# data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
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       "      <th>85</th>\n",
       "      <td>86.0</td>\n",
       "      <td>15400.000000</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>87.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>90.0</td>\n",
       "      <td>3681.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FlightNumber   PayloadMass  Flights  Block  ReusedCount  Orbit_ES-L1  \\\n",
       "0            1.0   6104.959412      1.0    1.0          0.0          0.0   \n",
       "1            2.0    525.000000      1.0    1.0          0.0          0.0   \n",
       "2            3.0    677.000000      1.0    1.0          0.0          0.0   \n",
       "3            4.0    500.000000      1.0    1.0          0.0          0.0   \n",
       "4            5.0   3170.000000      1.0    1.0          0.0          0.0   \n",
       "..           ...           ...      ...    ...          ...          ...   \n",
       "85          86.0  15400.000000      2.0    5.0          2.0          0.0   \n",
       "86          87.0  15400.000000      3.0    5.0          2.0          0.0   \n",
       "87          88.0  15400.000000      6.0    5.0          5.0          0.0   \n",
       "88          89.0  15400.000000      3.0    5.0          2.0          0.0   \n",
       "89          90.0   3681.000000      1.0    5.0          0.0          0.0   \n",
       "\n",
       "    Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  ...  Serial_B1058  \\\n",
       "0         0.0        0.0        0.0        0.0  ...           0.0   \n",
       "1         0.0        0.0        0.0        0.0  ...           0.0   \n",
       "2         0.0        0.0        0.0        1.0  ...           0.0   \n",
       "3         0.0        0.0        0.0        0.0  ...           0.0   \n",
       "4         0.0        1.0        0.0        0.0  ...           0.0   \n",
       "..        ...        ...        ...        ...  ...           ...   \n",
       "85        0.0        0.0        0.0        0.0  ...           0.0   \n",
       "86        0.0        0.0        0.0        0.0  ...           1.0   \n",
       "87        0.0        0.0        0.0        0.0  ...           0.0   \n",
       "88        0.0        0.0        0.0        0.0  ...           0.0   \n",
       "89        0.0        0.0        0.0        0.0  ...           0.0   \n",
       "\n",
       "    Serial_B1059  Serial_B1060  Serial_B1062  GridFins_False  GridFins_True  \\\n",
       "0            0.0           0.0           0.0             1.0            0.0   \n",
       "1            0.0           0.0           0.0             1.0            0.0   \n",
       "2            0.0           0.0           0.0             1.0            0.0   \n",
       "3            0.0           0.0           0.0             1.0            0.0   \n",
       "4            0.0           0.0           0.0             1.0            0.0   \n",
       "..           ...           ...           ...             ...            ...   \n",
       "85           0.0           1.0           0.0             0.0            1.0   \n",
       "86           0.0           0.0           0.0             0.0            1.0   \n",
       "87           0.0           0.0           0.0             0.0            1.0   \n",
       "88           0.0           1.0           0.0             0.0            1.0   \n",
       "89           0.0           0.0           1.0             0.0            1.0   \n",
       "\n",
       "    Reused_False  Reused_True  Legs_False  Legs_True  \n",
       "0            1.0          0.0         1.0        0.0  \n",
       "1            1.0          0.0         1.0        0.0  \n",
       "2            1.0          0.0         1.0        0.0  \n",
       "3            1.0          0.0         1.0        0.0  \n",
       "4            1.0          0.0         1.0        0.0  \n",
       "..           ...          ...         ...        ...  \n",
       "85           0.0          1.0         0.0        1.0  \n",
       "86           0.0          1.0         0.0        1.0  \n",
       "87           0.0          1.0         0.0        1.0  \n",
       "88           0.0          1.0         0.0        1.0  \n",
       "89           1.0          0.0         0.0        1.0  \n",
       "\n",
       "[90 rows x 83 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv')\n",
    "\n",
    "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
    "\n",
    "# X = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_3.csv')\n",
    "\n",
    "X.head(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then \n",
    "assign it  to the variable <code>Y</code>,make sure the output is a  Pandas series (only one bracket df['name of  column']).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "5    0\n",
       "6    1\n",
       "7    1\n",
       "8    0\n",
       "9    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We create a NumPy array to store to hold multiple values (all classes) in a single variable, Y\n",
    "Y = pd.Series(data['Class'].to_numpy())\n",
    "Y.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We need to scale the data, prior to modeling. Otherwise, we can obtain a biased outcome and questionable accuracy/\n",
    "#misclassification error\n",
    "transform = preprocessing.StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = transform.fit(X).transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the data into training and testing data using the  function  <code>train_test_split</code>.   The training data is divided into validation data, a second set used for training  data; then the models are trained and hyperparameters are selected using the function <code>GridSearchCV</code>.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function train_test_split to split the data X and Y into training and test data. Set the parameter test_size to  0.2 and random_state to 2. The training data and test data should be assigned to the following labels.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>X_train, X_test, Y_train, Y_test</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split to train and test\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see we only have 18 test samples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18,)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a logistic regression object  then create a  GridSearchCV object  <code>logreg_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters ={'C':[0.01,0.1,1],\n",
    "             'penalty':['l2'],\n",
    "             'solver':['lbfgs']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n",
    "lr=LogisticRegression()\n",
    "logreg_cv = GridSearchCV(estimator=lr, cv=10, param_grid=parameters).fit(X_train, Y_train) #grid search\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We output the <code>GridSearchCV</code> object for logistic regression. We display the best parameters using the data attribute <code>best_params_</code> and the accuracy on the validation data using the data attribute <code>best_score_</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hypyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "accuracy : 0.8464285714285713\n"
     ]
    }
   ],
   "source": [
    "#Grid search, best_parameters attribute\n",
    "#GridSearchCV tries all the combinations of the values passed in the dictionary and evaluates the model for each \n",
    "#combination using the Cross-Validation method. Hence after using this function we get accuracy/loss for every \n",
    "#combination of hyperparameters and we can choose the one with the best performance\n",
    "\n",
    "#Grid search, best_score attribute:\n",
    "#It receives a range of parameters as input and it finds the best ones based on the mean score. \n",
    "#Grid search trains different models based on different combinations of the input parameters and finally returns \n",
    "#the best model or the best estimator. Hence, best_score_ is the mean score of the best estimator.\n",
    "\n",
    "print(\"tuned hypyerparameters :(best parameters) \",logreg_cv.best_params_)\n",
    "print(\"accuracy :\",logreg_cv.best_score_) #accuracy is based only on validation data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy on test data : 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "acc_logreg_test_data = logreg_cv.score(X_test, Y_test)\n",
    "print(\"accuracy on test data :\",acc_logreg_test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Lets look at the confusion matrix:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logreg_yhat=logreg_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,logreg_yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining the confusion matrix, we see that logistic regression can distinguish between the different classes.  We see that the major problem is false positives.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  6\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a support vector machine object then  create a  <code>GridSearchCV</code> object  <code>svm_cv</code> with cv - 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'kernel':('linear', 'rbf','poly','rbf', 'sigmoid'),\n",
    "              'C': np.logspace(-3, 3, 5),\n",
    "              'gamma':np.logspace(-3, 3, 5)}\n",
    "svm = SVC()\n",
    "svm_cv = GridSearchCV(estimator=svm, cv=10, param_grid=parameters).fit(X_train, Y_train) #grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\n",
      "accuracy : 0.8482142857142856\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\n",
    "print(\"accuracy :\",svm_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  7\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test data : 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "acc_svm_test_data = svm_cv.score(X_test, Y_test)\n",
    "print(\"Accuracy on test data :\", acc_svm_test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_yhat=svm_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,svm_yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  8\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to ignore any warning messages.\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'criterion': ['gini', 'entropy'],\n",
    "     'splitter': ['best', 'random'],\n",
    "     'max_depth': [2*n for n in range(1,10)],\n",
    "     'max_features': ['auto', 'sqrt'],\n",
    "     'min_samples_leaf': [1, 2, 4],\n",
    "     'min_samples_split': [2, 5, 10]}\n",
    "\n",
    "tree = DecisionTreeClassifier()\n",
    "tree_cv = GridSearchCV(estimator=tree, cv=10, param_grid=parameters).fit(X_train, Y_train) #grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'criterion': 'entropy', 'max_depth': 8, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'splitter': 'best'}\n",
      "accuracy : 0.875\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\n",
    "print(\"accuracy :\",tree_cv.best_score_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  9\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test data : 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "acc_tree_test_data = tree_cv.score(X_test, Y_test)\n",
    "print(\"Accuracy on test data :\", acc_tree_test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_yhat = tree_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,tree_yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  10\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a k nearest neighbors object then  create a  <code>GridSearchCV</code> object  <code>knn_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "              'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
    "              'p': [1,2]}\n",
    "\n",
    "KNN = KNeighborsClassifier()\n",
    "knn_cv = GridSearchCV(estimator=KNN, cv=10, param_grid=parameters).fit(X_train, Y_train) #grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\n",
      "accuracy : 0.8482142857142858\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\n",
    "print(\"accuracy :\",knn_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  11\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy of knn_cv on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test data : 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "acc_knn_test_data = knn_cv.score(X_test, Y_test)\n",
    "print(\"Accuracy on test data :\", acc_knn_test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DtWXLFpUqVcrm3LfffisPDw8NGzZMWbJkUdmyZXX8+HHNmjVL77zzTqrvQUUDAABHc5Ghkz///FMeHh768ccfVblyZZtz27dvV/Xq1ZUly/+vSbzyyis6duyYLl68mOp7UNEAAMDRDJwMGhQU9MjzUVFRDz0XGBiowMDAFM+dPXtW5cqVs2krXLiwJOnMmTMqVKhQquKjogEAAJK5ffu2smbNatOWLVs2SdKdO3dS3Q8VDQAAHM3AisajKhbpkT17diUkJNi03U8wPD09U90PiQYAAI5msTg7gscqWrSozp8/b9N2//MiRYqkuh+GTgAAQDLVqlXTjh07lJSUZG37448/VLp0aRUsWDDV/ZBoAADgaC62j0ZK3nnnHcXHx2vgwIE6cuSIVqxYoblz56pjx45p6oehEwAAHO0p2IK8YMGCmj17tkaMGKGQkBA988wz6tu3r0JCQtLUD4kGAADQ6NGjk7X5+Pho6dKl6eqXRAMAAEdzo8fEk2gAAOBoT8HQiVFINAAAcLSnYHmrUVh1AgAA7IaKBgAAjsbQCQAAsBs3SjQYOgEAAHZDRQMAAEdjeSsAALAXi5lVJwAAAOlGRQMAAEdzo8mgJBoAADiaG83RYOgEAADYDRUNAAAczY0mg5JoAADgaMzRAAAAduNGiQZzNAAAgN1Q0YBDmEwm1WvXWAEf1lX+YgV0LvaMfprxg/74YaOzQwOchp8LN+ZGj4kn0YBDNOnVVG90fEuRE5cqds9R+dTxVYfJPWSxmLXlx03ODg9wCn4u3JgbDZ2QaMDusmbPqrptGmrdv1dr9fRISdKBzXv1fKWyCvq4AX+hwi3xcwF3QaIBu0tMuKuR73yua5fibNrvJtxVjtyeTooKcC5+Ltwcy1vtLzAwUCaTKVXXRkVF2Tka2JPFbNbJg8etn+cplFevvVdHL71WSfM/m+XEyADn4efCzbnRzqBOSzRCQkKsicbVq1e1ePFi1alTR76+vsqSJYv27t2rtWvXqk2bNs4KEXbg/2ZNdZzSU5K0J2qHor//3ckRAc7HzwUyMpPF4vypr6GhofLz81P79u1t2hcsWKD169dr3rx56b5Hm1LvprsPpN8zJYsof9ECerb88wrp3UwnDx7XmKaDnR0W4FT8XLieOce+s2v/N8e0Nqwvz37/Nqwve3CJfTSio6NVr169ZO2vv/66du/e7fiAYDcX/j6nmK0H9Mv8n/XN0H/Ly7+CylX3dnZYgFPxc+F+LGazYYerc4lEo3DhwoqOjk7Wvn79epUoUcIJEcFIuQvk0atv11bugnls2o/v+0uSlK9IAWeEBTgVPxduzmwx7nBxLrHqpG3btho+fLh27dqlSpUqyWw2a+fOnVq3bp3GjRvn7PCQTh7Zs6rdhG76buwirQ7/3tpeoVZlSdLJA8cf9lIgw+LnAu7CJRKNpk2bKmfOnFq4cKHWrl0rk8kkb29vhYeHq3bt2s4OD+l0+fRFbVwapTe7v6ukxCT9/WesylX3VoPQJvp9yXqdPnLS2SECDsfPhZtj1YnjNWrUSI0aNXJ2GLCT+Z9/rQt/n1PtD4JVsMQzunzmor6fsFRrZv3o7NAAp+Hnwo09BUMeRnGJVSeStH37du3cuVOJiYl6MKSuXbumu39WnQAAUsveq05uDGtuWF85By0yrC97cImKxrRp0xQWFqY8efIoV65cNudMJpMhiQYAAC7jKVgtYhSXSDS++eYb9ezZUx07dnR2KAAA2J8bDZ24xPLW69evMz8DAIAMyCUSDT8/P+3atcvZYQAA4BgWs3GHi3OJoZNGjRpp+PDh2rdvn8qUKaOsWbPanG/SpIlzAgMAwB7caOjEJRKNgQMHSpLmzp2b7JzJZCLRAADgKeUSicbBgwedHQIAAA7zNDyjxCguMUfjUc6ePevsEAAAMBbPOnGsEydOaMyYMYqJiVFSUpIkyWKxKCEhQZcvX9b+/fudHCEAAAZ6ChIEo7hERWPYsGE6dOiQ6tevr3Pnzqlhw4aqUKGCLl68qCFDhjg7PAAA8IRcoqKxc+dOhYeHy9/fXxs3blRwcLB8fHw0ceJEbdiwQe+//76zQwQAwDhPwbJUo7hERSMhIUElS5aUJJUuXVqHDh2SdG9Z6549e5wZGgAAxnOjORoukWiUKFFCMTExku4lGgcOHJAkmc1m3bhxw5mhAQCAdHCJoZOQkBD17dtXY8eOVUBAgFq1aqXixYtr06ZN8vLycnZ4AAAYyvIUVCKM4hKJRocOHZQtWzZZLBb5+Pioc+fOmj59uooVK6axY8c6OzwAAIzlRomGyWKxuMW7bVPqXWeHAAB4Ssw59p1d+7/e3bgHieae8h/D+rIHp1U0IiMjU30tW5ADADIUN9oZ1GmJRv/+/VN1Hc86AQBkOG40dOK0RIPnmwAAkPG5xGRQAADcihtVNFxiHw0AANyJxWIx7EiPu3fvavLkyapTp458fX3VvHlz7d6925g3+T8kGgAAOJqL7Aw6ffp0LVu2TMOHD1dkZKRKly6tdu3a6fz58wa9URINAADc1vr169WoUSO99tprev7559W/f39dv37d0KqGSyQaU6dO1a1bt5K1x8fHa8SIEU6ICAAAO3KRikbBggX166+/6uTJk0pKStLSpUuVNWtWlS9f3qA36sTJoEePHtXly5clSdOmTVP58uWVN29em2tiYmL07bffauDAgc4IEQAAuzByC/KgoKBHno+KinrouYEDB6pHjx4KCgpS5syZlSlTJoWFhVkfdGoEpyUaJ06cUKdOnWQymSRJXbt2TfG6d955x5FhAQDgNo4cOaLcuXNr2rRpKlKkiJYtW6ZPP/1UCxculLe3tyH3cOoW5KdPn5bZbFZwcLCWLVumAgUK/P/ATCZ5enoqX758htyLLcgBAKll7y3I4z56dBUiLfLOe3jF4lHOnDmjunXrau7cuapataq1/cMPP1S+fPkUHh5uSHxO3UejePHiku6VdYoXL26tbgAAkKG5wA7ke/bsUWJioipVqmTTXrlyZf3++++G3