MLP_LearningCurves.ipynb

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    "language_info": {
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      "pygments_lexer": "ipython3",
      "version": "3.6.3"
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    "colab": {
      "name": "MLP-LearningCurves.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Immaczx/neural-network/blob/main/MLP_LearningCurves.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9QKQqbW3jG7"
      },
      "source": [
        "# Supervised Classification Models & Learning Curves\n",
        "### MLP, Decision Tree, Logistic Regression\n",
        "https://github.com/annisap/irs-mlp/blob/master/MLP-LearningCurves.ipynb\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vgzu1gOX3jHB"
      },
      "source": [
        "![](./pics-iris/Untitled Diagram.jpg)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "viBXc7ee3jHC"
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "sd3U9LzZ3jHC"
      },
      "source": [
        "# Import the libraries for:\n",
        "# Mathematical computations on arrays and matrices\n",
        "import numpy as np\n",
        "# manipulating data via DataFrames, 2-D tabular, column-oriented data structure\n",
        "import pandas as pd\n",
        "# producing plots and other 2D data visualizations. Use plotly if you want interactive graphs\n",
        "import matplotlib.pyplot as plt \n",
        "# statistical visualizations (a wrapper around Matplotlib)\n",
        "import seaborn as sns \n",
        "import warnings # current version of seaborn generates a bunch of warnings that we'll ignore\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "sns.set(style=\"white\", color_codes=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8T8qvLOI3jHD"
      },
      "source": [
        "## Part 1: Exploratory Data Analysis\n",
        "### Tell me the summary of what happened"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GYpmEJj33jHD",
        "outputId": "47f48e4f-42b1-4343-fc6a-ae4acf607c82"
      },
      "source": [
        "# Assign the csv data to a DataFrame\n",
        "data = pd.read_csv(\"./Iris.csv\")\n",
        "\n",
        "# Print the first 10 instances\n",
        "data.head(10).style"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "</style>  \n",
              "<table id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3\" > \n",
              "<thead>    <tr> \n",
              "        <th class=\"blank level0\" ></th> \n",
              "        <th class=\"col_heading level0 col0\" >Id</th> \n",
              "        <th class=\"col_heading level0 col1\" >SepalLengthCm</th> \n",
              "        <th class=\"col_heading level0 col2\" >SepalWidthCm</th> \n",
              "        <th class=\"col_heading level0 col3\" >PetalLengthCm</th> \n",
              "        <th class=\"col_heading level0 col4\" >PetalWidthCm</th> \n",
              "        <th class=\"col_heading level0 col5\" >Species</th> \n",
              "    </tr></thead> \n",
              "<tbody>    <tr> \n",
              "        <th id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row0_col0\" class=\"data row0 col0\" >1</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row0_col1\" class=\"data row0 col1\" >5.1</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row0_col2\" class=\"data row0 col2\" >3.5</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row0_col3\" class=\"data row0 col3\" >1.4</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row0_col4\" class=\"data row0 col4\" >0.2</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row0_col5\" class=\"data row0 col5\" >Iris-setosa</td> \n",
              "    </tr>    <tr> \n",
              "        <th id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row1_col0\" class=\"data row1 col0\" >2</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row1_col1\" class=\"data row1 col1\" >4.9</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row1_col2\" class=\"data row1 col2\" >3</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row1_col3\" class=\"data row1 col3\" >1.4</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row1_col4\" class=\"data row1 col4\" >0.2</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row1_col5\" class=\"data row1 col5\" >Iris-setosa</td> \n",
              "    </tr>    <tr> \n",
              "        <th id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row2_col0\" class=\"data row2 col0\" >3</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row2_col1\" class=\"data row2 col1\" >4.7</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row2_col2\" class=\"data row2 col2\" >3.2</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row2_col3\" class=\"data row2 col3\" >1.3</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row2_col4\" class=\"data row2 col4\" >0.2</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row2_col5\" class=\"data row2 col5\" >Iris-setosa</td> \n",
              "    </tr>    <tr> \n",
              "        <th id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row3_col0\" class=\"data row3 col0\" >4</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row3_col1\" class=\"data row3 col1\" >4.6</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row3_col2\" class=\"data row3 col2\" >3.1</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row3_col3\" class=\"data row3 col3\" >1.5</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row3_col4\" class=\"data row3 col4\" >0.2</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row3_col5\" class=\"data row3 col5\" >Iris-setosa</td> \n",
              "    </tr>    <tr> \n",
              "        <th id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row4_col0\" class=\"data row4 col0\" >5</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row4_col1\" class=\"data row4 col1\" >5</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row4_col2\" class=\"data row4 col2\" >3.6</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row4_col3\" class=\"data row4 col3\" >1.4</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row4_col4\" class=\"data row4 col4\" >0.2</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row4_col5\" class=\"data row4 col5\" >Iris-setosa</td> \n",
              "    </tr>    <tr> \n",
              "        <th id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row5_col0\" class=\"data row5 col0\" >6</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row5_col1\" class=\"data row5 col1\" >5.4</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row5_col2\" class=\"data row5 col2\" >3.9</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row5_col3\" class=\"data row5 col3\" >1.7</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row5_col4\" class=\"data row5 col4\" >0.4</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row5_col5\" class=\"data row5 col5\" >Iris-setosa</td> \n",
              "    </tr>    <tr> \n",
              "        <th id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3level0_row6\" class=\"row_heading level0 row6\" >6</th> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row6_col0\" class=\"data row6 col0\" >7</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row6_col1\" class=\"data row6 col1\" >4.6</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row6_col2\" class=\"data row6 col2\" >3.4</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row6_col3\" class=\"data row6 col3\" >1.4</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row6_col4\" class=\"data row6 col4\" >0.3</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row6_col5\" class=\"data row6 col5\" >Iris-setosa</td> \n",
              "    </tr>    <tr> \n",
              "        <th id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3level0_row7\" class=\"row_heading level0 row7\" >7</th> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row7_col0\" class=\"data row7 col0\" >8</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row7_col1\" class=\"data row7 col1\" >5</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row7_col2\" class=\"data row7 col2\" >3.4</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row7_col3\" class=\"data row7 col3\" >1.5</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row7_col4\" class=\"data row7 col4\" >0.2</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row7_col5\" class=\"data row7 col5\" >Iris-setosa</td> \n",
              "    </tr>    <tr> \n",
              "        <th id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3level0_row8\" class=\"row_heading level0 row8\" >8</th> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row8_col0\" class=\"data row8 col0\" >9</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row8_col1\" class=\"data row8 col1\" >4.4</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row8_col2\" class=\"data row8 col2\" >2.9</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row8_col3\" class=\"data row8 col3\" >1.4</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row8_col4\" class=\"data row8 col4\" >0.2</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row8_col5\" class=\"data row8 col5\" >Iris-setosa</td> \n",
              "    </tr>    <tr> \n",
              "        <th id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3level0_row9\" class=\"row_heading level0 row9\" >9</th> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row9_col0\" class=\"data row9 col0\" >10</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row9_col1\" class=\"data row9 col1\" >4.9</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row9_col2\" class=\"data row9 col2\" >3.1</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row9_col3\" class=\"data row9 col3\" >1.5</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row9_col4\" class=\"data row9 col4\" >0.1</td> \n",
              "        <td id=\"T_7ae74298_77c2_11e8_b263_6c96cfdb01f3row9_col5\" class=\"data row9 col5\" >Iris-setosa</td> \n",
              "    </tr></tbody> \n",
              "</table> "
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x112c6b390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JwOoe4-y3jHE",
        "outputId": "f0b40aee-277d-409d-b9e6-395b6404a7db"
      },
      "source": [
        "# Information on the dataset\n",
        "print(data.info())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 6 columns):\n",
            "Id               150 non-null int64\n",
            "SepalLengthCm    150 non-null float64\n",
            "SepalWidthCm     150 non-null float64\n",
            "PetalLengthCm    150 non-null float64\n",
            "PetalWidthCm     150 non-null float64\n",
            "Species          150 non-null object\n",
            "dtypes: float64(4), int64(1), object(1)\n",
            "memory usage: 7.1+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7e9z7Bz3jHF",
        "outputId": "d7917822-88c4-40ac-fdaa-6ffb5175989a"
      },
      "source": [
        "# Total Instances on each iris plant class\n",
        "data[\"Species\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Iris-virginica     50\n",
              "Iris-setosa        50\n",
              "Iris-versicolor    50\n",
              "Name: Species, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfYr4sLC3jHF",
        "outputId": "358a056c-9e29-4fdb-815a-af3ac4a7fd55"
      },
      "source": [
        "# Dimensions of the dataset\n",
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Xi-d2aTu3jHG",
        "outputId": "4e2f2367-d5a9-4dfb-98fd-11e69a0d5468"
      },
      "source": [
        "# Statistics for each feature\n",
        "# No missing values (thank goodness)\n",
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\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>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>43.445368</td>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>38.250000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>112.750000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
              "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
              "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
