Iris_Data.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Mount the Drive**"
      ],
      "metadata": {
        "id": "Fl8n-BKsXNnr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bIvEuJIPOxDC",
        "outputId": "d7d59a31-1f1d-4143-9bda-379b1df8a92f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the required library**"
      ],
      "metadata": {
        "id": "GCBZj9plXpss"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "e1dgDmV8XgwY"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **load dataset into pandas Dataframe**"
      ],
      "metadata": {
        "id": "qGpcWCctXf6e"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "_QavrbrVOmQf"
      },
      "outputs": [],
      "source": [
        "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
        "df = pd.read_csv(url, names=['sepal length','sepal width','petal length','petal width','target'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
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          "height": 424
        },
        "id": "1Mtwg4yVhovF",
        "outputId": "f2365176-2611-4b08-9f3c-a10e4642bd30"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length</th>\n",
              "      <th>sepal width</th>\n",
              "      <th>petal length</th>\n",
              "      <th>petal width</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
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              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
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            "text/plain": [
              "     sepal length  sepal width  petal length  petal width          target\n",
              "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
              "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
              "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
              "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
              "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
              "..            ...          ...           ...          ...             ...\n",
              "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
              "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
              "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
              "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
              "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "lQrLpgt9b4vK"
      },
      "outputs": [],
      "source": [
        "# Data Standardization Section\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "features = ['sepal length','sepal width','petal length','petal width']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "SbjJKQ90hC-q"
      },
      "outputs": [],
      "source": [
        "# Separating out the features\n",
        "x = df.loc[:, features].values\n",
        "# Separating out the target\n",
        "y = df.loc[:,['target']].values\n",
        "#Standardizing the features\n",
        "x = StandardScaler().fit_transform(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "XtOOxDNChz_L"
      },
      "outputs": [],
      "source": [
        "# PCA Projection to 2D\n",
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=2)\n",
        "principalComponents = pca.fit_transform(x)\n",
        "principalDf = pd.DataFrame(data = principalComponents, columns = ['principal component 1', 'principal component 2'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "N2c3tZ7xjelR"
      },
      "outputs": [],
      "source": [
        "finalDf = pd.concat([principalDf, df[['target']]], axis = 1)"
      ]
    },
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      "execution_count": 9,
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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f7h544IFdtq1Zs4aZM2dG08Aao3MxROfCo/MwROdiiM6FpxbOg5kVDeqxSpQzMwP+H/BokIAuIiLxkMl4S1z09HirTqfT3tpVUlmxCurAycBHgD+Y2YPZbZ9zzq2qYptERKSE7m7o6IDBQejr8xajXLjQW4xyxoxqt66+xCqoO+e6Aat2O2JHl8AiElOZjBfQM5mhbX193mNHh7dIZcyHqBMlVkFdfOgSWERirKvL+3ryMzjo7dcilZUT2yltwq6XwLlL376+oe29vdVtn4jUvZ6eoa+nQn19sGFDZdtT7xTU4yzIJbCISBW1t3sdiH5aWmDKlMq2p94pqI9EJgPLlkFnp/eYP5gUJl0Ci0jMpdPQUCSSNDR4+6VyNKZerkqOcecugf0Cuy6BRSQGUinv66/wa7GhwduuJLnKUlAvR6XTPNNp74LBjy6BRSQmZszwvv66urwOxClTvK8nBfTKU1AvR6XTPHUJLCI1orVVWe5xoKBejmqMcesSWEREAlJQL0e1xrh1CSwiIgEo+70cSvMUEZEYU1AvR26MO5UampjZ0jK0XV3iIiJSRep+L5fGuEVEJKYU1EdCY9wiIhJD6n4XERFJCAV1ERGRhFBQFxERSQgFdRERkYRQUBcREUkIBXUREZGEUFAXERFJCAV1ERGRhFBQFxERSQgFdRERkYRQUBcREUkIBXUREZGEUFAXERFJCK3SFneZjLfMa08PtLd7y7ymUuUfIyIiiaegHmfd3dDRAYOD0NcHLS2wcCGsWuWt6x70GBERqQsK6nGVyXjBOpMZ2tbX5z12dMDGjeDc8MeIiEjdUFCPq64u7+7bz+Cgt9+54Y9505uia6OIiMSKEuXiqqdn6K67UF8fbNgQ7BgREakbulOPq/Z2b3zcL2i3tMCUKd6d+nDHiIhI3dCdelyl09BQ5J+nocHbH+QYERGpGwrqcZVKeRnsqZR31w3eY257a2uwY/xkMrBsGXR2eo/5iXYiIlKz1P0eZzNmeBnsXV3e+PiUKd7dd36wDnJMPk2BExFJLAX1uGtthXnzRn8MBJsmV+xiQEREYk/d7/UkyDQ5ERGpWQrq9URT4EREEk1BvZ7kpsn50RQ4EZGap6BeTzQFTkQk0RTU68lIp8CJiEhNUPZ7vSl3CpyIiNQMBfV6FHQKnIiI1BR1v4uIiCSEgrqIiEhCKKiLiIgkhIK6iIhIQiioi4iIJISCuoiISEIoqIuIiCSEgrqIiEhCKKiLiIgkhCrK1aJMxivz2tPjrbyWTnv120VEpK4pqFdafkCePNnb9uyzwYNzdzd0dMDgoLcGeksLLFzoLcgyY0b07RcRkdhSUK+kwoCcL0hwzmS852cyQ9tyr9PR4S3UooVZRETqlsbUKyU/IBcGdPC25Y7p7fV/ja4u74LAz+Cgt19EROpWyaBuZm1mdomZXWNmC8zsdT7HvMXM7oyuiQlRKiDnKxWce3r8LwjA275hw8jbJyIiNa9oUDezduAPwL8ApwBXAE+Y2fsKDt0LODWyFiZFqYCcr1Rwbm/3uun9tLR4a6OLiEjdKnWn/lXgceAg59xUYDJwC/BTM1tYicYlSqmAnK9UcE6noaHIP1lDg7dfRETqVqmgfhLwFefcXwCcc392zv0j8M/AV81saSUamBilAnK+4YLzxz8OTU3Q3Oz93tLiZcyvWqUkOZEEyGRg2TLo7PQe8/NiRYZTKvt9T2BL4Ubn3DVm9jxwg5kdCHw7qsYlSi7wlsp+b2goHpzzM+cHBrzA3tgIn/wkXHyxArpIAmjGqoxWqaD+ON5Y+h2FO5xzPzezdwM/B46PqG3JM2OGN+2sq8sbN580ydv+3HNel3s67R+c/aayDQx4j9/5jhfURaSmVWvGaq50xp57ej0DqmVV20oF9VuBc81ssXOuv3Cnc+7XZvb27HESVGsrzJsX7Njcp23lSujf7Z/Ak8uWD/qaIhJLQWashv0xz+8Z+OIX4dJL1TNQ60oF9a8DP6HEuLtzbr2ZHQscHnbD6l6pQjX5NJVNJJbKreYcdMZqWFWiVcsqmYoGdedcBlg/3As45/4M3BVmo+qe36etGE1lE4mdkYyN5ybI+AX23Mc8rDH3TMZLx9m61X+/OgBrV+wqypnZ98zsJTN7uNptqZqghWpAU9lEYsaveGSQgpHDzVjt6BjZ6xbq7oa2Nvjxj2HbNv9j1AFYu2IX1IHrgNOr3YiqClKoRlPZRGJppNWccx/nVGqopEX+x/zmm0dfJTr/gmP79uLHqQOwdsVuQRfn3N1mdnC121FVpfrhmpvhtNNg9uzi2fIiUjWjqeZcOEEmf1LML34x+irRQTsB1QFYu8w5V+027CYb1H+ZrWTnt38+MB9g4sSJx61YsWKX/b29vbTWcrAbHISHHvL/9DU0wNFHBytkQwLORYh0Ljw6D0OiOBebNnmrKRf7+E6eDBMmVOd1n38eXnzRf9+kSb08/3wrZt59Rb3+F6mFz8esWbPWOuem++50zg37A3wBOLDIvtcDXwjyOkF/gIOBh4Mce9xxx7lCq1ev3m1bzbnnHufGjXMOdv3Zc09vX0CJOBch0bnw6DwMieJcvPqqc6nU7h9d8LZnMtV73f/8T+daWvxf48orV7u5c0fevqSohc8H8IArEhODjqlfCkwqsu/A7H4JU7G78ddeKy8rRkQqarix8ZHeBIbxuqWS8czg29+u3zv0pAg6pm5AsX76ScBfwmmO7NTV5V1A+9F8E5FYKzU2Xs3X9atWnatQ/aY3wYoVo5//LtVVNKib2VxgbvZXB1xjZq8WHLYHcCTwP2E1yMxuAGYCE8zsOeBS59z/C+v1a4bWThepaeUUj6zk6/pdGEyeDOvXwyWXqOZ8rSt1p74FeDn7ZwNeATYXHDOAtxzrd8JqkHPuQ2G9Vk0LUolCRGQE8i8MMhlv3vqll+46/x1UWa4Wlaoo91/AfwGY2feBLznnnqxUw+peOu1dKvvRfBORyGT6M3St76Ln5R7a920nfUSaVHPt9UMHLSdbaprbjh0a6as1gcbUnXP/FHVDpECpwS8VnJEaUWsBsvuZbjqWdzDoBunb1kdLYwsLb1vIqjmrmHFQ7fRDl1NOttRI35YtsHq1gnotCVx8xsymAx/ES4zbo3C/c+7sENslEF22jUgF1FqAzPRn6FjeQWZgaM2Fvm1etOtY3sHGCzfS2hT/z165C7W0t8O4ccVf78Yb4dpra+trJ6xFb2pRoCltZvZx4D7gXOBNwH4+PxKF3ODX4sXeYy19sqRu5QfIXGDs29ZHZsDb3jsQvymZXeu7GHT+/dCDbpCuhwPUYY2BcsvUptNeN3sxY8YEK0EbF7na9gsWwJIl3mNbm7e9HgS9U78I+B7wMedciYrBIiLBAuS8Y+PVp9vzcs/OC5BCfdv62LC5NmaclDtxJpXyqk4XU0uTbbScbPAFXfYHblBAF5EgajFAtu/bTktji+++lsYWpoyvjRknuYkzfopNnJk1q3hRmlqabDPSxXSSJGhQvwU4McqGiEhy1GKATB+RpsH8vxIbrIH01NqYcTLcEq5+E2dKTaaplck2mQysXKnyHkGD+tXAXDO71MzeZmaHF/5E2UgRqS1RBshMf4Zl65bReXsny9YtI9OfGf5JAaSaU6yas4pUU2rnBUlLYwupJm97LSTJwcjKyaZS3h1+2KVtKyU3jr56dfFjaqnHYTSCjqnnTtWleIu75MuVkB0TVqNEpLblAmRh9nuDNYwqQEadUT/joBlsvHAjXQ93sWHzBqaMn0J6arpmAnrOSCbOtLbW5mQbv3F0P7XS4zBaQYP6rEhbISKJE3aArNSUs9am1tgl8Y3ESMrJRlXaNkrDrRHf3AxNTbXR4xCGoMVn7oq6ISKSPGEGyFrMqJfolcr2BzjtNPjJT+ojoEPwMXUAzOwMM7vEzL5rZgdlt73dzA6MpnkiIp5azKiX6A2X7T97dv0EdAhefGaimf0O+AXeym3zgAnZ3f8EXBJN80REPLWYUS/RG0m2f5IFvVP/FtAKvDn7Y3n7fgW8I+R2yUhkMrBsGXR2eo/DZY6I1JBamnIWt49i3NoTppFk+ydZ0ES504G5zrkNZlaY5f4c0BZusxIsqqLExVZw+PGPR//aIjEQVUZ92MpZTMVPWF8RuddZvRp++lPvrnXLlmSula5lMoYEXtAFKFZNbgLwWghtSb7RftqLKVUbsacHenvr83+3JE7cp5yNtkxpWF8RudfZscML5PmSWja1FjP3oxC0+/0e4FMFd+ku+/hR4M5QW5VE+Z/23Keqr29oe+8oFrgYbk5HPdRGlLqRy6hf/M7FzDt2XmwCOoyuTGlYXxH5r1MY0Mtpj9SmoEG9EzgeeBj4El5AP8/M7gJOAi6OpnkJEmVR4lJzOgYH66M2okgMlLuYSr6wviKGu8YP2h6pTYGCunPuYeA44AHgHGAH3trqzwEnOueeiKqBiTGaT/twSs3paGioj9qIIjEwksVUcsL6ihhu3nbQ9khtCjxP3Tn3R+fcR5xzBzrnmpxzBzjn5jjneqJsYGKM5tM+nFJzOnL7RSQ0xerPj2Z6VVhfEaVep1h7MhnYtCmZ2fH1pqziMzIKUU6mLDWno709OZkwUveiWsylHN3PdNN2VRsLbl3AknuXsODWBbRd1Ub3M92jml4V1lfEcNf4he3JLYby7LOwZAksWOD93t0d7P0kXgJnv5vZmXhd7pOAPQr3O+dOCLFdyZP7FBWmtjY0hDOZsticjgceCKf9IlUW9WIuQQSpPz9jRuuIpleF9RXh9zrjxnmZ8Gee6a2dnmtPflJdbhw+qdnx9SJQUDezy/BWZ3sIeAQYiLBNyRX1ZErN6ZCEqtRiLsMJWn9+pB/FsL4igr5OkOQ8faXUlqB36vOAK5xzn4uyMXVhNIE3qsI1IjEXl8VcKlF/Pqxr89ZWOPts7yvjiSdgxYrdvzJGmpynr6L4ChrUU8AdUTZEhhFV4RqRGhCXxVxy9ef92hK3+vNBvjJySXV+gb1Ycp6+iuItaKLcCrxSsVINURauEakBUS7mUk7yXa3Unw/6lVFucp6+iuIvaFC/A5htZt83sw+bWUfhT5SNrHs/+AEMFEljUFkoqQPlBtOggbp3oLdoJrufXP35VFNq50VGS2MLqaZUrOrPBy1kk5+tnwvupbL1o6yhJeEI2v2e+6c6GG/p1UIOKFzoRUaicLDqoIO8vq1t2/yPH64qxeCgN/FUg19Sw8pZzCVolnymP0PP5p6yk+/iXn8eyhsrzyXV3XorLFpUOjkvyhpaEo6gQf2QSFsRB3HI/CgcrBo3rnTxZihdlaK7Gx56CC65RINfUvOCBNNysuS71he/rRwu+S5Xfz6uyh0rb22FCRNg8eJwX1cqL1BQd849HXVDqioOmR+ZDJxxxq6DUsMFdChelSI3yHXppbsOfoEmoErNGi6YlpMl3/NyD/u7/X2PrWTyXRTSae8rzM9oal1F9boSnsAV5cxsrJmlzexbZrY8+3i2mZWzfGv8xCXz48tfLv+9mpqKV6XQ4JfUoXKy5Nv3bS86Th+3TPZyjaayXTVeV8ITtPjM/sD/AEcBTwF/wlud7RPAQ2b2bufcn6NqZKTiUH0hk4FvfrO85zQ1wVVXFe9JKGfwKw5DDyIhKGfKWfqINMueWOb7OnHKZB+pqGpdRV1DS0Yn6F32VcC+wFudc/flNprZ8cCN2f0fCb95FRCHzI+uLjAr7znNzTDXL2cxq9SqDs3N8OCDXgLdQQd5tSM16VQSIH1EmoW3+fcPFwbqVHOK9vHtpJpSwybf1aqoikyqeGV8BQ3qHcAn8wM6gHPufjP7V+BbobesUuKQ+dHTA/39pY/JtTFoMehSg1/9/V6q69137z5ur3F3qWHlZMmDN0YfdiZ73Dq+4tYeiVbQoN4MFKvIkAGawmlOFcQh86PUhQXAZz4DRxwxstUh7r+/+GuXSsRT4WeJqUx/hq71XfS83EP7vu2kj0iTah6KUuVOOQszkz0OObfVbk/cLyLi3r7RChrUfwt0mtmdzrmd0cHMWoDO7P7aFPXqaUGUurBobYXLLx9ZO2bM8IrWLF0KK1fCnXcWL2JTSJNOJYaCzkGvxpSz/JzbnGp2fFWjPcFghl8AACAASURBVHG7qKm19oUhaPb7hcARwLNmtsLMlprZDcCzwOHZ/bUrl/mxdKlXfWHpUu/3Sv0rl0opveWW0X3yGhq8u+2jjgoe0HPvr0mnEiP5c9BziXB92/rIDHjbeweqW6M0bhNOurq85VYr1Z64TCSq1faFJeg89QfNrB24CDgeLwv+BeBa4Crn3Kbomlgh1c78iDqldLgu/kKadCoxE5eV2oqJQ85tvtWri4+wRdGeOEwkKiXu7QtL4Dnm2cC9KMK2SJQXFqW6+KH8RDyRCovLSm3FxCHnNieTgZ/+tPj+cePCb0/cLmoKxb19YSmrcIyZ7QNMBV4PbATWO+f+GkXDpISRZHqUyh1YuRKefVaTTiXW4r7saRxybnO6uoqvvgZet3zY7YnTRY2fuLcvLEGLz4wF/g2v2My4vF1bzOw7wOedc0VWHJFQjSbTQ1UjpIaVMwe9GvKvm3cMOrb0GY179DOmwVj5s+20to4b/kVC0tNTenLLmWeG/7GP00WNn7i3LyzlFJ+ZD1wO/BR4CdgfmA1cDOwBfCqKBkqeMNJZq507IDJC5c5Br4YZM2Dl7+7lfRcvZ+ymQ9i2z6M0TvslZ/7uNVZN2TVDP0rD3ZXOmhX+e8ZhIlEtty8sQYP6R4DPOeeuytu2Gfg3M9uKF9gV1KP2gx8UL1KTpEwPkSLivuxppj/DmT87nf6jhi68twAMlF7ONWzVuiuNe2dg3NsXhqBBfRBYX2Tfw3jrqUuUurvhwguLT0tLUqaHSAlxXvY0Lhn6qRRccQV84hO777viimiDWNw7A+PevtEKOk/9R8C5RfadB1wfTnPEV67bvdQ883HjvEvQzk6vpnumWAFAEYnKSDL0MxnvIxvmRzeT8Upu+Fm0KDlzsmV3Qe/UnwZmm9l64OcMjam/H0gBV5rZBdljnXPumtBbWs9KTbDM2bLFy2LfsmXX5DmROjRcKdmoBMnQz5+8AvCd74Bz4VY4q5c52bK7oEH9yuxjG/AWn/35Y+0OUFAfrfxP/kMPBSsak0t3zU+e+9nPomujSAwFLSUbheEy9Ce/8iHa2oYStQqFVcY17nOyk15/vZqCVpQL2k0vYSicttZUYr2csWO9zBe/rvnBQdi8Obp2isRMfinZnNxdcyUS1Upl6K98/22c+dZxgbrXR3s3Hec52fVQf72aFKzjxq9Acamx9GIBHbznD7ekq0iCBElUi1ouQ3/p6UtZdPIilp6+lI0XbuSZe08adhQtZ7R30+l08eIz1ZyTXS/116up3Ipyh+F1we9RuM85pwHcMAw3ft7c7AXq3ATLj38crr66+CV5c3N0bRWJmbiUkvXL0C/VJV5otHfTcZ2TrbH+6AWtKHckcAPeeLr5HOKAMSG2q34N98k/7TQ4+uihCZbOwTVFUhgaGmD8+GjaKRJDcS4lW86aSmHcTcdxTnbcx/qTIOid+veAbcB7gQ1AGWt4SlmGGwybPXv3S9lSl+Tbt1em3SIxEOdSssOtqQTh303HbU52nMf6kyJoUH8LMNs5d1uUjalLhWmgHR3FP/lm/pfvpS7J16yJtPkicRLnUrLFusTNvCIxZvG4m45SvdRfr6agQf0+4KAoG1KXiqWBXnEFXHQRvPbarscPDsKDD/qniMbtklykSuJcSjaOXeKVFNex/iQJGtTnAzeY2RZgNbDbcqvOuRJrAsluSi3O0tkJY3xSFLZsGf0EVpE6EOdSsvV+/V3vFzZRCxrUNwFPAT8scYwS5cpRKg10YMDri/OjFFERqXH1fmETpaBB/XrgJODrKFEuHKXSQEvNS48qRVQlnkREal7QoD4LOM859+MoG1NXSqWBNjV5d+p+hWOiSBFViScRkUQIWlHuKbLLAkuZii3BVKrkU1MTNDb67ws7RVQlnkREEiNoUP8s8HkzOzi6piRQdze0tcGCBbBkiffY1uZtz6WBplLenTF4j6kU3HKL9+O3L+wU0SAlnkQSKJOBTZu0WrEkS9Du9y/iTWl7wsyewj/7/YQQ21X7SmW35zLYh0sDrUSKqEo8SR3KjTh98Yve9bZGnCQpggb1h7M/ElTQIsel0kBbW+Hss71jn3gCVqwIP4FNJZ4k4QrXVu84KE1HR4pMZugjGtaSpyLVFnTp1X+KuiGJE8YdcCUS2FTiSWKmMAinj0iTah7ZhWz3M92c8b2zGXjoAwz8eTJN+/0etj9Kw44l+M3C1YxRqXVlrdIGYGb7AuOBzc65l8NvUkKM9g44SPd9GLcTKvEkMdL9TPduJV4X3raQVXNWMeOg8i5kM/0Z3v1vl/PadY+Ba4BtrQw09sJgI+zwL6uhESepdYHXUzeztJk9CrwEPAa8ZGaPmtlZYTbIzE43s8fNbIOZLQrztStqtAsaVzKBLTe2v3QpLFrkPebG/EUqJNOfoWN5B5mBzM5V1vq29ZEZ8Lb3DpQ3E+MH9/2U1667EQb2gm3Zi9NtrbCjGW9hyd1pxEnCVGzyU5SCLr36IWA5cAuwGPgTMBFIAyvMbIxzbsVoG2NmY4CrgXcBzwH3m9nPnXOPjPa1K260d8CVTmBTiSepsq71XQw6/wvZQTdI18NdZZV+/eVNLeCKVGYsQiNOEpZqlf8I2v3+eeC7zrmPFWz/oZldC1wMjDqoAycAG5xzTwKY2Qrg/UDtBXUYXZFjJbBJnel5ucd3HXTw7tg3bC7zQnbzlKE79N0Y4HZWY9aIk4SpUqOnfoJ2v08Bbiyy78bs/jC0Ac/m/f5cdlvtyt0BL148lO0exGi770VqTPu+7bQ0tvjua2lsYcr48r5m3nvim6GpVJe9F9EXLqzMiFM1umKlOqpZ/sOc8x9b2uUgs6eBa51zi332/SvwMefcG0bdGLMzgdOdc+dmf/8IcKJz7pMFx83HWzmOiRMnHrdixa6dBL29vbQm4XK7t9frhgfvf0IuyLe3B744SMy5CIHOhafS52HQDbL5tc307+ineUwz4/ccT4PtfsE66AZ56E8P+XbBN1gDk/aaxMCOgZKvscvrDcJDDxX/cgWYPLmXhoZWJkwo+69VlhA+ypHT58MTxnl4/nl48cXi+w84wKtDNlKzZs1a65yb7rcvaPf794HLsmPeK/HG1PcHzsLret8t2I/Q88DkvN8nZbftwjn3XeC7ANOnT3czZ87cZf+aNWso3FazentHVYAmUedilHQuPJU8D37Z7A3WUDSbvemZpt2OdzicczRYQ6DX2OX1muC00xzbtvmPrX/962vYtGkmi8P6BvORyXhf4H535qlUfObF6/PhCeM8LFsGl15afPR06VKI6lQHDeqXA43AIrzqcjmv4a3cdnlI7bkfaDezQ/CC+d8DHw7ptWuTEtikRuVns+fkxsw7lnew8cKNtDbtGs1mHDSDjRdupOvhLjZs3sCkvSax6FeL6N3eG/g1dnm9GXDVVcZFF/mvj9TQMPL0lKDz6YPWoZLkqGb5j0Bj6s65Qefc5/HuomcCH8o+TnbOXeyC9OEHe5/twCeB24BHgZ8459aH8doiUllBstn9tDa1Mu/YeSx+52Kaxzbjikw/K/Ua+ebO9e7YixnJF2z3M920XdXGglsXsOTeJSy4dQFtV7XR/Uz3bseqEnP9KbW0R9TJmGUVn3HO/QW4J6K25N5jFbAqyvcQkeiFkc0exmuUml06kjHtcnsgNJGlPo1m8tNoFL1TN7PpZvaymXWUOKbDzDaZ2dHRNE9EalUY2exhZcQXq680ki/YcnsgNJGlfo108tNolOp+XwDcm71z9pXd1w1cGHbDRKS2pY9IF81Qb7AG0lOHj2ZhvEZOWF+w5fYeVLMrVupPqe73WUCRof5d3ABcGU5zRCQpUs0pVs1ZVTT7Pb+LuljSWTmvUSm53gO/wF6s96BaXbFSf0oF9Qn4TCfz8TywXzjNEZEkKcxmnzJ+Cump6V2C8XCLuAR5jUpKH5Fm4W3+9zuleg80kUUqoVRQ30ywam5t2WMlCpmMd3nf0+Nl3IS9nrrUvTCXOvWTy2Yv9t5Bks5am1o5+4iz6VrfxRMvP8GKh1eE3s6g4th74EdfHfWpVFC/C5gHDDdn5KPZYyVs1VoRQOpG2EudlntxEHQRlzDbGYa49R4UBvCDDoIzz9RXRz0qFdSvAH5nZt8DLnLO7XI3bmb74BWeORU4Mbom1qlqrgggdSHTn+GM5WfssqRpOYVd8o006AZJOhtJEZuRKPeipFQPRCUVXvuPGwdbtux6jL466kfR7Hfn3IN4RWbOBJ43s3vMbLmZXW9mdwMbgbOBDzvnHqpMc+tINVcEkLrw5Xu+XHSN8qCFXWB066CXmrI2rnEcG3s3ctZ/nUX/Dp9ycGW2s5RyisnESf61fy5wFwb0fPrqSL6SFeWccz8FDsOr7d4PHAscBwwAXwEOyx4jYVMZKolQpj/DN37zjaL7y1nqdKSV46D0lLUt27awcv1KbvvjbQzsGBh1O4sZzUVJtZW69vejr47kG7ZMrHPuBefc5c65dzrn3pL9eadz7svOuRcq0ci6lCtD5UdlqGSUutZ3Yea/yAlA85jmwIVdRlP1LZd0lmpK7bxjH9c4buf+LdtL3HYysiVZC43moqTaSl37+9FXR/IFXU9dKk1lqCRCPS/3FL37BXC4wIVdRlv1LZd0tvT0pSw6eRFnHn4m48aOK/mcnHIL0PgJoxRttZS69vejr47kU1CPK5WhkgiVCsQAnznxM7sVh1m2bhmdt3eybN0yMv1DSWthVH3LX8TlgJYDAt2hp5pSoUwhC6sUbTWUuvYHfXXUo7IWdJEKUxkqiUipAiqtja1cfOrFO38fLrM97HnbpSq2NY9p5rRDTmP2W2aHNoVst3PR3woPp2HzFLbv/xwd58f31rbUYjUrV8Kzz+qro94oqMedylBJBIIG4qDTycKct13qgqNpTBM/Oesnoc4Hzz8X2/7vRLb+4EagAQZasXE7OOyQMbGe361rf8mnoF4NpUo9qQyUVEiQQBy0OAyEN2+7GhXbZhw0g8fnb+SNBzXBwNDi61u3jGEr8K53wcc+BkccEc+PpK79JadoUDezYJkqWc650oNg4ilVJQ5UQU4qarhAXK0ksmpUbLv5plbGFJkQsHUrfPOb+khK/JW6U+8FXBmvNWaUbUm+UlXizjjDe+zt3X2fykBJlYxkRbKwVLpiW5DpYfpIStyVCuofpbygLsP5wQ+g378yFgMDUGzecK4MlPrXpMJGuiJZLcpNDwsy71sfSYmrokHdOXddBduRfN3dcOGFXvD2U2w7qAyUVE2trEgWhnTa61oPQh9JiSvNU6+EXLd7qcDd1ATNzf77VAZKqmjGQTN4/JOPM/stszmx7URmv2U2j3/y8aqsjhYlv9IQxegjKXEVOPvdzNLAecChwB6F+51z+4fYrmQJUqC5KZtx69c9rzJQUkWF89Qffulhbnrspqotexql/OlhjzwCV1+tj6TUlkBB3cw+DHwPuA44LfvnBuB9wF+BH0bUvmQYLgOnsRFuucX7s18VCZWBkioJc9nToEubjmRd9jDlTw/7wAf0kZTaEvRO/bPAl/DWWJ8PfMc5t87MUsDtgKazlVIqA6e5Ga68cmh+jKpISIyUM0+9lKDrrY90XfaoVKuwi8pVyEgFDertwK+dczvMbAewF4BzLmNmXwW+AXw9ojbWvlIZOE1NMHfu0O+qIiEhK7zzfaN7Y+DnhjFPPejdfpi9AmEK+yM5XMAuVcpCc+NlOEET5V4FcllczwNvydtnwL5hNqrmZDKwbBl0dnqP+fPQYfSLszzxBLztbfD613uPTzwRzd9DEqf7mW7armpjwa0LWHLvEhbcuoCH/vQQ3c90B3p+GIudBF3atJaXQA2quxva2mDBAliyxHtsa/O2w66lLHIde319Q9t747u0u8RE0KB+P3BU9s8/B75gZueZ2Vzga8Bvo2hcTRjuU5qT68dbuhQWLfIeN24c/tJ74UI47DD4zW/gxRe9x8MOCz73RupW/p1v7o63b1sfg26QjuUd9A4MHyHCWIEt6N1+LS+BGkSQgF0qpzY3N16klKDd74uBN2T//IXsn6/Buyi4Hzg//KbVgFIV4vxKTpXbj/fEE/CNb/jv+8Y3YMIE+Od/1mCb+ApjPDyMeepBq9JVs3pdWEp1rQcJ2KVyajU3XoIIdKfunPutc64r++e/OufeD7QA+zjnTnTOPRllI2Mr6svqc84pvf+SS/x7BUQI7843V4d96elLWXTyIpaevpSNF24MnLgW9G4/jF6Bahqu0y5IwM7l1PrR3HgJouziM+bZDxhwzr0aQZtqR9SX1f/3f6X3Dw5qsE2KCmM8PCdXh33xOxcz79h5ZSWs5e72U02pne1paWwh1ZTa5W4/6HFxFKRrPUjATqe9KXN+NDdegiin+EwHcDFwXPZ5281sLfBvzrmbI2pfvJWaqhbGZfUhh3jj6MNRIWrxEae67UFXXavG6mxhCNJpV2oSTC5gt7Z6ubOaGy8jFbT4zPnAd4A7gE8DLwH7Ax8Efm5mFzjn/iOyVsZVkE/paFx3nZcUNxwNtomPuNVtD7rqWqVXZwtDkE673GSX4QJ2tebGSzIEvVP/HPAfzrkLCrZfa2bXAp8H6i+oB/2UjtShh8JnPlM8WS5Hg21ShN+d7yGvHJK48q7VFrTTLmjAVrkKGamgQX1f4KYi+24E/iGc5tSgqC+rr7oKPvEJmDMHfvc7/2M02CYlFN75rlmzpnqNSahyOu0UsCVKQYP6auBUvJKwhU4F7g6tRbUojE9pqbkwb3oT/Pa3/qWmNNgmEljhx+yNwYvrlRR1p51IUEGD+r8Dy8xsX+BnDI2pfwA4AzjXzA7PHeyceyTshiZa0LqQGmyTGlDtBVmK8fuYfelLXqXmMMqv6uMpcRA0qN+WfTw/++PwysPm3Jp9tOy+MaG0rh5EXcAm9x5aHUJCMFzAjtuCLDvbXeRjNjjo/zEbKXWtS7UFDeqzIm1FPQsyF2Y03xK9vV4FDK0OIaM0XMCO64IsEP3HTCQuAgV159xdUTekbkVZwCaT8V4/aC+ASBFBAnZYy7RGQeVXpV6UXVFOQhZlXchSZWq1OoSUIUjAjvOCLMN9zCa9YSvL1i2j8/ZOlq1bRqY/43+wSMwVvVM3s5eA9zjnfm9mf8YbKy/KObd/2I2rC6MtYFNqvLynB/Yv8s+i2xMpQ5CAHecFWUp9zBzbWfTiIbhbM7HKAxAZiVLd71cDf8r7c8mgLiM0mrkww2XNt7fvvrZ7jgrWSBmCBOyzjzg7NmVpC5X6mLk5HfTai7DNOzYueQAiI1E0qDvnvpj358sq0pp6NZK5MEGy5tNpWLbM//kqWCNlCFJHvrWpNVZlaQv5fcz2mvgyDa/duzOg56t2HoDISASt/T4Z2M85t85n37HAn51zz4bduLpS7lyYUum8O3YMpfO2t3u3KaqIIaMQtI583BdkKfyYLf/F1pLDCr9/4kXmLoXHHoM3vxkWL4YDD6xQY0VGIOiUtmuAJ4DdgjrwYeAw4G/DapRkDTdeXiydd8sWWL3a+/ZqbVVFDAlF0IBdSwuyNI9pLjqs0LT201x92ed2/n7fffDDH8LVV8MFhatgiMRE0KD+VuDaIvtWA3PDaY7sFGS8fNw4L4D7ufFGuDb7T6aKGBKSWgrYQYzfczwN5jMJ6NUDGPjFN9i1xpbnE5+AD34QDjgg+vZVQu7eYc89vdE61aaqbUGntI2jdKJckckiMiL54+W5u/G+vqHtvb3eJ2/HjuKvMWaMpqyJDCM3fJBqStHS6H2NtTS2MHb110s+b9GiSrQuet3dXm2qBQvgxRe9x7Y2b7vUpqBB/Q/Ah4rs+xCwPpzmCBCs/FUqBbNnF38NTVkTCSQ3rLD09KUsOnkRS09fyrSxf4/fXXrO449Xrn1RCXLvILUnaPf7FcCNZtYMXAe8ALwer9t9dvZHwhK0/NWsWfDf/z38Is4iUlLhsMLdb4EH7i9+/GGHVaBREVPp3GQKdKfunLsJL4CfBPwCuD/7eBLwD865n0XWwnoUtMpcOu1lsvvRlDUJWaY/UzdV1xYvLr3/iisq044oqXRuMgW9U8c59yMzux4v031f4GXgceecitKELWiVOS3iLBEpXI3toL0O4sz/OjN2q69F5cADvSz3T3xi931XX52MJLncvYM6+pIlcFAHyAbwxyJqi+SUE6y1iLOErHA1tnFjx7Fl+66zLOqh6toFF3hZ7osWeWPohx3m3aEnIaDD6CtUSzwFDupmdiDwXmASsEfBbuec6wyzYXWvnGCtKWsSEr/V2AoDer6kV1074AC47jrvz6XKRtSiwnsHUEdfEgStKPcB4AZgDPASMFBwiAMU1MOmYC0VVmo1Nj/VXn2tUoYrG1Gr8u8d9tgDli5VR1+tC3qn/hXgf4BznHObI2yPiFRRqdXY/Iy1sWzMbCTTnyHVXMO3rSUEWWahloNg7t5hzRqYObParZHRCjpPfTLw7wroIsmWW40tqO1uOzc+eiNtV7XR/UwyK5YEmfolEhdBg/q9eFnvIpJg6SPS/mVTs8aNHbfbtr5tfWQGvLH43oHkVSzR1C+pJUGD+kJgvpnNNbMDzWxc4U+Ujax5mYxXVLmz03sstsa5SJXlVmMrLJuaakpx2z/cxpmHn0ljQ6Pvc3NJc0kTtGyESBwEHVP/3+zj9yleA37M6JuTQEnNsJHEKrUa2x1P3sG2QZ/Fx0lu0lylpn4lLbteqiNoUP8opRd0ET9Jz7CRxCq2GltuzN0vma6lsYUp45N321qJGk+69pewBArqzrnrIm5HMqm4siRM+og0C2/zv21tsAbSU5NZsSTKGk+69pcwBR1TFyh/bFwZNpIwpcbcV81ZlcjKcjm5qV+fuyyDO2YZX/pNODXwlV0vYSp6p25m9+HNS3/EzO5nmO5359wJYTcuVkbSP6biypJApcbck66whG4YNfB17S9hKtX9vh54Le/P9TumPtL+MRVXloQqNuaeZH4ldMOoga9rfwlT0aDunPunvD+fU5HWxNVIx8a1iprUgcIV3dJHpBNZXa5UCd3R1MDXtb+EadhEOTPbA3gFSEe5brqZnQVcBrwFOME590BU71W20fSPaRU1SbAouqPjqlQJ3dFM59O1v4Rp2KDunNtqZi8B2yNuy8PAB4H/iPh9yjfa/jEtzCIJFFV3dFxFOZ1P1/4SlqDZ7/8BfMrM/EtJhcA596