cclJoOWKFFCv/zyi95//31VqVJFVatWVbNmzbRu3TpnhwYAQIZUtGhRSdKhQ4ds2mNiYlSqVCnD7uMSicbatWvVtWtXFS5cWD179lTXrl1VsGBB9ejR45GTWAAAeBpZzBbDjifl4+Ojl19+Wf369dMff/yhY8eOadKkSYqOjlaHDh0Me68u8Zj4Jk2aKDg4ONmE0KlTp2rDhg1atmxZuu/BHA0AQGrZe47G1Q/qGNZXvm9+feLXxsXFadKkSfrtt98UFxencuXKqVevXqpevbph8bnEs07++usvNW7cOFl7o0aN9PXXXzshIgAAMr68efNq8ODBGjx4sN3u4RJDJ4ULF9bx48eTtR8/fly5c+d2QkQAANiR2cDDxblEotGoUSMNGTJEGzZsUHx8vOLj47VhwwYNHTpUDRo0cHZ4AAAYyhXmaDiKSwydhIaGKiYmRh07drQucbVYLAoICFCvXr2cHB0AAAZ7CioRRnGJRCNbtmwKDw/X0aNHFRMTI4vFIi8vL5UtW9bZoQEAgHRwiUTjvrx586pKlSq6vxDm9OnTkv7/xl4AAGQET8OQh1FcItHYuXOnBgwYoL///tum3WKxyGQy6cCBA06KDAAAO2DoxLG+/PJLPfPMM+rbty+rTAAAyEBcItE4fPiwIiMjmZMBAHALFioajlWsWDHduHHD2WEAAOAYbpRouMQ+GqGhoRo5cqQOHTqkxMREZ4cDAAAM4hIVjenTp+v06dNq0qRJiueZDAoAyEgYOnGw0NBQZ4cAAIDjkGg4VkhIiLNDAAAAduASiQYAAO6EoRMAAGA3JBoAAMBu3CnReKLlrVu3btXu3bsl3XseSadOndS4cWNNmzbNyNgAAMBTLs0VjcjISA0YMEBt2rRRlSpVNGjQIO3YsUM1a9bUjBkz5OHhoQ4dOjy2n23btqX6ntWqVUtrmAAAuC6LydkROEyaE425c+cqJCREffr00YULF7R582b17t1bbdu21Zw5c7R06dJUJRotW7aUyWSyPjjtvvtPbv1nG/toAAAyEncaOklzovHXX3/ps88+kyRt2LBBFotFQUFBkqRKlSpp0qRJqeonKirK+nF0dLTCw8P12Wefyc/PT1myZNHevXs1cuRItW/fPq0hAgAAF5HmRCNPnjyKj4+XJG3cuFHFixdXqVKlJEl///238ufPn6p+SpQoYf3466+/1ogRI1SjRg1rW82aNTV48GD179//oTuGAgDwNLKYGTp5KH9/f02dOlVHjhxRVFSUWrduLUlas2aNJk+erNdeey3NQZw/f16FCxdO1p4nTx5dvXo1zf0BAODK3GnoJM2rTgYOHKj8+fNr6tSpqlGjhjp27ChJGjVqlIoXL67evXunOQgfHx9NnjzZ5gmuV69e1VdffaXq1aunuT8AAOAaTJb7sy/T6fTp0ypevPgTvfbw4cP6+OOPdevWLZUqVUoWi0XHjh1TwYIFNW/ePJthlifVptS76e4DAOAe5hz7zq79n6oRaFhfJaJ/Mawve0jV0Mnp06dT1dn969KacLz44otas2aN/vOf/+jw4cMymUxq3ry5GjZsqBw5cqSpLwAAXJ07DZ2kKtEIDAy0WW76OE+yHDVXrlxq1qxZml8HAABcV6oSjZEjR6Yp0UiNoKAgfffdd8qfP/9jE5l/LoUFAOBpx6qTB7z99tuG3zgkJETZs2e3fmx0IgMAgKsyZnbk0+GJHqp2+fJlRUREaPPmzbpw4YJmz56t9evXq3z58goODk5VH127drV+3K1btycJAwCApxIVjUc4ceKEPvjgA925c0cvv/yyDh48qKSkJMXGxio8PFzh4eEKCAh4bD+RkZGpvicbdgEA8HRKc6IxZswYFSxYUAsWLJCnp6cqVqwoSRo/frzu3LmjGTNmpCrR6N+/v83n9597kj17dmXJkkXx8fHKnDmz8ufPT6IBAMhQ3KmikeYNu6Kjo9W5c2flyZMn2byKpk2b6vDhw6nq5+DBg9Zj3LhxKl++vCIjI7V7925t375dP//8sypWrGjdEAwAgIzCYjHucHVpTjQkKUuWlAshCQkJTzSpc9y4cRoyZIjKly9vbStVqpQ+//xzzZw580lCBAAALiDNiUbVqlU1c+ZM3bx509pmMplkNpv1zTffyM/PL81BXLt2TdmyZUvWbjabdfv27TT3BwCAK7OYTYYdri7NiUbv3r119OhR1atXT3379pXJZFJERITefvtt7dixQz179kxzEP7+/ho2bJhOnjxpbTt69KiGDh2aqvkeAAA8TSwWk2GHq0tzolGuXDktX75c/v7+2rJlizJnzqzNmzerZMmSWrJkiby9vdMcxJAhQ3Tt2jXVrVtX/v7+ql69uho1aiQPDw998cUXae4PAAC4BsMeqpZeSUlJ2rx5s/VZJ+XLl9crr7xi2EZePFQNAJBa9n6o2pGX6hvW1wv71xjWlz080YZdN2/e1Pfff6/t27fr2rVrKlCggF555RU1btxYWbNmfaJAMmfOrFq1aqlWrVpP9HoAAJ4W5qdgyMMoT7Rh10cffaTTp0/rueeeU8GCBXXs2DGtXLlS8+fP19y5c5U/f357xAoAAJ4yaU40Ro8eLZPJpMjISJvlqHv27FG3bt00atQojR071tAgAQDISJ6GSZxGSfNk0M2bN6t37942SYYkVa5cWb169dIvv/xiWHAAAGRE7rS8Nc0VDU9PT3l4eKR4rkCBAsqcOXO6gwIAICNzjWUYjpHmikbz5s01efJknT9/3qY9Pj5eM2fOVLNmzQwLDgAAPN1SVdFo1aqVzeexsbGqW7eu/Pz8VKhQIcXFxWnHjh0ym80qXry4XQIFACCjeBqGPIySqkTjwa027m8zfvfuXZ09e1aS9NJLL0mSzp07Z2R8AABkOCxvfcCCBQvsHQcAAMiAnujprQ9z8+ZN/f7770Z2CQBAhuNOzzpJ86qTU6dOaciQIdq6dasSEhJSvObAgQPpDgwAgIzKnVadpDnRGDVqlHbu3Kn33ntPO3fuVI4cOVSlShVt2rRJMTExCgsLs0ecAADgKZTmoZNt27apZ8+e+vzzz/X2228rW7Zs6tOnj5YvX65q1aopKirKHnECAJBhmC0mww5Xl+ZE48aNG/Ly8pIklSlTRvv375d076FoH374of744w9jIwQAIINxpTkakZGRatCggSpVqqSGDRvqp59+MuAd/n9pTjQKFy6sixcvSpKef/55xcXF6cKFC5KkfPny6dKlS4YGCAAA7OOHH37QwIED1bx5c61atUqNGjVSr169tGvXLsPukeZEo3bt2po0aZJ27dqlEiVKqGjRopozZ47i4+O1fPlyFSlSxLDgAADIiCwW444nj8GiyZMnq1WrVmrevLlKliyp0NBQvfrqq9q6dath7zXNk0G7d++uffv2afLkyZo7d6569uyp/v37a+7cuZKkQYMGGRYcAAAZkZFzK4KCgh55/mFzJ2NjY3Xq1Ck1btzYpj0iIsKw2KQnSDTy58+vZcuWWZ918uabb6p48eLavXu3fHx8VL16dUMDNMr809HODgFwObdOb3R2CIBbcoX9L2JjYyXd2wOrbdu22r9/v5599lmFhoYqMDDQsPuYLA/uL55BZclawtkhAC6HRANImUehMnbtf1uJEMP6qnbq+yd63Q8//KC+ffvq2WefVdeuXVW+fHmtWbNGM2bM0L///W/VqFHDkPie6KFqj2IymTRv3rwnDggAgIzOFZalenh4SJLatm2rkJB7iY+3t7f2799vaKKRqsmgFosl1YfZbDYkMAAAMiqLgceTur94o1y5cjbtL7zwgk6ePJmOnm3xUDUAANxQhQoVlDNnTu3Zs0dVq1a1tsfExKhkyZKG3SfNk0EBAED6uMLQSfbs2dWuXTtNmzZNRYoUkY+Pj1atWqVNmzZZV5IagUQDAAAHc4VVJ5LUuXNn5ciRQxMnTtS5c+dUtmxZhYWFyd/f37B7sOoEcGOsOgFSZu9VJ5uKvmtYXzXPfmdYX/ZARQMAAAdzp2UTJBoAADiYRa4xdOIIT5RoXL58WREREdq8ebMuXLig2bNna/369SpfvryCg4ONjhEAADyl0vxQtRMnTujNN9/Ut99+qyJFiujSpUtKSkpSbGysunfvrt9++80OYQIAkHGYLcYdri7NFY0xY8aoYMGCWrBggTw9PVWxYkVJ0vjx43Xnzh3NmDFDAQEBRscJAECGYXajoZM0VzSio6PVuXNn5cmTRyaT7ReqadOmOnz4sGHBAQCQEVlkMuxwdWlONCQpS5aUCyEJCQnJkg8AAOC+0pxoVK1aVTNnztTNmzetbSaTSWazWd988438/PwMDRAAgIzGbODh6tI8R6N379764IMPVK9ePfn7+8tkMikiIkJHjx7V8ePHtXjxYnvECQBAhvE0DHkYJc0VjXLlymn58uXy9/fXli1blDlzZm3evFklS5bUkiVL5O3tbY84AQDAU+iJ9tEoVaqUxo8fb3QsAAC4hadhyMMoaU40Tp8+/dhrihcv/kTBAADgDkg0HiEwMPCxK0sOHDjwxAEBAICMI82JxsiRI5MlGjdv3tT27du1ZcsWjRw50rDgAADIiNxpMmiaE4233347xfbmzZtr1KhRWrlyJTuDAgDwCGb3yTOebMOuhwkMDORZJwAAwMrQx8Tv2bPnobuGAgCAe9zpWSdpzgoGDBiQrM1sNuvs2bPatm2b3n33XUMCAwAgo3oKHrpqmDQnGlu2bEnWZjKZlCtXLrVv316dOnUyJDAAADIqlrc+wtdff62yZcvaIxYAAJDBpHky6IcffqjIyEg7hAIAgHswm0yGHa4uzRUNDw8P5c+f3x6xAADgFpij8Qg9evTQ2LFjdf36dZUvX16enp7JrmELcgAAIEkmi8WSpsSqQoUKSkpKeuQ25K64BXmWrCWcHQLgcm6d3ujsEACX5FGojF37X1qsuWF9NT2zyLC+7CHNFY0vv/zSHnEAAOA23Gln0FQlGq1atdLgwYNVtmxZhYSE2DsmAACQQaQq0di6datu3Lhh71gAAHAL7AwKAADsxp1WnRj6UDUAAIB/SnVFo0uXLsqaNetjrzOZTFq/fn26ggIAICNjMmgKXnrpJRUoUMCesQAA4BZ41kkKunTpIh8fH3vGAgCAW2COBgAAgAFYdQIAgIMxR+MBISEhPEgNAACDMEfjAaNGjbJ3HAAAIANi6AQAAAejogEAAOzG4kZzNFh1AgAA7IaKBgAADsbQCQAAsBt3SjQYOgEAAHZDRQMAAAdzpy3ISTQAAHAwdgYFAAB2wxwNAAAAA1DRAADAwdypokGiAQCAg7nTZFCGTgAAgN2QaAAA4GBmk3GHEWJjY+Xr66sVK1YY0+E/MHQCAICDudIcjcTERH366ae6efOmXfqnogEAgBsLCwtTrly57NY/iQYAAA5mMfBIj23btmnp0qUaPXp0Ont6OIZOAABwMLOB606CgoIeeT4qKirF9mvXrqlv3776/PPPVaxYMcPieRAVDQAA3NCQIUPk6+urxo0b2/U+VDQAAHAwIyeDPqxi8SiRkZHavn27Vq5caWAkKSPRAADAwZy9Ydfy5ct16dIlBQQE2LQPHjxYq1ev1uzZsw27F4kGAAAO5uzlrePGjdPt27dt2urVq6fu3bvrzTffNPReJBoAALiZIkWKpNhesGDBh557UiQaAAA4mFE7ej4NSDQAAHAwI5e3GuXQoUN26ZflrQAAwG6oaAAA4GCuV8+wHxINAAAczNmrThyJoRMAAGA3VDQAAHAwV5wMai8kGgAAOJj7pBkMnQAAADuiogEAgIO502RQEg0AAByMORoAAMBu3CfNYI4GAACwIyoaAAA4GHM0AACA3VjcaPCEoRMAAGA3VDQAAHAwhk4AAIDduNPyVoZOAACA3ZBowGHqBr+u6M2rdO3qER0+FK1ePTs6OyTAac6ev6Aa9d/V1p3/tWnfsmO3Pu7SR6/+6z3VbvyhPvnsS/198rSTooS9WAw8XB2JBhzCv7qffoicp0OHjuq999vpmyXfa/Soz9W3TxdnhwY43JlzF9Sh50Bdj79h077zv3+qQ8+Bypc3r0YP6qPPeobq+MlTahn6qa5cjXNStLAHsyyGHa6OORpwiMGDemv37n36uHV3SdKatb/JwyOL+vfrpilhEbp9+7aTIwTsz2w268efojRu2mxZLMl/QcxZuExlSpXUhC8/U6ZM9/4d6FvpJQW/3UqRq9ep9YfvOjpkIN2oaMDusmbNqtq1ayjyh59t2pcvX6U8eXLrtZrVnBQZ4FgxR2I1bFyY3vxXkEZ98Wmy85UqeKnl+02sSYYkFX6moHLl9NSJU2ccGSrszGzg4eqoaMDuypQpqWzZsinm8F827UeOHpMklStXVuujNjohMsCxihUtrNVLI1S08DPJ5mZIUsePPkjWtm3Xf3XterxeKP28I0KEg7jThl1OTTQCAwNlMplSdW1UVJSdo4G95M2TR5J0/Vq8Tfv16/c+z5Mnt8NjApwhb57cypuG7/crV+M0ZMwUFS5UUG+9EWzHyOBoT0MlwihOTTRCQkKsicbVq1e1ePFi1alTR76+vsqSJYv27t2rtWvXqk2bNs4ME+n0zzJwSsxmd/qRA1LnwsXL6tBroC5cvKzZk0cqZ05PZ4cEPBGnJhrdunWzfhwaGqqePXuqffv2NtcsWLBA69evd3RoMFDctWuSpFy5c9q0369kxMVdd3hMgCuLORqrLn2G6MbNm5oxYbh8KpR3dkgwmDsNnbjMZNDo6GjVq1cvWfvrr7+u3bt3Oz4gGObo0eO6e/euXihbyqb9/ucHDx52fFCAi9q6Y49ahX4qiyyaHz5Ofj4VnB0S7MCdJoO6TKJRuHBhRUdHJ2tfv369SpQo4YSIYJQ7d+5o48YtCmnSwKb97bcb6OrVOG3dtstJkQGu5UDMEXXpO1hFCz+jxTMn6oUyTADF089lVp20bdtWw4cP165du1SpUiWZzWbt3LlT69at07hx45wdHtJp5KjJWvPzEi35Zqbmzl2iGjWqqnevUH02cKRu3WIPDUCSBo2apMS7SerStoXOnDuvM+fOW8/lz5dXJZ8t7sToYCRzCvuoZFQuk2g0bdpUOXPm1MKFC7V27VqZTCZ5e3srPDxctWvXdnZ4SKdff9uk95q21+BBvbX8uwidOnVW/fp/qYmTZjo7NMAlnDh1RgdijkqSen4+Itn5t94I1ojPezs6LNiJ+6QZksmS0vZ0GVCWrAy/AA+6dZr9S4CUeBQqY9f+Wzz/tmF9LTy+wrC+7MFl5mhI0sGDBzVgwAA1a9ZM586d06JFi7RlyxZnhwUAgKHc6VknLpNo7Nu3T++9955Onjypffv2KSEhQQcOHFDbtm21YcMGZ4cHAIBhLAb+5+pcJtEYN26c2rRpowULFsjDw0OS9OWXX6p58+YKCwtzcnQAAOBJuEyisW/fPjVp0iRZe/PmzXX06FHHBwQAgJ240z4aLrPqxMPDQ/Hx8cnaz5w5oxw5cjghIgAA7ONpmFthFJepaAQHB2vSpEm69r/tqiXp6NGjGjFihAICApwXGAAABmOOhhP069dPN27c0CuvvKJbt27p7bffVqNGjZQ5c2b17dvX2eEBAIAn4DJDJ7ly5dKSJUsUHR2t/fv3y2w2q1y5cqpVq9Zjn/4JAMDT5GmYW2EUl0k07qtRo4Zq1Kjh7DAAALAbN9krU5KTE43AwECZTKZUXRsVFWXnaAAAgNGcmmiEhIRYE42rV69q8eLFqlOnjnx9fZUlSxbt3btXa9euVZs2bZwZJgAAhnKnVSdOTTS6detm/Tg0NFQ9e/ZU+/btba5ZsGCB1q9f7+jQAACwG3eao+Eysyyjo6NVr169ZO2vv/66du/e7fiAAABAurlMolG4cGFFR0cna1+/fr1KlODJqwCAjMOd9tFwmVUnbdu21fDhw7Vr1y5VqlRJZrNZO3fu1Lp16zRu3DhnhwcAgGGYo+EETZs2Vc6cObVw4UKtXbtWJpNJ3t7eCg8PV+3atZ0dHgAAeAIuk2hIUqNGjdSoUSNnhwEAgF2xj4aTbN++XTt37lRiYmKyP4SuXbs6KSoAAIzlTqtOXCbRmDZtmsLCwpQnTx7lypXL5pzJZCLRAABkGE/DJE6juEyi8c0336hnz57q2LGjs0MBAAAGcZlE4/r168zPAAC4BVdYdXL16lVNmDBBv/32m+Lj4+Xl5aXevXuratWqht7HZfbR8PPz065du5wdBgAAdmexWAw7nlSvXr20a9cuTZgwQcuXL5e3t7fatm2rv/76y8B36kIVjUaNGmn48OHat2+fypQpo6xZs9qcb9KkiXMCAwAggzl+/Lg2bdqkxYsX6+WXX5YkffHFF9q4caNWrlypHj16GHYvl0k0Bg4cKEmaO3dusnMmk4lEAwCQYTh76CR//vyaNWuWKlWqZG0zmUwymUy6du2aofdymUTj4MGDzg4BAACHMHLVSVBQ0CPPR0VFJWvLkydPss0w16xZo+PHj+uzzz4zLDbJheZoPMrZs2edHQIAABnWzp07NWDAANWrV08BAQGG9u0yFY0TJ05ozJgxiomJUVJSkqR7k2USEhJ0+fJl7d+/38kRAgBgDLOBO4P+lkLFIi3Wr1+vTz/9VH5+fnZ5tpjLVDSGDRumQ4cOqX79+jp37pwaNmyoChUq6OLFixoyZIizwwMAwDAWA4/0WLhwobp166Y6depoxowZypYtWzp7TM5lKho7d+5UeHi4/P39tXHjRgUHB8vHx0cTJ07Uhg0b9P777zs7RAAAMozFixdr+PDhatmypQYOHCiTyWSX+7hMRSMhIUElS5aUJJUuXVqHDh2SdG9Z6549e5wZGgAAhjLLYtjxJGJjYzVy5EjVrVtXHTt21MWLF3XhwgVduHBB169fN/S9ukxFo0SJEoqJiVGxYsVUunRpHThwQJJkNpt148YNJ0cHAIBxnL28dc2aNUpMTNS6deu0bt06m3MhISEaPXq0YfdymUQjJCREffv21dixYxUQEKBWrVqpePHi2rRpk7y8vJwdHgAAhnH2Y+I7deqkTp06OeReLpNodOjQQdmyZZPFYpGPj486d+6s6dOnq1ixYho7dqyzwwMAAE/AZHF2WuUgWbKWcHYIgMu5dXqjs0MAXJJHoTJ27b968dqPvyiVtp7eYFhf9uDUikZkZGSqr2ULcgBARmHkzqCuzqmJRv/+/VN1Hc86AQDg6eTURIPnmwAA3JGbzFqQ5EKTQQEAcBfOXt7qSC6zYRcAAMh4qGgAAOBgDJ0AAAC7caehExINAAAczJ2WtzJHAwAA2A0VDQAAHMzMHA0AAGAvDJ0AAAAYgIoGAAAOxtAJAACwG4ZOAAAADEBFAwAAB2PoBAAA2A1DJwAAAAagogEAgIMxdAIAAOzGnYZOSDQAAHAwi8Xs7BAchjkaAADAbqhoAADgYGaGTgAAgL1Y3GgyKEMnAADAbqhoAADgYAydAAAAu2HoBAAAwABUNAAAcDB2BgUAAHbjTjuDMnQCAADshooGAAAO5k6TQUk0AABwMJa3AgAAu3GnigZzNAAAgN1Q0QAAwMFY3goAAOyGoRMAAAADUNEAAMDBWHUCAADshqETAAAAA1DRAADAwVh1AgAA7IaHqgEAABiAigYAAA7G0AkAALAbd1p1QqIBAICDMUcDAABkeGazWVOmTFGtWrVUpUoVtW/fXidOnDD0HiQaAAA4mMViMexIj/DwcC1evFjDhw/XkiVLZDab1a5dOyUkJBj0Tkk0AABwOFdINBISEjRnzhx1795dAQEBKl++vCZOnKizZ89q7dq1hr1XEg0AANzQwYMHdePGDdWoUcPalidPHr300kvatm2bYfdhMigAAA5m5FTQoKCgR56PiopKsf3s2bOSpGLFitm0Fy5c2HrOCG6TaNxNOOXsEAAAkGTs76THJRoPc+vWLUlS1qxZbdqzZcumuLi4dMd1n9skGgAAZEQPq1g8Tvbs2SXdm6tx/2NJunPnjnLkyGFIbBJzNAAAcEv3h0zOnz9v037+/HkVKVLEsPuQaAAA4IbKly+vXLlyacuWLda2a9euaf/+/apWrZph92HoBAAAN5Q1a1a1aNFC48aNU4ECBVSiRAl99dVXKlq0qOrVq2fYfUg0AABwU927d9fdu3f1+eef6/bt26pWrZoiIiLk4eFh2D1MFnd6sgsAAHAo5mgAAAC7IdEAAAB2Q6IBAADshkQDAADYDYkGAACwGxINAABgNyQaAADAbkg03IiXl5dWrFghSQoLC1NgYOBDrz158qS8vLxstqa1h19//VVHjhx56Pl/xmwvj/taAGnF9y3w/5FouKk2bdrou+++c2oMp06dUqdOnXTp0iWnxgEAsB+2IHdTOXPmVM6cOZ0aA5vSAkDGR0Ujgzp79qxCQ0Pl6+ur119/XStXrrQ5/2DZNSYmRq1atVKVKlVUt25dRUdHP7L/FStWqG7dutb/V6xYUW+//bZ27Nhhveb27duaNGmSgoKCVKlSJb311ltas2aNpHtDM0FBQZKkVq1aKSws7LHvyWw2a+bMmapfv74qVqwoPz8/tWvXTn///bf1Gi8vL3333Xf6+OOP5ePjo9dee01Tp0616Wfp0qWqW7eufHx81KlTJ8XFxT323sCT4vsW7o5EIwO6e/eu2rVrpytXrmjhwoWaPHmyIiIiHnr99evX9fHHHyt37txatmyZhgwZounTpz/2PmfOnNGSJUv01Vdf6fvvv1eOHDnUv39/a6WiV69eioyM1BdffKEff/xRwcHB6tGjh9avX69ixYpp2bJlku4lPW3atHns/ebPn6+IiAj1799fa9as0bRp03Ts2DGNHj3a5roxY8YoJCREq1atUosWLRQWFqZt27ZJkv7zn/9o2LBh+vjjj/XDDz/Iz89PixYteuy9gSfF9y3cHYlGBhQdHa3Dhw9r7NixqlChgnx9fTVq1KiHXr9q1SrdunVLo0eP1osvvqiaNWvqs88+e+x9EhMTNXToUFWpUkUvvviiWrdurb///lsXLlzQ0aNHFRUVpcGDBysgIEClS5dWt27dFBQUpBkzZihz5swqUKCAJClv3rypGsYpWbKkxowZozp16qhEiRKqUaOG/vWvfykmJsbmuiZNmuitt97Sc889p06dOilPnjzauXOnJGnBggVq0KCBmjdvrtKlS6tDhw6qU6fOY+8NPCm+b+HumKORAcXExChv3rwqWbKktc3b21vZs2d/6PWlSpVS7ty5rW2+vr6pulfZsmWtH99/fWJiog4dOiRJevnll22ur1atmiZMmJC6N/KAwMBA7dmzR5MnT1ZsbKxiY2N15MgRFSlS5KEx3Y8rMTFR0r332rBhQ5vzvr6+Onjw4BPFBDwO37dwd1Q0MiCTySSz2ZysPUuWlPPKlK5/2LUPypo1a7K2R03ytFgsqe77QbNmzVKrVq105coV1ahRQ0OHDk1xyOVxMT34Xj08PJ4oHiA1+L6FuyPRyIC8vb11/fp1HT582Np27NgxxcfHp3h9+fLldezYMV2+fNnatm/fvnTF4OXlJUk2k0Mlafv27XrhhRck3Utw0mLGjBnq0qWLhgwZoqZNm6pKlSo6duxYmlaveHt7W8vR9+3duzdNcQBpwfct3B2JRgbk7++vypUrq2/fvtq9e7f27t2rvn37KlOmlP+4GzZsqIIFC6p37946ePCgtm7dqhEjRqQrhrJly6pOnToaOnSofvvtN8XGxmrq1KmKioqy/mvO09NT0r2y8PXr1x/bZ7FixbRp0yYdOXJEf/31lyZOnKi1a9cqISEh1XF16NBB69at0+zZs3Xs2DEtWLDAuhIGsAe+b+HuSDQyoEyZMmnmzJkqU6aM2rRpo44dO6phw4bWyZcP8vT01Lx58+Th4aEPPvhAffv2Vbt27dIdx4QJExQcHKyBAwfqzTff1K+//qqwsDD961//kiTlz59f77zzjsaOHavJkyc/tr+xY8fq9u3beuedd9SiRQvFxMRo6NChunTpkk6fPp2qmAICAjR+/HgtX75cjRs31tq1a1O14gV4Unzfwt2ZLOyaBAAA7ISKBgAAsBsSDQAAYDckGgAAwG5INAAAgN2QaAAAALsh0QAAAHZDogEAAOyGRAPIoNgiB4ArINEAUtCyZUt5eXnZHBUrVlRAQICGDh2quLg4u917xYoV8vLy0smTJyVJYWFh1mfHpMbZs2fVoUMHnTp1Kt2xnDx5Ul5eXlqxYsVDr2nZsqVatmyZpn6f5DUpefBrBcD18Jh44CFeeuklDR482Pp5YmKi/vzzT02YMEEHDhzQN998k+YHwz2J9957T7Vq1Ur19Zs3b9aGDRvsGBEApB6JBvAQuXLlUpUqVWzaqlWrphs3bmjKlCnas2dPsvP2ULRoURUtWtTu9wEAe2DoBEijihUrSpL1gVgtW7bUp59+qu7du6tKlSpq3bq1JOnOnTsaO3asateurYoVK6px48ZavXq1TV9ms1nh4eEKCAhQ5cqV1blz52TDMikNnURGRiokJESVK1e2PnArISFBK1as0IABAyRJQUFB6t+/v/U1y5YtU8OGDa1DQGFhYUpKSrLpd+3atXrzzTfl4+OjkJAQHTx4MM1fn8uXL2vo0KGqU6eOKlasqOrVq6tLly4pDm9MmzZNr776qnx9fdW5c2edOHHC5nxMTIw6duwoPz8/+fn5qUuXLsmuefDevXv3Vs2aNVWpUiW99dZbioyMTPN7AGAcKhpAGsXGxkqSnnvuOWvbTz/9pDfffFPTp0+X2WyWxWJRly5dtHPnTnXv3l1ly5bVunXr1LNnTyUkJKhJkyaSpK+++krz589XaGioKleurJ9++knjx49/5P0XLVqkYcOG6b333lOvXr104sQJjR07VnFxcfrkk08UGhqq6dOna+rUqdYEZebMmZo4caJatGihAQMG6MCBAwoLC9OZM2c0cuRISdIvv/yi7t27q3HjxurTp48OHDigPn36pOlrY7FY1LFjR8XFxenTTz9VoUKFdOjQIU2aNEmDBw9WRESE9dodO3bo0qVLGjRokJKSkjR+/Hi1atVKK1euVK5cuRQbG6tmzZqpTJkyGjNmjO7evavp06frgw8+0A8//KCCBQsmu3+fPn106dIlDR06VLly5dIPP/ygfv36qWjRonrllVfS9F4AGINEA3gIi8Wiu3fvWj+Pi4vT1q1bNX36dPn6+lorG5Lk4eGhoUOHKmvWrJKkTZs2aePGjZo4caIaNGggSapVq5Zu3bqlcePGqVGjRrp586YWLFig1q1bq2vXrtZrzp8/r40bN6YYk9ls1rRp0xQcHKwvv/zS2n7r1i2tWrVKuXPnVsmSJSVJ3t7eevbZZ3X9+nWFh4eradOm+vzzzyVJr732mvLly6fPP/9crVu31osvvqhp06bJx8dHX331lTUWSY9NfP7p/PnzypEjh/r166eqVatKkvz9/fX3339r6dKlNtdmzpxZc+bMsQ4LlSlTRk2aNFFkZKRatGihqVOnKkeOHJo7d65y5colSapRo4aCg4M1e/Zs9evXL9n9t27dqi5duig4OFiSVL16deXLl8/65wLA8Ug0gIfYtm2bKlSoYNOWKVMmvfrqqxo2bJjNRNAyZcrY/DKLjo6WyWRS7dq1bZKVwMBA/fjjjzp8+LAuXLigxMRE1alTx+Yeb7zxxkMTjdjYWF26dEl169a1aW/btq3atm2b4mt27dql27dvKzAwMFks0r2k6LnnntOff/6pHj16JIslLYlGkSJFNH/+fFksFp08eVLHjx/XX3/9pZ07dyohIcHmWj8/P5u5J97e3nruuee0bds2tWjRQn/88YeqV6+u7NmzW+POlSuXqlatqs2bN6d4f39/f4WFhWn//v2qVauWateunWJCAsBxSDSAh6hQoYKGDh0qSTKZTMqWLZuKFStm/df1P+XMmdPm86tXr8piscjPzy/Fvs+fP69r165JkvLnz29z7plnnnloTFevXpWkFIcNHveaDh06PDSWuLg4WSyWZLEULlw41fe578cff9SECRN05swZ5cuXT97e3sqePXuy6woVKpSsrWDBgtavy9WrV7V69epk81okqUCBAinee+LEiZoxY4Z++uknrVmzxiYxLFGiRJrfC4D0I9EAHiJnzpyqVKnSE702d+7c8vT01Pz581M8//zzz+u///2vJOnSpUsqU6aM9dz9xCAlefLkkXRv0uM/XblyRfv375evr+9DXzNu3DiVKlUq2flChQopX758ypQpky5evGhz7lGxpGT79u3q16+fWrZsqbZt26pIkSKSpLFjx2rHjh0216a0F8mFCxes7yF37tx69dVXrZNr/ylLlpT/6sqdO7f69OmjPn366K+//lJUVJTCw8M1dOhQzZo1K03vBYAxWHUC2EH16tV18+ZNWSwWVapUyXrExMRo2rRpunv3rnx9fZU9e3b9/PPPNq/99ddfH9pvmTJllD9//mTX/PDDD+rQoYMSExOVKZPtj3XlypXl4eGhc+fO2cSSJUsWTZgwQSdPnlS2bNnk6+urtWvX2uwo+ssvv6Tpfe/atUtms1ndunWzJhlJSUnWoQ6z2Wy9dseOHbp+/br18z179ujUqVPWSZvVq1fXkSNH5O3tbY25YsWKmjt3rtatW5fs3qdOnVLt2rWtX88yZcqoffv2evXVV60rhAA4HhUNwA5q166tatWqqXPnzurcubPKli2r//73v5oyZYpq1aplLf137txZkyZNUo4cOfTKK69ow4YNj0w0MmfOrG7dumnYsGEqWLCgAgMDFRsbqylTpqh58+bKmzevtYKxbt06vf766ypbtqzatWunyZMnKz4+Xv7+/jp37pwmT54sk8mk8uXLS5J69eqljz76SF27dlXTpk0VGxurGTNmpOl9+/j4SJKGDRumd955R3FxcVq0aJF1mezNmzetQ09ms1kdOnRQp06ddOXKFY0fP17lypXTm2++af3aNGvWTB07dtQHH3ygbNmyaenSpVq/fr2mTJmS7N4lSpRQ0aJF9eWXXyo+Pl4lS5bUvn37tGHDBnXs2DFN7wOAcUg0ADvIlCmTZs2apcmTJ2vmzJm6dOmSihQpotatW6tLly7W6zp27ChPT0/NmzdP8+bNk6+vr/r166chQ4Y8tO/mzZvL09NTERERWrp0qYoWLar27durffv2ku5NiHz11Vc1fvx4RUdHa9asWfrkk0/0zDPPaPHixZo9e7by5s2rGjVqqFevXsqdO7ckqWrVqvr66681YcIEde3aVc8++6xGjhypTp06pfp9+/v7a9CgQfr3v/+tn3/+WYUKFZK/v7+mTp2qLl26aMeOHapdu7YkKTg4WMWLF1efPn109+5d1alTRwMHDlS2bNkkSeXLl9eiRYs0ceJE9e3bVxaLReXKldO0adMUFBSU4v2nTp2qCRMmaPLkybpy5YqKFSumrl27PnR+CgD7M1l48hIAALAT5mgAAAC7IdEAAAB2Q6IBAADshkQDAADYDYkGAACwGxINAABgNyQaAADAbkg0AACA3ZBoAAAAuyHRAAAAdkOiAQAA7Ob/ARqVWBsKUj1uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_yhat = knn_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,knn_yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  12\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the method that performs best:\n",
