parthkacheria-term4machinelearningintermediate.ipynb

{
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   "cell_type": "markdown",
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    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Machine-Learning-to-Classify-Flowers-on-the-Iris-Dataset\" data-toc-modified-id=\"Machine-Learning-to-Classify-Flowers-on-the-Iris-Dataset-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span><center>Machine Learning to Classify Flowers on the Iris Dataset</center></a></span></li><li><span><a href=\"#-\n",
    "\n",
    "----\n",
    "\" data-toc-modified-id=\"-\n",
    "\n",
    "----\n",
    "-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span> \n",
    "\n",
    "<p><br>    </p>\n",
    "<center><img src=\"https://miro.medium.com/max/700/0*eZW9OBEUXqlThiwI\" width=\"500\"></center></a></span><ul class=\"toc-item\"><li><span><a href=\"#Table-of-Contents\" data-toc-modified-id=\"Table-of-Contents-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Table of Contents</a></span><ul class=\"toc-item\"><li><span><a href=\"#Problem-Statement:\" data-toc-modified-id=\"Problem-Statement:-2.1.1\"><span class=\"toc-item-num\">2.1.1&nbsp;&nbsp;</span>Problem Statement:</a></span></li><li><span><a href=\"#Importing-Libraries\" data-toc-modified-id=\"Importing-Libraries-2.1.2\"><span class=\"toc-item-num\">2.1.2&nbsp;&nbsp;</span>Importing Libraries</a></span></li><li><span><a href=\"#Data-Acquisition-and-Description\" data-toc-modified-id=\"Data-Acquisition-and-Description-2.1.3\"><span class=\"toc-item-num\">2.1.3&nbsp;&nbsp;</span>Data Acquisition and Description</a></span></li><li><span><a href=\"#Exploratory-Data-Analysis\" data-toc-modified-id=\"Exploratory-Data-Analysis-2.1.4\"><span class=\"toc-item-num\">2.1.4&nbsp;&nbsp;</span>Exploratory Data Analysis</a></span></li><li><span><a href=\"#Preparing-the-data-(OHE,-Train-Test-Split)\" data-toc-modified-id=\"Preparing-the-data-(OHE,-Train-Test-Split)-2.1.5\"><span class=\"toc-item-num\">2.1.5&nbsp;&nbsp;</span>Preparing the data (OHE, Train Test Split)</a></span></li><li><span><a href=\"#Linear-Regression\" data-toc-modified-id=\"Linear-Regression-2.1.6\"><span class=\"toc-item-num\">2.1.6&nbsp;&nbsp;</span>Linear Regression</a></span><ul class=\"toc-item\"><li><span><a href=\"#Linear-Regression---results\" data-toc-modified-id=\"Linear-Regression---results-2.1.6.1\"><span class=\"toc-item-num\">2.1.6.1&nbsp;&nbsp;</span>Linear Regression - results</a></span></li></ul></li><li><span><a href=\"#Logistic-Regression\" data-toc-modified-id=\"Logistic-Regression-2.1.7\"><span class=\"toc-item-num\">2.1.7&nbsp;&nbsp;</span>Logistic Regression</a></span><ul class=\"toc-item\"><li><span><a href=\"#Logistic-Regression-Results\" data-toc-modified-id=\"Logistic-Regression-Results-2.1.7.1\"><span class=\"toc-item-num\">2.1.7.1&nbsp;&nbsp;</span>Logistic Regression Results</a></span></li></ul></li><li><span><a href=\"#Classification-with-Decision-Tree\" data-toc-modified-id=\"Classification-with-Decision-Tree-2.1.8\"><span class=\"toc-item-num\">2.1.8&nbsp;&nbsp;</span>Classification with Decision Tree</a></span><ul class=\"toc-item\"><li><span><a href=\"#Decision-Tree-Results\" data-toc-modified-id=\"Decision-Tree-Results-2.1.8.1\"><span class=\"toc-item-num\">2.1.8.1&nbsp;&nbsp;</span>Decision Tree Results</a></span></li></ul></li><li><span><a href=\"#Random-Forest-Classification\" data-toc-modified-id=\"Random-Forest-Classification-2.1.9\"><span class=\"toc-item-num\">2.1.9&nbsp;&nbsp;</span>Random Forest Classification</a></span><ul class=\"toc-item\"><li><span><a href=\"#Random-Forest-Results\" data-toc-modified-id=\"Random-Forest-Results-2.1.9.1\"><span class=\"toc-item-num\">2.1.9.1&nbsp;&nbsp;</span>Random