hz7vGoXn9U0mnvstmP+sekhmT6M2zasonO20efuR2kOzpJSpXQDWM6X+7af/Fi71EBXUYiaFDfB5gKPGVmPzSzr5nZkryfr0bYxurL9Y+lUkP1Iltahrbr0yc1oPuZbtquauPZV59lyb1LWHDrglEtxBJVd3Rc1fN0Pqkd5tzwSe1m9n/DHOKcc28M8Dq/Ag7w2fV559x/Z49ZA1xUakzdzOYD8wEmTpx43IoVK3bZ39vbS2sUgXZwEDZvhv5+aG6G8eOL38HHRGTnogbV47kYdINsfm0zW7dv5aW+l3A4JjVP4rn+53Ye02ANHD3x6JILufjZtGUTz776rO/deoM1MHmvyUwYN2HUf4cojeT/RO6c9u/op3lMM+P3HF/2uYujevx8+KmF8zBr1qy1zrnpfvuCVpQ7JIyGOOfeGdLrfBf4LsD06dPdzIJFgNesWUPhtnqlczGk3s5FYRJbztcP/ToXPXHRzt9bGltYeujSsjO3M/0Z2q5q22VMPSfVlKqJMfV6+z9Ris6Fp9bPQ8nLSzPb08xmm9mFZvZhM5tYqYaJyMjlJ7EV6yLPGWlXubqjReKnVEW5NwK/Ag7O2/yqmZ3tnPufsBtiZh8AvgXsB9xsZg86594T9vvEipZlkoiUSmIrNJrM7XquLicSR6W635cAg8ApwFrgEOA7eJnwoXTH53PO3QTcFPbrxpaWZZIIlUpiKzTazO16rC4nElelgvpJwIXOuV9nf3/UzM7PPr7eOfdC9M1LKC3LJBErNac6p6WxhQZrKLurvF4qyInUolJB/fXAkwXb/ggYXga7gvpIaUlWiVipJVLNjIVvXcjh+x1edld5PVWQE6lFw83DqN9FXKKkZZkkYqWS2A4dfyhXvudK5h07r+w79MLku75tfWQGvO29A72R/F1EJLjhprTdZmZ+5WHvKNzunNs/vGYlnJZlkgoolsT2wL0jW1YhqgVNRCQ8pYL6FyvWinqjZZmkQsJMYqu3CnLD0eQViaNSS68qqEdFyzJJDYpyQZNao8krEleBKspJBLQsk9SYUsl3YSxoUis0eUXiTEG9mrQkq9SQXPJdYfb7SKbF1TJNXpE4U1AXkcBUQU6TVyTeFNRFpCz1XkFOk1ckzmp/vUARkQpKp4uvuKzJK1JtCuoiImXITV5Jpbw7c/Aec9uVJCfVpO53EZEyafKKxJWCuojsRou2DE+TVySOFNRFZBdatEWkdimoi8hO+Yu25OQqyHUs72DjhRtpbWrVnbxITCmoi8hOQRZtOWzCYbqTF4kpZb+LyE7DLdqy/qX1Wn5VJMYU1EVkp9yiLX5aGlt4+bWXh72TF5HqUVAXkZ3SR6RpMP+vhQZrYN9x+2r5VZEYU1AXkZ1yi7akmlI779hbGltINXnbD9/v8JJ38rW2/OqgG2TZumV03t7JsnXLyPRnhn+SSIwpUU5EdlFq0ZajJx6dmOVXu5/p5qE/PcQlv7lECX+SGLpTF5Hd5BZtWfzOxcw7dt7OVdiGu5OvldXaclP3chn8oIQ/SQbdqYtIWZKw/GqQqXv1vBKd1C4FdREpW60vvzrc1D0l/EmtUve7iNSd4abu1VrCn0iOgrqI1J3hpu7VUsKfSD4FdRGpO7mEvwZrqOmEP5FCGlMXkbo046AZDPxxgKWHLq3ZhD+RQgrqIlK3GqyhphP+RAqp+11ERCQhdKcuUmFai1xEoqKgLlJB3c90J3Yt8kwGurqgpwfa2yGdhpSuVUQqx83f7AAAHiRJREFUSkFdpEJypUkzA0OLhuQKoHQs72DjhRsjT9LK9GfYtGUTnbd37tZLMJoehO5u6OiAwUHo64OWFli4EFatghm1fa0iUlMU1EUqpNqlSXO9BF885IsseXzJLr0EwIh7EDIZL6Bn8hY468sWa+vogI0boVUJ5SIVoUQ5kQqpZmnS/F6C3IVFbgGTM5afwRnXn0FmIDOixU26urw7dD+Dg95+EakMBXWRCqlmadJSvQTbdmxjYHDAd1+uB6GUnp6hO/NCfX2wQWXURSpGQV2kQqpZmrRUL0H/jn4GdvgH9SA9CO3t3hi6n5YWmKIy6iIVo6AuUiHVXIu8VC9B85hmmsY0+e4L0oOQTkNDkW+ShgZvf1Jk+jMsW7eMzts7WbZuGZn+zPBPEqkgJcqJVFC11iJPH5Fm4W0Lffc1jmkEh+/depAehFTKy3IvzH5vaPC2JyVJLsnTESU5FNRFKqwaa5Hnegk6lnfsHAJoaWyhwRqKZr/n9gW54Jgxw8ty7+ryxtCnTPHu0JMS0OMwHVEkCAV1kTqR6yW49Ve3sujkRbv1Eoy2B6G1FeYltIx6tacjigSloC5SR1qbWpkwbgKLZy723afA5K+a0xFFyqFEORGRYVRzOqJIORTURUSG4Tsdsb8V1s5j4LbLee2+ObtU1BOpFgV1EZFh7DYd8emT4cqNcNs32Xb3QhZdtAdtbV4NfJFqUlAXEQkgl2j41VO+Q9OKX8FACga8RMK+vqEa+L2lq+qKREpBXURCl9QiLa1NrTQ//o80Nuzhu1+17qXalP0uIqEqp0jLaJZ7rRbVupc4U1AXkdCUU6SlViu05Wrd+wV21bqXalP3u4iEJkiRFtg1+I9kuddqqqda91J7FNRFJDRBi7QEDf5xlKt1n0oNrU7X0jK0PSmlcaU2qftdREKTK9LiF9jzi7TUeoW2pNe6l9qloC4ioSm1Glz+im9Bg3+cJbnWvdQudb+LVEBSp3gVCrpmvG+Ftqwgy72KiD/dqYtErFazvEcqyJrx+UvBjnS5VxHZnYK6SITqdR3uICu+BQn+IlIeBXWRCGkd7tK03KtIuDSmLhKhWs/yFpHaoqAuEiGtwy0ilaSgLhIhZXmLSCUpqItEKOgULxGRMChRTiRiyvIWkUpRUBepAGV5i0glqPtdREQkIWIT1M3sa2b2mJn9r5ndZGb7VLtNIiIitSQ2QR24HZjqnDsKeAL41yq3R6Ri6qU2vIhEKzZj6s65/8n79bfAmdVqi0gl1VtteBGJTpzu1PN9FLil2o0QiVp+bfhc5bm+bX1kBrztvQO9VW6hiNQSc85V7s3MfgUc4LPr8865/84e83lgOvBBV6RxZjYfmA8wceLE41asWLHL/t7eXlpbNV0IdC7yxfFcbNqyiWdffda3PnyDNTB5r8lMGDch1PeM43moFp2LIToXnlo4D7NmzVrrnJvut6+iQX04ZnYOcD7wDufcliDPmT59unvggQd22bZmzRpmzpwZevtqkc7FkDiei87bO1ly75Ki+xedvIjF71wc6nvG8TxUi87FEJ0LTy2cBzMrGtRj0/1uZqcD/wK8L2hAF6l1qg0vImGKTVAHvg2kgNvN7EEzu7baDRKJmmrDi0iY4pT9rlsSqTu52vCF2e8N1qDa8CJSttgEdZF6pdrwIhIWBXWRGFBteBEJQ5zG1EVERGQUFNRFREQSQkFdREQkIRTURUREEkJBXUREJCEU1EVERBJCQV1ERCQhFNRFREQSQsVnRETq3LZt22htbeXRRx+tdlOqbu+9947Nedhjjz2YNGkSjY2NgZ+joC4iUueee+45Jk6cyKRJkzCzajenqjKZDKlUqtrNwDnHyy+/zHPPPcchhxwS+HnqfhcRqXNbt25l7733rvuAHidmxr777svWrVvLep6CuoiIKKDH0Ej+TRTURUSk6lpbi69K+La3vS2y9/3KV74S2WtXg4J6vcpkYNky6Oz0HjOZardIRGpFhb4/tm/fDsC9994byeuDgrokQXc3tLXBggWwZIn32NbmbRcRKSXi7481a9Zwyimn8L73vY/DDz8cGLqLf+GFF3j729/OtGnTmDp1Kvfcc89uz1+/fj0nnHAC06ZN46ijjqKnpweA66+/fuf2888/nx07drBo0SJee+01pk2bxpw5cwD49re/zdSpU5k6dSrf/OY3Aejr6+Nv/uZvOProo5k6dSpdXV0AXH755Rx//PFMnTqV+fPn45wL5RyMhrLf600mAx0du15Z9/V5jx0dsHEjlOgGE5E6VqHvj3Xr1vHwww/vlvX94x//mPe85z18/vOfZ8eOHWzZsmW351577bV8+tOfZs6cOQwMDLBjxw4effRRurq6+PWvf01jYyMXXHABy5cv54orruDb3/42Dz74IABr167l+uuv57777sM5x4knnsipp57Kk08+yYEHHsjNN98MwCuvvALAJz/5Sb7whS8A8JGPfIRf/vKX/O3f/u2o//6joTv1etPVBYOD/vsGB739IiJ+KvT9ccIJJ/hO4zr++OP5/ve/z2WXXcYf/vAH36lnJ510El/5ylf46le/ytNPP82ee+7JHXfcwdq1azn++OOZNm0ad9xxB08++eRuz+3u7ua9730vLS0ttLa28sEPfpB77rmHI488kttvv53Ozk7uuece9t57bwBWr17NiSeeyJFHHsmdd97J+vXrQ/n7j4aCer3p6Rm6si7U1wcbNlS2PSJSOyr0/dHS0uK7/e1vfzt33303bW1tnHPOOfzwhz/kpptuYtq0aUybNo0HHniAD3/4w/z85z9nzz33pKOjgzvvvBPnHHPnzuXBBx/kwQcf5PHHH+eyyy4L3J5DDz2UdevWceSRR3LxxRdz+eWXs3XrVi644AJWrlzJH/7wB84777yyp59FQUG93rS3Q5EPDC0tMGVKZdsjIrWjyt8fTz/9NBMnTuS8887j3HPPZd26dXzgAx/YGaynT5/Ok08+yRvf+EY+9alP8f73v5///d//5R3veAcrV67kpZdeAmDz5s08/fTTADQ2NrJt2zYATjnlFG6++Wa2bNlCX18fN910E6eccgobN25k3Lhx/MM//AOf/exnWbdu3c4APmHCBHp7e1m5cmWkf/egNKZeb9JpWLjQf19Dg7dfRMRPlb8/1qxZw9e+9jUaGxtpbW3lhz/84W7H/OQnP+FHP/oRjY2NHHDAAXzuc59j/PjxfPnLX+bd7343g4ODNDY2cvXVV/OGN7yB+fPnc9RRR3HssceyfPly5syZwwknnADAueeeyzHHHMNtt93GZz/7WRoaGmhsbOSaa65hn3324bzzzmPq1KkccMABHH/88ZH+3QNzztX0z3HHHecKrV69erdt9cr3XNxzj3OplHMtLc6B95hKedsTTP8vPDoPQ3QuPI888oh79dVXgx2c8O+PwOehQh555JHdtgEPuCIxUXfq9WjGDC9LtavLGwObMsW7wlbWu4gMR98fsaagXq9aW2HevGq3QkRqkb4/YkuJciIiIgmhoC4iIpIQCuoiIiIJoaAuIiKSEArqIiJSddVaejWIjRs3cuaZZ47ouTNnzuSBBx4IuUXFKftdRETKkunP0LW+i56Xe2jft530EWlSzbvXYR+t7du3M3bs2EiXXi18Pz8HHnhgxSrG7dixgzFjxoz4+bpTFxGRwLqf6abtqjYW3LqAJfcuYcGtC2i7qo3uZ6q/9Oorr7zCG97wBgazi8709fUxefJktm3bxh//+EdOP/10jjvuOE455RQee+wxAM455xw+9rGPceKJJ/Iv//IvdHd376wlf8wxx5DJZHjqqaeYOnUq4AXdiy66iKlTp3LUUUfxrW99C4A77riDY445hiOPPJKPfvSj9Pf37/Z3u+GGGzjyyCOZOnUqnZ2dO7e3trZy4YUXcvTRR/Ob3/xmVOdPd+oiIhJIpj9Dx/IOMgNDS6/2bfMWeOlY3sHGCzfS2lS9pVf33ntvpk2bxl133cWsWbP45S9/yXve8x4aGxuZP38+1157Le3t7fzud7/jggsu4M477wTgueee495772XMmDGcccYZXH311Zx88sn09vayxx577PIe3/3ud3nqqad48MEHGTt2LJs3b2br1q2cc8453HHHHRx66KH84z/+I9dccw0LFizY+byNGzfS2dnJ2rVred3rXse73/1ufvazn/F3f/d39PX1ceKJJ3LllVeO+tzpTl1ERALpWt/FoPNfenXQDdL1cPWXXk2n03Rll4BdsWIF6XSa3t5e7r33Xs466yymTZvG+eefzwsvvLDzOWedddbOLu+3vvWtLFy4kH//93/nr3/9K2PH7nrv+6tf/Yrzzz9/5/bx48fz+OOPc8ghh3DooYcCMHfuXO6+++5dnnf//fczc+ZM9ttvP8aOHcucOXN2HjNmzBhmz5490tO1CwV1EREJpOflnp135oX6tvWxYXP1l1593/vex6233srmzZtZu3Ytp512GoODg+zz/9u7/+ioyjuP4+9PMBpMYC1oVwQRKBSFICAEW+VU3NVWEUFdMVXrLltBqaUiWEu3tqhYT6lue9ptyy4qiihiFtEja7PHXxBc9hx/gAtV5IeURatQFFgxQVOEfPeP5yYZhiHMhAyXmfm+zrlncu997r3feSbJM89zn/s8J5zQNJvbqlWrWLt2bcrrTZ06lQcffJDPPvuMc889t6mZPptKSkoO6z56Ii/UnXPOpaVP5z6UFqcucEuLS+ndKf6pV8vKyqioqGDy5MmMGjWKdu3a0bFjR3r27MnChQuBMJHZ6tWrU15j06ZNDBgwgGnTplFRUXFAoX7hhRcye/bspk51O3fupG/fvmzevJmN0Xzyjz76KOedd95+xw0bNoxly5axfft29u3bx4IFCw5I0xa8UHfOOZeWyv6VFCl1sVGkIirLsz/16sCBAxk8eDBVVVVMnjw5ZbrKykoee+wxKhOmgp0/fz5z5sxh4MCB9O/fn2eeeSblsbNmzWrqBFdcXMzFF1+83/7x48fTvXt3zjzzTAYOHMjjjz9OSUkJDz/8MGPHjmXAgAEUFRUxceLE/Y7r0qULM2fO5Pzzz2fgwIEMGTKEMWPGHGaOHEhhFrfcNXToUEt+BrCmpoYRI0bEE9BRxvOimedF4PnQzPMiWLt2Ld26dUt5jzrZ8veWM3L+SBqsgd2f76a0uJQiFVF9bTXDuw8/AtFmV21tbVr5cKSsXbuWM844Y79tklaa2dBU6b33u3POubQN7z6cLbduoeqtKjbu3EjvTr2pLK9sk17v7vB5oe5cnjhSA4I4V3ZsGdef5VOvHo28UHcuD6RqEp363NS8aRJ1zqXHO8o5l+MSBwRpfNxo9+e7qd0TttftqYs5QufckeKFunM57kgNCOKcO/p5oe5cjjtSA4I4545+Xqg7l+PiHhDEubaQ7alXp0+fzosvvpjRMYsXL2bmzJktpjmcaVmzwTvKOZfjKvtXMvW5qSn3HYkBQVzhqa2Fqip45x3o0wcqKyEbj3a35dSrM2bMSLm9palOR48ezejRo1s875GcljUdXlN3Lsd1OK4D1ddW0+HYDk019tLiUjocG7b788OuLS1fDl27wi23wL33hteuXcP2tpCtqVfHjRvXVPj26NGDadOmcdZZZ7Fw4UKqq6s5/fTTGTJkCLfddhujRo0CYO7cuUyaNAkIU7TefPPNnHPOOfTq1avpXOlMyzpjxgwqKiooLy/nhhtuIJuDvnlN3bk84AOCuCOhthZGjgyvjXZH3TlGjoQtW6CFVvS0ZWPq1WSdO3fmjTfeoL6+nj59+vDyyy/Ts2fPFpvSt27dyvLly1m3bh2jR48+IG2qaVkBJk2axPTp0wG47rrrePbZZ7n00ktblTeH4jV15/JE44AgP7vgZ1x/1vVeoLs2V1UFDakftKChIexvC2099WoqjdvXrVtHr169mq43duzYg8Z12WWXUVRURL9+/di2bdsB+1NNywqwdOlSzj77bAYMGMCSJUtYs2ZNS2//sHih7pxzLi3vvNNcM0+2ezdsbKMHLdp66tVMrtGS4447runndJvQ6+vruemmm3jyySd58803mTBhAvX19RlfO11eqDvnnEtLnz5wsLKwtBR6Z/lBi9ZOvdqSvn37smnTJjZv3gzAokWLWh1fqmlZGwvwE088kbq6uqx3qvN76s4559JSWQlTUz9oQVFR2J9NNTU13HfffRQXF1NWVsa8efNSpqusrGTs2LHU1NQc8pzt27dn1qxZXHTRRZSWljJo0KCU9+DTMX78eDZs2NA0beuECROYNGkSEyZMoLy8nJNPPpmKiopWnTtdPvVqnvO8aOZ5EXg+NPO8CDKaenV56BTX0BCa3EtLQ4FeXQ3Dc3Sagbq6OsrKyjAzJkyYQP/+/ZkyZUrcYQE+9apzzrksGj489HKvqgr30Hv3DjX0tuj1HpcHHniARx55hD179lBeXs6NN94Yd0it5oW6c865jJSVwfV5NPPqlClTmmrmtbW1HH/88TFH1HreUc4555zLE16oO+ecy+ooZ651WvOZeKHunHMFrqSkhF27dnnBfhQxM3bs2EFJSUlGx/k9deecK3DdunVj9erV1NXVxR1K7Orr6zMuSLOlpKSEbt26ZXSMF+rOOVfgiouLqaurY+jQlE9JFZSamhoGDx4cdxit5s3vzjnnXJ7wQt0555zLE16oO+ecc3ki54eJlfQR8G7S5hOB7TGEczTyvGjmeRF4PjTzvGjmeRHkQj6cZmYnpdqR84V6KpJWHGxc3ELjedHM8yLwfGjmedHM8yLI9Xzw5nfnnHMuT3ih7pxzzuWJfC3U7487gKOI50Uzz4vA86GZ50Uzz4sgp/MhL++pO+ecc4UoX2vqzjnnXMHJ20Jd0t2S/iBplaTnJZ0Sd0xxkXSfpHVRfjwt6YS4Y4qDpLGS1khqkJSzvVsPh6SLJK2XtFHSD+OOJy6SHpL0oaS34o4lTpJOlbRU0tvR38bkuGOKi6QSSa9JWh3lxV1xx9Qaedv8LqmjmX0S/Xwz0M/MJsYcViwkfR1YYmZ7Jf0cwMymxRzWESfpDKABmA1838xWxBzSESWpHbABuBB4H3gduNrM3o41sBhI+hpQB8wzs/K444mLpC5AFzN7Q1IHYCVwWYH+TggoNbM6ScXAcmCymb0Sc2gZyduaemOBHikF8vPbSxrM7Hkz2xutvgJkNu1PnjCztWa2Pu44YjQM2Ghmm8xsD/AEMCbmmGJhZi8DO+OOI25mttXM3oh+rgXWAl3jjSoeFjROU1ccLTlXbuRtoQ4g6R5JfwKuBabHHc9R4tvAf8YdhItFV+BPCevvU6D/wN2BJPUABgOvxhtJfCS1k7QK+BB4wcxyLi9yulCX9KKkt1IsYwDM7HYzOxWYD0yKN9rsOlReRGluB/YS8iMvpZMPzrn9SSoDFgG3JLVyFhQz22dmgwitmcMk5dytmZyeT93MLkgz6XygGrgji+HE6lB5IWkcMAr4W8vXjhRk9DtRiD4ATk1Y7xZtcwUsun+8CJhvZk/FHc/RwMw+lrQUuAjIqc6UOV1Tb4mkPgmrY4B1ccUSN0kXAT8ARpvZp3HH42LzOtBHUk9JxwLfBBbHHJOLUdQ5bA6w1sx+GXc8cZJ0UuOTQZLaEzqU5ly5kc+93xcBfQm9nd8FJppZQdZKJG0EjgN2RJteKcQnASRdDvwGOAn4GFhlZt+IN6ojS9JI4FdAO+AhM7sn5pBiIWkBMIIwI9c24A4zmxNrUDGQNBz4L+BNwv9KgB+ZWXV8UcVD0pnAI4S/jSLg381sRrxRZS5vC3XnnHOu0ORt87tzzjlXaLxQd8455/KEF