    "Evaluate it one last time on the test set.\n",
    "Hence, generalization is now checked by running on unused test data, the holdout set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>Accuracy Train</th>\n",
       "      <th>Accuracy Test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logreg</th>\n",
       "      <td>0.846429</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Svm</th>\n",
       "      <td>0.848214</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tree</th>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knn</th>\n",
       "      <td>0.848214</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Accuracy Train  Accuracy Test\n",
       "Logreg        0.846429       0.833333\n",
       "Svm           0.848214       0.833333\n",
       "Tree          0.875000       0.833333\n",
       "Knn           0.848214       0.833333"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "methods = ['Logreg','Svm','Tree','Knn']\n",
    "accs_train = [logreg_cv.best_score_, svm_cv.best_score_, tree_cv.best_score_, knn_cv.best_score_]\n",
    "accs_test = [acc_logreg_test_data, acc_svm_test_data, acc_tree_test_data, acc_knn_test_data]\n",
    "\n",
    "dict_meth_accs = {}\n",
    "\n",
    "for i in range(len(methods)):\n",
    "    dict_meth_accs[methods[i]] = [accs_train[i], accs_test[i]]\n",
    "\n",
    "df = pd.DataFrame.from_dict(dict_meth_accs, orient='index')\n",
    "df.rename(columns={0: 'Accuracy Train', 1: 'Accuracy Test'}, inplace = True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>LogReg</th>\n",
       "      <th>SVM</th>\n",
       "      <th>Tree</th>\n",
       "      <th>KNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1_Score</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.869565</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LogReg       SVM      Tree       KNN\n",
       "F1_Score  0.888889  0.888889  0.869565  0.888889\n",
       "Accuracy  0.833333  0.833333  0.833333  0.833333"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the scores from the test Dataset\n",
    "\n",
    "#Since we're dealing with binary classification, lets also evaluate using F1 score\n",
    "#F1 score is usually more useful than accuracy, especially if we have an uneven class distribution. Accuracy \n",
    "#works best if false positives and false negatives have similar cost. However, if the cost of false positives and \n",
    "#false negatives are different (such as our case) it's better to look at both Precision and Recall.\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_scores = [\n",
    "             f1_score(Y_test, logreg_yhat, average='binary'),\n",
    "             f1_score(Y_test, svm_yhat, average='binary'),\n",
    "             f1_score(Y_test, tree_yhat, average='binary'),\n",
    "             f1_score(Y_test, knn_yhat, average='binary'),\n",
    "            ]\n",
    "\n",
    "accuracy = [acc_logreg_test_data, acc_svm_test_data, acc_tree_test_data, acc_knn_test_data]\n",
    "\n",
    "scores = pd.DataFrame(np.array([f1_scores, accuracy]), index=['F1_Score', 'Accuracy'] , columns=['LogReg', 'SVM', 'Tree', 'KNN'])\n",
    "scores\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>LogReg</th>\n",
       "      <th>SVM</th>\n",
       "      <th>Tree</th>\n",
       "      <th>KNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F1_Score</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.916031</td>\n",
       "      <td>0.958678</td>\n",
       "      <td>0.900763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.877778</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.855556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LogReg       SVM      Tree       KNN\n",
       "F1_Score  0.909091  0.916031  0.958678  0.900763\n",
       "Accuracy  0.866667  0.877778  0.944444  0.855556"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the scores from the whole Dataset\n",
    "\n",
    "f1_scores = [\n",
    "             f1_score(Y, logreg_cv.predict(X), average='binary'),\n",
    "             f1_score(Y, svm_cv.predict(X), average='binary'),\n",
    "             f1_score(Y, tree_cv.predict(X), average='binary'),\n",
    "             f1_score(Y, knn_cv.predict(X), average='binary'),\n",
    "            ]\n",
    "\n",
    "accuracy = [logreg_cv.score(X, Y), svm_cv.score(X, Y), tree_cv.score(X, Y), knn_cv.score(X, Y)]\n",
    "\n",
    "scores = pd.DataFrame(np.array([f1_scores, accuracy]),\n",
    "                      index=['F1_Score', 'Accuracy'],\n",
    "                      columns=['LogReg', 'SVM', 'Tree', 'KNN'])\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qVZOHh0e21ytXrpwk6bvvvrOZ57pgwYLr3u/zWoUKFZKTk5MuXLhgE8AuX76sN998U5JsnjDVqlUrGYahiRMn2hxzzZo1Onr0aKZfD7dp00YpKSmaNGmSvLy81Lhx42zVlcHJyUktW7bU3r17tXDhQqWmpmZ61K2bm5uaN2+uw4cPa/bs2TbLtm/frvHjx2vx4sVZ/rr7WuXKldPx48d16NAha9uJEycy3T4qOTlZH330kWbMmGETKBMTE3X+/Hndfffdmeal3kz9+vXl6empBQsWaO/evTaj0Ney95wzRvpz+rSw6tWrq3Tp0lq3bp2+/fZbm2VTp07VX3/9pUcffVSurq66++67Vb9+fW3bts3msb2JiYmZpkd4eXlp7ty5mjx5cqY+++eff0q6+u8IQM4w5QDADS1fvlyGYahly5Y3HP1q27attm7dqkWLFumLL75QZGSkFi9erNatW6t+/fpKSkrSqlWr5OXlpZEjR0qS6tWrl631Mu4ju2vXLnXs2FE1a9bUwYMHtW3bNgUHB2v37t03PQ8PDw9FREQoNjZW7du3V926dXXx4kV98803+vfff1WkSBElJCQoPT1dTk5OateundatW6dly5bp4MGDqlWrlk6ePKl169apZMmSNheGZZzLXXfdpRMnTqht27Y5mrf6+OOP68MPP9SMGTOsPyRca+jQodq1a5fGjRunr776SlWqVLHW5eLiojFjxtzwvrcZnnjiCb355pvq0qWLWrRooeTkZK1Zs0YBAQH68ccfretVqVJFTZs2VWxsrB5//HE9/PDDSk1N1YYNG3T27FmNHj3a7vN0c3PTI488ojVr1sjV1fWmTwez55wzLuJbs2aNPD099fjjj+vBBx/Mdm1OTk5666239Oyzzyo6OlqNGjWSv7+/du3apbi4OJUvX15Dhgyxrj9y5Eh16NBBAwYMUHh4uIoXL65vvvkm03vg6uqqfv36adSoUWrRooUiIiLk7u6uHTt26Oeff1br1q2tP7gBsB8jtABuKGO6QcZo6vVERETIx8dHP//8sw4ePKjRo0fr1Vdflbu7uxYuXKj169erQYMG+vTTT22ujs/ueu+9954ef/xx/fHHH/rkk0906dIlffzxxwoODs72uYwZM0ZPP/20EhIS9Mknn+i7775TUFCQPv30U7Vp00aXL1+2PlnK2dlZM2fO1IABA3T58mXNnz9f27ZtU8uWLbVgwYJMo6AuLi6KiIiQJLunG2R44IEHVLlyZeuUhax+gPDz89Pnn3+ubt266eTJk5o3b55+/PFHhYWF6fPPP7c+gOFmOnfurJdeeklFihTRZ599pu+//149e/bUSy+9lGnd8ePHa9CgQUpLS9PChQu1ZMkSlSpVSjNnzlS7du1ydK4ZtxKrW7fudacbZLDnnEuUKKEBAwbIYrFo/vz52rNnj921hYSE6IsvvlDz5s21a9cuzZ8/X+fOnVOvXr20aNEiFS1a1LpuqVKltHDhQjVv3lw7duzQ4sWLVbFiRc2cOTPTfrt06aLJkyerZMmSWr16tebPn6/k5GQNHz5cY8aMsbtOAP9jMbKalAQAsFuHDh108uRJff3117c8lxMAkH2M0AKAA2zevFm7du1SZGQkYRYA8hgjtABwC0aPHq2dO3fq4MGD8vHx0dq1a21+JQ0AyH2M0ALALbjnnnt05MgRlS1bVjNnziTMAkA+YIQWAAAApsYILQAAAEyNQAsAAABT48EKki5cuKS0tMzPNMet8/X10tmzSfldBm4T9Cc4Ev0Jjkafyh3Ozk4qXNjjhusQaCWlpaUrNZVA62gZdy5KS0sXM7Vxq+hPcCT6ExyNPpW/mHIAAAAAUyPQAgAAwNSYcgAAAAq09PSCPy3QYpHS0tKUns6Ug5xycsr5OCuBFgAAFEipqSk6e/YfpaWl5ncp2XLqlJMpwndB5ezsIl/fe+TiUsjubQm0AACgQDp79h+5uXnK27uwJEt+l3NTLi5OXGSeY4YSEy/o7Nl/dPfdJezemkALAAAKnPT0dKWlpcrbu7CcnJzzu5xscXJy0i381vyO5+1dWBcvXlB6errd0w942QEAQAFW8Edm4Sg5f68ZoQUAAKZiycWMywVd5kSgBQAAplGkiKdcXXNvCkJycprOn7+Ya/tH7iDQAgAAU7BYJFdXZz3zRqwuXnb8nQ883V00Z2RTWSz2jdQahqGnnmorDw8PzZ69wOF15ZW9e3/RhAljJEkXLpxXQkKCSpQoKUmqXbuuevbsk+19vfXWm3rkkUaqXbtertR6LQItAAAwlYuXU3XpSsG5lde2bVtVtKivzp8/px07tqtmzVr5XVKOVKpUWXPmXA3kq1evUGzsGk2ZMiNH+xo2bIQjS7spAi0AAMAtWLLkc9WtW1+GYeizz+ZbA+22bVv13nvTlJaWJheXQnr++RdUrVp1HTiwT5MnT1BSUpKcnCzq2rWHGjUKV7t2LTVixJsKDq4qSXryyTYaOvQV3Xff/erZs6sCAgJ1/PhxjRv3tv7++y/NmfOBUlNTdfr0adWqVUdDh7583eOeOXNGn3wy2zqCnJiYqPbtW2nBgsXy9fXN1nm2a9dSFStW1u+/H9ZTT3VR1aohmjTpLV28eFH//ntK999fQm+88ZaKFi2qvn2fU8uWbdS0aXPVq1dDPXr00rZtW3Tq1Cm1aROpzp2fceh7QKAFAADIoRMnjuuHH7bpxReHy9nZolmz3tPvv/8mX18/vfrqcL399nRVqlRZcXE/6Z13JurDD+dq2LBBGjDgRTVs2Fjx8cc0cGBf1apV54bHOXPmtDp06KwaNUJlGIbefnu8Bg9+SeXKPWANp61bt1Xx4vde97gxMW9r375fVLFiZa1Zs1J16tTNdpjNUKqUv954Y6wk6b33pqtRo3C1bNlG6enpGjiwr9asWamnnuqcabtChVw1c+ZHOnz4kJ59trPatGknb29vu459IwRaAACAHFq69AuFhNRQ8eL3ysXFSdWqVdfChfNVp049lSzpr0qVKkuSqlYN0Zw5C3Tw4AGlpCSrYcPGkq4GxEWLvrzpcSwWi4KCgq1fv/XWJG3dulmbNm3UsWNHlZx8RZcuXdSePbuyPK4ktW7dVsuXL1HFipW1fPniHE0LCA6uZv26R49e2rnzBy1YME8nTsTr6NE/VKVK1Sy3q1//EUnSAw88KMMwlJiYQKAFAADIb1euXNbq1Svk5OSkdu1aSpIuX76sPXviVLVqiCzX3F/s8OFDcnFxydT+xx9HdN999/9/+/+uRktJSbF+7eTkJDc3N0nSpUuX1LVrJ9WtW09Vq4YoIqKZdu/eJcMw5Oycef+HDx9SmTJl1bp1W3Xq1F5hYRFydXVV5cpV7D5nNzd369evv/6KLl26pPDwpqpZM1SXL1+ScZ2r6TJql64G8uutl1M8WAEAAJiKp7uLPNwc/8fT3b5xvvXr18rd3V3Llq3RF1+s0LJlq7Rs2Rrdffc92rv3Z/311wn9+usBSdIvv+zRwIF9VaqUv9zdPbR58yZJV6csREd308WLF1W0aFH9+utBSdL+/Xt16tQ/WR73+PFjOnfurJ57rrfq12+o48fj9c8/J5Wenq5KlYKyPK4k+fkV08MP19HEiWP1+OPtc/Ta/9f27VsVFdVNTZo0k7e3j378cYfS0/Pn0b+M0AIAbOTmTesLijvhHG9HhnH1PrFzRjbNtWMkJ6dl+5ZdS5YsUvv2T8nF5X9xysXFRU8//aymT39HL7/8uiZMGKOUlFS5urpq7NhJ///3RE2ZMkkffDBTTk4WjRz5pnx9fdWjR2+9884ErVy5XAEBgapUKSjL45Yv/6AaNQpX585PyMensO66625Vrhyk48ePqUaNUL3xxluZjptRY4sWrfX991sUEdHsll+r6Ojn9frrr8jHx0eFChVStWrVdfz4sVveb05YDEeP+ZrQ2bNJSk3Nn58obmcWi3TXXT76998EnryCW0Z/yhu5fdP6giQlNV3nzyXRnwqo9PR0/fNPvO65p5ScnGx/oVxQnxTm4uJUoPOEYRiaMWOqLBapd+/++V1OJtd7z11cnOTr63XDbRmhBQBIyv2b1hckOb2BPgoG3jP7Xb58WW3aPKoHHnhQY8dOyu9yHI5ACwCwUdBuWg/g1rm7u2vt2m/yu4xcw0VhAAAAMDUCLQAAAEyNQAsAAABTYw4tAAAwlYJ6lwPkHwItAAAwDb+ibnIu5Jpr+09LSdaZc1dybf/IHQRaAABgChaL5FzIVUen9lD6lUsO37+Tm4dK9/tAFssVRmpNhkALAABMJf3KJRnJjg+0OX0kgmEYeuqptvLw8NDs2QscWlNe2rv3F02YMEaSdOHCeSUkJKhEiZKSpNq166pnzz527W/r1s2Ki/tJvXv3c3it1yLQAgAA3IJt27aqaFFfnT9/Tjt2bFfNmrXyu6QcqVSpsubMuRrIV69eodjYNZoyZUaO97d//15duHDeUeXdEIEWAADgFixZ8rnq1q0vwzD02WfzrYF227ateu+9aUpLS5OLSyE9//wLqlatug4c2KfJkycoKSlJTk4Wde3aQ40ahatdu5YaMeJNBQdXlSQ9+WQbDR36iu6773717NlVAQGBOn78uMaNe1t///2X5sz5QKmpqTp9+rRq1aqjoUNfvu5xz5w5o08+mW0dQU5MTFT79q20YMFi+fr63vQck5ISFRMzWb/+elBpaamqUKGS+vUbKE9PLy1YMFexsatVqJCrXF1d9eKLw5WQcEHLly9RamqqPD091a/foNx58f8fgRa4DeTmFb8FxZ1wjsDtKKf/ds3yb/7EieP64YdtevHF4XJ2tmjWrPf0+++/ydfXT6++Olxvvz1dlSpVVlzcT3rnnYn68MO5GjZskAYMeFENGzZWfPwxDRzYV7Vq1bnhcc6cOa0OHTqrRo1QGYaht98er8GDX1K5cg9Yw2nr1m1VvPi91z1uTMzb2rfvF1WsWFlr1qxUnTp1sxVmJSkmZrL8/cto2LARMgxD06ZN1syZ0xQd3Ufvvz9Dq1ZtkJeXt9auXaW4uJ/Utm17tW7dVqdO/ZPrYVYi0OYLs/wjvVV3ynnmtyJFPOXq6pzfZeSJlNScznADkB9u5fMpLS1Np045ycXFSU5OeXvbfGfn7B9v+fLFql69pkqUuF+GIVWrVl0LF85XnTr1VLKkvypVqixJqlo1RHPmLNDBgweUkpKshg0bS5JKlfLXokVf3vQ4FotFQUHB1q/femuStm7drE2bNurYsaNKTr6iS5cuas+eXVkeV5Jat26r5cuXqGLFylq+fLGGDRuR7fPcvHmTihbdo3Xr1kiSUlNTVLSor7y8vBUa+rC6deus2rXrqkaNUIWHN832fh2FQJvH7qTwIRFAcpvFIrm6OuuZN2J18XJqfpeTqzzdXTRnZFNZLNwnEjCDW/18KuRsUY9mxXUp/bxkuRownZws8vX1cnSpmRz564LS02/+QZN85Yq+/HK5nJyc1KLlo3JxdtLly5e1Z0+cqlYNkeWakZ3Dhw/JxcUlU/sffxzRfffd///t/ztuSkqK9WsnJye5ublJki5duqSuXTupbt16qlo1RBERzbR79y4ZhiFn58z7P3z4kMqUKavWrduqU6f2CguLkKurqypXrpLt1yQ9PV0jR76pgICHJEkXLyZZ6xs//h0dOnRQO3fu0Pz5H2vZssWaOHFqtvftCATaPHQnhQ+JAJKXLl5O1aUrt3+fAmA+Of18SnW2yDAMGemGZLn6n0jGfyVObh45viPBjTi5eVw9Tvr/H/cmtmzaIFc3N709bb4KuRZS+RJFdPlysjp1aqe9e3/WX3+d0K+/HlBAwEP65Zc9eumlwfriixVyd/fQ5s2bVK9eA504cVzR0d306adLVLRoUf3660EFB1fT/v17derUP1ke9/jxYzp37qyee6633NzctX379/rnn5NKT09XpUpBWR53yZJV8vMrpocfrqOJE8cqKqqbXa/Nww/X0eeff6rhw0dKkkaNek2+vr7q1Olp9esXrQ8+mKsOHTorIOAhvfba1bm8zs7OSktLs+s4OUWgzQeEDwAA7GcYhlKuXFHpfh/k2jFSrlyRkc1RmPWxy9TssUi5uPwvTrm4uOjpp5/V9Onv6OWXX9eECWOUkpIqV1dXjR076f//nqgpUybpgw9mysnJopEj35Svr6969Oitd96ZoJUrlysgIFCVKgVledzy5R9Uo0bh6tz5Cfn4FNZdd92typWDdPz4MdWoEao33ngr03EzamzRorW+/36LIiKa2fW6DBjwoqZOfVtPP/2U0tPTVKFCRfXpM0Cenp6KjHxSffv2kJubu5ydnayht0aNUC1d+oXGjn3D2pZbLEZ237Xb2NmzSUrNg1+NWyzSXXf56ImXVt0RgdbDzUWfj3lMp08nKJ2ZB7niTupT9KfcR3+CI91qfyrkbNHzre6Xt09x65SDjP1e+yt1RzIMI0e/VbQ4WVS+RJE8yRM5ZRiGZsyYKotF6t27f36Xk0l6err++Sde99xTymbetIuL002nmjBCCwAATMMwlO0RVPzP5cuX1abNo3rggQc1duyk/C7H4Qi0yHVXf5rO7ypyH5+veYP+BEe6U/qTRJ+607m7u2vt2m/yu4xcQ6BFrink4iQjNUV+fj75XUqeSEtJ1plzV/K7jNsW/QmOdKf1J4k+ldsskmQYcnHJ21uM5RfDMJSW5uifknK+PwItco2Ls5MsLoV0dGoPpV9x/DO3CxInNw+V7veBLJYrjILkEvoTHOlO6k+SOftUSpqh1DRDUrqkgh8SLf8/3H/l5FHJKLjzaB3C4iS34qV1KwE0K1fviGDJ0RxpAi1yXfqVSzKSb+//MG7zj64Chf4ER7oT+pNk3j61+/dE1a7kLle3ovldyk0ZxtWLmtLT0+6AQGv8/7k67jwNw9CFC2fl4eFNoAUAALePrfsTdH8xV93ne+X/f6dfcDk5WfS35bxSL5y9/ScsWyxyMZwdGmglycXFVT4+RXO2rUMrAQAAcJCUNEOffvuvXJxy91ZdjuBb2E3vDw/X0XfGy0i5nN/l5CpLIXeVHjBLp08nOiy7X52xkfOpJQRaAABQoF29tWvBHvVMTbv6ZCxLeqqUlnLzDUzM4uwiZ2dnOTk5FZjB6II/yxoAAAC4AQItAAAATI1ACwAAAFMj0AIAAMDUCkSg3bx5syIjIxUcHKywsDDNmjXrhs9pTk1N1fvvv68mTZqoatWqat26tVavXp2HFQMAAKCgyPdAGxcXp+joaJUrV04xMTFq2bKlJkyYoA8++OC628TExGjy5Mlq1aqVZs6cqerVq+uFF15QbGxsHlYOAACAgiDfb9sVExOjChUqaMKECZKkBg0aKDU1Ve+++66ioqLk7u6eaZvFixerRYsW6tu3rySpdu3a2rt3rz755BM1bdo0T+sHAABA/srXEdrk5GRt375dERERNu1NmzZVUlKSdu7ced3tvL29bdqKFi2qc+fO5VapAAAAKKDyNdDGx8crJSVFZcqUsWkvXbq0JOnIkSNZbhcVFaVly5Zp06ZNSkxM1JdffqnvvvtOrVu3zu2SAQAAUMDk65SDhIQESco02url5SVJSkxMzHK7Z555RnFxcerRo4e1LTIyUt27d89xLXnxRL0C/tQ+OEBev8f0qdsb/QmOlpfvMf3p9leQ3uN8DbTp6ek3XO7klHkAOTk5WZ06ddKpU6f0+uuvq1y5ctq1a5dmzpwpT09PvfLKK3bX4evrZfc2QFaKFfPJ7xJwG6E/wdHoU3CkgtSf8jXQ+vhcfSGSkpJs2jNGZq8duZWk2NhYHThwQLNnz1adOnUkSaGhofL29tYbb7yhJ554QgEBAXbVcfZsktLSbhyuHcFiKVhvPhzv9OmEPH2uNX3q9kZ/gqPlZZ+iP93+8qo/OTs73XTwMV8Drb+/v5ydnXX06FGb9mPHjkmSypcvn2mbP//8U5IUEhJi016zZk1J0uHDh+0OtJLy9D8N3L4Mg74Ex6E/wdHoU3CkgtSf8vWiMDc3N9WoUUPr16+3eZBCbGysfHx8VKVKlUzblCtXTpL0448/2rT/9NNPkqSSJUvmYsUAAAAoaPL9PrS9evVS165d1b9/f0VGRmrXrl2aNWuWBg0aJA8PDyUmJurw4cPy9/eXn5+fwsLCFBwcrMGDB+v5559XuXLltGfPHs2cOVNhYWFZhmAAAADcvvL9SWG1a9dWTEyMjhw5oj59+mjFihUaMmSI9Q4Ge/fu1ZNPPqmNGzdKkpydnfXRRx+pefPmmjFjhnr06KFly5apV69emjJlSj6eCQAAAPJDvo/QSlJERESmhytkqFWrlg4ePGjT5u3trREjRmjEiBF5UR4AAAAKsHwfoQUAAABuBYEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYWoEItJs3b1ZkZKSCg4MVFhamWbNmyTCMG26zceNGtWvXTlWqVFGDBg00atQoXbx4MY8qBgAAQEGR74E2Li5O0dHRKleunGJiYtSyZUtNmDBBH3zwwXW3+frrr9WrVy89+OCDeu+99/Tcc89pyZIlGjFiRB5WDgAAgILAJb8LiImJUYUKFTRhwgRJUoMGDZSamqp3331XUVFRcnd3z7TN2LFj1bRpU40dO1aSVLt2baWlpWnevHm6dOmSPDw88vQcAAAAkH/ydYQ2OTlZ27dvV0REhE1706ZNlZSUpJ07d2baZt++fTp27Jg6d+5s0/70009rw4YNhFkAAIA7TL4G2vj4eKWkpKhMmTI27aVLl5YkHTlyJNM2+/fvlyS5ubmpZ8+eqlKlikJDQzV69GglJyfnuBaLJW/+4PaWV/2IPnVnoD/B0ehPcKSC1I/ydcpBQkKCJMnb29um3cvLS5KUmJiYaZszZ85Ikvr27asWLVqoa9eu+vnnnxUTE6MzZ85o0qRJdtfh6+tl9zZAVooV88nvEnAboT/B0ehTcKSC1J/yNdCmp6ffcLmTU+YB5JSUFElSRESEBg8eLEl6+OGHZRiGJk2apL59+6ps2bJ21XH2bJLS0m5ciyNYLAXrzYfjnT6doJvcoMOh6FO3N/oTHC0v+xT96faXV/3J2dnppoOP+TrlwMfnakdPSkqyac8Ymb125Fb63+htw4YNbdrr168v6X9TEuxlGHnzB7e3vOpH9Kk7A/0JjkZ/giMVpH6Ur4HW399fzs7OOnr0qE37sWPHJEnly5fPtE3GfNtr58tmjNy6ubnlQqUAAAAoqPI10Lq5ualGjRpav369/vsghdjYWPn4+KhKlSqZtqlRo4Y8PT21atUqm/avv/5aLi4uqlatWq7XDQAAgIIj3+9D26tXL3Xt2lX9+/dXZGSkdu3apVmzZmnQoEHy8PBQYmKiDh8+LH9/f/n5+cnLy0v9+vXTW2+9pcKFC6tJkyb66aef9OGHHyoqKkp+fn75fUoAAADIQ/n+pLDatWsrJiZGR44cUZ8+fbRixQoNGTJEPXr0kCTt3btXTz75pDZu3GjdpmvXrhozZox27NihHj16aPHixXr++eetF4kBAADgzpHvI7TS1TsWXPtwhQy1atXSwYMHM7VHRkYqMjIyt0sDAABAAZfvI7QAAADArSDQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMza5AO2DAAG3YsEHJycm5VQ8AAABgFxd7Vo6Li9PatWvl7e2tiIgINW/eXHXq1JGzs3Nu1QcAAADckF2BduPGjfrpp5+0evVqrVu3TkuXLpWvr6+aNm2q5s2bKzQ0NLfqBAAAALJkV6CVpJCQEIWEhOjll1/Wjz/+qDVr1mjjxo1auHCh7r77bjVv3lwtWrRQ5cqVc6NeAAAAwEaOLwqzWCyqWbOmRo4cqXfeeUdNmzbVP//8ozlz5qh9+/Zq0aKFli5d6shaAQAAgEzsHqGVJMMwtGPHDq1du1br1q3T6dOn5e3trccff1wtW7aUxWLRwoUL9dJLL+nXX3/V0KFDHV03AAAAIMnOQLt9+3atXbtW69ev1+nTp+Xq6qqGDRuqRYsWeuSRR+Tq6mpdt3bt2urSpYs+//xzAi0AAAByjV2B9umnn5aLi4sefvhhtWjRQhEREfLy8rru+qVKlZKnp+ctFwkAAABcj12BdsSIEXr00Ufl5+eXrfXHjBmTo6IAAACA7LLrorBOnTrp6NGjev755/Xvv/9a28eOHatevXrp0KFDDi8QAAAAuBG7Au22bdvUpUsX7dq1SxcuXLC2lyhRQvv27dMTTzyhffv2ObxIAAAA4HrsCrRTp05VYGCg1q5dq3Llylnbo6KitHr1apUtW1aTJk1yeJEAAADA9dgVaA8ePKj27dvL29s70zIvLy9FRkZqz549DisOAAAAuBm7Aq2Hh4f+/vvv6y4/e/asnJxy/KwGAAAAwG52pc969epp3rx52rt3b6Zlhw4d0ty5c1WvXj2HFQcAAADcjF237XrhhRe0efNmPfHEEwoKClKZMmUkSfHx8dq9e7eKFi2qQYMG5UadAAAAQJbsGqEtXry4VqxYoaioKCUkJCg2NlarV6/Wv//+q6eeekrLli3T/fffn1u1AgAAAJnYNUIrSb6+vho6dCiPswUAAECB4LAruNLT05WQkKD169c7apcAAADATdk1QnvhwgWNGDFCW7Zs0cWLF2UYRpbr7d+/3yHFAQAAADdj1wjthAkTFBsbq4ceekh169aVYRhq06aNGjRoIDc3N7m5uWnatGm5VSsAAACQiV2BduPGjWrWrJk++eQTjRs3TpLUoUMHvffee/riiy9UqFAhHThwIFcKBQAAALJiV6A9e/asateuLUny8/PTPffco927d0uSHnjgAbVv315r1qxxfJUAAADAddgVaH18fJScnGz9vmzZsjp48KD1+3LlyunPP/90XHUAAADATdgVaKtVq6Zly5bp4sWLkqSAgAD98MMP1pC7f/9+eXp6Or5KAAAA4DrsCrTR0dH69ddf1bBhQ507d05PPvmkTpw4ocjISEVHR+vTTz9V/fr1c6tWAAAAIBO7Am2VKlW0aNEiNW/eXEWLFtUDDzygcePG6eLFi9qxY4eaNm2q4cOH51atAAAAQCZ23Yf2448/VmhoqF577TVrW8uWLdWyZUtH1wUAAABki10jtO+8846+++673KoFAAAAsJtdgbZo0aJKSUnJrVoAAAAAu9k15eDll1/Wyy+/rISEBFWvXl2+vr5ycsqciUNCQhxWIAAAAHAjdgXavn37SpLmzJmjjz/+ONNywzBksVi0f/9+x1QHAAAA3IRdgXbMmDGyWCy5VQsAAABgN7sCbdu2bXOrDgAAACBH7Aq0P/30U7bWYw4tAAAA8opdgbZjx47ZmnLAHFoAAADkFbsC7dixYzO1paWl6cyZM1q3bp0SExM1atQohxUHAAAA3Ixdgfbxxx+/7rLu3burS5cuio2NVY0aNW65MAAAACA77Hqwwg135OSkli1bauXKlY7aJQAAAHBTDgu0knTixAlduXLFkbsEAAAAbsiuKQcrVqzIsj05OVkHDhzQp59+qgYNGjikMAAAACA77Aq0gwcPlsVikWEYWS6vWLGiXn75ZYcUBgAAAGSHXYF27ty5WbY7OTnp7rvvVunSpR1SFAAAAJBddgXa0NBQSdLBgwdVrlw5FSpUSJL0448/KikpyfHVAQAAADdh10VhSUlJ6tmzp9q0aaPff//d2v7JJ58oMjJSQ4cOVUpKisOLBAAAAK7HrkA7bdo0bd26VS+88IJKlSplbR85cqSGDh2q1atX6/3333d4kQAAAMD12BVoY2NjFRUVpeeee06enp7Wdj8/Pz3zzDPq1KmTli9f7vAiAQAAgOuxK9CeOXNG999//3WX+/v76+TJk7dcFAAAAJBddgXacuXKacOGDdddvnHjRvn7+99yUQAAAEB22RVoo6Ki9P3336tv377aunWrjh8/ruPHj+v777/XwIED9d133ykqKiq3agUAAAAyseu2XW3atNHff/+tGTNm6KuvvrK2G4YhFxcX9enTR+3bt3d4kQAAAMD12BVoJSk6OlodOnTQ999/rz///FNpaWm67777VKdOHRUrViw3agQAAACuy+5Am5qaqp9++klhYWFyc3OTJG3YsEG7du1S48aNZbFYHF4kAAAAcD12zaE9ffq0nnjiCfXp00eHDx+2tq9evVp9+/bV008/rcTERIcXCQAAAFyPXYH27bff1tGjRzVp0iQ99NBD1vaJEycqJiZG+/bt09SpUx1eJAAAAHA9dgXaTZs2qWvXrmrevLmcnZ3/txMnJ0VERKhLly43vK0XAAAA4Gh2BdrExEQVLlz4usuLFSumM2fO3HJRAAAAQHbZFWgrVqyo5cuXKyUlJdOytLQ0rVy5Ug8++KDdRWzevFmRkZEKDg5WWFiYZs2aJcMwsrVtamqq2rVrpy5duth9XAAAAJifXXc56NGjh3r16qVOnTqpffv21qeCxcfHa8mSJYqLi9O0adPsKiAuLk7R0dF69NFH1b9/f+3cuVMTJkxQWlqannvuuZtu//777+vnn39WaGioXccFAADA7cGuQNuwYUO99dZbGj9+vEaMGGG9RZdhGPL19dVbb72l8PBwuwqIiYlRhQoVNGHCBElSgwYNlJqaqnfffVdRUVFyd3e/7rYHDhzQe++9p7vvvtuuYwIAAOD2YdeUA0lq3bq1vvvuO33++eeaPHmyJk2apAULFmjTpk0qUqSIBgwYkO19JScna/v27YqIiLBpb9q0qZKSkrRz584bbjtkyBB16dJFZcuWtfc0AAAAcJuw+8EK0tW7GlSpUkVVqlTRgQMHtGzZMvXr10+nT5+2az/x8fFKSUlRmTJlbNpLly4tSTpy5Ijq1q2b5bbTp09Xamqq+vXrp2effTYnp2EjL54HwTMnbn95/R7Tp25v9Cc4Wl6+x/Sn219Beo9zFGhPnz6tFStWaNmyZTp48KAMw1ChQoXUvHlzPfnkk9neT0JCgiTJ29vbpt3Ly0uSrvuQhj179uijjz7S/Pnz5erqmpNTsOHr63XL+wAkqVgxn/wuAbcR+hMcjT4FRypI/SnbgTYlJUVfffWVli1bps2bNystLU2GYchisahTp07q06eP/Pz87Dp4enr6DZc7OWWeEXHlyhUNGzZMTz/9tKpUqWLX8a7n7NkkpaXduBZHsFgK1psPxzt9OkHZvEGHQ9Cnbm/0JzhaXvYp+tPtL6/6k7Oz000HH28aaHfv3q2lS5dqzZo1unDhggzDUMWKFRUREaGKFSuqZ8+eqlOnjt1hVpJ8fK529KSkJJv2jJHZa0duJemdd95Renq6evfurdTUVEmy3uIrNTVVzs7O1ovV7JGX/2ng9mUY9CU4Dv0JjkafgiMVpP50w0DbrFkzHT16VM7OzgoJCVHjxo0VHh6uEiVKSJJOnDhxSwf39/eXs7Ozjh49atN+7NgxSVL58uUzbRMbG6sTJ06oWrVqmZZVqlRJY8eOVdu2bW+pLgAAAJjHDQPtH3/8IU9PT3Xo0EENGzZUcHCw3NzcHHZwNzc31ahRQ+vXr9ezzz5rHVmNjY2Vj49PllMKZs6cqeTkZJu2V199VZL0+uuvq2TJkg6rDwAAAAXfDQPttGnTtHLlSi1YsECzZ8+Wm5ub6tSpo/DwcDVq1MghBfTq1Utdu3ZV//79FRkZqV27dmnWrFkaNGiQPDw8lJiYqMOHD8vf319+fn4KDAzMtI+Mi8iCgoIcUhMAAADM44aBNjw8XOHh4UpKStL69eu1cuVKbdq0Sd98842cnZ0VEBAgi8WSacTUHrVr11ZMTIymTp2qPn36qHjx4hoyZIi6desmSdq7d6+ioqKYSgAAAIAsZesuB15eXmrTpo3atGmjs2fPas2aNVq5cqV27dolwzA0ePBgLV++XJGRkWrUqJFcXOy7G1hERESmhytkqFWrlg4ePHjD7efNm2fX8QAAAHD7sPs+tL6+vurYsaM6duyov//+WytWrNCqVau0ceNGffvttypatKi+//773KgVAAAAyMTuR9/+17333qsePXpo2bJlWr16taKjo1W4cGFH1QYAAADc1C0F2v8qV66c+vfvr9jYWEftEgAAALgphwVaAAAAID8QaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBFoAAACYGoEWAAAApkagBQAAgKkRaAEAAGBqBSLQbt68WZGRkQoODlZYWJhmzZolwzCuu35ycrLeffddNWvWTFWrVlXTpk01bdo0JScn52HVAAAAKAhc8ruAuLg4RUdH69FHH1X//v21c+dOTZgwQWlpaXruueey3GbUqFH68ssv1bt3bwUFBennn3/W9OnT9eeff2rMmDF5fAYAAADIT/keaGNiYlShQgVNmDBBktSgQQOlpqbq3XffVVRUlNzd3W3WP3v2rD7//HO9+OKL6t69uySpdu3akqRJkybpxRdflJ+fX96eBAAAAPJNvk45SE5O1vbt2xUREWHT3rRpUyUlJWnnzp2ZtklMTFSHDh0UFhZm016uXDlJUnx8fO4VDAAAgAInX0do4+PjlZKSojJlyti0ly5dWpJ05MgR1a1b12ZZqVKl9Nprr2Xa11dffaVChQpl2ld2WSw52qzAHQP5K6/fY/rU7Y3+BEfLy/eY/nT7K0jvcb4G2oSEBEmSt7e3TbuXl5ekq6Ox2bF+/XotXbpUnTt3VpEiReyuw9fXy+5tgKwUK+aT3yXgNkJ/gqPRp+BIBak/5WugTU9Pv+FyJ6ebz4hYt26dBg0apOrVq2vw4ME5quPs2SSlpd24FkewWArWmw/HO306QTe4QYfD0adub/QnOFpe9in60+0vr/qTs7PTTQcf8zXQ+vhc7ehJSUk27Rkjs9eO3F5rzpw5GjdunEJDQzV9+nS5ubnluJa8/E8Dty/DoC/BcehPcDT6FBypIPWnfA20/v7+cnZ21tGjR23ajx07JkkqX758ltsZhqHRo0dr3rx5atGihcaOHStXV9dcrxcAAAAFT77e5cDNzU01atTQ+vXrbR6kEBsbKx8fH1WpUiXL7d5++23NmzdPXbt21cSJEwmzAAAAd7B8vw9tr1691LVrV/Xv31+RkZHatWuXZs2apUGDBsnDw0OJiYk6fPiw/P395efnp/379+uDDz5QUFCQmjVrpt27d9vs74EHHrjpVAUAAADcPvI90NauXVsxMTGaOnWq+vTpo+LFi2vIkCHq1q2bJGnv3r2KiorS2LFj1bZtW61bt06GYejnn3/Wk08+mWl/c+fOVa1atfL6NAAAAJBP8j3QSlJERESmhytkqFWrlg4ePGj9vn///urfv39elQYAAIACLl/n0AIAAAC3ikALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMjUALAAAAUyPQAgAAwNQItAAAADA1Ai0AAABMrUAE2s2bNysyMlLBwcEKCwvTrFmzZBjGDbdZuXKlHnvsMVWpUkWPPvqoli5dmkfVAgAAoCDJ90AbFxen6OholStXTjExMWrZsqUmTJigDz744LrbxMbG6sUXX1TdunU1ffp0hYaGatiwYVq1alUeVg4AAICCwCW/C4iJiVGFChU0YcIESVKDBg2Umpqqd999V1FRUXJ3d8+0zdtvv61mzZrppZdekiTVr19f58+f15QpU/TYY4/laf0AAADIX/k6QpucnKzt27crIiLCpr1p06ZKSkrSzp07M21z/Phx/fHHH1luc/ToUf3xxx+5WTIAAAAKmHwdoY2Pj1dKSorKlClj0166dGlJ0pEjR1S3bl2bZb/99psk3XCba5fdjLNz3uR6i+Xq3xXK+OpyclqeHDM/FfZylSS5lwxUevKVfK4mdzm5ukmSXFycdJPp3w51J/Up+lPuoz/dvvKjT91J/Um6s/pUXven7OS0fA20CQkJkiRvb2+bdi8vL0lSYmJipm0y2uzZ5mYKF/awe5tb8fpzdfL0ePntvqdG5HcJeaZoUa98Oe6d1KfoT7mP/nT7yo8+dSf1J+nO6lP59RmVlXydcpCenn7D5U5OmcvLyTYAAAC4feVr+vPx8ZEkJSUl2bRfbxQ2p9sAAADg9pWvgdbf31/Ozs46evSoTfuxY8ckSeXLl8+0TdmyZSUp0zYZ32e1DQAAAG5f+Rpo3dzcVKNGDa1fv97mQQqxsbHy8fFRlSpVMm1TunRplSxZUrGxsTbt69atU5kyZVSyZMlcrxsAAAAFR77fh7ZXr17q2rWr+vfvr8jISO3atUuzZs3SoEGD5OHhocTERB0+fFj+/v7y8/OTJPXp00fDhw9X0aJFFRYWpq+++kpr1qzR5MmT8/lsAAAAkNcsxs2eMZsH1q9fr6lTp+rIkSMqXry4OnXqpG7dukmStm/frqioKI0dO1Zt27a1bvPZZ5/po48+0l9//aVSpUrpueeeU5s2bfLpDAAAAJBfCkSgBQAAAHKKe1wBAADA1Ai0AAAAMDUCLQAAAEwt3+9ygPzTpUsXSdK8efPyuRLcaX799VfNnDlTP/zwg86fP6+iRYuqRo0aio6O1kMPPZTf5cEkhg0bpqVLl95wndDQUD7jYLfr/f+YmJiobt26ad++fXrnnXcUHh6eH+UhCwRaAHnq0KFDevLJJ1W1alW98sorKlasmP7++2998skneuKJJzR37lxVrVo1v8uECfTu3VsdOnSwfj9jxgzt27dP06ZNs7bx9Eg4SmJiorp3764DBw5o+vTpeuSRR/K7JPwHgRZAnpo9e7Z8fX31wQcfyMXlfx9B4eHhatasmWbMmKH3338/HyuEWfj7+8vf39/6vZ+fn1xdXfmBCA6XEWb379+vmTNnqm7duvldEq7BHFrc0JYtW9SxY0dVr15dtWrV0qBBg/TXX3/ZrLNr1y516tRJVatWVcOGDfXxxx/rmWee0bBhwyRJx48fV2BgoGbPnq1mzZopODhYixcvlnT1V889e/ZUSEiIQkJC1KdPH8XHx9vs/7ffflOPHj0UEhKiOnXqaPLkyRo+fLj1V0Iwl3///VeGYSg9Pd2m3dPTUy+99JIeffRRvfvuu6pcubLOnz9vs86cOXNUqVIlnT59WkuWLFFQUJB+/PFHRUZGKigoSE2bNtXXX3+t33//XU8//bSCg4MVERGhVatW5eUpogBZsmSJKlasqEWLFqlu3boKDQ3V4cOHJUkbNmxQ27ZtFRQUpLp162rUqFG6ePGizfbZ+YzC7S0pKUk9evTQwYMH9f7779uE2bCwME2dOlXjxo1TnTp1VKVKFT377LP6448/rOsMGzZMzzzzjBYvXqymTZuqcuXKat26tTZt2pQPZ3P7ItDiupYtW6Zu3brpvvvu09tvv63hw4dr165devLJJ3X69GlJV8PmM888I0l6++239fzzz+v999/Xzp07M+0vJiZGPXr00Pjx41W3bl0dOXJEHTp00OnTpzVu3DiNHj1a8fHxeuqpp6z7P3PmjDp37qy//vpLY8eO1SuvvKK1a9dq5cqVefY6wLEaNmyoP//8Ux06dND8+fP122+/WR993axZMz3++ONq2bKlUlNTtW7dOpttV61apXr16qlYsWKSpNTUVA0aNEgdOnTQzJkz5eHhoRdffFHR0dFq2LCh3n33Xd1zzz0aOnSo/v777zw/VxQMaWlp+uijjzR69GgNHz5c5cuX14oVK9SnTx+VK1dO06dPV9++ffXll1+qd+/e1v6Ync8o3N4uXryo5557Tvv27dMHH3ygWrVqZVpn7ty5+v333zV27FiNGjVKv/zyi4YOHWqzzi+//KJZs2apX79+mj59upydnfX8889n+qEdOceUA2QpPT1dEydOVL169TRp0iRre0hIiJo3b65Zs2ZpyJAheu+99+Tj46MPP/xQHh4ekqRy5crZzGvL8OijjyoyMtL6fcbjjefMmWOd51a7dm2Fh4frww8/1NChQzVv3jwlJSVp2bJlKl68uCQpODhYTZs2zc3TRy7q2LGjTp06pVmzZumNN96QJPn6+qpevXqKiopSlSpVVKJECdWsWVMrV65U+/btJUnHjh3Tnj17bB5xnZ6erujoaOs6Fy5c0AsvvKCnn35aXbt2lST5+PgoMjJSv/zyi+699948PlsUFBk/5EiSYRiaOHGi6tevr4kTJ1rXKVOmjJ555hl9++23atiwoaZNm3bTzyjcvjLCbMYAzbWj9xkKFy6sGTNmyNnZWdLVz6qYmBidPXtWvr6+kqSEhAQtWbLEOkXG09NTnTt31rZt2/j/zEEYoUWWjhw5olOnTqlFixY27f7+/qpWrZp++OEHSdK2bdvUoEEDa5iVpGrVqqlEiRKZ9lmhQgWb77dt26bQ0FC5u7srNTVVqamp8vb2Vo0aNbR161brOtWqVbOGWUkqUaKEqlWr5rBzRd7r37+/vvvuO02aNEnt2