Forest Results</a></span></li></ul></li></ul></li><li><span><a href=\"#Naive-Bayes\" data-toc-modified-id=\"Naive-Bayes-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Naive Bayes</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Naive-Bayes-Results\" data-toc-modified-id=\"Naive-Bayes-Results-2.2.0.1\"><span class=\"toc-item-num\">2.2.0.1&nbsp;&nbsp;</span>Naive Bayes Results</a></span></li></ul></li><li><span><a href=\"#K-Means-Clustering\" data-toc-modified-id=\"K-Means-Clustering-2.2.1\"><span class=\"toc-item-num\">2.2.1&nbsp;&nbsp;</span>K Means Clustering</a></span><ul class=\"toc-item\"><li><span><a href=\"#K-Means-Clustering-Result\" data-toc-modified-id=\"K-Means-Clustering-Result-2.2.1.1\"><span class=\"toc-item-num\">2.2.1.1&nbsp;&nbsp;</span>K Means Clustering Result</a></span></li></ul></li><li><span><a href=\"#SVM\" data-toc-modified-id=\"SVM-2.2.2\"><span class=\"toc-item-num\">2.2.2&nbsp;&nbsp;</span>SVM</a></span><ul class=\"toc-item\"><li><span><a href=\"#SVM-Results\" data-toc-modified-id=\"SVM-Results-2.2.2.1\"><span class=\"toc-item-num\">2.2.2.1&nbsp;&nbsp;</span>SVM Results</a></span></li></ul></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-2.2.3\"><span class=\"toc-item-num\">2.2.3&nbsp;&nbsp;</span>Conclusion</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><center>Machine Learning to Classify Flowers on the Iris Dataset</center><h1> \n",
    "\n",
    "<br>    \n",
    "<center><img src=\"https://miro.medium.com/max/700/0*eZW9OBEUXqlThiwI\" width=\"500\"/></center></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem Statement: \n",
    "\n",
    "- The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant\n",
    "- One class is linearly separable from the other 2; the latter are NOT linearly separable from each other\n",
    "- We must build a model that can classify these plants into their correct species with the best results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.set_printoptions(precision=4)      # To display values only upto four decimal places. \n",
    "\n",
    "import pandas as pd\n",
    "pd.set_option('mode.chained_assignment', None)       # To suppress pandas warnings.\n",
    "pd.set_option('display.max_colwidth', None)          # To display all the data in the columns.\n",
    "pd.set_option('display.max_columns', None)           # To display all the columns.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')     # To suppress all the warnings in the notebook.\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style(\"dark\")\n",
    "                                       \n",
    "!pip install -U pandas-profiling\n",
    "!pip install -q --upgrade pandas-profiling         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Acquisition and Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</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  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/insaid2018/Term-3/master/Projects/Iris.csv')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 4 feature columns present in the data. The ID column is redundant for the analysis and can be dropped. The Species column is the target variable and contains 3 unique entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Id'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As there are duplicate values, they must be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>34</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm         Species\n",
       "34             4.9           3.1            1.5           0.1     Iris-setosa\n",
       "37             4.9           3.1            1.5           0.1     Iris-setosa\n",