              "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
              "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
              "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
              "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
              "max    150.000000       7.900000      4.400000       6.900000      2.500000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufF6ZIVv3jHG"
      },
      "source": [
        "## Visualizing continuous data \n",
        "<p> 1. **Univariate** Distribution of each feature on species</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1P_RAwZ93jHG",
        "outputId": "886de79c-58ea-4e24-cb95-86789219552e"
      },
      "source": [
        "# Plot a histogram of SepalLength Frequency on Species (matplotlib)\n",
        "Iris_setosa = data[data[\"Species\"] == \"Iris-setosa\"]\n",
        "Iris_versicolor = data[data[\"Species\"] == \"Iris-versicolor\"]\n",
        "Iris_virginica = data[data[\"Species\"] == \"Iris-virginica\"]\n",
        "\n",
        "Iris_setosa[\"SepalLengthCm\"].plot.hist(alpha=0.5,color='blue',bins=50) # Setting the opacity(alpha value) & setting the bar width((bins value)\n",
        "Iris_versicolor[\"SepalLengthCm\"].plot.hist(alpha=0.5,color='green',bins=50)\n",
        "Iris_virginica[\"SepalLengthCm\"].plot.hist(alpha=0.5,color='red',bins=50)\n",
        "plt.legend(['Iris-setosa','Iris_versicolor','Iris-virginica'])\n",
        "plt.xlabel('SepalLengthCm')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('SepalLength on Species')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OIiIqQUWGO2+jR0T0ajLpR0zFodfrMWvWLNy+fRtKpRILFy5EnTp1pOqOiIhy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\nJEMMdyIiGfp/VMXLqYD/+3kAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x116166fd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zH8s1L0s3jHH",
        "outputId": "ac48de46-d04a-43ad-c1c5-16d0561768ec"
      },
      "source": [
        "# Plot a histogram of SepalWidth Frequency on Species (sns)\n",
        "sns.FacetGrid(data, hue=\"Species\", size=5).map(sns.distplot,\"SepalWidthCm\", hist_kws={\"alpha\":.2}).add_legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x116150da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Rb5mIGV3X2d28nyxbBstyFk96TaASM7UisT27mnYvZhXK88x09/nDLtMUKFIZ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\nnFx89bUYo6MoY+dxJZPl5U4sJpUDNV1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H6ymt6vp9CsJA122lckFbRxvKaLZbmZEyWdMr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\nAe//A6PWBY5RIcIhAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x116150f98>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdXu_Q3X3jHH",
        "outputId": "aa82f0c7-6019-47df-c226-e85bf8325020"
      },
      "source": [
        "# Fit and plot the probability density function of PetalLength.\n",
        "# kdeplot visualizes a kernel density estimate(estimated PDF)\n",
        "sns.set_style(\"whitegrid\")\n",
        "sns.FacetGrid(data, hue=\"Species\", size=6) \\\n",
        "   .map(sns.kdeplot, \"PetalLengthCm\") \\\n",
        "   .add_legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x11636cac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5HQLXEcgcMiAiIilhIKhHi+cQcC8CIiKSEAaCerha/s1Zdlj764mIiKSAgaAezR8yYCAg\nIiLpYSCoR8snFXKVARERSQcDQT1qOgTKJn2dxj2p0Ob1moiIiHyFgaAezZ9UqKz+enYIiIhIOhgI\n6tHSZYecQ0BERFLCQFAPSwvnEHAfAiIikhIGgnq4fqA393AjdgiIiEhKGAjq0dKzDNghICIiKWEg\nqEdLdyrkskMiIpISBoJ6tHRjIrOFyw6JiEg6GAjq4dpHoOmTCl2rDNghICIi6fAoEOzbtw8zZ868\n6PEVK1bg+uuvx8yZMzFz5kxkZWV5vUCxWKwOKOQyKBVNy0warjIgIiIJanQbvnfffRc//PADdDrd\nRc8dOnQIzz33HHr37u2T4sRkttibvMIA4OFGREQkTY3++pucnIzXXnutzucOHTqE5cuXY9q0aXjn\nnXe8XpyYzFZbk4cLAEChkEMhl7FDQEREkiITBEFo7EU5OTl44IEHsHLlyvMef/311zF9+nTo9Xrc\nd999mDZtGkaOHNnge6Wnp7esYj95aVUu5DJgwfjEJn/tM1+dQXS4EveOjfdBZURE0pWWliZ2CVSP\npp3cU4sgCLjtttsQEREBABg+fDj++uuvRgMB4L9viPT09GZfS1j1MyIjtM36et3qQiiUSq/eZ0vu\nJdDwXgJTsNxLsNwHwHsh/2r2KgODwYAbbrgBRqMRgiBgx44dQTWXwGyxN3nJoYtGpeCQARERSUqT\nOwSrV69GZWUlpkyZgoULF2LWrFlQq9UYMmQIhg8f7osa/c7hEGCxOZo1hwAAtGoFisusXq6KiIjI\ndzwKBElJSe75A+PGjXM/PmHCBEyYMME3lYnIYmveOQYuWrUSJgs7BEREJB3cmKgOzd2l0EWjVsBm\nd8Bu5+ZEREQkDQwEdWjuOQYuPOCIiIikhoGgDi3tEGjVzpEYDhsQEZFUMBDUocUdguogYeIBR0RE\nJBEMBHVoeYfAdeIhOwRERCQNDAR1sHhrDgEDARERSQQDQR3cQwbNXmXgmkPAIQMiIpIGBoI6cMiA\niIhCDQNBHVo6qdAVCLjKgIiIpIKBoA41HYLmnf2k4bJDIiKSmGafdhjMvLcxEecQEBEFKrvdjhdf\nfBFHjx6FIAhISEjA0qVLodFomv2eDz30EJ5//nkvVuk/7BDUwdLCSYWcQ0BEFPg2b96MyspKfPDB\nB1ixYgWio6Px3Xffteg9pRoGAAaCOrl+kKtVzft4uFMhEVHgS0xMxJ49e7Bp0yZUVVXhH//4B664\n4gpMnz4dd9xxB8aPH481a9YAcIaHqVOnYurUqfjkk08AABkZGZg6dSpuuukmPProoxAEAWPGjKn3\n9R999JH79b/++qs4N90ADhnUoWbIoJlzCFTsEBARBboePXpg0aJF+Oyzz/DII4+gX79+uPvuu5GX\nl4effvoJNpsN06dPx7XXXosXXngBX3zxBcLDw3H77bdj9OjReOWVV7B48WL07dsXy5cvR2FhIQDA\n4XDU+fqffvoJL774IqKjo7FlyxaR7/5iDAR18MZphwD3ISAiCmRHjx5Fjx498Oabb8Jms2H58uX4\nz3/+g969e0On0wEAdDod8vLykJeXh3vvvRcAUFZWhpycHGRnZ6NPnz4AgLvuusv9viUlJXW+funS\npXjllVdQUFCAiRMn+vluG8chgzq0fNmhM2exQ0BEFLi2bt2K119/HQCgVCrRvXt3qFQqHDt2DDab\nDeXl5TAYDIiPj0dSUhLef/99fPLJJ5g8eTI6duyIdu3a4fDhwwCAZ555BkeOHAEAxMTE1Pn67777\nDs888ww++OADvPPOO6Ldd33YIaiDtzoEPP6YiChwzZgxA//3f/+H8ePHQ6fTITY2FvPnz8dDDz2E\nu+++G6WlpVi0aBEUCgXmzp2LWbNmwWq1IjU1Fa1bt8Y///lPLF26FA6HA926dUP37t0BAHK5vM7X\nd+zYEZMnT0ZUVBSmTp0q8t1fjIGgDi09y8C9MZGZQwZERIFKrVZj2bJl5z2Wk5ODdu3a4f333z/v\n8dGjR2P06NHnPdalSxd8+umn5z32yy+/1Pv6GTNmYMaMGd4q3+s4ZFAHs9UOuVwGpaJ5H49KKYdc\nxlUGREQkHQwEdTBb7M0eLgAAmUwGjVrJIQMiIolxjf2HIgaCOpittmYPF7ho1AqYucqAiIgkgoGg\nDi3tEADOeQQcMiAiIqlgIKiD2WpvcYdAq1Zy2SEREUkGA0EdvNEh0KjYISAiIulgILiAwyHAYnNA\n3dJAoFbAZnfAbnd4qTIiIvK2nJwc3HLLLec9tnnzZvz3v//1+rV+++035Ofne/19vYWB4AIWW8v2\nIHDhAUdERNI0bNgwTJkyxevv+/HHH8NgMHj9fb2FGxNdoKW7FLrU3q0wXKdqcV1ERMHsg9WHsGXf\nGa++5xX92mHOuF4evXbmzJmIiYlBeXk5rr/+emRnZ2PevHmYP38+DAYDTCYTHnzwQQwaNOi8r1u7\ndi3effddKJVKtGvXDs8//zyMRiMeffRRlJSUAAAee+wx5Obm4vDhw1i0aBE+//xzfPrpp/jpp5+g\nVCpx6aWX4sEHH0R6ejqee+45KJVKREZG4sUXXwQAPProo6ioqEBJSQluvvlmTJ8+3aufkwsDwQVa\neo6Bi5YHHBERScq4ceNw9dVX49tvvwUAnDp1CkVFRfjwww9RXFyMkydPXvQ1P/74I/72t7/h+uuv\nx6pVq2AwGPDOO+9g8ODBmD59Ok6ePIlHHnkEX3zxBS655BIsXboUJ06cwJo1a/Dll19CqVRi3rx5\n2LhxI/78809cffXVuP3227FhwwaUl5ejpKQE119/Pa655hrk5+dj5syZDAT+4vUOAYcMiIgaNWdc\nL49/m/eVTp06nffnrl27YsaMGXjggQdgs9kwc+ZM7Nq1C6+88goA4Pbbb8cjjzyCd955B1988QVS\nUlIwevRoHDt2DNu3b8eaNWsAAOXl5ee9b1ZWFvr16weVytk9vvTSS5GRkYF77rkHb7/9Nm677TbE\nx8ejb9++aN26NT766COsXbsWer0eNpvvfslkILiA9zoEPPGQiEhKZDLZeX8+evQojEYjli9fjoKC\nAkydOhUbNmzAJ5984n7Nyy+/jHnz5qFVq1Z4/PHH8dtvvyElJQU33ngjxo0bh+LiYnz11Vfu9xcE\nASkpKVixYgVsNhsUCgV27tyJCRMmYPXq1Zg4cSIWLVqEd955BytXrkRFRQVSU1Mxffp0bN++Hb//\n/rvP7p+B4ALug4281CHgkAERkTR17NgRb7zxBlatWgWVSoX777//otf07dsXs2fPRnR0NMLDwzFi\nxAiMGDECjz76KFauXAmDwYD77rsPANC/f3889NBD+OCDD3Dddddh2rRpcDgcSEtLw+jRo7F//348\n/PDDCAsLg0qlwhNPPIEzZ85g6dKlWL16NaKjo6FQKGCxWKBWq71+vwwEF/DWkEHNHAJ2CIiIAlVS\nUhJWrlx53mOTJk1y//err77a4NePGjUKo0aNuujxN99886LHFi5ciIULFwIAZs+ejdmzZ5/3fL9+\n/dzzF1zat2/vPkHR17js8ALeGjLQcMiAiIgkhIHgAt7vEHDIgIiIAh8DwQW8PamwyswOARERBT4G\nggvUdAhaNr1Cp2GHgIiIpIOB4AIWL3UIdJrqDoGJgYCIiAIfA8EFXEMGalXLPhqtKxCwQ0BERBLA\nQHABb00qdHcIzAwERESByp+nHTZm+fLl2L9/f5O+ZubMmcjMzPTK9bkPwQVqJhW2dA5B9WmHDARE\nRJIybNgwUa571113iXJdFwaCC3hv2SE7BEREnvpk7zfYfnq3V99zcPsBmJk62aPXNue0Q6vVirFj\nx+L7779HWFgY3nvvPSiVSlx77bVYsmQJzGYzNBoNnnzySdjtdtx7772Ijo7GsGHDEBYWhlWrVkEu\nl2PAgAFYtGgRHn74YYwdOxaXXXYZHnnkEZw9exZWqxVLlixB7969sXjxYpw+fRp2ux2zZ8/G2LFj\n3bWUl5fjwQcfhMFggN1ux/z58zFkyBDccMMN6NixI9RqNV566aUGPwMGggt4a9mhSimHUiGHicsO\niYgkoamnHapUKlxzzTVYu3YtJkyYgJ9//hnvv/8+li1bhpkzZ2L48OHYtm0bXnzxRSxcuBCFhYX4\n5ptvoFarMXnyZCxZsgSpqan4/PPPzzu06Msvv0S7du3wn//8B8eOHcPWrVtx6NAhxMTE4IUXXoDB\nYMCkSZMwePBg99e89dZbuPzyy3HbbbchPz8f06ZNw7p161BZWYm///3v6NmzZ6P3z0BwAW91CADn\nsEElOwRERI2amTrZ49/mfaU5px3efPPNWLp0KVJSUtCxY0fExMTg2LFjeOedd/Dee+9BEAT3qYZJ\nSUnuMwieeeYZfPDBB3jxxReRmpoKQRDc183KynIPW3Tr1g3dunXDsmXLcPnllwMA9Ho9OnfujNOn\nT7u/JjMzE+PGjQMAxMfHQ6/X49y5c3XeV30YCC5gca8y8EYgUHAfAiIiiWjOaYcAIAgC3nvvPUyb\nNg0AkJKSgjlz5mDAgAHIzMzEzp07AQByec08/pUrV2LZsmXQaDS4/fbbsWfPHvdznTt3xoEDBzB6\n9GicPn0aL7/8Mvr3749du3bh6quvhsFgwLFjx5CUlHTe1+zatQs9e/ZEfn4+ysvLER0dfdF1G8JA\ncAGz1Q65XAalQtb4ixuh0yhRXGbyQlVERORvnpx2CAA33XQTXnnlFXcLf9GiRVi6dCnMZjNMJhMe\nffTRi76me/fuuOmmmxATE4P4+PjzDjaaOnUqFi9ejFtvvRV2ux2LFy9G9+7dsWTJEkybNg1msxn3\n3XcfWrVq5X6/u+++G4sXL8avv/4Kk8mEJ554Akpl037Ey4TafQo/SE9PR1paWsBea/6/NyG32IiV\nT1/f4uv/89XNyMwpxXfP39ji9/Ln5+ZrvJfAFCz3Eiz3AfBeyL+4D8EFzFZbiycUuug0StjsAqw2\nTiwkIqLAxkBwAbPF7pUJhUDtzYkYCIiIKLAxEFzAbLV7tUMAcC8CIiIKfAwEFzBbHV7rEGirgwV3\nKyQiokDHQFCLwyHAYrV7ZckhwA4BERFJBwNBLRabd3YpdGEgICIiqWAgqMWbuxQCDARERIHOF6cd\nNuXUwsau9e2332L9+vXNrqUpuDFRLd46x8BF6zrxkLsVEhFJRktPO2zKqYWNXWvSpEktqqUpGAhq\n8VmHwMRAQETUkBMrPkLx1m1efc9Wlw9Bp9m3efRab552eOTIEYwdOxZFRUX45ptv4HA4cP/99yMn\nJwefffYZoqKioFKp3KcVZmVlYerUqfjHP/6BhIQEnD59Gn369MGyZcvw2muvoXXr1pgyZQqeeuop\n7N+/H1arFfPmzcPIkSPx+OOPIy8vDyUlJRg2bBgWLFjQ7M+LgaAWi5c7BO5AYOE+BEREgc5bpx0e\nOXLE/ZrIyEi89dZbOHfuHJYuXYpVq1ZBrVZj1qxZF13/5MmTeP/996HT6TB69GgUFha6n1u/fj1K\nSkrw9ddfo7CwEJ9++il69OiB1NRU3HzzzTCbzQwE3uQeMuAcAiIiv+o0+zaPf5v3WQ1eOu2wrvc8\ndeoUOnfuDJ1OBwDo37//RddPTk6GXq8HAMTFxcFsNrufO3HiBFJTU93PLVy4EAaDAQcOHMD27duh\n1+thsVhadP8MBLW4hgy8teyQ+xAQEUmHt047rM110mBycjKysrJgMpmgVquxf/9+pKSkNHj92lJS\nUvDLL78AACoqKrBgwQIMHz4cEREReOKJJ5CdnY2VK1dCEIQG36chDAS1uDoEWm8NGWjZISAikqrm\nnnZYl9jYWNx5552YPn06oqOjYTaboVQqYbN59vPhqquuwrZt2zBt2jTY7XbMnTsXbdu2xQMPPID0\n9HTodDp06NABBQUFiI+Pb9b9MhDUYnJNKlR752PRqRkIiIgCWVJSElauXHneY7Vn9r/66quNvse4\nceMwbtw495+fffbZi15js9lQUFDgnp8wY8YMJCYmYuDAge7X1K7D9d/z5s1zP7ZkyZKL3nf16tWN\n1ucpBoJauA8BERH5glKpRFVVFSZOnAiVSoW+ffvi0ksvFbus8zAQ1GKu3i/AW0MGapUCMllN54GI\niELXAw88gAceeEDsMurFnQpr8fbGRHK5DFq1gvsQEBFRwPMoEOzbtw8zZ8686PENGzZg8uTJmDJl\nykVjMFLk7SEDwDlsUMWdCokROM7zAAAgAElEQVSIKMA1OmTw7rvv4ocffnCvnXSxWq145pln8PXX\nX0On02HatGkYOXIk4uLifFasr7la+1ovTSp0vVcl5xAQEVGAa7RDkJycjNdee+2ixzMzM5GcnIyo\nqCio1WqkpaVh165dPinSX7w9ZAA4lx5yHwIiIgp0jf4qfO211yInJ+eixw0GAyIiItx/Dg8Ph8Fg\n8Oii6enpTSixZZpyrbO55wAAx47+hYIc73QJbJYqmCx27Ny1C/Jmbhbh4s/Pzdd4L4EpWO4lWO4D\nCL57SUtLE7sMqkezf+rp9XoYjUb3n41G43kBoSH++oZo6jffrwf+BFCJSwekIkqv8UoNP+7ZjuyC\nfPTq3Q9hWlWz3yeY/iLxXgJTsNxLsNwHwHsh/2r2KoPOnTsjOzsbpaWlsFgs2LVrV517M0uJT4YM\nuBcBERFJQJM7BKtXr0ZlZSWmTJmChx9+GLfffjsEQcDkyZObvV1ioPDVKgOAexEQEVFg8ygQ1N7a\nsfb2jKNGjcKoUaN8U5kIzBYbNGpFsw+GqItW4wwX3IuAiIgCGTcmqsVstXttl0IX95AB9yIgIqIA\nxkBQi8li9+pwAcADjoiISBoYCGoxW+xenVAI1ByBzL0IiIgokDEQ1GKy2L129LGLlh0CIiKSAAaC\nag6HAIvVB0MGXHZIREQSwEBQzWJznWPg3UAQVj1kwFUGREQUyBgIqrn3IPDRKgMecERERIGMgaCa\nLzYlAmp1CBgIiIgogDEQVHNtW+zNo4+BWh0CDhkQEVEAYyCoZqreOMjbQwauA40qTVavvi8REZE3\nMRBU89UcAq1aAZmMQwZERBTYGAiqmXw0h0Amk0GnUXLIgIiIAhoDQTVfHH3sEqZRskNAREQBjYGg\nmmvIwNuTCgHn9sXsEBARUSBjIKhmdk0q9PKQAQCEaVSoMnNSIRERBS4Ggmo1yw69Hwh0WiVsdgHW\n6t0QiYiIAg0DQTWTj1YZANyLgIiIAh8DQTVfziHgboVERBToGAiq+XSVgXtzIgYCIiIKTAwE1Uxm\n300qrBky4MRCIiIKTAwE1Xy9DwHAIQMiIgpcDATVarYu9s0+BACHDIiIKHAxEFSrmVTIDgEREYUe\n7/86LFFmqx0KuQxKhfczEicVkqdyynPxy7FNOFRwDEVVJYjWRiItsTfGdr8KbcJbiV0eEQUxBoJq\nJovNJ/MHgFqTCrlbIdXDZDXh8wPf49eM3yFAgE6lRUJ4axRVleDnjI1Yf2Ir7h04E5cnp4ldKhEF\nKQaCaiaz3f2D29t03IeAGpBXUYDn/3gbOeW5aBsRj2l9x2Ng236Qy+Ww2W3YnP0nPtyzEi9vew9m\nmxkjUy4Xu2QiCkIMBNWqLDaEV7f2vc09h4BDBnSB48Un8fTm12GwGDGm6wjc2m8S1Iqa70OlQolR\nKZejS2wH/GvjS1i+6zPEhbdC7/juIlZNRMGIkwqrmcw26DQ+GjJwrTJgh4BqOVKYiSc2vQyjtRJ3\nXToDcwZMOS8M1JYc3Q4PXnEPAOCNHR+h0lrlz1KJKAQwEABwOASYLHZofTRk4JpUyA4BuWSey8Yz\nm1+H1W7FwiF3YHTnoY1+Tc82XTGx5xgUV5Xgs33f+aFKIgolDARwTigEfHOOAQColXLI5TLuVEgA\ngFOlZ/B/v78Gk92MeYPnYHD7AR5/7cRLxiApMhHrMv/AqdIzPqySiEINAwFqTjoM81GHQCaTIUyj\n5KRCQr6hEE/+/ioMFmOzVg2oFCrc2m8iBAj478HVPqqSiEIRAwFqzjHw1ZAB4DzxkHMIQlu52YCn\nf38dZaZyzBkwBSM6DWnW+/RP7I3urVKw88w+HC8+6d0iiShkMRCgZjmg1keTCgHnXgScQxC6LDYL\nnv/fW8g1FGDCJddiTNcRzX4vmUyGm3vfAAD48eg6L1VIRKGOgQA1gUDnozkEgHNiYaXZBkEQfHYN\nCkwOhwOvbl+BY8VZGJo8EFP73Nji9+wT3wMdotphe84eFBnPeaFKIgp1DASomUPgyyEDnUYJh0Nw\nn6pIoUEQBHy49yv8eWYverXphnsvmwm5rOV/7WQyGcZ2GwWH4MAvxze1vFAiCnkMBKjVIfBlIOBu\nhSHpt8zN+CVjE9pHJuKfV9wNVT37DDTH0A4DEaEOx8YT22Cz8/uKiFqGgQA1kwp9tTERwN0KQ9Hh\nwgys2L0SERo9Hh42F+HqMK++v0qhwpUdB6HCbMCus/u9+t5EFHoYCODcthjw3T4EAHcrDDVFlefw\n0pZ3IQB44PI7EeejkwpHdXKea7DxxFafvD8RhQ4GAtReZeDDSYUa7lYYKmwOO/69ZTnKzBX4W/+b\n0atNN59dKzm6HbrEdsTevL9QWlXms+sQUfBjIIDzpEPAdxsTAbWOQOZuhUHvq4M/IvNcNoZ1GIRr\nuwz3+fWGdhgIQRCwI2evz69FRMGLgQD+25gI4KTCYHe4MAOrDv+KNuGtMCdtCmQymc+vOTjJufXx\nttPpPr8WEQUvBgLUnkPgw0mFnEMQ9CotVXh9+4eADJg3eDbCVDq/XDc2LBrdW3fG4cLjHDYgomZj\nIICflh1ylUHQ+3jfNyisPIfJPa9D99ad/XrtIe0HQACHDYio+RgIUDOHwLdDBs5JhewQBKfjxSex\nIWsLkqPaYVLPsX6/vmvYYHvObr9fm4iCAwMBnB0CuVwGtdJ3H4d7yICTCoOOQ3Dg/fQvAQBzBkyB\nUu67oaf6uIYN/irMQKmp3O/XJyLpYyAAYLLYoFMrfDoBzD1kwA5B0NmYtRWZJdkYmjwQPdt0Fa2O\nIe0HQBAE/JmzR7QaBIcD1ooKmAsLUXk6B4asLFSezoG5sBDWigqe5UEUwHzXI5eQSpPNp/MHgNrL\nDhkIgonJbsbnB76HVqnBramTRK1lcNIAfLjnK2w/vQfX+Hi5o8NmQ2X2KRgyjqMiIwNVOWdgOXcO\nlnMlEGz1f4/LNRroktohskcPRA9IRXRqP8iV/GeIKBDwbyKcbfyYSK1Pr8Flh8Hpz9IDqDAbML3v\nBMTqokWtJTYsGp1jO+BwYQYqLVUIU3t/lUP54SPIX/sbinf8CbuxsuYJuRzqmGiEp3SCOiYaCq0O\ncq0WcrUKDosVDrMJ9qoqmAsKUXnqNIyZWcj96WeooqKQeMNYJN4wFsow727tTERNE/KBQBAEGE02\nJLXx3qEzdVEpFVAq5FxlEEQMZiPSSw8hShuJ67qOFLscAMCAxN7IPJeN/fmHMbj9AK+9b0XGcZx4\nfwUqDh8BAGjiWqP10KGI6NoZ+q5dENa+PWQKz+ZOOKxWVBw7huIt21D4+/9w6rMvcHb1T0i5Yw5a\nDxvql70biOhiIR8IzBY7HA7B/Ru8L4Vplag0c1JhsPjp2AZYBCumdL8RGqVa7HIAAAPa9sFXh37C\n7rMHvRIIBLsdJz/+FGe/Xw0IAmIuTUO7CTcisldPyOTNm4IkV6kQ1asXonr1QvKtM5D708/I+eob\nHHvpZRTv+BNd758Lhda3HTsiuljIBwJj9az/cK1vOwSAcx4BOwTBwWipxM8ZGxCm0OLqLleKXY5b\np5j2iNJGYk/uQTgEB+Sy5s8btlZU4MizL6D84CFo2yai8713I7pvHy9WCyjDdGh/82TEDRuKjJdf\nQ/GWrTCdPYtLljwKTatYr16LiBoW8qsMXJP8wnS+DwTODgEDQTD4+dgGVFlNuCy6D7RKjdjluMll\ncvRP7IUycwWyzp1q9vtYKypw6PFlKD94CLGDLkO/fz/v9TBQmzY+Hr2e+BcSxlwD44mTOPjoEpgL\ni3x2PSK6WMgHgpoOgT+GDFSoMtu49EriTFYTfj62AREaPfpH9RS7nIuktXX+4N6de6BZX283m/HX\n0idhzDqB+GtGo8fDD/plwp9cpULKPXch6aZJMOXm4eDjS2Et554KRP4S8oGgsqq6Q+CnIQNBAEwW\nu8+vRb6zOXsHjNYqjOkyHGq5779vmqpv/CVQyBXYffZgk79WEAQcf/UNGI5nos2oEeh8793NnivQ\nHDKZDMm3Tke7yRNhOpuLw089C7vZ7LfrE4WykA8Efu0Q8AhkyRMEAWsyNkEhV+DqzoEzd6A2nUqL\nnnFdkFVyCiVNPOzo7A+rUfTHFkRc0gOd/36PX8OAi0wmQ4eZM9B62JWoOHoUmW8tZ1eNyA9CPhC4\nfjj7Yw6BjnsRSN6B/CM4U56Hy9unIVoXJXY59eqf6Bw22JN7yOOvcRQUIvvjz6CKjkaPRf+EXCVe\n90Mmk6Hr/XOh79oFhRs3IX/tb6LVQhQqQj4QGF1DBj7eqRDgboXBYE3GRgAImH0H6jMgsRcAYG+e\nZ4FAsNth/X41BJsNnf9+D9QxMb4szyNylQrdH/oHlBF6ZL37AYzZzZ8kSUSNC/lA4M8OgWueApce\nSlO+oRC7zx5E19iO6NKqo9jlNCgxIh5x4a1wIO8w7I7G56zk/vwLhNw8xI0cgVaDBvqhQs9o27RB\nl3lzIVityHj5VTisHG4j8pWQDwT+3ocA4BHIUvVrxu8QIOC6boHdHQCcLfd+CT1htFYh81x2g6+1\nlpXh1BdfAlotOs2e5acKPddq0GVoc9UoGLNOIOerb8QuhyhohXwgcO9D4KedCgGgirsVSo7VbsWm\nk9sRpYnA4CTvbQnsS6kJziWRjQ0bZH/2JezGSiiHXwlVVGDOi+h0x2yoW7VCzjffoTLnjNjlEAWl\nkA8ExqrqDoE/JhW6jkDmkIHk7Dq7HwaLEcM6DoJSIY0NPnvHd4dCJse+3L/qfU1Vbi7yf1sHXVI7\nKC4N3KCjDAtDyp1zINhsyFr+HlcdEPlAyAcCd4fAD5MKXR0CDhlIz4asrQCAkSmXi1yJ58JUOnRr\n3RnHz2Wjwmyo8zWnv1wJOBxInj7V48OJxBI7eBBi0vqjbN9+FP2xVexyiIJOyAcCo8kKrVoBhcL3\nH0WYxtmF4CoDaSmqPIf9eYfRrVUKkiITxS6nSfolXAIBAg7kH7noucrTOSj8/X8I69gBrYYMFqG6\nppHJZOh05x2QqVQ48f4K2CorG/8iIvJYoz8FHQ4HHn/8cUyZMgUzZ85Edvb5E5SeeuopTJo0CTNn\nzsTMmTNRUVHhs2J9odJk9csuhQD3IZCqTSe2Q4CAkZ2GiF1Kk6UmVC8/rGPYIOeb7wBBQPK0KaJs\nQNQcusQEJN00CdaSEpz6/L9il0MUVBrtk69btw4WiwX//e9/sXfvXjz77LN466233M8fOnQI7733\nHmJjpXkyWYXRgrgY3+/TDnCnQilyCA5sOrEVGoUaQ5LTxC6nyTrGJCFSo8e+vL8gCAJkMhkAwFxY\nhKLN/4MuKQmxlwXOMkNPJE2agMKNvyPv5zVIHHstdG3bil0SUVBo9NeC9PR0XHmlc4vW1NRUHDxY\nsz+6w+FAdnY2Hn/8cUydOhVff/217yr1AbvdAaPJhogw/5xlH8YOgeT8VXAMBcZiDGmfhjCVTuxy\nmkwuk6NfQk+UmMpwqqxmdv7Z1T9CsNvRbuJ4yXQHXORqNTrMuhWC3Y7sTz4XuxyioNFoh8BgMECv\n17v/rFAoYLPZoFQqUVlZiVtvvRWzZ8+G3W7HrFmz0Lt3b/To0aPB90xPT2955R5q6FoGk3PDFpvF\n6Jea7A7nzOj8wpJmXc+fn5uvSeVeVuc5dyZsa21Vb82Bfi9RJmeQ+WnXbxgU0xeCyQTzml+BCD1O\nR+qRU6v+QL8XF0GjgqxdWxRv3Yad3/8AeVK7856Xyn14ItjuJS1Nep22UNFoINDr9TAaje4/OxwO\nKJXOL9PpdJg1axZ0Ouc/OIMHD8aRI0caDQT++oZo7JvvVF45gFx0SIpHWlo/v9Sk/iYPCrWuyZ9B\nMP1Fksq9GC2VeOnER0jUt8H4K8a62+21SeFeupi64sfvf0eRogxpaWk4u/pHnLBY0GHKzUi67DL3\n66RwL7WV6cJwcPESaLf/id5PP+n+/yO1+2gI74X8qdFe4YABA7B582YAwN69e9GtWzf3cydPnsT0\n6dNht9thtVqxe/du9OrVy3fVellFpXMsPyLMf4e4hGmU3IdAIrac2gmr3YqRKZfXGQakIkobiU4x\n7XGkKBNV5krk/rQGMpUK8deMFru0Fonq1ROxgwai/K/DOLfjT7HLIZK8RjsEV199NbZs2YKpU6dC\nEAQ8/fTTWLFiBZKTk3HVVVdh3LhxuOWWW6BSqTB+/Hh07drVH3V7RbnRAgCIDPfPHALAudKAOxVK\nw8asbZDL5BjeMfCX5DUmNaEXTpScxoHNv8Cam4c2o0ZCFRkpdlkt1mHWrTi3Mx3ZH3+K2IGXBvxe\nCkSBrNFAIJfL8cQTT5z3WOfOnd3/feedd+LOO+/0fmV+UFHpDAT+mlQIOCcWllaY/HY9ap7s0hxk\nlmRjQGJvxATwMcee6pfQE98d/gXnftuICACJ118ndkleEZaUhPirRyP/17Uo2LAR8VdLu+tBJCZp\nTS/2sorqDkGEPzsEGiWqzHb3BEMKTBurdyYclXKFyJV4R7fWKYixqhB+7CzCO3WCvkvnxr9IItpP\nuQlytRqnvvwKDotF7HKIJCu0A0Gl/4cMwt1HIHPYIFBZ7Vb8L/tPRGkiMKBtH7HL8QqlXIEri8Ih\nFwDtUGntO9AYTatWSBg7BpaiIuT9ulbscogkK6QDgXsOgR+HDFyHKBmqGAgCVfrZA6iwGHFlx0FQ\nyoNjTFoQBHQ4cg52OZDdRfpDIBdKmjwRCp0OOV99A4FdAqJmYSCAf4cM9NWBwMhAELA2ZG0BAIzq\nJJ2DjBpjzDoBeW4RTrTTYF/FCbHL8TpVZCTaTrgR1rJy2HfsFLscIkkK6UBQUWmBXFbTxvcHV4fA\nyCGDgFRUeQ778g6ja6tOSIqS1kFGDSlY79xgKa9nAg4UHIXNHnxLX9veOA7KyEjYtm6HVWJnqhAF\ngpAPBOE6NeRy/60xD2eHIKBtPrmj+iCj4OkOOKxWFG7eDFVUFNoMvAxmmxlHijLFLsvrlGE6JN00\nETCbcebbVWKXQyQ5IR0ISissiNL7b7gA4JBBIHMIDmzMch5kdLkEDzKqz7mdu2CrMCBu5HCkJvUG\nAOzLu/j0w2CQMOZaICICuT/+DMu5ErHLIZKUkA0EVpsdFZUWxEZq/XrdmkmFwdeylbrDhceRbyzC\n4PYDJHmQUX1cwwVtRo1Ez7huUMmV2BukgUCh0UA5bCgcFgtOfyWtw9aIxBaygaCk3AwAiI0SJxCw\nQxB4XHsPBNNwgeVcCUp274G+S2eEd0iGRqnGJXFdkV2ag5KqMrHL8wlFal9oExOQv3YdTPn5YpdD\nJBkhGwjOlTt3C4yN8G8g0HNSYUCqMBuwLWc3EvRxuCSui9jleE3Bpt8BhwNtRo10P9YvoSeA4B02\nkCkUSJ4+FYLNhtNfrhS7HCLJYCDwd4egekWDoZJrpQPJ+qwtsNqtuLbLcEkfZFSbIAgo2LARMqUS\nrYcNdT+emhjcgQAAWg+9AmEdklGwaTMqT50WuxwiSWAg8HOHoGbIgHMIAoXdYcfa45uhUagxotMQ\nscvxGkPGcVSdzkHsoIFQRUS4H0+KTESsLhr78w7D4XCIWKHvyORydLh1OuBw4NTnX4hdDpEkMBD4\nuUOg0yghk3HIIJCknz2AospzGNZxEMLVYWKX4zUFG5yTCeOvGnXe4zKZDKkJPVFhMSKr5JQYpflF\nzMBLEdG9G4q37UBFxnGxyyEKeAwEfl5lIJfLEK5VcVJhAFmT4fzBOabrCHEL8SKHxYLCzX9AFROD\n6NR+Fz3fr3rYIFhXGwDO4JN863QAwKlPPxe5GqLAF7qBoMwZCGIiNX6/drhOxbMMAsSp0jM4VHAM\nvdt0R/uotmKX4zXFO3bCbjSizcjhkCkuPo+hT3wPyGSyoJ5HAADRffsgOrUfSvfuQ9mBg2KXQxTQ\nQjYQlFSYEa5VQqtW+v3a4Tp2CALFz0HYHQBqhgtqry6oTa8OR9fYTsgoPgGjpdKfpfmdq0uQ/enn\nEAQeO05Un5ANBIWlVX6fP+Ci16lQZbbBbg/OCV1SUWgsxu8ntyMxog0ubdtX7HK8xlxcjNK9+6Dv\n1hVh7ZPqfV1qYk84BAcO5B/xY3X+F9G1C2IHD0LFkaMo2ZUudjlEASskA0FFpQXGKisSWoWLcv2I\n6uOWKyrZJRDT94fXwu6wY9Il10EuD56/CoUbnXsPXDiZ8EKpCb0ABPc8ApcOM6YCcjlOfvgJHDau\n8CGqS/D8K9gEuUVGAECiSIHAdX5CmcEsyvUJKK4swYYTWxEf3hpDOwwUuxyvEQQB+es3Qq5Wo/XQ\nKxp8bUpMMvTqcOzL/SvoW+lhycmIv3o0qnJykLfmF7HLIQpIIRkI8oqdgUCsDkGU3jmRsZSBQDTf\nH1kLm8OGiT2vg0J+8aQ7qao4egyms2cRO/gyKPUNf3/L5XKkJvREcVUJTpbm+KlC8XS4dRoU4eE4\n9cV/YS0Lzm2biVoiJANBbnUgSGwtbiAoN3C3QjGUVJVhfeYfiAuLxbCOg8Qux6sK1m8AUP9kwgsN\nTHIuSdx5Zq/PagoUqshIJE+bAruxEtmfcbMioguFZCDIK3LOqk5oJc4mNNHsEIhq5cEfYa3uDiiD\nqDtgN5tR9MdWqFu1QnTfPh59TWpCL6jkSvyZs8/H1QWGhOuuha59EvLXroMhK0vscogCSkgGgtxi\nI2QyID5WnEAQyTkEoskuzcGGE1uQFJmIkUG0TTEAFG/bAXtlZb17D9RFp9KiT3wPnCo7gxJruY8r\nFJ9cqUTKHXMAQUDWO+9BCNKtm4maIzQDQZERraN1UCnF+e2QHQJxCIKAD/d8BUEQMCt1clDNHQBq\n7T1wlWfDBS6XJaUCADIMJ71dUkCKTu2HVpcPQcWRo5xgSFRLyAWCMoMZ58pN6JAQKVoNkeHODkG5\nkXMI/GnzyR04VHAMaW37IDWxl9jleJW5sBBl+w8g4pIe0LVt2o6LaW37QAYZjhmzfVRd4Em5+w4o\nI/Q4+fFnMOXni10OUUAIuUCQdcY5uzilXZRoNUSEqSGXAaUV7BD4S5mpHB/v+wYahRpzBkwRuxyv\ny/9tPSAIiB/d8N4DdYnSRqJ76xScMeWj1BT8wwYAoI6ORqc7bofDZMLx198K+mWXRJ4I2UDQWcRA\nIJfLEKnXcA6BnwiCgHd2fY4KswFT+9yIuPBWYpfkVYLdjvx1G6DQ6Rrde6A+rmGDXWf2e7O0gBY3\n/ErEDExD2f4DyF/7m9jlEIku5AJBpisQJEWLWkdUuBplHDLwi/VZf2DXmX3o1aYbruvWtPF1KSjZ\nvQeW4mLEjRgGhbZ523EPbOdcfrgjZ483SwtoMpkMne+9G4rwMJxc8TGqcvPELolIVKEXCHJKodep\n0CZGJ2odUXoNjFVWWG1292OCIOBQVrH7aGZquYziE/hg90ro1eH4+2WzIJcF37d83q/O327jr7m6\n2e8Rr49DgqY1DuQfQbmpwlulBTxNq1ZIuesO2KuqcPSFl+CwcjtxCl3B969jA4rLqnC2yIhuyTGQ\nyWSi1uI6WKm4rOaH/9cbMvDwG39g/kubOL/AC0pN5fj3luWwC3YsGHJ70A0VAIC5qBgl6buh79oF\n+pROLXqvS/Sd4RAc2HZ6t5eqk4Y2I4ajzaiRMGZm4uSKj8Quh0g0IRUI9hwtBAD0795G5EqA+Bjn\nHggFJc5NkipNVny1/hgA52TDX7afFKu0oGCz2/Dy1vdwrqoU0/qMR9+ES8QuySfy1613HmTUgu6A\nyyURKZBBhj9O7fRCZdKScvcdCEtuj9yf1jg/U6IQFFKBYPfRAgDAgO5xIlcCtKneFKngnDMQ7Pwr\nH1VmOyaO6AKVUo5t+3PFLE/SHA4HXtvxIf4qzMCgpP4Y3+MasUvyCcFuR/5v6yHXahF3ZfMmE9YW\noQxHzzZdcbQoE4XGYi9UKB0KrRY9Fj8MpV6PzLeWo+zQIbFLIvK7kAkEVpsde44WoHWUFu3jI8Qu\nx90hyD9XBQDYdsAZAK66tD0u6RiLrLNlXIXQDIIg4L3dX2Lb6XRcEtcF8wb9TfThIV8p2bMXlqIi\nxA0fBoXOO3NihiY7T37ccmqXV95PSnSJCej+0D8AAIf/71kYT54UtyAiPwuZQLDtQC4MVVYMTW0X\nED8gXAcr5RRUwGy1I/1IPtq2DkdyQgR6pTjHujNOl4pZouQIgoAvDnyPdZn/Q4foJDw09F6olWqx\ny/KZs9+vBgAkjPFeB2RQ+/5QyBUhGQgAILpfX3Sdfx/sxkoc+teTqDx1WuySiPwmZALBL9ucu7CN\nGdJR3EKqxcXoEK5T4cTZcuw7VgiTxY4hfRIhk8nQqa1zj4QTZ3lEq6cEQcDHe7/BqsO/IkEfh0eH\nz0O4WpyzKvzBkHUCZfsPIKpvnxZPJqxNrw5H/4ReyC7Nwemys157XymJG3YlUu6+E9bSUhx87F/s\nFFDICIlAsP94IQ5kFiG1axzaxenFLgcAqn/wR+JskQFrdzjDyuA+iQCATm2d2yqfzA2NXeNayuFw\nYPmuz/HTsfVIikzE0lEPIFor3tbU/uDqDrQdP87r7z20w2UAgN9Pbvf6e0tF4tgxSLnnTljLynDg\nkSUo3Rc6GzZR6Ar6QGB3CHjv+4MAgFnXB9ZM835d4yAIwI5DeUhsHY7uyTEAgDYxYdBpFAwEHrA5\n7Hh9x4dYn/UHOkW3x9JRDyBWJ+6mU75mLi5G0f/+gC4pCTED+nv9/Qe264sIdTg2ndgGm93m9feX\nisTrxqDbPxbAYbHgr2VP4ewPP3KLYwpqQR8I1v15CifOlmPUpe3RtX2M2OWcZ8SAJCgVzv8Fk0d2\ndc9tkMtl6JgYhZwCw3kbF9H5rHYrXtr6Lv44tRPdW6Xg8ZELEKkJjA6QL+X+tAaC3Y6242+ATO79\nv8IqhQrDOw1BudmAP8/s8/r7S0ncsCvR64l/QanX48T7K3DkmedgOVcidllEPhHUgcBsdeDTNYeh\nVSswa2xgdQcAIKFVOLR2s1cAABn7SURBVF5aMAwP3zYQ1wxKPu+5jomRcDgEnMoLnV3jmsJss+D5\nP97CrjP70LtN96CfM+Bir6pC3i9roYqKRJsRw312ndEpzmWM6zL/57NrSEVUr57o958XEdm7F87t\n2Ind981H/roN7BZQ0AnqQLAzw4hSgxkTR3RBqyhxtyquT6e2Ubiib9uLVj4kJziXRp7OZyC4UKW1\nCk9vfg378g5jQNs+eHjYXGhVzdvDX2ry12+E3WhEwtjrIFf7bgVF28gE9GrTDQcLjiK3osBn15EK\nTatY9H5yKVLuuROC3Y7jr72BQ0uWoiLjuNilEXlN0AYCs9WOrYcrEK5V4sZhncUup8nat3EGgpwC\ng8iVBBaD2YgnN72Cw4XHMaR9Gv55xd1QK1Ril+UXDosFZ779DnK1GonXXevz612VMhSA83AoAmRy\nORKvG4MBr7/sPCXxwEHs/+ciHHn+36g6G5orMii4BG0g2HEwF5VmB8YM6Qi9Tno/MJLinWPhpwvY\nIXApNZVj6cb/IPNcNkZ0HIL5g+dAKVeIXZbf5P+2Dpbic0gYOwaqKN8f3z0oKRUR6nBsPLENVjsP\n/XHRxMWh52OL0evJpdB37YLiLVuxe+58ZLz2Bkz5+WKXR9RsQRsI1u90bihy1cDkRl4ZmGIjtdBp\nlOwQVCsynsO/Nvwbp8rOYEyXEbjnslsh98GEukBlN5tx+qtvIddqkTRpgl+uqVKoMDLlClSYDdh8\ncodfrikl0X37oO8Lz6L7on9C164tCtZtwO575+H462/BlM9hFpKeoPwXtcxgxt5jBWjXSh0Q2xQ3\nh0wmQ1IbPc4WGmC3O8QuR1RnyvOwZP2LyK0owPge12D2gFuC8hjjhuT+tAbWkhIk+qk74DK220go\n5Ur8cOQ3OByh/X1YF5lMhtaXD0H/V15Ct38shDYhHvm/rcPuv8/D8TffgbmwUOwSiTwWlP+q7sso\nhEMALmkv7Ylm7eMjYLMLyK8+ACkUZZ7LxuPrX0RxVQlm9J2IGf0mBsTW0/5kLS9HzlffQBmhR9Lk\nSX69dqwuGsM6DkKuoQA7zuzx67WlRKZQIG7YUPR/7WV0XTgfmjZxyP91LdLvuQ+Zby+HuSi0Dosi\naQrKQLD3mDOVpyRIOxAktameRxCiKw0O5h/Bso3/gcFaibsvnYHxlwTnqYWNOf3lStgrK9F+yi1Q\n6sP9fv3xPa6BDDKsOvwrl9o1QqZQoM2IYRjw+ivoOn8eNK1bI2/Nr0i/Zy5OffFf2M08sIwCV9AF\nAkEQsOdYISLCVEiIlt5kwtqSQnilwZ85e/H05jdgc9ixcMgduKrzULFLEoUhKwu5a36Ftm2iVw8x\naorEiDYY1L4/TpScxv78w6LUIDUyhQJtRo3AgDdfRZd5f4dSr8fpL1diz30LULzjT7HLI6pT0AWC\n3CIjikqr0LdLHORyabeW24foSoMNWVvw763LoZAr8PCVf8fg9gPELkkUgsOBzLeWAw4HUu66A3KV\neAF3Qg/nMsdv//qFXYImkCkUiB99FQa8+RraTRwPS3Exjjz9HI489yKsZTy8jAJL0AWCvRnO4YJ+\n3eJErqTlElqFQyGXhUyHwCE48Pn+VXh756cIV4XhXyMWoG9C4O0w6S95a36F4VgGWl95BWL6p4pa\nS0psMvon9sbhwgzszTskai1SpAzToePfZiH1lZcQcUkPFG/dhj3zFqBoyzaxSyNyC75AUD1/ILWr\n9AOBUiFH27hw5ORXBP1vZRabBa9s+8B9fPFTox9El1YdxS5LNFVnz+LkR59AGaFHpzmzxS4HADC9\n73jIIMNn+1ZxxUEzhbVPQp//ewId5/wN9ioTjj7/Io795xXYKqvELo0ouAKB3SFg//EitIkNQ0Kr\n4NjXPqlNBIwmGwym4P0HuNxUgSc2vYJtp9PRo3VnPDX6IbSNiBe7LNE4bDZkvPw6HGYzOt9zF9Sx\ngXEoV4foJAzrOAinys5gw4ktYpcjWTKFAu3Gj0Pqy/+GvltXFG7ajH0P/BOG45lil0YhLqgCQWZO\nKYxVVvTvFhc0S9NcKw2KyoNzp7jjxSfxyG/P4lhxFoZ2uAxLRswPiRMLG5L9yWeoOHoUrYcNReuh\nV4hdznmm9R0PnVKLz/d/jwpzaAxl+YquXVv0eeYptJs0AabcPOxftBhnVv0Agd0XEklQBQLXcEG/\nIBgucHFtrFRYFlzn0guCgF8zfsfjG/6NosoS3NJ7HOYN+htUIXIuQX2KtmzD2VU/QNeuLTrfe4/Y\n5VwkVheNm3vfAIPFiE/2fit2OZInVyrR8baZ6LXscSj1epxc8RH+evJpWEo54ZD8L6gCwb7qCYV9\nu7QWuRLvqekQBE8gsDiseG37Cry/+0voVFo8Onwebuo1Nmi6Os1VcfQYMl5+FXKtFj0efhDKsMA8\noXNM1xHoFNMem05uw+6zB8QuJyhEp/ZD6iv/RnT/VJTu3oO9Cx5A6d59YpdFISZoAoHJYsNfJ84h\npV0UovQascvxGtdeBMEyZJBRfAIfnV71/+3deVxVdf7H8ddduFz2CynKLm5oGe5ZJglqiplLLiPq\nT1Obmhx/Ls1koqLlWBJqTdu4VDqO2owb7k5q5oJZKlKoJEgqJFwNkEVkEe72+8PkV1O5oM7hXj7P\nx4PHQzh3eV+83Pu533PO58MXF5Jp8UAoCb1n1OszCW6oyM3l9OvxWM1mwqb9CdfgujuDQ6vWMPGR\nZ9GoNSxNXkPJtVKlIzkEncHAg3Nm0WTsGMylV/n21b9g2rMXa3W10tFEPeEwBUHauULMFivtHeB0\nw59ycdbi94AbFwursVrt90wDk8XEP09uIe7zhRSZrtCvZU/mRv2JBq4+SkdTXEWukbS4VzGXltLs\nxefx6dRR6Ui3FGwIYMTDAym5Vsr7R1bIWQf3iEqtJuCZgTycMB+9vz+WI8c48fJ0yrO/VzqaqAcc\npiBISb8+drRjK8c7Or1VE2+umWzk2mmDovNF3xO7J54t6btp6OrDiIB+PNt+KFqNVuloirv63VnS\nZs7GVFxC6PPP0biP/bRn7h/Wi47+D3Mq7wyrTiQqHceheLRoTru/LkTTsT0V31/gxJ9fwbhVDjgU\n95dDFAQ2m43jGXm4OGtpHep4nzhbN7n+mNKzixVOcmdKq8r46Pg/mbE3gZzSS/Ru9gSL+sQR7OKn\ndLQ64fLhL0mbNQfT1as0ffEF/J9+SulId0SlUvG/XcYS6OnHvzP3sePM50pHcigavR6nfn1pHTcD\nrZsr2Sv+QVrcq1RcyFE6mnBQDlEQ5OaX8UNhBe1aNkSrcYiH9DOtfiwIMrKLFE5yeyxWC7u+O8CU\nf7/KZ+cO4e/eiNmRU/h9pxHonex74NS9YKmq4vyHH3NmwVugUtF6xiv49e2jdKxacdO5EvvERAx6\nT1albmTXdweUjuRwfDp3ot17f8WnyyOUfnua1Kl/JnvVGhmUJO45h1izPfB1LgCPh/srnOT+CG7s\nibOTihNnC7DZbHX2aHyrzcqx3FQ2pO0gp/QSLk56xrQbSnSLSLRqjdLx6oSSEyc5t2QZ1y79gEtQ\nIK2mT8M1KFDpWHfF1+0BXo16idf2/5UVX6+juPIKwx/uj1rleMW5UnQGA61nTqfwaDJZH32MMXEz\nBQeSCIoZhm+PKNRah3gpFwqz+2eR1Wpjf0oOLs5aurRprHSc+0KjVhEW4MLJ7ArOXCimVUjd2i1i\ntlo4lvsNm0/v4vsrRlQqFT1CuzIifCBeek+l49UJV787S+76jRQdSwaVCv+B/QkeNQKNs2OcERPg\n2Zi/9Pgz8UkfsDl9F9klOUx85Fk89R5KR3MoD3TpjKHtw+RuSOTith2c+9tSjImbCRo+jAYR3RQd\ngCXsn90XBEe//YGC4kqefCQYvc7uH85vahNyvSA49I2xzhQERZUlHMj6ij1nkyiqLEGlUvFESBcG\nP9S3XrcevsFmsVB0PIWLW7ZRevr62GDPB1sT+tw43Js3Uzjdvefn4cvrvV7h/SN/55tL3/LSp3MZ\n1fYZIps8hlotqwX3ikavJ2T0KPz6PUXuxkR+2P0Z3737AdkrV9HoyV406v0k+ka+SscUdsiu30Et\nFiurPz2NWgXPRDZXOs591cxPj4erjv0pOYyKboWrXplPAkWVJaRe+pbDF5JJy8vEhg0XrZ6+LaKI\nbhGJn0f9fiGyVFVx5VQahV8dpehYMubS6+foGzq0J2Bgf7zahtfZXT73gqezOzOemMinmftZm7ad\npclr2J6xl74tI3k8uDNuOseYMVIX6Hy8afrC7/EfNIBLOz8l//N95G7cRG7iZjxatsSnS2d8unTG\nJSDAoZ9z4t65ZUFgtVp57bXXOHPmDDqdjtdff52QkJCa7evXr2ft2rVotVomTJhAVFTUfQ18g81m\n4+87TpOTV0bvLiE1LX4dlUatYlD3Zqz+NJ2PtqQxeXi7+/5HbrFauFSWz/miC5wtyubb/Exyrlys\n2R7WoBndgjsT0eQRXJ3qZle9+8lSWYn10g8UHDxE2blzlKZnUH4+C5v5eldJJ28DjftG4/dUnzrd\naOheU6vU9AvrSZeg9mz89t8cyPqKj1PW8o9vNtIxIJzwRq1o3bAF/h6N5I3qHtD7+hI67lmCR8ZQ\nePgr8j7fR+npdK6eOcP3q9ag8/HBI6wlHq3CcGsaiou/P7oHfOR3L37hlgXB3r17qa6uZt26daSm\npvLmm2+yZMkSAAoKCli9ejWJiYlUVVUxcuRIHn/8cXQ63X0LfK3azCe7Mjh17jLncq8Q0NCdcf0f\num/3V5c8E9mMwycvsjf5AsaCMqaP6cQDXrf/RlxWVU52SS5mq/nHLwsmi5lqSzVXq8sprSqjtOoq\nV65dJb/sMvnll7HY/v+8ZyeNE20bP0jbxg/ySEBbfN3tu0W0zWqlPCsby7Vr2CyWn3+ZLVirrmEu\nr8BSWYmlogJzeTnVxSVUFxVTXVSIqbgEgMwfb0+l0eDWNBTPB1vzwKNd8GgVhqoeL5U3cPXhxc7/\nw/A2/UnKPsr+rC85kvM1R3K+BsBFq8fPw5fGHr54673wcHbDXeeKu84NZ60zWrUGd50bTb2D5c3r\nNmicnfHtEYlvj0hMpaUUH0+h6NhxSjMyKPzqCIVfHam5rFqvR+/bECeDAScvT5y8vHDy8kLr5ora\n2Rm1zhm1s+76MS5qNSqNBpVajVuTEDQu9a/4ry9uWRCkpKQQEREBQLt27UhLS6vZdvLkSdq3b49O\np0On0xEcHExGRgbh4eH3LfDlkkq2HDyHSgWPPezHhCHhuLvUjwNpnLQa/vLCY7y/PpXk9Dzyiyrv\nqCBYeHgp6QVnb+uyns7uNPNpgp+HL818QmjqHUwT7yB0DjR8KG/v55z729I7vp5ap8PJ24BX23DK\ndE6EtG+Ha5MQ3Js3d5iDBO8lbxcvBrbuzYBWT2K8+gPp+Wc5XZDJhRIjOVcucr74wk2vH9d9srS3\nvkNOnp749ojCt0cUNpuNqoICrmZkUpGTQ6XRyLWLl7iWX3DHPQ28O3fiwbgZ9ym1UJrKZrPdtB/u\nrFmz6N27N927dwcgMjKSvXv3otVq2bp1K5mZmUybNg2AV155hUGDBtG1a9ffvL2UlJR7GF8IIYS9\n6dix7rfnro9uuULg7u5OeXl5zfdWqxXtj+e8/ue28vJyPDxuvi9fnghCCCFE3XPLHZwdOnQgKSkJ\ngNTUVFq2bFmzLTw8nJSUFKqqqrh69Srnzp372XYhhBBC2Idb7jK4cZZBZmYmNpuN+fPnk5SURHBw\nMD179mT9+vWsW7cOm83GH/7wB/r0sc8WrEIIIUR9dsuCQAghhBCOr/6eEyWEEEKIGlIQCCGEEMK+\nWxffzIkTJ1i0aBGrV69WOspdMZlMzJw5E6PRSHV1NRMmTKBnz55Kx7pjFouFuLg4srKy0Gg0xMfH\nE2zn3fsKCwsZPHgwK1asoFkz+51NMGjQoJqzgwIDA4mPj1c4Ue0tW7aMffv2YTKZGDFiBMOGDVM6\nUq1s2rSJzZs3A1BVVUV6ejqHDx/G09P+hoWZTCZiY2MxGo2o1WrmzZtn138vjswhC4KPPvqIbdu2\n4eIAHbW2bduGwWBg4cKFFBcX88wzz9hlQbB//34A1q5dy9GjR4mPj6/peGmPTCYTc+bMQa/XKx3l\nrlRVVQHYfeEMcPToUb755hv+9a9/UVlZyYoVK5SOVGuDBw9m8ODBAMydO5chQ4bYZTEAcPDgQcxm\nM2vXruXw4cO88847vP/++0rHEr/CIXcZBAcHO8wTLjo6milTptR8r9FoFExTe7169WLevHkAXLx4\nkQYN7LvtcUJCAjExMfj62vcwp4yMDCorKxk/fjxjxowhNTVV6Ui19sUXX9CyZUsmTpzIiy++SGRk\npNKR7tqpU6c4e/Ysw4cPVzpKrYWGhmKxWLBarZSVldX0sRF1j0P+z/Tp04fc3FylY9wTbm5uAJSV\nlTF58mSmTp2qcKLa02q1TJ8+nc8++4z33ntP6Ti1tmnTJnx8fIiIiODDDz9UOs5d0ev1PPfccwwb\nNozs7Gyef/55du3aZZcv2sXFxVy8eJGlS5eSm5vLhAkT2LVrl13PQVi2bBkTJ05UOsZdcXV1xWg0\n0rdvX4qLi1m69M7bhYv/DodcIXA0ly5dYsyYMQwcOJD+/fsrHeeuJCQksHv3bmbPnk1FRYXScWol\nMTGRL7/8ktGjR5Oens706dMpKChQOlathIaGMmDAAFQqFaGhoRgMBrt9LAaDgW7duqHT6WjatCnO\nzs4UFRUpHavWSktLOX/+PI8++qjSUe7KypUr6datG7t372br1q3ExsbW7KoSdYsUBHXc5cuXGT9+\nPNOmTWPo0KFKx6m1LVu2sGzZMgBcXFxQqVR2u/vjk08+Yc2aNaxevZrWrVuTkJBAw4YNlY5VKxs3\nbuTNN98EIC8vj7KyMrt9LB07duTQoUPYbDby8vKorKzEYDAoHavWkpOTbzoXxl54enrWHLTq5eWF\n2WzGYrEonEr8GvtbF6xnli5dSmlpKYsXL2bx4sXA9YMm7e1gtt69ezNjxgxGjRqF2Wxm5syZOMtk\nQMUNHTqUGTNmMGLECFQqFfPnz7fL3QUAUVFRJCcnM3ToUGw2G3PmzLHbohMgKyuLwMBApWPctbFj\nxzJz5kxGjhyJyWTipZdewtXVVelY4ldIp0IhhBBCyC4DIYQQQkhBIIQQQgikIBBCCCEEUhAIIYQQ\nAikIhBBCCIGcdigcXG5uLtHR0TRr1gyVSoXJZMLX15f4+HgaN278q9dZv349rq6uPP300795uzda\nY0+aNImwsDDOnDlzX/LD9TkQ2dnZjBs37mf3+5/y8/NZsGAB6enpaDQa/Pz8iIuLIygo6L5lE0I4\nDlkhEA7P19eXrVu3smXLFnbu3ElYWBgLFiz4zct//fXXVFdX/xcT3lxaWhplZWU3vUxFRQWjR4+m\nc+fO7Nixg23bttGvXz/GjRuHyWT6LyUVQtgzWSEQ9U6XLl14++23OXnyJPHx8Vy7dg1vb2/mzp1L\nTk4O+/bt48iRIzRs2JBGjRoxb948KioqKCoq4oUXXmDEiBG3dT9JSUm89957mM1mAgMDmTdvHt7e\n3vTo0YMBAwbwxRdfUFlZSUJCAm3atCEzM5PY2FgsFgudOnUiKSmJJUuWsHbtWgD8/f0BOHnyJDEx\nMeTl5TF48GAmTZrEzp078fHx+dkQnAEDBqDT6aiurmb79u0cOHCAkpIS8vPziYmJwWg0cuTIEQwG\nAx9//LE0ihKinpMVAlGvmEwmdu/eTZs2bYiLi+Ott95i8+bNjBs3jtmzZ9O1a1d69OjB5MmTiYiI\nYMOGDfzxj38kMTGRVatW3XRl4aeKiop46623WL58OVu2bKFbt24sWrSoZrvBYGDjxo3ExMTUtHSO\njY1lypQpbN26laCgICwWC82bNycmJoaYmBiGDBkCQGFhIatWrSIxMZHly5dTVlZGeno6Dz300C9y\nREdH1wzIOnXqFIsXL2b58uXEx8fzxBNPsH37dgAOHTp0V79XIYT9kxUC4fDy8/MZOHAgANXV1YSH\nhzNkyBB27drFhAkTai73a8vysbGxHDp0iGXLlpGZmXnbA5lOnDhRM5QKwGq14uXlVbM9IiICgBYt\nWrBnzx5KSkowGo10794dgCFDhrBq1apfve2IiAh0Oh0+Pj54e3tz5coV1Go1Op3uppk6dOiAu7s7\n7u7uADz22GMABAQEUFpaeluPSwjhuKQgEA7vxjEEP5WRkUFgYGDNzy0WC5cvX/7FdadOnYqnpydR\nUVE89dRT7Nix47bu02Kx0KFDh5pRr1VVVZSXl9dsv7E8f2M0r0aj4Xa7iP901oBKpcJms9GmTRs2\nbdr0i8vOmjWLsWPHAuDk5PSbtyOEELLLQNRLTZs25cqVKxw/fhy4PtL45ZdfBq6/Od+Yxnb48GEm\nT55Mr169SEpKAritSW1t27YlNTWVrKwsABYvXnzT3Q0eHh4EBQVx8OBBgJql/Bt5zGbzTe8vOjoa\no9HIhg0ban6WmJjIsWPHCAkJuWVeIYSQjwiiXtLpdLz77ru88cYbVFVV4e7uTkJCAgBdu3bl7bff\nxsPDg0mTJjFy5EicnZ1p1aoVAQEB5Obm/uL22rdvX/Nvf39/du7cyfz585k6dSpWq5VGjRqxcOHC\nm2ZasGABM2fO5J133iEsLKxmomXnzp2ZPn06DRo0+M3r6vV6Vq5cyfz581m5ciUqlYrAwEBWrFhx\ny10JQggBMu1QiDrjgw8+4He/+x2+vr7s2bOH7du31/QdEEKI+01WCISoI/z9/Rk/fjxarRZPT0/e\neOMNpSMJIeoRWSEQQgghhBxUKIQQQggpCIQQQgiBFARCCCGEQAoCIYQQQiAFgRBCCCGQgkAIIYQQ\nwP8BquUanyE6Wb0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x116282710>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGFcTlKG3jHH",
        "outputId": "7dd1eaf1-eac7-459e-9cb4-f4503107a0d4"
      },
      "source": [
        "# Boxplot of SepalWidth\n",
        "sns.set_style(\"darkgrid\")\n",
        "sns.boxplot(x=\"Species\", y=\"PetalWidthCm\", hue = \"Species\", data=data, palette=\"Set3\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x1164675f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bhbNnz1C/fgOef/4F1q5dSaVKlfj733uxePF8jh2LISsrm7CwkbRq1Yb582dz4UI8SUlJ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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1162efe10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtKEOO7k3jHI",
        "outputId": "a29efd4e-ea3e-4ac3-a72a-4fa9ac34c6dc"
      },
      "source": [
        "# A violin plot combines the benefits of boxplots and individual scatter points and aggregates them\n",
        "# Denser regions of the data are fatter, and sparser thiner in a violin plot\n",
        "sns.set_style(\"darkgrid\")\n",
        "sns.violinplot(x=\"Species\", y=\"PetalWidthCm\", hue = \"Species\", data=data, palette=\"Set3\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x11663a438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sa9Z8xYwZU/n661Wkpd0EoG7deqZRgbNnz2Xjxh+YMWMqCQnxpY4bExNN27bt/oihKaGh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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1160c5358>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8xatzdR-3jHI"
      },
      "source": [
        " <p> 2. **Multivariate** Distribution of the four features on species</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-2TpmwK3jHI",
        "outputId": "6a71d6f2-0e03-4f23-be3f-3932c6fe6b66"
      },
      "source": [
        "# A boxplot with Pandas on each feature split out by species\n",
        "# .drop returns new object with labels in columns (axis=1) removed.\n",
        "\n",
        "data.drop(\"Id\", axis=1).boxplot(by=\"Species\", figsize=(12, 6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a1f4df4e0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x1161aa748>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1161355c0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x116279a58>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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raPnytblcFQAAKGwIznB69kKvv38Jxcdfy6dqAABAYcVQDQAAAMACgjMAAABg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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x116135780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kS-sCjpA3jHJ"
      },
      "source": [
        "<p> One of the clusters contains Iris setosa, while the other cluster contains both Iris virginica and Iris versicolor and is not separable without the species information. Iris virginica has bigger petal length and a slightly bigger sepal length and width </p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kT_xKTl33jHJ",
        "outputId": "163dff58-8774-409f-d951-0f64f4568021"
      },
      "source": [
        "# Bivariate Pairwise relationships between columns\n",
        "# The diagonal elements in a pairplot show the histogram by default (if diag_kind=\"kde\" is deleted)\n",
        "sns.pairplot(data.drop(\"Id\", axis=1), hue=\"Species\", size=3, diag_kind=\"kde\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x116387be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Rx74Fy8svZNQHmuZNkg5a3GYg1TZAAFCoCN6WPEu3JROSk5RbWzkb1CtbWsL/V3V18dLE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ypo7zSw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\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f/hSA+fPnc/jwYY4cOWJJ7NGtOxa6qVSqAd+n6i5Td/wBZGRkkJ+fz/jx4y2f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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x116387358>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wB48KooQ3jHJ",
        "outputId": "86f406ad-e4d1-4437-abf0-c4c48106ce23"
      },
      "source": [
        "# A final multivariate visualization technique pandas has is radviz\n",
        "# Which puts each feature as a point on a 2D plane\n",
        "from pandas.tools.plotting import radviz\n",
        "radviz(data.drop(\"Id\", axis=1), \"Species\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x116123c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xxc/Pn6ZN70Cr1dpjaNWqNceO/Y+JExPIysqkf//B9Ov3BFu2fMXy5atRqVQA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\nIYQbkGQvhBBu4P8BfZdpGuDuq98AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1a20593c50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zk8Fr-nF3jHK"
      },
      "source": [
        "## Part 2: Data Preprocessing\n",
        "1. Encoding Categorical Variables (Categorical To Numerical)\n",
        "2. Removing Correlations (Ordinal To Discrete)\n",
        "3. Split the dataset\n",
        "4. Mean Normalization (or Feature Scaling)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yGDFcbs93jHK"
      },
      "source": [
        "<p>1. Encoding categorical values with LabelEncoder</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmCYkNnQ3jHK",
        "outputId": "a2bdadb2-b005-48b4-bdb1-80757d23070b"
      },
      "source": [
        "# Replace Iris-setosa with 0, Iris-versicolor:1 and Iris-virginica:2 \n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "labelencoder = LabelEncoder()\n",
        "data[\"Species\"] = labelencoder.fit_transform(data[\"Species\"])\n",
        "# data[\"Species\"]\n",
        "# Construct a dataframe from a dictionary\n",
        "species = pd.DataFrame({'Species': ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']})\n",
        "species"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Species\n",
              "0      Iris-setosa\n",
              "1  Iris-versicolor\n",
              "2   Iris-virginica"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xkA5i5x3jHL"
      },
      "source": [
        "<p>2. Transform Species to  a three dimensional vector</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbP4iCFh3jHL",
        "outputId": "4d759207-94b0-4ecc-9d93-ad576eac4152"
      },
      "source": [
        "# Before the transformation\n",
        "# The first ten rows are setosa (Species= 0)\n",
        "data.head(n=10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
              "0   1            5.1           3.5            1.4           0.2        0\n",
              "1   2            4.9           3.0            1.4           0.2        0\n",
              "2   3            4.7           3.2            1.3           0.2        0\n",
              "3   4            4.6           3.1            1.5           0.2        0\n",
              "4   5            5.0           3.6            1.4           0.2        0\n",