+rOOedcnvBC3TnnnMsTXqg755xzecILdVcwJN0pyRKWLZIWSfpSGsfOldTmE8BEMW1v6/NG5x4Xvc+yNNIOklQl6c+S9kR5M19SRTZiyzeSrooGeEonbaWkpyRtjT6ftI5zLh1eqLtCswv4arR8HxgEvCSp9BDH3Q2My0I8DwKxPisv6QrgNaAzMAW4ALgV+Cvg+RhDyyVXkf7vx5VAD+DZbAXjCldODxPrXCvsTZhK8RVJ7xEG3xgJLExOLKm9mX1mZn/MRjBm9j5hYpVYSDqFMODGAmBc0hDCCySNiieyvFZpZg1RC8r4uINx+cVr6q7QrYxeewBI2izpF5J+Iul94JNo+37N7wlN2wMkvSBpt6R1Ua13P5Iul/SapM8k7ZBULem0aN9+ze+SRkTn/bqkZ6PzvidpYtI5vyppcdSEu1vSKknXtuL9jweOBW5NNSeAmTXVJqMZrO6M4vmLpDWSrkmKa66kFZIukfS2pE8l/V5SJ0m9JS2N4l0RjeCVeKxJmirp15J2SvpY0m+iIW0T0w2S9FJ07v+LbhP8dcL+HtG5rpI0W9IuSe9LuktSUdK5yqP4aqNloaSTE/Y3fh4jon11kjZJuinxPQN/B5yXcGvnzoNluJk1HGyfc4fLC3VX6HpEr39O2HYNcB5wE1B5iOMfJ4yffjnwDvCEpKb56iVdBzwF/JHQRPuPwAbCULUtmQP8AbiCMBnRvybVmk8D/hu4HriUMCHHw5KuPsR5k50HrDCzdO7rzwBuB+4HRkfXn5/imt2jtD8GbgDOiY55IlquJLQSPiFJScfeSpho5lrgp9HxTUPZSjoJqAGOJ3xO34vewwvJhT9wL1AXXe8xwvTLVyacq3f0HkqAbxGaz/sD/5EirgeA1YTPuQb4naRh0b67gaXA/9B8a+dBnIuDmfniS0EswJ3AdkKBcgzwZcI/40+ALlGazcBWoCTp2LmEwq9xfRxgwLcTtnUmTG07MVovIsyC9tShYkpYHxGd9/6kdC8QxuxPdQ5F72c2sCRFjGUtXH8dsCCNvOsE7CaMkZ64vRpYn5RPe4EvJWy7N4rj7xO2jYy2nZGwzaJ4ihK23Q58CnSK1mcSxu3vmJDm7OjYq6P1HtH6vKRYVwFPJKw/CqwHjk3Y1gfYB1yS9HnMSEhTDHwEzEzY9iRQk+HvY1l07nFx/234kj+L19RdoekMfB4t64FehHucWxPSvGRm9Wmer6kjmZntAD4k1DQhTCh0CvBwK+J8Omn9KWCIpHYAkr4g6V8kvUvz+7mB8EUlU+lMAFFOqB0n9zuoAr4c1aAbbbb9+yBsjF6XpNjWNel8z9j+zdNPAe2j6wMMA563hDm/zexVwpex4UnnSu7k9zbNnw2EDoFPAw2SjpF0DPC/0bmGHuxcZvY5oVWmG84dZbyjnCs0uwj/zI3Q5L7FzJILtW0ZnO/jpPU9hOZcCF8gINT8M/VhivVjaJ5VbC7wFULT79uE1obvEKYZzsQHhObyQ+kSvSbnTeN6J0LtFVLnSfL2xm0lSWlTve/E63cB1qSIb1sUQ6KWPhsIeTktWpKdmrR+qHM5d1TwQt0Vmr1mdqjnzdtq6sLGqW67tJgqtS+mWN8LbJdUAowCvmtm/9aYILkTWJpqgNsldTKznS2ka/xi8kWa3xdAYwe1lo7NRKr3nXj9rSnSNMaxMsX2luwk1NRT3f/OytgBzmWbN787lz3rCTXhf2jFsZenWF9pZvuA4wh/u39p3CmpA6HzWqbmEJru/znVTkmXRD++Rbi3PTYpyVXABjP7iLYxJunLyRXAZ9H1AV4FvhG938YYKwj30ZdneK2XCB3jVprZiqRlc4bn8pq7Oyp4Td25LLHwLPIPCD3E5xOeBTfgbwid01pqMbhY0j3AMkLBdiFR07qZ7ZL0OjBd0idAA/BDwq2FjhnGuEVhRLMFUa/9hwhfRLoC3wS+RuiktlPSr4AfS9oLrIjiGglk2uO+JR2AhZIeIBS4PwF+l9CK8EvCbYbnJP2c0NlsJvAm4QmATNxJGHTn95IeItTOuxLyeq6Z1WRwrnWELySXEcYd2GJmW1IllNQP6Efzl4ChkuqAj8xsWYbvwbn9eKHuXBaZ2eOS6gm9uJ8k9CB/heb7zwczHriFMMLbTkJT++KE/dcQervPIzSH/5bQkW1SK2JcJOls4J+AX9N8f3wJof9Bo+mEWwDfITR3bwS+ZWZPZHrNFvyC0HlxAaE1Yg7wo4RYP5J0fpRuAaGGXA1MMbM9B57u4Mxsg6SvEB6du5/QIe8DQg1+Y0vHpjALGEz4UvQF4C7Cl4ZUrgLuSFj/brQsI/S2d67VdGAfIedcXCSNIDxmN8DM3jpE8rwiyYDvmdlv447FuVzl99Sdc865POGFunPOOZcnvPndOeecyxNeU3fOOefyhBfqzjnnXJ7wQt0555zLE16oO+ecc3nCC3XnnHMuT3ih7pxzzuWJ/wesw5FUyJsCjAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Visualize 2D Projection\n",
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure(figsize =(8,8))\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
        "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
        "ax.set_title('2 component PCA', fontsize = 20)\n",
        "targets = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n",
        "colors = ['r', 'g', 'b']\n",
        "for target, color in zip(targets,colors):\n",
        "    indicesToKeep = finalDf['target'] == target\n",
        "    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1'],\n",
        "               finalDf.loc[indicesToKeep, 'principal component 2'],\n",
        "               c = color,\n",
        "               s = 50)\n",
        "ax.legend(targets)\n",
        "ax.grid()   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pTmg0PDpmvf4",
        "outputId": "b24a6638-c95c-4fcf-9182-6bb8cebaaec7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.72770452, 0.23030523])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Explained Variance\n",
        "pca.explained_variance_ratio_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKi15GbmnTcp",
        "outputId": "130346d8-0691-4716-c2cc-677594223f2b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   sepal length  150 non-null    float64\n",
            " 1   sepal width   150 non-null    float64\n",
            " 2   petal length  150 non-null    float64\n",
            " 3   petal width   150 non-null    float64\n",
            " 4   target        150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "CTXfNkw9rOhf",
        "outputId": "cc460d1e-46f6-4f54-c790-e298c637e353"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df.hist(edgecolor='black', linewidth=1.2)\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(12,6)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ZdVbXBKGsU-O"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "4nZsF9Oerqj4",
        "outputId": "cb44319f-e297-498b-9ee2-73209ebecf4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0905b88ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "plt.subplot(2,2,1)\n",
        "sns.violinplot(x='target', y = 'sepal length', data=df)\n",
        "plt.subplot(2,2,2)\n",