       "142            5.8           2.7            5.1           1.9  Iris-virginica"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.duplicated()==False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>147.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>147.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.856463</td>\n",
       "      <td>3.055782</td>\n",
       "      <td>3.780272</td>\n",
       "      <td>1.208844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.829100</td>\n",
       "      <td>0.437009</td>\n",
       "      <td>1.759111</td>\n",
       "      <td>0.757874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>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>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\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>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": [
       "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count     147.000000    147.000000     147.000000    147.000000\n",
       "mean        5.856463      3.055782       3.780272      1.208844\n",
       "std         0.829100      0.437009       1.759111      0.757874\n",
       "min         4.300000      2.000000       1.000000      0.100000\n",
       "25%         5.100000      2.800000       1.600000      0.300000\n",
       "50%         5.800000      3.000000       4.400000      1.300000\n",
       "75%         6.400000      3.300000       5.100000      1.800000\n",
       "max         7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The duplicate entries have been dropped\n",
    "- Here we can observe that the data for each feature has a low standard deviation. The dataset features all lie within a small range of each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_profiling import ProfileReport\n",
    "profile = ProfileReport(df, title='Pandas Profiling Report', minimal=True, progress_bar=False)\n",
    "profile.to_file(\"Iris Dataset.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From the report we can observe that the dataset is well balanced. \n",
    "- We can see that there is a visible gap between petal length values and petal width values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>5.010417</td>\n",
       "      <td>3.431250</td>\n",
       "      <td>1.462500</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>5.936000</td>\n",
       "      <td>2.770000</td>\n",
       "      <td>4.260000</td>\n",
       "      <td>1.326000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>6.604082</td>\n",
       "      <td>2.979592</td>\n",
       "      <td>5.561224</td>\n",
       "      <td>2.028571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "Species                                                                  \n",
       "Iris-setosa           5.010417      3.431250       1.462500      0.250000\n",
       "Iris-versicolor       5.936000      2.770000       4.260000      1.326000\n",
       "Iris-virginica        6.604082      2.979592       5.561224      2.028571"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_mean= df.groupby(by=\"Species\").mean()\n",
    "iris_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Thus we can see that the Iris Sentosa Species has a very low Petal Length and Petal Width compared to the Versicolor and Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RRxyRsWPHln04AABorRoM6Zdffjn/8i//ssH1p59+el588cW/+eA33nhjBg8enMGDB+fqq6/eYP+cOXMyfPjwDBo0KBdeeGHWrl37CUYHAICW02BIV1Z+9BLqNm3apEOHDg0+8MyZM/O73/0ukyZNygMPPJD//M//zG9+85t6t6murs7FF1+cadOmpVQqZcKECZ9wfAAAaBkNhnSXLl3y1ltvbXD9n//857Rt27bBB66qqsr555+f9u3bp127dtl1113zzjvv1O2fN29eamtrs88++yRJhg8fnocffrjInwEAAJpdgyF9yimnZOTIkXniiSeyatWqrFy5Mk888URGjRqVk046qcEH3n333esi+Y033shDDz2U/v371+1fuHBhqqqq6rarqqq8gREAgI1Ggx9/N2DAgCxbtiw1NTWZN29eKioqsvPOO2fUqFH13oDYkJdffjmnnXZazj333Oy88851169fvz4VFRV126VSqd52Y3Tt2uUT3R42N1VVW7b0CLBZcuxBy2juY+9vfo700KFDM3To0Lz//vtp06ZNttpqq0Y/+LPPPpszzzwzo0ePzuDBg+vt6969exYtWlS3/d5776Vbt26fYPRk8eJlWb++9Inu87f45cemZNGipS09wifi+GNT4diDllGOY69Nm4qPPXnbqG82rK2tzW9/+9ssWbIkpdJ/h+vJJ5/8sfeZP39+Ro0alWuvvTZ9+/bdYH+PHj3SoUOHPPvss/nCF76QyZMnp1+/fo0ZBwAAWlyjQvrss8/Ou+++m8997nONXn5x5513ZtWqVbnyyivrrjvmmGMyY8aMnHnmmdl7771zzTXXpKamJsuWLUuvXr1y4oknFvtTAABAM2tUSL/88suZNm1a2rRp8L2J9dTU1KSmpmaD67/1rW/V/bznnnvmvvvua/RjAgBAa9GoMu7atasvSwEAgP+hwTPSP/vZz5L85aPpTjjhhBx88MFp165d3f6G1kgDAMCmrMGQ/tOf/pTkL1/M0qVLl7z++uvNMhQAALR2DYb0D3/4wyTJ//t//y+HHHJIvX0PPPBA+aYCAIBWrsGQnjFjRtauXZurr746pVKp7qPv1q5dmxtuuCHDhg1rliEBAKC1aTCk58yZkyeffDKLFy/OL37xi/++U2Xl3/yKcAAA2JQ1GNKjRo3KqFGjMn78+Bx33HHNNRMAALR6DYb0X9dBb7HFFh+5JtrSDgAANlcNhvTDDz+cJFm0aFFee+219OnTJ5WVlXnqqafSs2dPIQ0AwGarwZC+9dZbkyT/8i//kmuvvTY77rhjkuSdd97JRRddVP7pAACglWrUNxvOnz+/LqKTZIcddsi7775btqEAAKC1a/CM9F9VVVXl+uuvz9e+9rUkyT333JPPfvazZR0MAABas0adkb7yyiszd+7cHHnkkRk2bFjmzZuXH/zgB+WeDQAAWq1GnZHu1q1bbrrppnLPAgAAG40GQ/o73/lOrrvuugwdOvQj90+ZMqUsQwEAQGvXYEiPGDEiSXxCBwAA/C8NhvRPf/rT9O/fP1/96lez3XbbNddMAADQ6jX4ZsO99torU6ZMyaGHHppjjjkmt9xyS+bMmdNcswEAQKvV4BnpU089NaeeemrWrFmT559/Pk8++WR+8IMfZP78+enbt2++//3vN9ecAADQqjTqUzvatWuX3XbbLQsXLsz777+fJUuWZO7cueWeDQAAWq0GQ/qNN97IjBkzMn369Lzyyivp06dPDjzwwIwcOTJdu3ZtrhkBAKDVaTCkDzvssBx00EE544wz8sUvfjGVlY06gQ0AAJu8Bt9sOGLEiLz11lu59NJL86Mf/ShPPfVU1q1b11yzAQBAq9VgSH/ve9/LlClTcuedd6ZHjx65/fbb069fv5x11ll58MEHm2tGAABodRoM6b/q0aNHjj/++IwbNy7nn39+XnvttZx33nnlng0AAFqtBhc9f/jhh3n66acza9aszJo1K++880769u2bE088Mf369WuuGQEAoNVpMKT79u2bPfbYI1/96ldz4YUXpnfv3mnbtm1zzQYAAK1WgyH97//+76mqqmquWQAAYKPRYEifcsopDd55ypQpTToMAABsLBoM6Ysuuqi55gAAgI1KgyG933771f38/vvvZ+XKlSmVSlm3bl3+/Oc/l304AABorRr1VYXXXXddbr/99iRJ27Zts2bNmuy2226WdgAAsNlq1OdIT548OY8++mgGDRqURx55JD/84Q+z2267lXs2AABotRoV0tttt126deuWXXbZJS+99FKGDRuWP/3pT+WeDQAAWq1GhXRlZWX+/Oc/Z5dddskzzzyTtWvXZtWqVeWeDQAAWq1GhfRpp52Wiy66KAceeGAeeeSRHHjggenTp0+5ZwMAgFarUW82HDBgQAYMGJDkL+ul33zzzeyxxx5lHQwAAFqzRoX08uXLc9NNN+V3v/td2rZtm4MOOii77LJL2rdvX+75AACgVWrU0o6amposWLAgF1xwQaqrq/Pqq6/m8ssvL/dsAADQajXqjPSLL76YadOm1W336dMngwcPLttQAADQ2jXqjHS3bt2yZMmSuu0VK1Zk2223LdtQAADQ2jXqjHT37t3z9a9/PYcddljatm2b6dOn5+/+7u/qlnfU1NSUdUgAAGhtGhXSO+20U3baaae6bcs6AADY3DUqpL/97W+ntrY2b775ZnbfffesWrUqnTp1KvdsAADQajVqjfTzzz+fQw45JKeddloWLlyYAw88MM8991y5ZwMAgFarUSF91VVX5ec//3m22WabdO/ePVdffXWuuOKKcs8GAACtVqNCura2Nrvttlvddv/+/bNu3bqyDQUAAK1do0K6srIyH3zwQSoqKpIkr732WlmHAgCA1q5RbzY8/fTTc/zxx2fRokU5++yz8/jjj2fMmDHlng0AAFqtvxnSpVIp/fr1y6677prHHnssy5cvz2mnnZY99tijOeYDAIBWqcGlHa+88koOPvjg/Md//Ee233773H333XnwwQdz6qmn5vHHH2+uGQEAoNVpMKSvvvrqfPe7382AAQMyderUVFRUZOrUqZkwYUJuuOGG5poRAABanQZDev78+TniiCOSJE899VQOPvjgtGnTJn//93+fZcuWNcuAAADQGjUY0m3a/Pfu3//+9/nSl75Ut71q1aryTQUAAK1cg2823HrrrfPSSy9l2bJlWbRoUV1IP/fcc9l+++2bZUAAAGiNGgzps88+OyeddFKWLVuWc845J507d86dd96ZW2+9NTfddFNzzQgAAK1OgyG9zz775Le//W1qa2uz1VZbJUl69+6de++9NzvvvHNzzAcAAK3S3/wc6fbt26d9+/Z12/vuu29ZBwIAgI1Bo74iHAAAqE9IAwBAAUIaAAAKENIAAFCAkAYAgAKENAAAFCCkAQCggLKG9LJlyzJkyJC8/fbbG+y78cYbM2DAgBx55JE58sgjM378+HKOAgAATepvfiFLUc8//3xqamryxhtvfOT+2bNnZ9y4cendu3e5RgAAgLIp2xnpCRMm5JJLLkm3bt0+cv/s2bNz2223ZejQoRkzZkxWrVpVrlEAAKDJle2M9BVXXPGx+5YvX56ePXumuro6O+20U84///zcfPPNOeussz7Rc3Tt2uXTjgmbtKqqLVt6BNgsOfagZTT3sVe2kG7IFltskTvuuKNu+5RTTsno0aM/cUgvXrws69eXmnQ2v/zYlCxatLSlR/hEHH9sKhx70DLKcey1aVPxsSdvW+RTO955553cd999ddulUimVlS3S9AAAUEiLhHTHjh0zduzYvPXWWymVShk/fnwOPfTQlhgFAAAKadaQHjFiRP74xz9mu+22y5gxYzJy5MgcdthhKZVKOfnkk5tzFAAA+FTKvp5ixowZdT//z3XRgwYNyqBBg8r99AAAUBa+2RAAAAoQ0gAAUICQBgCAAoQ0AAAUIKQBAKAAIQ0AAAUIaQAAKEBIAwBAAUIaAAAKENIAAFCAkAYAgAKENAAAFCCkAQCgACENAAAFCGkAAChASAMAQAFCGgAAChDSAABQgJAGAIAChDQAABQgpAEAoAAhDQAABQhpAAAoQEgDAEABQhoAAAoQ0gAAUICQBgCAAoQ0AAAUIKQBAKAAIQ0AAAUIaQAAKEBIAwBAAUIaAAAKENIAAFCAkAYAgAKENAAAFCCkAQCgACENAAAFCGkAAChASAMAQAFCGgAAChDSAABQgJAGAIAChDQAABQgpAEAoAAhDQAABQhpAAAoQEgDAEABQhoAAAoQ0gAAUICQBgCAAoQ0AAAUIKQBAKAAIQ0AAAUIaQAAKEBIAwBAAUIaAAAKENIAAFCAkAYAgAKENAAAFCCkAQCgACENAAAFCGkAAChASAMAQAFlDelly5ZlyJAhefvttzfYN2fOnAwfPjyDBg3KhRdemLVr15ZzFAAAaFJlC+nnn38+3/rWt/LGG2985P7q6upcfPHFmTZtWkqlUiZMmFCuUQAAoMmVLaQnTJiQSy65JN26ddtg37x581JbW5t99tknSTJ8+PA8/PDD5RoFAACaXGW5HviKK6742H0LFy5MVVVV3XZVVVUWLFjwiZ+ja9cuhWaDzUVV1ZYtPQJslhx70DKa+9grW0g3ZP369amoqKjbLpVK9bYba/HiZVm/vtSUo/nlxyZl0aKlLT3CJ+L4Y1Ph2IOWUY5jr02bio89edsin9rRvXv3LFq0qG77vffe+8glIAAA0Fq1SEj36NEjHTp0yLPPPpskmTx5cvr169cSowAAQCHNGtIjRozIH//4xyTJNddckx/+8Ic57LDDsmLFipx44onNOQoAAHwqZV8jPWPGjLqf77jjjrqf99xzz9x3333lfnoAACgL32wIAAAFCGkAAChASAMAQAFCGgAAChDSAABQgJAGAIAChDQAABQgpAEAoAAhDQAABQhpAAAoQEgDAEABQhoAAAoQ0gAAUICQBgCAAoQ0AAAUIKQBAKAAIQ0AAAUIaQAAKEBIAwBAAUIaAAAKENIAAFCAkAYAgAKENAAAFCCkAQCgACENAAAFCGkAAChASAMAQAFCGgAAChDSAABQgJAGAIAChDQAABQgpAEAoAAhDQAABQhpAAAoQEgDAEABQhoAAAoQ0gAAUICQBgCAAoQ0AAAUIKQBAKAAIQ0AAAUIaQAAKEBIAwBAAUIaAAAKENIAAFCAkAYAgAKENAAAFCCkAQCgACENAAAFCGkAAChASAMAQAFCGgAAChDSAABQgJAGAIAChDQAABQgpAEAoAAhDQAABQhpAAAoQEgDAEABQhoAAAoQ0gAAUICQBgCAAoQ0AAAUIKQBAKCAsob0lClTcvjhh2fgwIEZP378BvtvvPHGDBgwIEceeWSOPPLIj7wNAAC0RpXleuAFCxbk2muvzcSJE9O+ffscc8wx2X///bPbbrvV3Wb27NkZN25cevfuXa4xAACgLMp2RnrmzJnp06dPttlmm3Tu3DmDBg3Kww8/XO82s2fPzm233ZahQ4dmzJgxWbVqVbnGAQCAJlW2M9ILFy5MVVVV3Xa3bt3ywgsv1G0vX748PXv2THV1dXbaaaecf/75ufnmm3PWWWc1+jm6du3SpDPDpqaqasuWHgE2S449aBnNfeyVLaTXr1+fioqKuu1SqVRve4sttsgdd9xRt33KKadk9OjRnyikFy9elvXrS00z8P+fX35sShYtWtrSI3wijj82FY49aBnlOPbatKn42JO3ZVva0b179yxatKhue9GiRenWrVvd9jvvvJP77ruvbrtUKqWysmxdDwAATapsIX3AAQfkiSeeyJIlS7Jy5co88sgj6devX93+jh07ZuzYsXnrrbdSKpUyfvz4HHrooeUaBwAAmlTZQnr77bfPWWedlRNPPDHDhg3LkCFD8vnPfz4jRozIH//4x2y33XYZM2ZMRo4cmcMOOyylUiknn3xyucYBAIAmVda1FEOHDs3QoUPrXfc/10UPGjQogwYNKucIAABQFr7ZEAAAChDSAABQgJAGAIAChDQAABQgpAEAoAAhDQAABQhpAAAoQEgDAEABQhoAAAoQ0gAAUICQBgCAAoQ0AAAUIKQBAKAAIQ0AAAUIaQAAKEBIAwBAAUIaAAAKELbm4N4AABFKSURBVNIAAFCAkAYAgAKENAAAFCCkAQCgACENAAAFCGkAAChASAMAQAFCGgAAChDSAABQgJAGAIAChDQAABQgpAEAoAAhDQAABQhpAAAoQEgDAEABQhoAAAoQ0gAAUICQBgCAAoQ0AAAUIKQBAKAAIQ0AAAUIaQAAKEBIAwBAAUIaAAAKENIAAFCAkAYAgAKENAAAFCCkAQCgACENAAAFCGkAAChASAMAQAFCGgAAChDSAABQgJAGAIAChDQAABQgpAEAoAAhDQAABQhpAAAoQEgDAEABQhoAAAoQ0gAAUICQBgCAAoQ0AAAUIKQBAKAAIQ0AAAUIaQAAKEBIAwBAAWUN6SlTpuTwww/PwIEDM378+A32z5kzJ8OHD8+gQYNy4YUXZu3ateUcBwAAmkzZQnrBggW59tprc/fdd+eBBx7IPffck1deeaXebaqrq3PxxRdn2rRpKZVKmTBhQrnGAQCAJlVZrgeeOXNm+vTpk2222SZJMmjQoDz88MP59re/nSSZN29eamtrs88++yRJhg8fnuuvvz7HHntso5+jTZuKph88yd9tu0VZHheaW7mOkXJqv1XXlh4BPrWN8dj7uy7btfQI8KmV49hr6DHLFtILFy5MVVVV3Xa3bt3ywgsvfOz+qqqqLFiw4BM9x7ZlCt7rLxhWlseF5ta1a5eWHuET2/v0q1p6BPjUNsZj75pvXNLSI8Cn1tzHXtmWdqxfvz4VFf9d8KVSqd7239oPAACtWdlCunv37lm0aFHd9qJFi9KtW7eP3f/ee+/V2w8AAK1Z2UL6gAMOyBNPPJElS5Zk5cqVeeSRR9KvX7+6/T169EiHDh3y7LPPJkkmT55cbz8AALRmFaVSqVSuB58yZUpuu+22rFmzJkcddVRGjBiRESNG5Mwzz8zee++dl156KTU1NVm2bFl69eqVH/7wh2nfvn25xgEAgCZT1pAGAIBNlW82BACAAoQ0AAAUIKQBAKAAIQ0AAAUIaTawxx57fOT1v/rVr/KrX/2qLM/51ltvZfTo0WV5bGhNWuL4aowFCxZkxIgRhe77cX8maM3KeSxOnz4911133Se6z5FHHtnkj0n5+dQONrDHHntk7ty5zfqcTz31VG688cbcddddzfq80Nxa4vgqt03xz8Smz/9uaQqVLT0ArddTTz2VsWPHZv369dl9993zmc98Jkly+umnZ/To0Xn55ZeTJMcee2y++c1vbnD/q666Ko8//njatGmTQw45JN/+9rezfPnyjBkzJi+//HLWrVuXESNGZMiQIbn88svz9ttv57LLLssll1ySW2+9NQ8++GDatm2bL3/5y6murs7KlStz9tln57333kuSjBo1KgcffHBmzZqVa6+9NrW1tfnwww9zwQUX5JBDDmm+FwoK+DTH1/Tp03Pvvffm1ltvTZLcddddefPNN3PBBRfk6quvzqxZs7Ju3boMHz48J5100gbPNWzYsIwdOzZJsvXWW+dHP/pRVqxYkRNPPDEzZszIvHnzcsEFF2TJkiXp2LFjLr/88uy55565//7787Of/SwVFRXp1atXLrroomyxxRZ1c61cuTI1NTWZO3duKioq8s///M8ZNmxYJk6cmEmTJuX999/PgAEDcvbZZzfHSwyNUo5jca+99sqsWbNy5ZVX5qCDDsrnP//5zJkzJ3fffXemTp2aX/7yl9lyyy2zyy67ZMcdd8wZZ5xRF/Y33HBDFixYkDfffDPz5s3LN77xjYwcOTITJ06se8yZM2fmyiuvTKlUyg477JAf/ehHSZLRo0dnwYIFWbhwYfr27ZsrrrgiFRUVzfhqbn4s7aBBb7zxRv71X/81V111Vd11v//97/PBBx/kgQceyG233ZZnnnlmg/vNmzcvv/3tb/Pggw/mV7/6VV555ZWsWrUqt9xyS3r16pWJEydm/PjxufXWW/PWW2+lpqYm/+f//J9ccskleeyxxzJjxozcf//9mTRpUt5888383//7f/Ob3/wmPXr0yMSJE3PFFVfUPe8vf/nLXH755Zk0aVIuv/xy//TFRqPo8dWvX7/Mnj07H3zwQZJk6tSpOeKIIzJhwoQkyaRJk3Lfffdl+vTpdff/n891880359JLL83EiRNzwAEH5MUXX6z3+JdddlkGDRqUX//61znjjDNyyy23ZO7cubn11ltz1113ZcqUKenUqVNuvPHGeve74YYbsu222+bXv/51/vVf/zU33HBDXnrppSR/WToyadIkEU2r1NTH4kfdbtq0aVm0aFHGjx+fiRMn5u67786bb775kfPMnTs3d955Z+69997cfvvt+fDDD+v2rV69Ouecc06uuuqqTJkyJZ/73OcyadKk/Pu//3t69uyZe+65J9OmTcvTTz+d//zP//y0Lw1/gzPSNOgf/uEfsuWWW9a7bvfdd8/rr7+ef/7nf06/fv1y7rnnbnC/7bffPh06dMgxxxyTAQMG5JxzzkmHDh0yc+bM1NbW5v7770+SrFixIi+//HK9s1pPPvlkBg8enE6dOiVJvv71r+eBBx7IOeeck3HjxmXBggU58MADM2rUqCTJ2LFj8+ijj+bhhx/O888/n+XLl5fr5YAmVfT4ateuXQ499NA88sgj+fKXv5z3338/n//85/OTn/wkc+bMyZNPPpnkL8fX3Llzs9tuu9V7roMPPjjf/va3c8ghh+Tggw/Ol7/85bz99tt1j//0009n3LhxSZL+/funf//++eUvf5kBAwZk2223TZIcffTRueCCC+rN9eSTT+YHP/hBkmS77bar+xejLl26ZK+99kplpb9yaJ2a+lh85ZVX6t3uH//xH5MkTzzxRAYMGJAuXbokSQYPHlwvkv9q//33T/v27dO1a9dss802Wbp0ad2+uXPnZvvtt0/Pnj2TJN/73vfq9r3wwgv5+c9/ntdeey3vv/9+VqxYUfAVobH8VqNBHTt23OC6bbfdNlOnTs3jjz+exx57LF/72tcyderUnHDCCXW3mTx5cu69997MmjUrv/3tb3PMMcfkrrvuyvr16zN27Nj06tUrSfLee+9l6623znPPPVd33/Xr12/wnGvXrs3OO++chx56KP/xH/+RRx99ND/96U/zb//2bzn22GOz//77Z//990/fvn1zzjnnlOGVgKb3aY6vI488Mtddd10++OCDDB06NEmybt26VFdXZ+DAgUmSJUuWZIsttsgf/vCHes910kknZcCAAXn00UczduzYvPDCC3WPkaRe8JZKpbz66qsbHJelUilr167d4Lr/vb1u3bqP/bNCa9HUx+L/1qFDhyRJmzZtPvLvuI+7fZJUVFTUO7batWtXb7nG0qVLs3z58vzmN7/JtGnT8s1vfjMHHHBA/vSnP21wTNL0LO3gE5s+fXqqq6tz4IEHpqamJp07d878+fMzefLkuv+8+OKLOf744/OlL30p5513Xnbddde8/vrr6dOnT927oRcuXJgjjjgi8+fPT9u2bev+Uu7Tp0+mTp2a2trarF27Nvfff3/69OmTX/7yl7nhhhvyT//0T7nkkkuyZMmSfPjhh3njjTfyne98J/369cv06dPr/uKGjVFjjq8k2WeffbJw4cJMnjy57p+S+/TpkwkTJmTNmjVZvnx5jj322PzhD3/Y4Dm+8Y1vZPny5TnppJNy0kknbbC044tf/GKmTp2aJJk5c2Yuuuii7LfffpkxY0bef//9JMmECROy//7717tfnz59ct999yX5S8RPnz49++23X9O+QNBMPs2x+HH69u2bxx57LMuWLcvq1avzyCOPfOI1zP/wD/+QxYsX1531/slPfpJf/epXefzxx3P00UfniCOOyKpVq/LSSy81Ktr5dJyR5hPr169fHnnkkQwePDgdOnTIEUccscHHCO21117ZZ599MmTIkHTq1Cn77rtv+vXrl/322y+XXnpphgwZUnf2bMcdd8yWW26ZpUuXprq6OmPHjs2cOXPy9a9/PWvXrs1XvvKVHH/88amtrc3ZZ5+doUOHpm3btqmurs4222yTo446KoMHD05lZWX69OmT2trarFixIp07d26hVwiKa8zx9Vf/9E//lN/97nf57Gc/myQ55phj8uabb+ZrX/ta1q5dm+HDh2f//ffPU089Ve9+Z599ds4///xUVlamc+fOufzyy+vtv/jii1NTU5O77747nTp1yuWXX57ddtstp512Wk444YSsWbMmvXr1ymWXXVbvfqNGjcqll16aoUOHZt26dTn99NPTq1cvn4zARunTHIsf53Of+1xOPPHEHH300encuXO23XbbemefG6NDhw4ZO3Zszj333KxZsyY77rhjrr766rzwwgu59NJLc/vtt6dLly7p3bt3vSVblIePvwMAaAavv/56HnvssZx00klJkpEjR+Yb3/hGDjrooJYdjMKckQYAaAY9evTIH//4xwwZMiQVFRX5yle+kgEDBrT0WHwKzkgDAEAB3mwIAAAFCGkAAChASAMAQAFCGqAV+8Mf/pATTjghQ4cOzZAhQ3Lqqafm5ZdfbvLnGTFixAbfxgZAw7zZEKCVWr16db761a/mpz/9ad23gU6ePDnXXnttpk+fnrZt27bwhACbNx9/B9BKrVy5MkuXLs2KFSvqrjviiCPSpUuXPPHEE7nuuuuyww475LXXXkvHjh1z5ZVXZtddd83q1atzzTXX5Omnn866deuy1157paamJl26dMnrr7+eiy++OEuWLEmbNm0ycuTIHH744TnooINy3XXXZe+9986MGTNyyy23ZM2aNenYsWPOO++89O7dO6+++mouvPDCrF69OqVSKUcddVSOO+64FnyFAFqWpR0ArdTWW2+d6urqnHrqqTn44INTXV2d+++/PwcccEDatWuX2bNn54QTTsiUKVMyfPjwVFdXJ0luv/32tG3bNhMnTsyDDz6Ybt265Zprrknyl281POywwzJ16tTcfvvtGTduXJYtW1b3nG+88Uauvfba3H777XnggQfy/e9/P2eccUZWrFiRO++8MwcddFAmTpyY22+/Pc8884yvIAY2a5Z2ALRyy5Yty9NPP52nn34606dPT5JUV1fnpptuyqRJk5L8ZRnIP/7jP2bmzJkZMWJEli5dmo4dOyZJ1qxZk65du+aGG25I37598/zzz6d9+/b1nuOvZ6RfeOGFXH/99enevXvdviVLluSOO+7I/Pnzc95552W//fZL3759c9hhh6Vr167N9CoAtD6WdgC0Us8++2x+//vf59RTT82AAQMyYMCAnH322RkyZEjWrl37kWuk27Ztm/Xr12f06NHp379/kmT58uVZtWpVKiv/8iu/oqKi7vavvfZadthhh7rt9evXp2/fvvnxj39cd938+fPTrVu37Lnnnpk2bVpmzpyZJ554IjfddFMmTpxYL7oBNieWdgC0Utttt11uueWWPPPMM3XXLVq0KMuWLcv777+fl156KS+99FKS5J577knv3r2z1VZb5Stf+UrGjx+f1atXZ/369bnooosybty4dOnSJb169coDDzyQ5C+B/K1vfStLly6te/y+ffvm8ccfz6uvvpokeeyxx3LEEUektrY23/ve9/Jv//ZvGTx4cC655JJ06dIlf/7zn5vxFQFoXSztAGjFnnzyydxwww15991306FDh2y55ZYZNWpUOnTokPPOOy977rln5s2bl+222y5XXHFFPvOZz6S2tjZXXXVVZs2alXXr1qVnz575/ve/ny5duuTNN9/MZZddlvfeey8VFRU544wzcsghh9R7s+FDDz2UW2+9NaVSKZWVlRk9enS++MUv1r3ZcMWKFWnbtm369u2b6urqeme4ATYnQhpgI/TUU0/l+9//fn7961+39CgAmy1LOwAAoABnpAEAoABnpAEAoAAhDQAABQhpAAAoQEgDAEABQhoAAAoQ0gAAUMD/B2k1VaWyuPk6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = df.drop(['Species'], axis=1)\n",