              "5   6            5.4           3.9            1.7           0.4        0\n",
              "6   7            4.6           3.4            1.4           0.3        0\n",
              "7   8            5.0           3.4            1.5           0.2        0\n",
              "8   9            4.4           2.9            1.4           0.2        0\n",
              "9  10            4.9           3.1            1.5           0.1        0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dWuDEHv13jHL",
        "outputId": "27d75eb1-c13a-4e3e-a6e7-17971b8b0df8"
      },
      "source": [
        "# After the transformation\n",
        "\n",
        "# Create dummy columns for each unique value in species to remove numerical correlations that labelencoder gave to data: \n",
        "# Iris-versicolor is not higher than Iris-setosa and Iris-setosa is not smaller than Iris-virginica\n",
        "# Onehotencoder works with matrix of integers whereas getdummies works with dataframe\n",
        "\n",
        "def create_dummies(df,column_name):\n",
        "    dummies = pd.get_dummies(df[column_name],prefix=column_name)\n",
        "    df = pd.concat([df,dummies],axis=1)\n",
        "    return df\n",
        "data = create_dummies(data,\"Species\")\n",
        "data.head(n=10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "      <th>Species_0</th>\n",
              "      <th>Species_1</th>\n",
              "      <th>Species_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species  \\\n",
              "0   1            5.1           3.5            1.4           0.2        0   \n",
              "1   2            4.9           3.0            1.4           0.2        0   \n",
              "2   3            4.7           3.2            1.3           0.2        0   \n",
              "3   4            4.6           3.1            1.5           0.2        0   \n",
              "4   5            5.0           3.6            1.4           0.2        0   \n",
              "5   6            5.4           3.9            1.7           0.4        0   \n",
              "6   7            4.6           3.4            1.4           0.3        0   \n",
              "7   8            5.0           3.4            1.5           0.2        0   \n",
              "8   9            4.4           2.9            1.4           0.2        0   \n",
              "9  10            4.9           3.1            1.5           0.1        0   \n",
              "\n",
              "   Species_0  Species_1  Species_2  \n",
              "0          1          0          0  \n",
              "1          1          0          0  \n",
              "2          1          0          0  \n",
              "3          1          0          0  \n",
              "4          1          0          0  \n",
              "5          1          0          0  \n",
              "6          1          0          0  \n",
              "7          1          0          0  \n",
              "8          1          0          0  \n",
              "9          1          0          0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kMPj4QQj3jHL"
      },
      "source": [
        "<p> Splitting the dataset into the Training set and Test set</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "sciKBwHM3jHM"
      },
      "source": [
        "X = data[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]\n",
        "y = data['Species']\n",
        "\n",
        "# Sample the train data set while holding out 20% for testing (evaluating) the classifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, shuffle = True)\n",
        "# X_train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4iFHQ4pz3jHM"
      },
      "source": [
        "<p> 4. Mean Normalization </p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "mqTqtrCY3jHM"
      },
      "source": [
        "# Features before mean normalization\n",
        "unscaled_features = X_train\n",
        "\n",
        "# Mean Normalization (Standarize the features to follow the normal distribution, to obtain a faster & better classifier)\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler() \n",
        "X_train_array = sc.fit_transform(X_train.values) #calculate μ & σ(fit) and apply the transformation(transform)\n",
        "\n",
        "# Assign the scaled data to a DataFrame & use the index and columns arguments to keep your original indices and column names:\n",
        "X_train = pd.DataFrame(X_train_array, index=X_train.index, columns=X_train.columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "vYA060ff3jHN"
      },
      "source": [
        "# Center test data. Use the μ & σ computed (fitted) on training data\n",
        "X_test_array = sc.transform(X_test.values)\n",
        "X_test = pd.DataFrame(X_test_array, index=X_test.index, columns=X_test.columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u145PwJs3jHN",
        "outputId": "0d9938d2-37d0-4353-8dc4-aa2739b4e01a"
      },
      "source": [
        "# Before & After Mean normalization\n",
        "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 5))\n",
        "\n",
        "ax1.set_title('Before Scaling')\n",
        "sns.kdeplot(unscaled_features['SepalLengthCm'], ax=ax1)\n",
        "sns.kdeplot(unscaled_features['SepalWidthCm'], ax=ax1)\n",
        "sns.kdeplot(unscaled_features['PetalLengthCm'], ax=ax1)\n",
        "sns.kdeplot(unscaled_features['PetalWidthCm'], ax=ax1)\n",
        "ax2.set_title('After Scaling')\n",
        "sns.kdeplot(X_train['SepalLengthCm'], ax=ax2)\n",
        "sns.kdeplot(X_train['SepalWidthCm'], ax=ax2)\n",
        "sns.kdeplot(X_train['PetalLengthCm'], ax=ax2)\n",
        "sns.kdeplot(X_train['PetalWidthCm'], ax=ax2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wP/74I23atGHPnj08//zzzJ//FoFAgDPPHIzVKjNv3uvMmFGK3+9n+vTp7Nu3j86dOzFs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zE8Dfl18DnSzITO/o4QxSZc1d70zAWmaaI27USpq0f0ODnUg8gyyZHdUBVEEe/ESai7mjG13J8H\nQGVIQZFFdheY/DmO3OCSe1ChOWa9rxuD+57HPR1TyyYT0+JMniwSlfxIEatLdbxy3wvgbKg6WS7D\nhdss1JCfo8A7xYr/renoT7xe3/Ard63LdspM6r2TGDR+QNURAO8IkTvYMQS6jtrSMqzjBEGgtspP\na1e8pN0JYwXJpFXoeP0e2pKtAEyryO+OtFQwtdySFUPV1qaqErFcX+OV+14AV5YplNzr86vcfVMt\ncq/KQu5u5T4Mzb3vZipYzVGyIBHX+ssbiaSGV5EQRzBa2evE/+7cPuxj6yr9xJM64TEykSmf2cCJ\nRIJLL72Uc889lwsvvJCOjo49veyckLATIf1+Dz1GG6YmM7lqwiivanQxrcwid93XSVT24VWtrJ3o\neOU+9uE4UfLW3Hc1I1fXIHq9eR0vllcQ8wpUtvcX3Xs199zlDHVn5mYqWBWwX/YT1/pfI65qI6a3\nO3D8//nEENRWWha01s4smxKjgPTZwIsWLWL58uUZj7/44ot873vfy5gN/MgjjzB79mwefvhhTjvt\nNO688849vewhYRoGqm59wUtegaQQxoiXuXbUfRVTyiYhINCq7kL1Bt1M91gJb/KXELnnr7nr8Th6\nV5fbLJQPdAxaq2QCPUn0WDTjMSWPDdVkU6bH3YHf4yOWhdxjCY3ACJO7mw6Zx+CO+iqL3Hd3jQ1y\nz2c2cPoxJ5xwAq+88sqeW3COMGIxUpK1gR0VwiCYSMlylDzvaEsFPtlHfbCOreEd+GqqEDFRPEJJ\nV+5jb3xKntA0Hdkj5jXxKdVibTrlK8kAaIZGa5WHabtSJHfsIDD7APcxR3NXh0HuatNOkKR+a/LL\nfjoSXRm/M02TWFKjcUIw7/XnArm8HKmsLL/K3SH3MVK55zMbOBKJUFZmjesLBoOEw0OPCSx0NvBw\nEdcibmhYyk5CDAo1Y27M4GisZ079/jy76SXKJgfhY/BIBrFo/5nEY+21Ssdw1lY65J4y8v4QuZnp\nBVTuFrlbL2dy29YMcleGKcuYpona3ORmyqQjIPvRDA1VT6FIlosmoeqYJiNeuYNVvcfXf4iRiCP6\n/EMfYKOucmyRez6zgdOPiUajlJeXD3mdQmcDDxeJ7bvduN/NXdbeSLlYvUfn1Q6FPT0/18Ekn3Xn\nmSiz9n0MNU7KCLBzR6f7GR2tteWCbGsbjOxLSJbR3Xmlw4XaUphTBvqS+7aMx7ze4VXuWqfdUDWx\nf4pfwJ7AlK67OzbIgG/kyb0323140kxNhQ9JFGgZI3bIfGYDz507l9WrVwPwwgsvMG/evBFdYz7Q\nIxGb3E12xK3ejX3Z456O/SpnANDqtTZTjYT9RV2i0kzJkHtKM/J3ytiVu6cAck8ZGl1lErosktze\nh9wdK2SObplsThkHzrCFdHKPJSxyD3pHfpJ7bwzB8MhdEkUaqgPsbItijAE75CmnnIKiKJx99tks\nW7aMJUuWsGrVKv7xj38MeMw555zDxx9/zDnnnMOjjz7KwoUL9+CKc4MeCaNKPrwegV3xFoykj5qy\n4Y1GLFVU+6qo9lXxkWFFIMuqRe6xErVDlpAsoyOX5+cIUHftQvD6kCtzm76U9fqGhikKJGrLkZp2\nYmqaK6kMt4nJqfwde2U6Anb7dPqmajRhN63sgcq9kJF7k2qD7GyL0t6doL5uaEljJJHPbGC/38/t\nt98+4msrBHo4TEr04veKRPUIZryWytp92ymTjlkVM1gbW4sJBFMRooxX7mMapmnmrbmbhkGqZRdK\nQ0Nem7EOnPmpifoqq9HHjjOA4ccPJLZusY6bOr3fY07lHkulVe6OLLMHNHfvxEkgCP3uTnLBlDqr\ngtyxO1LsZY3DRqonjCZ5kRTr7siIhajcx22Q6di/cgaGKGCE/IRSpR1BUBLk7oSGyXlo7qn2NkxN\nK0hvB9wRe2p9FQCJNN1dlEQkWcyZ3JPbtiEGg8jV1f0e87uae28jkyPL7AnNXfT58NTVk9y+bdjd\nppNqbXJvHSf3kUIiHMUUREzFeq+Z8TIqQ8NLOS1lzLJ191jQQ5XaCUB729jYByo2SoLcC+lO/f/b\ne/f4OOr73vs9s7P3Xd1XF9uSLMuWr8g3LjY3lxBCQmiT8gpgfDBQcvKkhB7SllIa2qS0xw8hfU5y\nmrzaEkhSntS5AMGkJ0+bAEmgARwC2Fi+3yRLsu731d5nd2fm+WN2V5YtaXd1W60877/sndmd745m\nPvud7+97iSV6ylim2VMmZUNiOHasUq8ElDvGVzxaLCaicvqwjBIKERvox1azfMInCUfSc4/nxnMH\nPVykhsPELyjwyYTqhLh3DATT7GkwXcJ+/bqQzXqoQQ25Dc/9AiocHlxmJyNWBXtMdzJ6+hdmdsxM\nWSTinqhOnYbnPhtpkDAm7kpFqR62uChjJjmNKR3JcIe1tnbC7Q5Jj7mPy5aZR88dxpqkRc5fWrI/\nFSUFVuxWiS7Dc58zIon+5H5TADQBLeKk0Gl47kkEQWBV0QpGrCoWJYKmaYx6F2e30rRqmA89OOKp\nQR3Ze+4zbRiWJJYQd5PNpg/MvihsYbFIGaVCyonza51gMRX0ClW4eEE1kS1jm/tsGRgT94ufTtIh\nCALVHie9w6FU/3mD2SUS0s+rV/UiRt247dbLvjr1YtaWNhBwiAhoCIJKTI7jX4RDO9KKez704JiR\n597bA4KAuXxmLVGTnrtZNGOrqUUNh4kNDqS2m62ZzVGNnG8DwDbBYipcEHMft6A6f9kyMNYB8+Kn\nk0xYXlWApkFzpzf9zgZZIyccCFkMEQ+6KCmw5diihce6ktUEHLpW2CUNC/Deiew6neYDadUwH3pw\nzCTmHu3twVxalvVovUtsSIi7JJqwJgZmy62tqe2WRCvedKEZ+fx5BKsNc/nEhSfJIqbQBHnu8xVz\nlwoKMBUVZR2WAahfWgjA6faR2TbrskdTFJLZtnEphhJ0UWqI+yUU24qwFuvzZAsscUQE3jvam2Or\nZp+04j5ZD44k1113HcXFxZe8J9seHDMh1cs9y6ZhSiiI4vNNe0DHOBsSqZCSIGGrWwFApG1M3JNz\nVKcSd1WWifZ0Y6upQRAn/i5JcQ/Gxlb4w6nh2PP3+G2rqUXxeon7fFm9r36Jnt9++rwh7rONEgym\n+soo5ihqyEXJNGs/FjvLluntQaT4KAA9fX56hxdX1kxaV2++enDMpMHScJ9+rMJiR1ZNgPyn9Xh7\nYV3tjJsF2bz6OSkpcrOsYT2doojS2Z763ILEqDmXy0aZZ+KKQf/pHtA0ilavnNIep9lORA2n9onG\nNexWicqKwrR2zlZTpNCaVQSPHMbmG6C4PvNBEGVluuCcbh+mrMw1o9oCg/Eofr06FUCRYmhht+G5\nT8LK2kbgFwjhYTBVYAHePdbLFasXz8SqtOK+ZcsW3nzzTW677base3A0NjZm3INjJg2Whob07AtZ\njo1rrJOuCdDoyRYAlKLSGTcL8vqSpcwxhu0xLFVL8De30N/rpbyyCEXVQ0d9vaNowsRxd+/hkwCo\nniVT2uOUnHgj/tQ+owEZu9WU9jvMZlMkpVR/2uk/eor4sksrO6dieWUBH54Z4My5wXmLCS/kTn+z\nhRLwp5qGCWbQojZD3Ceh3tPACYuAOTgEBeAQRd493svn7mjMtWmzRto4Rj704JhuzH02Wv2mbEjF\n3PXfS1tdHVo0qg+5ZiwsM9U0pvC55tR7p8JpdhKMhVA1/XuH5DiOeegrcyG2ZMbMNOLuKxKhmXPd\n2YV0DKZG99ztgIZNcgOCsaA6CSbRRNztwBXSr8HqEgeDoxGOnxvKsWWzR1rPPR96cIxNYcpO3Gcr\nxx3GUiHNom6DvX4lvnfeJtx8Frasy2hBNdJ8FtHhTJuW6bI4UTWVSDyCTbITkePzluOeRCorQ3Q4\nxlXiZkoy7t7SPcqVa4yOhbNFUtw1UUaK6+e41Ii5T4q5uJSCNj3Lr8yhJ1T88v3z3PvRVbk0a9ZY\nHEVM8em1H4j29iDa7ZgK0seq09qQ8tx1D9q+Sg9fhZvPAHoRE0zePCw+6iU2MIB95cpJF1OTuM36\nUA5/LEhEjqMxf5kySQRBwFpdQ6y/DzWSXRFIbaUbUYAWw3OfVZSAn6hkRzVFiAddSCYRt1HANClF\n5UuxxMNoaIiKSlmhjf1HulMJCvnOohD32DTmp2qKQrS/D0tl1aws6qWyZRKeu7miEpPLTfjsWSD9\nqL1ws76ffWV6r8GZEPdANEggVcA0/w0+rdU1oGnIndkNzLZZJGqrCmjv9RNP9AUymDnyaABFNBM3\nywRH7JQUWOd0YHq+YystR0RDM4Xx+cJcf0UVclThwKn+XJs2KywOcU8IpiUL7zU2OACKMuPK1CTJ\nxmGSoNsgCAK2VauIDw8hDwyMee6TeAXhs7qHb8tA3F2WhLjHggRCegGTyzG/MXeYWdy9oaaYWFyl\ny+gzM2uE/PoTVMwSxTdkNRZT02BONOYThDChQIzt6ysQBHjnaE+OLZsdFpW4JwU0E6KJhmGzkeMO\nly6owpgXPnr0+Ng0pkkWVEMnTiBYLKkc+alwm/VUykAsQCCcEHf7/It7skXCdOLuq2v02oiW7tFZ\ntelyJpToS645NFDMRo57GsxleiGTWQ2BBnbJxMaVHs52ji6KnPfLWNxnp6dMkrH2A2Pi7ly3AQBv\n02EsibCJPIHnHhsZIdrdhb1hNaI5vUg7EwM7AtEggbB+Q+dC3C2VVQiSNC3PfXWtLu5GxszsEUxc\nC2a77rEbnvvUSGV6B1e3qp+3vqFhbr5ad1j2LwLvfZGIe0JYs0iFTA7TmI1MGRjLlrnQc7csW4ap\nsAhvUxOWxGJvNHKpuIdOHAfAuW59RsdyW5Kee5BAWP+8XIi7IElYllUT7e5CjcWyeu+ycjd2q8kQ\n91kkLOvrFxaL7rEbaZBTYy4uAUGgKK43PDzZ3cL2K6qwWyV+e6wXVc39OMiZsCjEPTodz727G0wm\nLDNsGJbkwt4ySQRBwLl+PbFRHwzqnsBEnnvo+FEAHBmKe2pBNZZbzx3AVrcCLR5H7shuUVUUBZZX\nFtA7HEqNCTSYPpqmIcf1a08U9BYVhuc+NYIkIRUX4w7phX1tfT1YzSauWVvOiF/mZJ73P1oU4h6L\nKkhmMeOsF03TiPZ0YymvSM05nSkXp0ImcW7QK97CRw8jisIlnrsajRI43ITZ40nNJ02H68JsmRx6\n7gD2VB+dc1m/t36pnovdanjvM0YNh4gm+8ooetiutNAQ93SYyzxYfXr3Vu9oEL8cYNt6fR3uvZP5\n3Sly0Yi7xZK5SCujXtRweNZCMnBBWEYY//TgbNyIaLEQ+OB3WGzSJZ578OgRNFnGdeXVGf84WU0W\nzKKU8NyT2TK5yWe2rUiI+7mWrN+7okqvLzBCMzNHGR1NVKdCOJQIy7iNBdV0mEvLsCYmMkmyjQNd\nR1i5rJBit5UPTw8Qi+dvqu6iEfesQjLJytQlS2bPBjWGWZQuEWjRZqPk6quI9fVhMWmXeO7+938H\ngPuqqzM+liAIOM3ORCqkHpbJRZ47gLm8AtFuJ9KaveeebEPQ2mOI+0yJekeISnZAZcgfo9BlMYZ0\nZIBUVoZFiWASwSLb2X/+AKIgcNWackJynOOtcztoaC5ZHOIey07c5e4uACxVmXczTGuDGsMsThwa\nKbtR721vigTHee6xoUEChz7Esqw6NQAjU9xmJ4FogEA4jt0qIZly86cURBFb3QpifX0ogezG5xU4\nLRS5LHQYY/dmzOhgD1GTHZMpxpBPprLEmWuT8gJzWRkC4LKCTXZxtO8Uo7Kfa9bpa3Hv53FoJu/F\nXdO07D337tnNlAGIKTEspolDIyVXbsFcUYHgG0KJq6n+8yO/fB1UlZKPfTzrKlm3xU1UjeGPhHDZ\nc+O1J5mof32mVJe7GfbJqfCSwfQY6e8karIjWTQUVaOy1BD3TEjmujtEGUExIcYlDvY3sbzSjafI\nxqGzg8ix9OMxFyJ5L+7JpmHZhWW6QRBmLccdxsIyEyGYTJR84nakRMqVHIkTaT2H981fI5WU4L76\nmqyPl0yHDMaDOVtMTZIS92mEZmoq9O/R0bc4J9DPF77hIVTRhJS4D6rKDHHPBHMi192u6JXSVtnJ\nBw1vZHMAACAASURBVL2HEASBq9dWIMcUjrbkZ6fIvBf3