        "sns.violinplot(x='target', y = 'sepal width', data=df)\n",
        "\n",
        "plt.subplot(2,2,3)\n",
        "sns.violinplot(x='target', y = 'petal length', data=df)\n",
        "plt.subplot(2,2,4)\n",
        "sns.violinplot(x='target', y = 'petal width', data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G6T-0KTHs8r4",
        "outputId": "70fd6e11-25ec-4893-d7f7-1a183779c946"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "11TBPutHsSJO"
      },
      "outputs": [],
      "source": [
        "# importing alll the necessary packages to use the various classification algorithms\n",
        "from sklearn.linear_model import LogisticRegression # for Logistic Regression Algorithm\n",
        "from sklearn.model_selection import train_test_split # to split the dataset for training and testing \n",
        "from sklearn.neighbors import KNeighborsClassifier # KNN classifier\n",
        "from sklearn import svm # for suport vector machine algorithm\n",
        "from sklearn import metrics # for checking the model accuracy\n",
        "from sklearn.tree import DecisionTreeClassifier # for using DTA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "KUFUlI_puf-o",
        "outputId": "a7c67f4a-8b6a-4b3e-8325-a5c70f363f31"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(8,4))\n",
        "sns.heatmap(df.corr(), annot=True, cmap='cubehelix_r') # draws heatmap with input as correlation matrix calculated by df.corr() \n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['sepal length', 'sepal width', 'petal length', 'petal width']]\n",
        "Y = df['target']"
      ],
      "metadata": {
        "id": "FibcgZvMdVWI"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train, test = train_test_split(df, test_size = 0.3, random_state=1212)# in this our main data is split into train and test\n",
        "# the attribute test_size=0.3 splits the data into 70% and 30% ratio. train=70% and test=30%\n",
        "print(train.shape)\n",
        "print(test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQmUh9_WeKpv",
        "outputId": "48f61c26-6571-46d1-c957-30318d5c811e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(105, 5)\n",
            "(45, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "TZJ4bteB2y05"
      },
      "outputs": [],
      "source": [
        "X_train = df[['sepal length','sepal width','petal length','petal width']] # taking the training data features\n",
        "y_train = df.target # output of the training data\n",
        "\n",
        "X_test = df[['sepal length','sepal width','petal length','petal width']] # taking test data feature\n",
        "y_test = df.target # output value of the test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ihT12GQwvM8n",
        "outputId": "15cec11f-c643-4f1b-8a8d-f23c855d7d37"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of Logistic Regression is:  0.9733333333333334\n"
          ]
        }
      ],
      "source": [
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)\n",
        "prediction = model.predict(X_test)\n",
        "print('The accuracy of Logistic Regression is: ', metrics.accuracy_score(prediction, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "jHHb3GeA2yxn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79bbe6a0-b883-4929-a386-7e9f55bbd121"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of the Decision Tree is 1.0\n"
          ]
        }
      ],
      "source": [
        "model = DecisionTreeClassifier()\n",
        "model.fit(X_train,y_train)\n",
        "prediction = model.predict(X_test)\n",
        "print('The accuracy of the Decision Tree is',metrics.accuracy_score(prediction,y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6l-38qec4wm",
        "outputId": "22f71a5e-df6c-4c9f-b155-78453d9bd9fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of the SVM is:  0.9733333333333334\n"
          ]
        }
      ],
      "source": [
        "model = svm.SVC() # select the svm algorithm\n",
        "\n",
        "# we train the algorithm with training data and training output\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# we pass the testing data to the stored algorithm to predict the outcome\n",
        "prediction = model.predict(X_test)\n",
        "print('The accuracy of the SVM is: ', metrics.accuracy_score(prediction, y_test)) # we check the accuracy of the algorithm\n",
        "#we pass the predicted output by the model and the actual output"
      ]
    }
  ],
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      "name": "Iris Data.ipynb",
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      "name": "python3"
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    "language_info": {
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