rWTt7e3VqxYYb0oTJJatWqlHTt26NSpU5Kujs56e3srLCzMZl//7QsZI7fBwcHWtqJFi0q6GnZx5/rv58/vv/+uv//+W2FhYdbPntTUVNWsWVPe3t7asmWLpOx9RuH29csvv+jQoUOaP3++SpcurWHDhlk/j/4rKCjIGmYlWX9wvnTpkrXNz8/PZr53Vuvg1hBokaVz585Jku66665My+666y4lJCRIujolICNEXLvOtTw9PTMdY/Xq1apUqZLNn2+++Ub//POP3fuHuRQpUkQtWrTQ6NGjtWHDBi1dulTly5fXhAkTdPbsWTVr1kwuLi5as2aNpKuBtmnTpnJ3d7fZT1ZXsf/3ByxAsv38yfh8e/311zN9/iQmJlo/f7LzGYXbV5EiRfTxxx+revXqGj9+vM6fP6+hQ4dap6RkuPbzxsnparT673UC165jsVgyrYNbw5QDZCljVOvff//NtOzUqVPWX6Pce++9Wa5z+vRplStX7obH8PHxUZ06day/Gv6vjKvfb7R/mM/JkycVGRmp/v37W6cJZKhYsaJeeOEF60U3VapUUVhYmNasWaOHH35Yhw4d0ogRI/KpctxOChcuLEkaMmSIQkNDMy0vUqSIpOx9RuH2FRgYaL0vdnBwsHr27Knp06frww8/VI8ePfK5OlyLEVpkqWzZsrr77rszXXwVHx+vuLg4hYSESJJq1qyp7777TleuXLGus2/fPh0/fvymx8i42rhChQoKCgpSUFCQKleurDlz5mj9+vXW/cfFxdn8mueff/5RXFycA84See2uu+6Si4uLFixYYNNnMvz+++9yc3NT6dKlJUmtW7dWXFycPv30U91///1Zhg/AXuXKlVOxYsV0/Phx62dPUFCQihcvrkmTJmnfvn2SsvcZhTtH7969FRQUpClTpmjPnj35XQ6uwY+Yd7i///5bc+bMydQeEBCggQMHavjw4Ro0aJBatWqls2fPatq0aSpSpIh1xCI6OlqrV69W9+7d1a1bN124cEFTpkyRk5OT9Vcq15NxU/SePXvqqaeekpubmxYuXKgNGzZo6tSpkqSoqCjNnz9fzz77rPr06SPp6s3TU1JSbrp/FDzOzs567bXX1KdPH0VGRqpTp04qX768Ll26pC1btmj+/Pnq37+/dYSsfv36Klq0qBYuXKju3bvznsMhnJ2d9cILL2jkyJFydnZWo0aNdOHCBc2YMUMnT55UpUqVJGXvMwp3DhcXF02YMEGPP/64Bg4cqGXLluV3SfgPAu0d7tixYxo7dmym9nbt2mn06NHy8vLSe++9pz59+sjb21v169fXwIEDdffdd0uSSpcurVmzZmn8+PHq16+fihUrpp49e2rmzJny8vK64bEfeughzZ8/X5MnT9aQIUNkGIYCAgI0ffp0NW7cWNLVXw3OnTtXo0eP1pAhQ+Tl5aWOHTvKw8Mj05xcmEPDhg31+eefa9asWXr33Xd15swZubq6qmLFipo8ebKaNGliXdfFxUWPPfaY5s2bp1atWuVj1bjdtG/fXl5eXvrwww+1cOFCeXp6KiQkRBMnTlSpUqUkZe8zCneWsmXLasiQIXr99df16quv5nc5+A+Lce3sZsAO33//vQoVKqQaNWpY2y5cuKA6depoyJAhioqKuqX97969W+fOnbN5xGBqaqoaNmyoxx57TMOHD7+l/QMAAPNjhBa3ZO/evZo6daoGDhyoSpUq6dy5c5o9e7Z8fHwy3fIrJ/7880/rhUKhoaG6dOmSFi5cqISEBD3xxBMOOAMAAGB2jNDilqSnp+vdd9/V8uXL9ddff8nT01OhoaEaNGiQ9cKeW/Xpp59qwYIFio+PV6FChRQcHKz+/fsrKCjIIfsHAADmRqAFAACAqXHbLgAAAJgagRYAAACmRqAFAACAqRFoAQAAYGoEWgBwsGHDhikwMFCVK1dWQkJClusYhqF69eopMDBQMTExdh8jPj7e+vX27dsVGBio5cuX57jm7Hj55ZcVGBiYq8cAgJwg0AJALklJSdGmTZuyXBYXF6dTp07laL8jRozQyJEjb6U0ALitEGgBIBc4Ozvrvvvu09dff53l8g0bNsjPzy9H+96yZYu44yIA/A+BFgBySVhYmDZt2qTU1NRMy7766iuFhYXlQ1UAcPsh0AJALgkPD9eFCxe0Y8cOm/bffvtNR44cUXh4eKZt1q1bp3bt2qlKlSqqVauWBg8erJMnT1qXBwYG6sSJE/r+++8VGBio7du3W5clJiZqxIgRqlWrlqpVq6bo6GgdP37cZv8XL17UuHHj1LBhQ1WuXFlNmjTRjBkzMoXuP/74Q7169VL16tVVt25dTZs2LdOocHJyst544w2FhYWpcuXKCgsL07hx43T58uUcv2YAkBMu+V0AANyuatasKR8fH3399deqXbu2tX3Dhg0qUaKEKlSoYLP+woULNXLkSDVo0EBDhw7Vv//+q/nz52vHjh1asmSJ/Pz8NH78eI0dO1Z33XWXevToofLly+u3336TJE2YMEGVKlXSgAEDdPToUc2bN0+9e/fWl19+KelqAO3atav27Nmj9u3bKzAwUD/88IOmTJmi/fv3Wy9O+/fff9WxY0elpaWpe/fucnZ21ty5c5WUlGRT7+uvv67Vq1crKipKJUuW1N69ezV79mydP39eY8aMyc2XFgBsEGgBIJcUKlRIjzzyiL755hu9/PLL1vYNGzYoIiLCZt2EhAS99dZbatOmjcaNG2dtb9WqlVq2bKkZM2bolVdeUevWrTVlyhTdddddat26tSRZA21gYKDmzp0rZ2dnSVdHYxcuXKj4+HiVKlVKixYtUlxcnF577TU99dRTkqROnTpp7NixmjNnjjZu3KiGDRtq1qxZOnfunJYuXWq9q0GzZs3UqlUrm5pXrlypdu3a6YUXXpAktW/fXtLV0V0AyEtMOQCAXNS4cWPFx8fr119/lSSdPHlSP//8c6ZAu3XrVl28eFFhYWE6c+aM9U+RIkVUuXJlffvttzc9VpMmTaxhVpKCgoIkyXo3hW+++UZFixa1Bs8M0dHRkq7O65WkTZs2KTg42OYWXf7+/mrYsKHNdvfee6/WrFmjZcuWKTExUZL02muvac6cOTetFQAciRFaAMhFDRo0UKFChfT1118rICDAeneDkJAQ/fPPP9b1jh07Jknq169flvspVKjQTY9VrFgxm+9dXV0lXb19mCSdOHFCJUuWlIuL7Ue/r6+v/Pz89Oeff1rXq1SpUqb9lytXzub7V199Vf3799fQoUNVqFAhVa9eXU2aNFFkZKTc3d1vWi8AOAqBFgBykbe3t2rVqqWvv/5a0dHR+vrrrxUWFiYnJ9tfkKWnp0uSxowZo/vuuy9Hx7JYLDdcfqNbfaWnp9uE5itXrmS5zn/VqVNH33zzjb766itt3LhRW7Zs0bZt2/TZZ5/piy++kJubm51nAAA5w5QDAMhljRs31p49e3TkyBFt374903QDSbr//vslXR1lrVOnjs2flJQUh4TDEiVK6Pjx45nuaHD27FmdO3dOxYsXlySVLFlSR48ezbT9f59OlpycrN27dyspKUmtW7fW5MmTtXXrVnXr1k2//vqrNm/efMv1AkB2EWgBIJc1btxYkjRq1Ci5urra3PEgQ926deXq6qpZs2YpLS3N2n7o0CH17t1bH3/8sbXNyckp02hpdjRq1Ejnzp3TokWLbNo//PBDSbLOkW3cuLH2799vc0uwkydPWufYSlcvYuvQoYPef/99a5uLi4sqVqxo/RoA8gqfOACQy4oXL65KlSpp8+bNat68uXVu63/5+flpwIABGj9+vDp27KjHHntMFy9e1Lx58+Th4WEzt9bPz0/79+/XZ599pkceeSTbdTzxxBNasmSJ3njjDe3fv18PPfSQfvzxR61atUqNGzdWo0aNJEndu3fXihUrFB0dra5du8rLy0vz58+Xu7u7Ll26JOnqSHKbNm00f/58Xb58WVWrVtXJkyc1b948lSlTJsvQDgC5hUALAHmgcePG+uWXX7J8mEKGZ599Vvfcc49mz56tiRMnysvLS1WrVtWAAQP0wAMPWNfr06ePXn31VY0ePVqenp7WqQI34+rqqrlz52rKlCmKjY3V0qVLVaJECb344ovq1q2bdT0fHx99+umnGjdunObNmycnJye1bdtWbm5umjlzpnW91157Tffee69Wr16tFStWyNvbW2FhYRowYECWoR0AcovF4IHgAAAAMDHm0AIAAMDUCLQAAAAwNQItAAAATI1ACwAAAFMj0AIAAMDUCLQAAAAwNQItAAAATI1ACwAAAFMj0AIAAMDUCLQAAAAwtf8DplDIYR37WUsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Multiple Bar Chart \n",
    "\n",
    "sns.set(font_scale=1.0)\n",
    "plt.figure(figsize=(10, 7),dpi=80)\n",
    "\n",
    "w=0.4\n",
    "x = ['Logreg','Svm','Tree','Knn']\n",
    "accuracy_train = df[\"Accuracy Train\"]\n",
    "accuracy_test = df[\"Accuracy Test\"]\n",
    "\n",
    "bar1 = np.arange(len(x))\n",
    "bar2 = [i+w for i in bar1]\n",
    " \n",
    "plt.bar(bar1, accuracy_train, w, label='Accuracy Train')\n",
    "plt.bar(bar2, accuracy_test, w, label='Accuracy Test')\n",
    "\n",
    "plt.xlabel('Methods', fontsize=16)\n",
    "plt.ylabel('Accuracy', fontsize=16)\n",
    "plt.title('Accuracy Verus Methods', fontsize=18)\n",
    "plt.tick_params(labelsize=15);\n",
    "plt.xticks(bar1+w/2,x)\n",
    "plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "As can be seen from the table in Task 12 above, all machine learning models produced the same test accuracy, apart from the decision tree (DT) model. Indicating that they perform reasonably well in terms of predictive accuracy on the test/ unseen data, with the tree model performing slightly better. Accuracy is 0.94 and F1 score is 0.96, on the unseen/ test data. Partly due to the DT model's false positives and false negatives having a similar cost (refer to the Decision Tree Confusion matrix in Task 9 above).  \n",
    "\n",
    "And since the model demonstrates strong performance on both the training data and the test/evaluation data, it indicates that our model is not overfitting. This is a positive outcome, indicating that the model generalizes well and can effectively handle unseen data.\n",
    "\n",
    "We can also consider other evaluation metrics or criteria to determine the best model:\n",
    "\n",
    "**Interpretability**: We can consider the interpretability of the models. Some models, such as decision trees or linear models, provide more interpretable results compared to complex models like neural networks or ensemble methods. In our case, the Logistic Regression and Decision tree are considered interpretable, unlike SVM and KNN. And the higher the interpretability of a machine learning model, the easier it is for someone to comprehend why certain decisions or predictions have been made. \n",
    "\n",
    "**Data**: In general we lack test data, as we only have 18 test samples to work with.  Hence, the test dataset is an unrepresentative sample of data from the domain. Possibly a reason why all the test accuracies are the same, (apart the decision tree).\n",
    "\n",
    "Overall, the evaluation metrics and criteria may vary depending on our specific problem, data, and objectives of our project. Therefore, it is recommended to consider a combination of metrics and criteria to make an informed decision about the best model, taking into account our specific requirements and constraints.  In our case, **the decision tree method/ model performs the best**, when we take into consideration both accuracy and F1 Score (other evaluation metrics or criteria). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description      |\n",
    "| ----------------- | ------- | ---------- | ----------------------- |\n",
    "| 2021-08-31       | 1.1     | Lakshmi Holla   | Modified markdown |\n",
    "| 2020-09-20        | 1.0     | Joseph     | Modified Multiple Areas |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
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