ZBqkJUtxN5d5Zjxab9xnqvFJwzIABdu2\nY3PrC14dzz5H17f+N6gqlX/036eVsVNg0fuTq6YILntum0PNRNyry3VxP99vhGZmQsirj10ULXqG\nTFWZI5fm5A1SUTGIIrawnva40raS8/5O+kIDbF2te/WHzg5M9RELlrwX92R1aqa93BW/H8Xvn9WQ\nDCQ8d9Pk4i5IEkWb9bRI/7l21GiM8l334li7blrHK0h47pjlnIdlpMJCpNJSIq3n0g4Av5iaCv1H\n6nyfIe4zIRbS13LiiUlMRlgmMwSTCXNJKVaf3iys1qLPUvig9xC1FW6K3VaOtAzlZYO7RSPumaZC\nysnK1CWzt5iqaipxNY5lCs8dwObWb7jSzz7Eiq/9L4puunnax0x67oI5mnPPHXTvXfH7iQ1k5+WU\nF9mxmEU6DM992sTUOJqsOzfBhAZVGeKeMVJZGRav3mvKrRRhEc180HcIgM2ryghG4pzt8ObSxGmx\naMQ905j7WNuB2UuDHOsrM7W4J5uHqXY3JtfEc1QzxZ0S99x77jDWJC3Z3TJTRFGg2uOiZyiY1znF\nuaQv2I+g6Dnto5EYVrOJIiPHPWMs5eXY47pz4R+RafSsZzA8RJuvg80NydDMYC5NnBZ5L+7J4Re5\nFPeoqmd6TBWWAVLNwyKzkBmSXFAVzFHcOSpguhB7gz5NPltxB6iucKOoGt2DRvvf6dA90oGKHmMf\nDEbxFNmNweNZYK6oxKQp2CwCI0MhrqrYDMAHfYdYXV2E3Spx6OxA1iHHXJP34p61596l57hbl8xu\nGiQwabZMEnui57o8C+JeYB3z3AtduRd367JqRLt9WuJek1pUNTJmpkN/7zkikh6GCSoqlaXGYmo2\nJLPmnFKMUW+Y1YUrcVtcfND7IRoKG+tLGfLJebcudFmJu6ZpRDrOY/aUI9rss2dDwnNPG3NPpCxG\nwjMf4+WQ7AgIurg7c/8ILogitvqVxPp6iY9m16M9mTHT2T9/nruqqnzlK1/h7rvvZvfu3bS3j29b\n/NJLL3HHHXdw11138eabbwLg9Xq55ppr2L17N7t37+b73//+vNk7FaN9nciSEwGNGFBZYoh7NiTF\n3a6E0FSNcDDO9qqrCMXDHBo4yqZVerrk4Zb8Cs3kvbgnJxtlMoUpPjKCGgikhkzMFqnh2GnCMmPi\nPnPPXRREJNUO5ihFC8BzhwvmxmbpvS9J5GR3Dc6fZ/SrX/2KaDTKiy++yKOPPsrTTz+d2jYwMMDe\nvXt54YUX+N73vsc3vvENotEoJ06c4Pbbb2fv3r3s3buX+++/f97snQxN0wgN9RGRHJhN+ppFlSHu\nWWEuKwOTCXsiHdLnDXNtld4O5J2u99hQV4pJFDjcnF/57nkv7nKiV4s1g94qcqc+MSjbUv90RJXE\nkIS0nvvsxdwBBMWKIMk5L2JKMl1xt1slygptdM7jyL2DBw9yww16W4hNmzZx7Nix1LYjR46wefNm\nLBYLbrebmpoaTp06xbFjxzh+/Dj33nsvjzzyCP39uZ+1ORr1YfKFiUqO1N1shGWyQ0i0/raM6hkz\noyMRPI5S1hSvomW0FZ8yxKplhbT2+BgNRnNsbebkPs1ihqTEPQPPXT4/N+KeDMukE3fJbEI0CbMm\n7mrMiuBQUYUYkPsmUba6OgRJmlbcfWmZk8MtQ/hCUQrmYYE4EAjguiBjyWQyEY/HkSSJQCCA2+1O\nbXM6nQQCAVasWMGGDRu49tpr+dnPfsaePXv41re+NeVxiosdSNLc/W06uttwhKzIgohi0iCmsaGh\nHACPx53m3bljodk2WFuN/dA5KNbXxDweN7et/T1O/fYsB0cOcd2mzZw676WtP8BHr67NmZ3ZnLf8\nF3c5C8+9Y64898xi7oIgYLebZ03c4xEzOMAX9WOTct+7WzTrM2DDzWdRwmFM9szXNZZ6XBxuGaJ7\nIEhB7dyLu8vlIhgce1JQVRUpUSl88bZgMIjb7aaxsRF74jvdcsstaYUdYGRkbmdxHu9qwRaxgg28\ncSh0WQj6IzhsZgYGFuYCtcfjXnC2acVlOGMfAtDd4WVgwE+tpQ63xcWb537L/1irtwh5p6mLjXUl\nObFxovM2ldgvorBM+tCE3NGB6HQiFRfPqg2ZxtwBrLMk7rG4SiyiH88XXTir+PZVDaBpWXvvyzx6\n3L1znjpEbtmyhbfeeguApqYmGhoaUtsaGxs5ePAgsizj9/tpaWmhoaGBv/mbv+G1114D4N1332X9\n+swmZ80lnf5upJj+gzMc01hiFC9NC0tlJZIaw2GFkSH9B1kSJXYsvZZwPEJL+CjlxXaOtQ7nTbVq\nWnFf6FkFciSGKApI5qm/ihoJE+vvw1pdM+s5wGNhmfRPDza7maisoMzwAvEFo2gx3cP1RReOF5Rs\np5CcC5spSz16iKRrnnLdb7nlFiwWCzt37uSrX/0qX/rSl3j++ef59a9/jcfjYffu3ezatYv777+f\nP/uzP8NqtfLoo4/y4x//mN27d/PCCy/w13/91/Ni61R0BroxxXVBlwVY6jHEfTokM2YKpCgBn5ya\ny3zDsu1YRDNvdLzDFfUlyFGF03lSrZpWjS7MKmhqauLpp5/mmWeeAcayCvbt24csy+zatYvrrrsu\nlVXw5S9/ec6/gByJY7FdOiTjkv06OgGwzXJIBsYWVCdr+XshyYwZORLH4Zx++MEblCGmp0D6F5Dn\nblu5CsFiIXTiWPqdL6CyxIEoCHTN06KqKIr8/d///bjX6uvrU/++6667uOuuu8Ztr66uZu/evfNi\nXyZE4hFGRweIiXp1cBRY5plZ5fPlSirXPeYDyhgZClFeVYDL7GT7kqv5Ted+HBV6b/fDzYOsX56b\n0Ew2pPXcF3pWQTQSzzDerj9xzHYaJICcEHerKX2+uc0xO+mQvsCY5+5fQJ67aDZjb1hDtLub2HDm\nqWNmSaSy1EHXYCDvKgFzRVegl4KgQtisC7qM4blPF5PTicldgC2RMZMMzQDcXH0DoiByPHQAm0Xk\nSPNQXlyjaVVxIWcVaJqGLMcpKXNOurCQfH10QP+jVTauxTnLK/VS4rerorQorR0liZiozWKeUcaA\n0jyElvDco6Kc8WfNR5ZCbNtWWo8dwdTRgmf18oxtWbG0kHcOB8Es4Sk20vnS0RnopjCgEpZcCCio\nCEbMfQZYq6uxtHbC0g3jxL3UXsKW8kYO9DVRvSrA2eMqvcOhBd+cLa24L+SsglhUQVU0REmccPX9\nwtVl79kWBEkiaC0gNMsr9cM+/fPCfoUBcWo7kr/4Pd2jOAqmH5bp7BlNiXu/bzij7IP5ylJQa/Qw\nQe+7BxA3TjwbdiJbygr073P0dB+N9WWzatNCS72bDTr8Xbj9ChGzC0WNU1bsxp5BSrDBxFhranGe\n1ge9j1y09vPJulv4sP8IowVHgas43Dy04MU9bVhmIWcVpNIg01zQaixGtLMDy7LqaQ3GSGtHKiyT\nXqwdiWrS8AyLIYb9MigSJsG0oBZUQR9fKBWXEDp5HE3NfOE4GS+ez2KmfKbVd56CkB1NMBHWtFQb\nB4PpYa2pwaJEsJi0cZ47QLnDw7VVV+FXRpA8XRzJg1YEaZXulltuYf/+/ezcuRNN03jqqad4/vnn\nqamp4eabb05lFWiaNi6r4IknnuDHP/4xdrudPXv2zInxckSPW6eLucsd59Hi8dTEoFm3Q9HH52US\nc08uogaD8oyOOTQaAQTcFteCWlAFPZ/fsX4DvnfeQm5vy/i8J+PFXcbA7LSEYmF6g31cJRcybIaA\nYKKhcvE9ncwntppafVi2EGFkREBRVEwXDJ7/RN1Hea/3IGL1Oc40LSUUieHIIAU7V6QV94WcVZBp\n64HIOX38m31F/ZT7TduOxGxUq5S55x4KzMxzHxwNU+iyUGBx0xPsRdO0BdXm1ZkQ9+DRIxmLu6fI\njkUSDc89A9r9HQCYZAeYISiaWG6I+4wwl1cg2mw4IsMMS0vxDoUoveBpqMhayE3VN/B6+5uIVS0c\na72Cq9dW5NDiqcnrIqbMxV2Po9lWzJXnnnm2TNJzD80gLKOqGsM+mbICGwUWFzE1TkSJTPvz5gLH\n+g1gMuH/8GDG7xEFgSVlTnqGgihZhHMuR1pH2xEVDTmuX3MRoLbCEPeZIIgizuW1OEf0H86B3kvD\nnbfW3oRLciNVtfJ+S9s8W5gdeS3ukZAelrGlaZwVaW1BdDoxl8/Nr6ysyJgEU0ZFTBarhGQWZ+S5\newMyiqpRWmhLjdtbSFWqACaHA+e69UQ7O4j29WX8vqUeJ3FFo38kPIfW5T+tvvMUBhRC5gIArE4z\nha7ct37Od5wr6iiI6KMi+ycQd5tk4zMNtyOIKifj76CqCzclMq/FPen92qcoBop7R4gNDGCvXzln\nYQtZiWa0mJrE4bTMyHMfHNW99LJCe2rcnk9eWIuqAK6tVwEQ+PBAxu8xFlXTo2kabaPnqZHthMyF\noMapqSrMtVmLAteKOlzyCKIAAz0T31NXVmzCrVZBQR+/PJP5tT3f5LW4JzNOpqr0DJ0+DYyNgZsL\nZEXOKCSTxOGyEg5Gp/2rP5QS9zHP3R9bWJ47gGvTZhBF/AczvwGMRdX09IcGCMXD1EacRMwu4lqc\nlcsMcZ8NnCtWIKJSIMkM9gcmbBMiCAIfW/JxNFXg9e5XU40DFxp5Le6hDMQ9fGY+xD17z13TIBKa\nnvc+OKqHLEoLbalZqgstHRLA5HLhWL0Wua2V2FBm1apLyxI9ZgzPfVJafXp3U9eoA00QCQErlxri\nPhs4amsQLBbcoQFURWN4kuvwupUNqP3LieDn9fY35tnKzMhrcU967smS/gn3OXMawWrFVjN3PZiz\n9dydiYyZ4DTj7kO+CTz3BRiWAXBt3QpkHpopcllw2qR56w6ZjyTFXRnVK7pHTRYjU2aWECUJe/0q\nnCN6u5KJFlUBrBYTq8xXoco2Xm//L3qCma8rzRd5Le6hYBSb3TwuF/VCoiMjRHu69Xj7HBQvASiq\nQkyNZ+W5uxKVmAHf9DJcBrz6+0oS2TKw8BZUk7g2bwVBwPfe7zLaXxAElnpc9I+EicaUObYuPznn\nbcOChD+si7u7xInFnPthLYsF++rVFET0J83+SeLuAJvrK4m1r0PRFH58ah+qtrAyvPJc3GOpvPGJ\n8B46DCTS8ubKhrgeInGYM++F4i7UB2v4Rqcn7t1DQUoLrFjNprEF1QUYlgGQCgtxNm5Ebmslcr49\n/RuAao8LDWNRdSJ8UT/dwV42UEFA0kMxK+pmdz7B5Y69YTXO6AiioE26qAqwsb4M1VuOI7KMltE2\n3u35YB6tTE/eins8rhCV49inCMmMHDoEgHPDFXNmRyimlyk7pMynDhUU6fv6vdmLezASYzQQZUki\nNm2XbEiitOCqVC+k8IYdAIy+/VZG+y+v0n+wWnt8c2ZTvnJmuBmAhkgBAWsxqDE2rJrdPjyXO7a6\nFZjMEoXKKEMDAaKJNicXU1poY+XSQoZPrsQqWvlp888XlJOVt+IeDuor1JN57pqq4j10GKm4GMuS\npXNmRzDhuTun4bn7p+G5Jxcal5bpWSWCIOA2uxbURXUxzisaMRUW4f/db1Hl9G0Xllfpudtthrhf\nwukRvSCvZFAgKjmQtTirjMXUWUU0m7GtqKfQ246mQU/H6KT7bltfgRaz0SBdQzgeZt/Z/28eLZ2a\n/BX3RKaJY5JhypFzLcT9fhzrr5jTsvzpeO5Wm4TZYppWWKY70a1uSdlYR7oCqxt/1L9ge0wLJhOF\n112PGg5ntLBaVeLAajFxzhD3cWiaxsnhM9glO74u/doRi91GvH0OsDespjistwnvOj/55KWr1pRj\nEgV6z5azvKCGA31NHB86PV9mTkneinvS63UWTJylEkjkVru2bJlTO8Zi7pmLuyAIuAtt+EcjWQty\ncgzdhUMZCiwu4ppCOL5wqzoLrr8RAO9/vZl2X1EUWF7hpncoRHiSR+LLke5gLyOyl3VFKxny6Yt3\ntSvLc2zV4sSxdh2FkX5ENLraRibdz+2wsL6uhPN9AW6pvA1REHnx9E9T09lySd6K+2iiPL2w6FJR\n1TQN/4cHMDkcONbO7RDjUCz7BVXQQzOxqJL1RKak515VOna8ImsRACPy5I+PucZSXo7zikYiLc2E\nTp9Ku39dVQEa0D5JKtrlyNHBEwBsVCrwWUpB07h685IcW7U4sdevxOy0UxQdYLA/QNA/eThx2zq9\nrcm5c3Bz9Y0MRYb5eeuv5svUSclbcfclFiMLii8V90hrK/GhIYqv3IpontuWnMF49mEZgKISfX/v\nUHZDSroGg5QV2rBZxlI7S216tsRgeDirz5pvSm7/AwCG/+NnafddsUSPu5/tzI9hxPPBkcETiIJI\naVcUn82DSJQqY2bqnCCYTLiu2EjpaBsA589Nfm9tXuXBZjGx/2gPt9Z+hFJbMb/ueIuuQM88WTsx\neSzuusdckFicHLdt/9sAeHbcMOd2hGPZL6gClCRuyqEs0v2GfRF8weglQxlK7fqw3uHI5I+PCwF7\n/Uoc69YTOnkC38mpvffVNfrTyKkp4p2XE4PhYdp9HawqWkH74U40QcRVUZBrsxY1ri1bKQt1AtB6\ndvLhHFaLiWs3VDLilznZ6ufu1Xegaio/ynHue/6K+0gYp9uCdNFikirL+N//HVJxMcWbN825HYHU\ngmp24l6aiJlPVt48Eee69QXGpFeb+qyE5z60wD13GPPeO178yZT7uR0WqstdnO0cJRY3ipkO9DUB\ncGVZI76ALhibrqrLpUmLHseGK3CZ47jio3ScG04NB5qImzbrGXm/OtDB+tLVbC3fSJvvPG93ZVa8\nNxfkpbgrikrAL6fyxS/E/8F7qOEwBduvQzDNfRbBaCLOXWjNrvy7uNSBIGTnubd068dasWR86lup\nTffcByMLX9wdDauxr1mL91ATwRPHp9x3bW0xcUWluevyzprRNI0P+g4hiRKV3SJD9mUImsLqNZ5c\nm7aoEc1m3FdeTcVoM6qqce705N77Uo+L9cuLOXXeS2uPj880/AF2yc7PWn6BN0drYXkp7j5vGE3j\nEnHXVJWR114Fk4nC3/vIvNjilUdxm11IGfRyvxDJbKKg2M7wQCDjjJmT7SNIJuESz91pdmA1WRZ8\nWCaJ566dIAgM/OgHaPHJs2HW1OpPJCfaFv6P1lzSMtpGb7CPK8rW0fJmE2FLAcVFpkueWg1mn8Ib\nf48KfyugcerI1DH027bp/av+47dtFFjc/GH9bUQUmZ+c+T/zYOml5KW4D/Xr3m6pZ/z08eDhQ0R7\nuim4ehvmkpI5t0PTNLzyKEW26RWReCrcRGUF73D6FMZAOEZHX4CVSwuxXnRTC4JAqa2EofDwgs11\nvxBbTS2VH7+VaG8PQ/8x+YW/uroIsyTy4ZmBvPhec8V/de4H4JrSqwl59dDApusapnqLwSxhW76c\n4ppySkNd9Hb5GOybvBJ8TW0x9UsLOHR2kJauUbYvuYr6wjqaBo7xbs/8933PS3Ef7NPT48oqxhYW\ntXicgX0/AUGg+BO3zYsd4XiYqBqjyDq9ha0lNfqPQncGi4ZHWgbRgPV1E/9oeeylRBR5wTYQu5ja\n++5FKi1l+Of/SfjsmQn3sVslGutL6RkKXbZ9ZvpDgxweOMZSVxXtv2xm0FWHCYUVC3h252Kj+NZP\nsMyrJwA0vXd+0v0EQeAzO/Q5zS++2QwI3LfubuySjRdP/5TuQO98mJsiL8W9NxGDvVDcvW/+mlhv\nL4U3/h7WOWw3cCHJvPJknnm2LKnRww7d59OHUw6e1kd/bWmYOM66xFUFQFege1q2zDeSw07lZ/8v\nALq//c/ERiY+B8kBxO+fXHgtVeeD/2x9HVVT2V52HfLxs0QlBytXFmE2QjLzhmvzVqqKwSUP03yy\nn5HByR2N1TXFbG3w0Nw5ytuHuymzl7B77V3E1BjfPbaXcHz+Zh3nnbjH4wr93T7KKlxYbXoOe7S3\nh8FXXkZ0uSj91B/Omy2DYb0taIlteuJeVGLH4bLQ2e5FnWIgtC8Y5UjLEMs8TqpKnRPusywh7p15\nIu6gL656PnMXyugoXf/4dZTApU8djfWlWC0mfnusl1h8YbVUnWvOjbZzoK+JatdSTv1igKCjBjSN\nLR9Zm2vTLisEUaT8rp2sGD6EpsE7vzw7ZZhw1y0N2K0mXnijmd7hEBs9G/hI9Q30hQb49pHn5616\nNe/EvavNi6JoLFuue71KMEj3P30LLRajYvf9SAXzl/vb6deFdKlrelWCgiBQt6qMSChGR+vk3vtv\nmrpQVI0bNk5+nGVufVuHv2tatuSKoltupegjHyXa1UnH//M0ce/482A1m9ixcQkjfpn9x3JbFDKf\nRJUoe0++iIBARWgrnvNtBK3F1NfYKSrJLu3WYOY412+gbt0SSoJddLZ7OXl48mux2G1l98dWI0cV\n/vmVowQjMf5w5SfZXN5Is7eV7xzdS1yd+7YaeSfuZ0/oj+d1q8pQAgG6vvkNor09FN9yK+7EQOb5\nojNRgbZsmuIOsKaxEoCTTRNfLIFwjNc/6MBpk7j+iqpJP6fUVkKRtZAzIy0LbmjAVAiCgGfnLoo+\ncjPRrk7a/+eTBI8fG7fPx6+pQTKJ/PzdduTo4s95VzWV//fEC/SHBqm3bCTy2gmGCxuQiHPdH2zO\ntXmXLRW772ND/CSSIvP2a2foap/cIdu2vpKPXrmMrsEg//jSYUIRhQfW7WRd6WpODJ/mn5q+SyA2\nt+tIeSXuPm+YllMDFJU6KAj3cf7//nsi51pwb9tO2Z13z6stmqbR7jtPgcWddY77hXgq3XgqXbSe\nHaSnY/zCqqZp/OD10wQjcT65fTl26+TploIgsKZkFYFYkHZf57TtyQWCKOK55148d92D4vfT9b//\nF93P/BPRgX4AilxWPnrlMgZHI/zwlxMvvi4WZCXK9479kMMDxyjUquA1GatjKYpo5sZbVuJ0Zz7O\n0WB2MTmcrPwfX6DR+zs0VeE/X2jizNHJPfidN69i+/pKWrp97Pm3A5zvDfG5Dfex0bOBs95zfPX9\nf6Rp4NicZYKZnnzyySen2kFVVf72b/+Wb3/72/zsZz9j69atFBWNxZhfeuklvvzlL7Nv3z7Kysqo\nq6tjeHiYhx9+mBdffJG3336bHTt2YE7T4yWUZlh0LKrwi58cIeCPsp5zRF/5N9RwiJLbbqf8nnsR\nxEt/p5xOa9rPnS4to238V+d+Gss2sKl86klPU9khCAIlHienjvTS0TZMWYULRdEQTfDjXzez/2gv\n9UsKuO/jqxHFqVsXmwSRA31NRNUom8svHVAyl+cjWy62RRAE7PUrcW7chNzZSej4Mbxv/IrwuRYQ\nRNatq+ZYV5Cj54YIhGKsW16c9nxcfLwLWSjXdRJFVTjUf4Tnjuyl1ddOSU8RDQcqEezVxCUrV20u\nofGGzIe8L6S/9cXks22S241n/SqE/a/SJ1XQ0jxC57E2BFHE7rZhMonEYwqiSUAUBDY3lKGoGk1n\nB3n7cDeDXpnra7ZSUeTk2NBJDvY1cWzoFHbJSrGtCPMU9TIT2XbxdX0hgpbmZ+P111/njTfe4Omn\nn6apqYlnn32WZ555BoCBgQEefPBB9u3bhyzL7Nq1i3379vEP//APrFu3jjvuuIPnnnsOi8XCAw88\nMNVhGBi4tPufGo3ie+ct5J5uerpDvKusodLXzLr+d7Atr6P87nuwr5o839fjcU/4uTNFURW+duBb\ndAV6+NPNf8yq4hVT7p+JHYff7+C3b7Sk/t9nETkfjbO0zMlf7NxEoSu9x6ZqKk9/8E26Aj1sLm/k\n0/WfoMxempUd88VUtmiqiv+D9/G+8SsiLc2p18WyctoVB92aA8HuoGpZGUUlhZQU2ih1W3GsXYtU\nNPHIOY9n/NNVLq9r0Cdq/a7lLGf8JxiQ+xmK9xAXZCyyxqdfDxEyN9BWshGrJnP9R+pouCa7vPaF\n9Le+mMVgmxIM0vaT/8OBVhi2XxqWrTT7uO2uzVirqwE41T7Cj351NjX4vdBpYelSjVDxCfo0/b43\nCSbqCmqoclVSZi/BIdmxiGZsko11paupKC+8xLaLr+sLSVtWefDgQW64QW/AtWnTJo4dG4uHHjly\nhM2bN2OxWLBYLNTU1HDq1CkOHjzI5z//eQBuvPFGvvGNb6S9CSYifOY0/T/6AQB24Ialg3iuqMO9\n9UvYVzXM6RCOqVA0FU3T+FjtTWmFPVPWbFrCv75xlhL07zSsqXxiWw2/f+3ycR0gp0IURD67/r/x\nz4e/x6H+I2wqWz9O3PMFQRQpuGYbBddsQ+7qJHi4idCpk0TaWqkO9VMN4AUST8QxoBcouP4GKh/4\nbEbHyOV1DfBvr57msPpzTEV6SbsataF5a6mKraTE/AaV5Rq168ys+uj1mDP8+xvMHyank/oHdlEz\nNMj5N96l/ewgwxETCiYETaPM34zcWZoS9zW1xTz54FWc7fDy22O9HDk3xIkzMWAVgq0KU0kvanEf\nzVorzaOtlxzvz7d8gYoJnsanIu1VEwgEcLnG8slNJhPxeBxJkggEArjdY78cTqeTQCAw7nWn04nf\nn/6XcKJfIM9N17L8pmsz+iLZfO5s8I+3/+2s2/Gjr39quuaMO84zy5+akR3zRUa2eNbCptlP/cvl\ndQ3wlc9tB7ZP8q7PZPw9pnPshcCisc3jZsmaOrZluHtFeQHXb62Zll2QnW1pF1RdLhfB4Niqrqqq\nSJI04bZgMIjb7R73ejAYpGAe0xMNDDLBuK4NFjtpxX3Lli289ZY+tb6pqYmGhrHYX2NjIwcPHkSW\nZfx+Py0tLTQ0NLBlyxZ+85vfAPDWW2+xdevWOTLfwGB6GNe1wWIn7YKqqqo8+eSTnDlzBk3TeOqp\np3jrrbeoqanh5ptv5qWXXuLFF19E0zQ+//nPc+uttzI4OMjjjz9OMBikuLiYr3/96zgcRuGFB2qH\nkgAABRlJREFUwcLBuK4NFjtpxd3AwMDAIP/IqyImAwMDA4PMMMTdwMDAYBGS9wm0ydjp6dOnsVgs\n7Nmzh9ra2tT2l156iRdeeAFJknjooYe46aab5sSOWCzGE088QVdXF9FolIceeoibb745tf3555/n\n5ZdfpiQxROTv/u7vWLFidnLkJ+LTn/50Km1v2bJlfPWrX01tm69z8sorr/DTn/4UAFmWOXnyJPv3\n709lmezZs4cPP/wQp1PvdPkv//Iv41IQDaaH3+/nscceIxAIEIvF+Ku/+is2b85tT5p092kuSXfv\nLgSGhoa44447+Nd//Vfq6+sze5OW57z22mva448/rmmaph06dEj74z/+49S2/v5+7fbbb9dkWdZ8\nPl/q33PByy+/rO3Zs0fTNE0bHh7WduzYMW77o48+qh09enROjn0xkUhE+9SnPjXhtvk8Jxfy5JNP\nai+88MK413bu3KkNDQ3N+bEvN775zW9qzz//vKZpmtbS0qJ9+tOfzq1B2tT3aa5Jd+/mmmg0qn3h\nC1/QPvaxj2nNzc0Zvy/vwzKZVhq63e5UpeFc8PGPf5wvfvGLqf+bLhrOffz4cZ577jnuuecenn32\n2TmxIcmpU6cIh8M8+OCD3HfffTQ1NaW2zec5SXL06FGam5u5++6x5m6qqtLe3s5XvvIVdu7cycsv\nvzynNlxOPPDAA+zcuRMARVGwWnPfbGyq+zTXpLt3c83XvvY1du7cSXl5eVbvy/uwzHQqDeeCZGgh\nEAjwyCOP8Kd/+qfjtn/yk59k165duFwu/uRP/oQ333xzzsIhNpuNz372s9x55520tbXxuc99jldf\nfXXez0mSZ599locffnjca6FQiHvvvZc/+qM/QlEU7rvvPjZs2MCaNWvm1JbFxk9+8hO+//3vj3vt\nqaeeorGxkYGBAR577DGeeOKJHFk3xlT3aa5Jd+/mkldeeYWSkhJuuOEGnnvuuazem/szO0OmU2k4\nV/T09PDwww+za9cufv/3fz/1uqZp3H///alj79ixgxMnTsyZuNfV1VFbW6sPA6mro6ioiIGBAaqq\nqub9nPh8Ps6dO8e2beMLtO12O/fddx92ux2Abdu2cerUKUPcs+TOO+/kzjvvvOT106dP8+d//uf8\n5V/+JVdffXUOLBvPVPfpQmCyezfX7Nu3D0EQePfddzl58iSPP/44zzzzDB7PxOM2LyTvwzLTqTSc\nCwYHB3nwwQd57LHH+MxnxvcGCQQC3H777QSDQTRN47333mPDhqnbBM+El19+maeffhqAvr4+AoFA\n6mKYz3MC8MEHH3DttZf2B2pra2PXrl0oikIsFuPDDz9k/fr1c2bH5URzczNf/OIX+frXv86OHTty\nbQ4w9X2aa6a6d3PND3/4Q37wgx+wd+9e1q5dy9e+9rWMhB0WQRHTdCoN54I9e/bwi1/8YlwGzJ13\n3kk4HObuu+/m3//939m7dy8Wi4Xt27fzyCOPzIkdANFolC996Ut0d3cjCAJ/8Rd/weHDh+f9nAB8\n97vfRZKkVPfE559/PmXHd77zHV599VXMZjOf+tSnuOeee+bMjsuJhx56iNOnT7N0qT4o3uVypdoZ\n54qJ7tOMsz7mmInu3e985zvYbLYcWnUpu3fv5sknn8z4vOW9uBsYGBgYXEreh2UMDAwMDC7FEHcD\nAwODRYgh7gYGBgaLEEPcDQwMDBYhhrgbGBgYLEIMcTcwMDBYhBjibmBgYLAIMcTdwMDAYBHy/wO0\nvUYdiRq51AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1a20bc6400>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw_isFRl3jHO"
      },
      "source": [
        "# Predicting Flower Species\n",
        "\n",
        "Multi-class Classifier: Classify instances into one of three or more classes.\n",
        "A classifier: correlates features of data with class properties\n",
        "Gives the predicted classes: Softmax classifier\n",
        "The cost function (how good are the parameters):Categorical Cross Entropy\n",
        "Optimization algorithm finds optimal set of weights: Stochastic Gradient Descent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VxGIJGUv3jHO"
      },
      "source": [
        "## Part 3: Build the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HorKSyEM3jHO"
      },
      "source": [
        "![](https://github.com/annisap/irs-mlp/blob/master/pics-iris/Input-computauionunit-output.jpeg?raw=1)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "mE__f_Du3jHO"
      },
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "mlp = MLPClassifier(hidden_layer_sizes=(10),solver='sgd',learning_rate_init=0.01,max_iter=500)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_l6OFZqm3jHP"
      },
      "source": [
        "![](./pics-iris/multilayerperceptron_network copy.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0tD4ykp3jHP"
      },
      "source": [
        "## Part 4: Train the model\n",
        "<p>Minimize the cost function. \n",
        "Gradient Descents => Find the values of Weights that minimize the cost function. \n",
        "We need to find the change rate of the cost function(dJ) so as to select the direction that goes downhill. This would save us time because it would prevent us from searching of weights values that increase the cost.</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KyXpM9Lg3jHP",
        "outputId": "0fe4dbec-9c15-4e61-e3dd-182e1c2fd7eb"
      },
      "source": [
        "mlp.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
              "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
              "       hidden_layer_sizes=10, learning_rate='constant',\n",
              "       learning_rate_init=0.01, max_iter=500, momentum=0.9,\n",
              "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
              "       shuffle=True, solver='sgd', tol=0.0001, validation_fraction=0.1,\n",
              "       verbose=False, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JV41U4Ha3jHP"
      },
      "source": [
        "![](.\\pics-iris/gradientDescent.gif)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YG1rSU8t3jHQ"
      },
      "source": [
        "## Part 5: Test the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VbsbvLv_3jHQ",
        "outputId": "6b8fbb6d-e609-40e8-a96d-12d34b3ea701"
      },
      "source": [
        "\"\"\"\n",
        "Predictions for X_test are compared with y_test\n",
        "\n",
        "Score for mlp is the mean accuracy given the test data and labels\n",
        "We can use accuracy (instead of F1 score) because our data labels are balanced (not skewed data)\n",
        "Multilabel Classification: subset accuracy(harsh measure, see haming score or exact match ratio)\n",
        "\"\"\"\n",
        "\n",
        "# It's expected to see such a high result since the Iris dataset is so simple, clean and structured. \n",
        "# But it is not going to generalize into new data\n",
        "mlp.score(X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ODaB_n0a3jHQ"
      },
      "source": [
        "### Part 5.1 Diagnose Sources of Error: Bias or Variance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmRfOEmw3jHQ",
        "outputId": "ffc5b5bd-3ad4-4eda-b28e-6a4e48d38aaf"
      },
      "source": [
        "# Score on train data in order to compare it with validation score and check for bias or variance error\n",
        "mlp.score(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9833333333333333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IKamtilD3jHR"
      },
      "source": [
        "![](pics-iris/cross validation.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LuHGK1Da3jHR"
      },
      "source": [
        "<p><b> Validation Curve</b> error vs number of hidden nodes </p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4zio6gR3jHR",
        "outputId": "9dc2f553-3d8f-487b-8421-0f7292c29481"
      },
      "source": [
        "# Train-Test may not be randomly splitted. --Should I split the data into 60-40?--\n",
        "# Hyperparameters may be tweaked in test data and cause overfitting\n",
        "# One solution: Cross Validation \n",
        "\n",
        "# train_CV  = pd.concat([X_train, y_train], axis=1).reindex(X_train.index)\n",
        "X_train_array = X_train.values\n",
        "y_train_array = y_train.values\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "scores = cross_val_score(mlp, X_train_array,y_train_array,cv =3, scoring = 'accuracy')\n",
        "\n",
        "#the samples are balanced across target classes hence the accuracy and the F1-score are almost equal.\n",
        "f1_scores = cross_val_score(mlp, X_train_array,y_train_array,cv =3, scoring ='f1_macro')\n",
        "print(\"Accuracy: \\n \", scores, \"\\n\", \"Accuracy: \\n \", f1_scores, \"\\n\", \"Mean Accuracy: %0.2f (+/- %0.2f) \\n\" % (scores.mean(), scores.std() * 2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: \n",
            "  [1.         0.925      0.97435897] \n",
            " Accuracy: \n",
            "  [0.97616007 0.92624799 0.95054945] \n",
            " Mean Accuracy: 0.97 (+/- 0.06) \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X58VoPyt3jHS",
        "outputId": "95ef3ce2-2a27-444e-db5a-6f5a7f28dce0"
      },
      "source": [
        "# Validation Curve\n",
        "from sklearn.model_selection import validation_curve\n",
        "\n",
        "X = data[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]\n",
        "y = data['Species']\n",
        "\n",
        "# Calculate accuracy on training and validation set using range of parameter values\n",
        "train_scores, valid_scores = validation_curve(mlp, X, y, param_name='hidden_layer_sizes', param_range=np.arange(1,12), cv=3, scoring=\"accuracy\")\n",
        "\n",
        "# Columns represent the splits (cv=3) while rows represent the training size\n",
        "print('train_scores:\\n', train_scores, '\\n valid_scores:\\n', valid_scores)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train_scores:\n",
            " [[0.33333333 0.33333333 0.33333333]\n",
            " [0.96969697 0.33333333 0.96078431]\n",
            " [0.33333333 1.         0.96078431]\n",
            " [0.33333333 1.         0.97058824]\n",
            " [0.23232323 0.33333333 0.97058824]\n",
            " [0.96969697 1.         0.33333333]\n",
            " [0.96969697 1.         0.96078431]\n",
            " [0.95959596 0.33333333 0.1372549 ]\n",
            " [0.35353535 1.         0.97058824]\n",
            " [0.97979798 0.53535354 0.98039216]\n",
            " [0.96969697 1.         0.97058824]] \n",
            " valid_scores:\n",
            " [[0.33333333 0.33333333 0.33333333]\n",
            " [1.         0.33333333 1.        ]\n",
            " [0.33333333 0.90196078 0.97916667]\n",
            " [0.33333333 0.92156863 0.95833333]\n",
            " [0.31372549 0.33333333 1.        ]\n",
            " [1.         0.94117647 0.33333333]\n",
            " [1.         0.94117647 0.95833333]\n",
            " [1.         0.33333333 0.20833333]\n",
            " [0.33333333 0.92156863 1.        ]\n",
            " [1.         0.52941176 0.97916667]\n",
            " [1.         0.94117647 1.        ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F4p3xz5x3jHS",
        "outputId": "518d8f9e-6e7c-4d0b-c832-d33a2cc304f7"
      },
      "source": [
        "#Plot the Validation Curve: The result is 7 hidden_layer_sizes\n",
        "\n",
        "# Create range of values for parameter \"Number of hidden nodes\"\n",
        "param_range = np.arange(1,12)\n",
        "\n",
        "# Calculate mean and standard deviation for training set scores\n",
        "train_scores_mean = np.mean(train_scores, axis=1)\n",
        "train_scores_std = np.std(train_scores, axis=1)\n",
        "\n",
        "# Calculate mean and standard deviation for test set scores\n",
        "test_scores_mean = np.mean(valid_scores, axis=1)\n",
        "test_scores_std = np.std(valid_scores, axis=1)\n",
        "\n",
        "# Plot mean accuracy scores for training and test sets\n",
        "plt.plot(param_range, train_scores_mean, label=\"Training score\", color=\"darkorange\")\n",
        "plt.plot(param_range, test_scores_mean, label=\"Cross-validation score\", color=\"navy\")\n",
        "\n",
        "# Plot accurancy bands for training and test sets\n",
        "plt.fill_between(param_range, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, color=\"gray\")\n",
        "plt.fill_between(param_range, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, color=\"gainsboro\")\n",
        "\n",
        "# Create plot\n",
        "plt.title(\"Validation Curve MLP\")\n",
        "plt.xlabel(\"Number of hidden nodes\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.tight_layout()\n",
        "plt.legend(loc=\"best\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IytPy2fM5PPPBiyhU9v1uy4JkR+Txo6onJCSkcMmG/Pw89uzZgSA4/zoLgoDB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            "text/plain": [
              "<matplotlib.figure.Figure at 0x1a20d21208>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KaOoHc4_3jHS"
      },
      "source": [
        "<p><b>Learning curve </b> (error vs number of training samples)</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mnaml2S3jHT"
      },
      "source": [
        "# Learning curve (error vs number of training samples) \n",
        "from sklearn.model_selection import learning_curve\n",
        "# train_sizes means you progressively add more data into your model. \n",
        "# For each of these train_sizes you have a different model trained upon\n",
        "# We have 150 tr.examples where the 20% of them are the validation set, so the plot will reach 120 tr.examples\n",
        "\n",
        "# Put 7 hidden nodes\n",
        "mlp = MLPClassifier(hidden_layer_sizes=(7),solver='sgd',learning_rate_init=0.01,max_iter=80)\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "# Cross validation with 100 iterations to get smoother mean test and train\n",
        "# score curves, each time with 20% data randomly selected as a validation set.\n",
        "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
        "train_sizes, train_scores, validation_scores = learning_curve(\n",
        "                                                   mlp, X,\n",
        "                                                   y, cv = cv, n_jobs=5)                                               "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "IbWduc1V3jHT",
        "outputId": "0e6edac1-2f3f-449e-bb94-925bece5d6ca"
      },
      "source": [
        "\"\"\"\n",
        "To plot the learning curve, \n",
        "we need only a single error score per training set size, so we average row-wise for the y axis of the plot.\n",
        "\"\"\"\n",
        "plt.style.use('seaborn')\n",
        "\n",
        "# Calculate mean and standard deviation for training set scores\n",
        "train_scores_mean = np.mean(train_scores, axis=1)\n",
        "train_scores_std = np.std(train_scores, axis=1)\n",
        "\n",
        "# Calculate mean and standard deviation for test set scores\n",
        "validation_scores_mean = np.mean(validation_scores, axis=1)\n",
        "validation_scores_std = np.std(validation_scores, axis=1)\n",
        "\n",
        "# Plot the std deviation as a transparent range at each training set size\n",
        "plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"lightgreen\")\n",
        "plt.fill_between(train_sizes, validation_scores_mean - validation_scores_std, validation_scores_mean + validation_scores_std, alpha=0.1, color=\"b\")\n",
        "\n",
        "# Plot mean accuracy scores for training and test sets score lines at each training set size\n",
        "\n",
        "# Easy to fit a model with a small number of data points (1 to 40)\n",
        "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"lightgreen\", label=\"Training score\")\n",
        "# Usually,its hard to predict for model with a small number of data points(1 to 40)\n",
        "plt.plot(train_sizes, validation_scores_mean, 'o-', color=\"b\", label=\"Cross-validation score\")\n",
        "\n",
        "# Create plot\n",
        "plt.title(\"Learning Curve MLP\")\n",
        "plt.xlabel(\"Number of training examples\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.tight_layout()\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qrwCzyelAXS2bnPo4Xlv78Nrag9fVPry29hlSkAM0SLacu0eanIiIiIi6CwMN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\nxhhjFo8bGsYYY4xZPG5oGGOMMWbx/gfU4/CU2wxCZwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1a20c5b5f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tv6IlQra3jHT"
      },
      "source": [
        "Our y-axis is 'score', not 'error', so the higher the score, the better the performance of the model.\n",
        "Training score decreases, increases and plateau.\n",
        "Cross-validation score increases and plateau (able to learn from data).\n",
        "Our true score is 0.75. If we had lower scores (high errors):tweak model (perhaps increase model complexity with more hidden nodes or max_iteartions).\n",
        "If we had a big gap between the two lines (high variance): add more data.\n",
        "In our example, we know that mlp needs more data in order to work properly, otherwise there is no need to use such a complex model as Neural Nets. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "q9aIp7jQ3jHT"
      },
      "source": [
        "## Part 5.2 See other classifiers reults"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZKV30_bX3jHT"
      },
      "source": [
        "### Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unFVqtm53jHU",
        "outputId": "c2d9f12a-ae7e-4f8d-a2fe-f4ce2bee75bc"
      },
      "source": [
        "# Check if a decision tree outputs more accurate results\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "tree = DecisionTreeClassifier()\n",
        "tree.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
              "            max_features=None, max_leaf_nodes=None,\n",
              "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "            min_samples_leaf=1, min_samples_split=2,\n",
              "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
              "            splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FqLXicI53jHU",
        "outputId": "25995c17-6017-481b-a0e7-231f61ff918c"
      },
      "source": [
        "# Test the model\n",
        "tree.score(X_test,y_test) # metrics.accuracy_score is an alternative"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LsgHXsyh3jHU"
      },
      "source": [
        "How would look like the decision tree splits on iris\n",
        "![](.\\pics-iris/dendogram-iris.jpg)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gACQDFqZ3jHU",
        "outputId": "b6d3376e-056d-4949-ec42-673c80e2d20c"
      },
      "source": [
        "# Learning Curve DT\n",
        "#4 tr.examples \n",
        "train_sizes = [1,30,50,99] #  put aside 50 examples as a validation set?\n",
        "train_sizes, train_scores, validation_scores = learning_curve(\n",
        "                                                   tree, X,\n",
        "                                                   y, train_sizes = train_sizes, shuffle = True, cv = 3)\n",
        "# Calculate mean and standard deviation for training set scores\n",
        "train_scores_mean = np.mean(train_scores, axis=1)\n",
        "train_scores_std = np.std(train_scores, axis=1)\n",
        "\n",
        "# Calculate mean and standard deviation for test set scores\n",
        "validation_scores_mean = np.mean(validation_scores, axis=1)\n",
        "validation_scores_std = np.std(validation_scores, axis=1)\n",
        "\n",
        "# Plot the std deviation as a transparent range at each training set size\n",
        "plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"lightgreen\")\n",
        "plt.fill_between(train_sizes, validation_scores_mean - validation_scores_std, validation_scores_mean + validation_scores_std, alpha=0.1, color=\"b\")\n",
        "\n",
        "plt.style.use('seaborn')\n",
        "\n",
        "# Plot mean accuracy scores for training and test sets score lines at each training set size\n",
        "# Easy to fit a model with a small number of data points (1 to 40)\n",
        "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"lightgreen\", label=\"Training score\")\n",
        "# Usually,its hard to predict for model with a small number of data points(1 to 40)\n",
        "plt.plot(train_sizes, validation_scores_mean, 'o-', color=\"b\", label=\"Cross-validation score\")\n",
        "\n",
        "# Create plot\n",
        "plt.title(\"Learning Curve Desicion Tree\")\n",
        "plt.xlabel(\"Number of training examples\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.tight_layout()\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N3/tU/5JvvpMIWtd8+zaexMMcQPg/scH7hDuJuzwq3Er8PYemeRtDfT0027A9mpPQGGiI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\nd2yaIAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1a232015f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0tTd31MY3jHV"
      },
      "source": [
        "Perfect training and CV score. Better to use Decision Tree instead of MLP even with less tr.examples"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bFSzhcId3jHV"
      },
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtssqHD03jHV"
      },
      "source": [
        "# The ‘newton-cg’ optimization algorithm supports L2 regularization\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression(multi_class = \"multinomial\", solver = \"newton-cg\")\n",
        "lr.fit(X_train, y_train)\n",
        "pred = lr.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CafC_Q6N3jHV",
        "outputId": "adb11c82-f04a-40ea-ceac-336875c5ef30"
      },
      "source": [
        "# Test the model\n",
        "lr.score(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9333333333333333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_ocfBKl3jHV",
        "outputId": "abdff483-2c2f-4712-bc0c-8fde75e26f4b"
      },
      "source": [
        "# K-fold CV: Split the TRAINING set into K folds. Train the model into k-1 folds. Test the model in the remained fold\n",
        "# Good for large data sets\n",
        "# How many folds? The more we have, the more prone to variance (although we reduce bias) & increased complexity (time & memory)\n",
        "\n",
        "from sklearn.cross_validation import KFold\n",
        "size = 150\n",
        "cv = KFold(size, shuffle=True)\n",
        "train_sizes, train_scores, validation_scores = learning_curve(\n",
        "                                                   lr, X,\n",
        "                                                   y, shuffle = True, cv = 3)\n",
        "# Calculate mean and standard deviation for training set scores\n",
        "train_scores_mean = np.mean(train_scores, axis=1)\n",
        "train_scores_std = np.std(train_scores, axis=1)\n",
        "\n",
        "# Calculate mean and standard deviation for test set scores\n",
        "validation_scores_mean = np.mean(validation_scores, axis=1)\n",
        "validation_scores_std = np.std(validation_scores, axis=1)\n",
        "\n",
        "# Plot the std deviation as a transparent range at each training set size\n",
        "plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"lightgreen\")\n",
        "plt.fill_between(train_sizes, validation_scores_mean - validation_scores_std, validation_scores_mean + validation_scores_std, alpha=0.1, color=\"b\")\n",
        "\n",
        "# Plot mean accuracy scores for training and test sets score lines at each training set size\n",
        "\n",
        "# Easy to fit a model with a small number of data points (1 to 40)\n",
        "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"lightgreen\", label=\"Training score\")\n",
        "# Usually,its hard to predict for model with a small number of data points(1 to 40)\n",
        "plt.plot(train_sizes, validation_scores_mean, 'o-', color=\"b\", label=\"Cross-validation score\")\n",
        "\n",
        "plt.style.use('seaborn')\n",
        "\n",
        "# Create plot\n",
        "plt.title(\"Learning Curve Logistic Regression\")\n",
        "plt.xlabel(\"Number of training examples\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.tight_layout()\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OTfySc9M4dmo7+Nb8VYXlp+inB0JNXc/N559/yq5dO5k27fr6HqaoRLQ+N1WNFCwlNEII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\nXBahUs+cPgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1a234606d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nniBWe8g3jHW",
        "outputId": "9b5f0d06-7828-4713-8277-33f15b16c63f"
      },
      "source": [
        "# Takes more time then the KFold\n",
        "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
        "train_sizes, train_scores, validation_scores = learning_curve(\n",
        "                                                   lr, X,\n",
        "                                                   y, shuffle = True, cv = cv)\n",
        "plt.style.use('seaborn')\n",
        "\n",
        "# Calculate mean and standard deviation for training set scores\n",
        "train_scores_mean = np.mean(train_scores, axis=1)\n",
        "train_scores_std = np.std(train_scores, axis=1)\n",
        "\n",
        "# Calculate mean and standard deviation for test set scores\n",
        "validation_scores_mean = np.mean(validation_scores, axis=1)\n",
        "validation_scores_std = np.std(validation_scores, axis=1)\n",
        "\n",
        "# Plot the std deviation as a transparent range at each training set size\n",
        "plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"lightgreen\")\n",
        "plt.fill_between(train_sizes, validation_scores_mean - validation_scores_std, validation_scores_mean + validation_scores_std, alpha=0.1, color=\"b\")\n",
        "\n",
        "# Plot mean accuracy scores for training and test sets score lines at each training set size\n",
        "\n",
        "# Easy to fit a model with a small number of data points (1 to 40)\n",
        "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"lightgreen\", label=\"Training score\")\n",
        "# Usually,its hard to predict for model with a small number of data points(1 to 40)\n",
        "plt.plot(train_sizes, validation_scores_mean, 'o-', color=\"b\", label=\"Cross-validation score\")\n",
        "\n",
        "# Create plot\n",
        "plt.title(\"Learning Curve Logistic Regression 3-Fold\")\n",
        "plt.xlabel(\"Number of training examples\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.tight_layout()\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1uS+wd+8uwNtpGKBZQiJ2eyE5OUcA2PLrJlq1asOvv2wiKSmZqU88y8jRY3n7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\nrqKiQpMmTdLu3btVXl6u0aNH60c/+pEmTpwol8ulq6++Wo8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            "text/plain": [
              "<matplotlib.figure.Figure at 0x1a230b2160>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "HzpTe1g83jHW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}