    "for feature in features:\n",
    "    plt.figure(figsize=(12,10))\n",
    "    sns.set(font_scale = 1)\n",
    "    sns.barplot(y=df[feature], x=df['Species'],)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- While the Sepal Lengths of all 3 varieties are similar, the Sentosa Species has different Petal lengths and widths compared to the Veriscolor and Virginia as seen above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d398b76a00>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TMUgBPXL8qA7cPpJAYV6A7dU7r1duli9a7FsvqzX7CRJBQbAHFU07AzFz/NkYonvWGu8qJKUUysrK+M9//pNuWbo9QikuP2M0/7ttHlsrGrFMyQ++fTAGdGgUryngZxcezm8e/4S6RpvMgMl1Fx6Oyf6SE1WjiRqVfzLxMu786BHqIw1kWEF+ctQPMBxLz/N9ICml0Lt3b8rKyigpKUm3PN0az1MEDMltsyYRcTwsUyKhQ1cJEHUDLc4NcP/VxxGxPTKCFspxtFeGZr/CdRSFZhF3nvQLIl4En+FD2iauLs+9TySlFIQQnH766YwePTrOpvB///d/aROsu7LjwesTgJvOrOet47keAvBLyM8JUF5et9dzNJruhucAjomv+VGmX3v2naSUwsknn8zJJ5+cblk0KcYXMAnZHqFI4lcnn8/A9qKutAYkzHtkmga252EZEidNldksy8B2FU1hBylF0qk2NBpN6tmrUrBtm2nTppGZmQnAihUrGDx4cNyKYU888MADvPHGGwghOOecc7jkkkvijj/88MM899xz5OTkAHDeeedxwQUXtOd3aHZBSsA0eWHOWpasqWDEwHxmHHcQwvXwvOZ3KVOyobyBZ95ZhRSC86cNoyg3GKuFIKXAk5Jn56xhxYYqDh9ZzNQJA1COk1IvKmFKPl9Twevz1lOQG+B7J48gwxTaz1yj6SRaVQpbt27l4osv5kc/+hGnnXYaAH/4wx9Yvnw5f/nLX1q1MSxYsID58+fz0ksv4TgOp5xyCscddxyDBw+OfWbJkiXce++9jBs3LkU/RwPgSYM/PLuYhUvLAFixsYp1W2r4yfnjwAPTlFTU29z0f3NjD/gvVm7joZ8eT6Yl8LxoLYTb/7IwFuuwYmMVm8sbuPhbIyBFtgnTlHy2uoL7//l5rO2z5VE5tElco+kcWg1eu+uuuzj77LNjCgHg/vvv59vf/jZ33313qx1PmDCBJ598EtM0qaiowHXdFgFvS5Ys4dFHH+X000/nlltuIRwO78NP0exAKVi0rCyu7cvV23Gbt2X8fpPX56+Pe+P3FLy9cBOBgA8Ax1UxhbCDD77YjEphIJ7tKV75eF1cW0PIYf2W2qj7rEaj6XBaXSmsWrWKe++9t0X7rFmz4hTFnrAsiwcffJDHH3+c6dOnx60sGhoaGDlyJNdddx0DBgzg+uuv55FHHuHqq69OWviCgqykP5sOioqyO/X790R5VSMBn0lTeKctwWdKpBQUFURlzs8OtDivIMdPZmb03/bqJgwpYooEovURDEOS3yM10eyNIZu8bH+L9txsP/n5nXtt90ZXvfa7013khO4j6/4uZ6tKwTQTH5ZS4ve3vJkTcdVVVzFz5kxmzZrF008/zXe+8x0AMjMzeeyxx2Kfu/TSS7nxxhvbpBQqKuo7zShZVJTdZT16TJ/BRaeM5NEXvoq1nT9tOKYgJvPUI/vz5oINVNREA9xK8jOYPKZP7LgwJOecMJR/vb0y1sdlZxyCcN2U/W4pBRd9ayRfrt5OOBLdLjrkoALys/1ddmyha1/7XekuckL3kbW7yyml2OvLdKtKISsri02bNtGvX7+49o0bN2IYrUcNrlmzhkgkwsiRIwkGg0ybNo0VK1bEjm/ZsoW5c+dyzjnnANHMq3tSQpq24URcjjq4J2OGFrJiQxVD+/UgI2Di7uI95JNwz1XHsmpTFYaUDO6Ti4liR9YY5XpMP7I/R4/pzfrSWob2y8NnyDbXXGgNz1NkB0weue4EVmyspDAvg6LcAOgiPBpNp9GqTeHSSy/liiuuYN68eYTDYZqampg3bx5XXnkl3//+91vt+JtvvuGmm24iEokQiUR45513GD9+fOx4IBDg7rvvZtOmTSileOqpp5g6dWpKfpQmWswmwxQcMbyYAb1ywIl/0EYiLsp2GNkvj2F9slG208IlVbkeWT7JoYPyo3EXXuq9wD3XQ7guB/fvwciB+boIj0bTybT6an788cdTX1/PTTfdxObNmxFCMHDgQK688sq92hSOO+44vvzyS2bMmIFhGEybNo1TTz2VmTNnctVVVzF69GhuueUWrrjiCmzb5rDDDmvhstpdEUJgWRLPU+1+szZNic9n4jgukUj73pyllJhmMolw92zUlVJiGAKl2h8/4POZmKYgFHJ3usTuRneJtrasfc+rs6OPPdXDNozomDuOp2M2NB2OUCq597Lq6mqklLGYgq5Al7QpSElD2GHuV6X075nNiP49opHNbXj9FaZBRW2IT77eyrB+PRjSLxfVxsAxYRlsKW/g85XbOHRoEf2KsmIxCADSEDgI5n9VipSCow7uhYGKezgL02BjWR3L1ldy2PBiCnMDbXJHlRKUYbJ8QyXrNtcycXRPcrP8LVYtO+jK+7WGIciUYZrWLAInTMawo2hUQdpyWQxD4PoifPLN5xhScnjvMRgRX1xMhgy4bKrbwoqKNYzteTAF/nxUuH2KqCuP5+50F1m7u5zJ2BSSUgqhUIg333yTysrKuIdbZ7/ZdzWlYJqSFZtr+e3sBbG2UYPy+en3xie9T+7zW3yweAt//PdOI/Gk0b247IxD9vgw3R3DZ/LyR2t59t3VsbZTJw/i3OOH4DkuQoAjJD++930aQ1EPpewMi/uvnoLYIach+cebK3lr4cZYH5ecNoopY3snX0nONHjo6S/4fGV5rOkn54/jiOHFhMMtc9535Rsu1xehdPbPcBuibrrCCtD7B7+jTmUmtd0lBLj+MD9767c02VHjfpYvkzun3ghN0UBQ4XN5ZvnLvLd+buy8Cw89i6N7HYXbjhIBXXk8d6e7yNrd5UxGKSRVZOeaa67hiSeeYPny5axcuTL2TxOP7cFfX4svUbp0XWWca+jeCLse/3xrRVzb3K9K41xD94bjKV78YG1c2+vz1seyZvt8Jm/MXx9TCAB1jTYffLEZny/6VuopeHvRxrg+/vnWymiJzySJOF6cQgD4x5srCHVCedJ9wbIMGlcviikEAGWHqF34Mj4rqVsIn8/knbUfxRQCQH2kgbkbF+0cc+ny/vp5cec9t/RVPENneNN0HEm5+6xatYo33ngDKZO7AQ5k7AQPPMfziGYX2jtiD314nkqyB0DRQol4SqFQCKLBbeEEdopIxGGHfUE1f25XHNdrzfyQUObdif627rdPruzIHtqS+y1KQdht2UfEjbBzzKP/7YrjOW0ac41mX0nqKV9QUIDj6LeVvWEZcPbxQ+La+hZnkZ3hS7oPUwpOO3pQXNuoQflYSRmMoxgCjh8f70Y86dBeGM3RyLbtcsqkQZi7RA37TMnxh/fDtp1YH+NHFMf1ccqkgclNmGYCPoOD+uTGtZ1+zGD8ZvcqgmLbLpkjjkL4dgn4E5KcCadhJ3lb2LbLtIOOxZA7f7tlWBwz8MidY65MxvU6JO68aUOORbraVVvTcbRqU5g9ezYAn332Gdu2bePEE0+MS4SnbQot9+x2GGffXriJgb2yOeHwfs2J6JKXU1omS9dXMvfLLQztl8dxh/UFx9uj507CPnwmi5aX8cWKckYfVMiRh/QEx4l5lUpD0BjxePGDtUhDcMYxgwmYAq95j0kIEKbJ/CWlLFlbwVGH9OSQQQV4Sdo1IGpj8aTk7YUbWV9ay7Fj+zC0X94ejeZdeb/WMiBIA7ULX0HZYXKOOI2IlUvYTV5NSlPRJBt5ZeU7GEJyytAT8XvBaPpnomMugx4LNn/Bsu0rOaL3WEYWDsVr0obmrkJ3l3OfDc033HBDqyfffvvtSYqYHrqiUoCoS6GUO9/K24Pfb4IQCCAUal8h8kDAQgE52YnrKQghMJv3xB07cXyAZcnoppNqv3ttMGjhNe9HhVuxr3T1G04I8FmS7Gw/lVWhds09IQSmTyCUwG5lzJEK5Yp9Chbs6uO5K91F1u4u5z5HNO946L/99tucdNJJccf+/e9/t1XOAwbX9fY5KLe1h2eyhEI2QgAJ8hxBNIrc3ksMhG3vu1G4qal9Sq2rEbXFeORIs90vI0op7HDr59q2h5SJbTIdTVSOzpZC05G0qhTeffddHMfhrrvuQikVc0d1HIeHHnqIGTNmdIiQmrYjDIGHYHtNCMwmhCHjyoLuqJdQ3xhVHJkBC6l0sFRnY/kdHOlS2VhNfmY+pmMQiXS8g4cMOoS9CPWRBvIzemC6Puyw1g4HAq0qhWXLljF//nwqKip48sknd55kmntNc6HpPAxTsmpzLbf/ZSGup5BScM13D+OQQT3wmrcjlGFwy5/ms760FoBh/fK44ftHgKfzDnUWPr/HwrIveezzf0VzgUmTG46exYDMgYQ78IEsgg7PLnuFd9Z+DEC2L5Nfn3AtmWZOSnNfabomrSqFK6+8kiuvvJKnnnpKV0TrRjgKHnz6i5hbqucpHnluMQ9dOwVBtAznnMVbYgoBYOWmar5YWc74oUU4bTAma1KHIx1mL35u54rcc3hk0VP89vhrgeQ92PaVJrcpphAA6iIN/HXx81x+2AXgdC/PMU3baVUp7LAbZGZmJrQh6O2jrolSUNsQ7xPfGHJwPRW94EKwbktti/M2bK1jwohitPdx52B7DvZuocuVjdVRC3cHYZqSisbqFu1b68txlINMPlpG001pVSm8/vrrAJSXl7N27VqOOuooTNPkk08+YeTIkVopdFGkgJED81m2vjLWNrhPLqYU4Cpcx+XEI/rx9sL4iOVjx/Vpt7eUZt/xSZNe2SWU1u2smjemZCTSEx1WnNRxPPrm9MRv+OKC7Sb2O4wgQcLo7aP9naRyH11++eXcdNNN9O/fH4jWQvjlL3/Jn//857QL2Bpd1SW1s5FS4ArJ7P98zddrKxg+MJ/LTj8YExUbL2FIFq+p4Jl3ViGl4HvThjOif16cMbqj6cpjuivpktPnMwjLeh7//BnWVm1kdPFwLjh0Birkb1OMyr7Kafqhwq7gic+foaKxikn9D+fUYSfgNqRvlXCgX/tUkzaX1B2UlpbGFAJA79692bp1axvF1HQUnqeQwuMHp4/C9SA700dTQzhOgSrX47ChhYweXACAz0iN+6mm/UQiLpaZyaxxF+AqB0tY2CHZLoWwLzhhKPIV8+Mjf4ASioAIEG7Qc+NAISmlUFRUxIMPPsiZZ54JwL/+9a8W1dg0XQulFDgKCWQELBrqQi0+49huLK2O1gddA8fxmo25BuFOlCNaw8NCgN4yOsBIygH6jjvuYMWKFZxxxhnMmDGDzZs3c9ttt6Vbtg5HCAGGRDX/M9qQb2gHUkb78OSe+7B8JsowUIaBMA3SkWfQaP4NnpRU14UwDJ1VrTUMQ5Bp2uSYTWSbYUyza46XlOALuMhgBBmM4E8Ql2iYAgI2FY1VCL8bi65PrRwC/C4qEIGAk3C8DF9UDgI2wue1sJfvOuZOfVW7xtwwJPidqBx+R8/zFJDUSqG4uJjf//736Zal8zEN/vDcYhYtKyMjYDHzjEMYc1BB0vvsQgg8IfndPz7j67UV5GX5+dF5YxncMxvVvAUgDIMPvtzC315bTijiMPGQXlx+5miIpM7lxzAk9RGX2/+ykG+21dOrMJPrLzqcvKDVbSqcdSRSCjJpoPz5e4hsXYuRXUDRjKsR2X2TTnjXURhBlycXP89HmxbhM3ycN3I6x/Q7EjsUvZWlBd+EvuH3HzxBVaiGoQWD+PFRP8AI+1NmfxNCoPwRHl4wm2Xlq8n1ZzPriP9iYOYAPDv6UBY+l4XbFvP3L/9NyAkzoe9YLhn7HdzG6BtQKsbcMCQNspZ7P/ojm+u20jOriGsmXk6OkRdXuEjTNlp9R/3xj38MwOmnn57w3/6EYRm8+MEaFi4tQyloaLK5/5+fE2nD5BJSMPuVqHEXoLo+zO1/WYDX/IokJdSHHR779xKawg5KRWslvD5vPf6A1UrPbcMBbnsiqhAASrc3cOvsBXSx51uXISAjVPznYSJbozUo3LoKtj19G0HRMtV1ZxIIGszdsJAPNy5EKUXYCfPXr16kKlIbS2uvTIe7PnqEqlANAKsq1vHnz/6JMlPnvyQsj799+QLLyqNFnGrCddwz91E8MzrDhIAm1cifP/snTU4IheKTbz7nvXUfY1rReyEVY+6aEe6Z+yib66L2za315dz18R9wrf0jrUpn0SDHwNcAACAASURBVOpKYebMmQD88pe/7BBhOhPb8Vi8qrxF+/rSWkb0zUnqzcNRsGRNRXybq6ioaaIo24dlmSxfWdbivC9Xb2fqhP4pS5vvKdhcXh/XVl7VhOuq5JaGBximVIQ2xRdH8sKNKDtERwaN7Q1PRfh82/IW7cu2reSYviWEQh4NdgOR3WIdlpevRkmXJHeL94orHJaWxxfZcj2XqqZq8kURhiFZXbGhxXmLy5ZxbL9JgJGSMfeEF+e+C7C9sRJXORh6prebVmfJ448/zosvvsiQIUOYMGFCi3/7E5YhGDWwoEV7/5LspJeihoDh/XvEtUkpKMgNoBTYtsPQfnktzhs5MB9/khW8kkEKKMnPiGvLzwno/dY94HoCf+/4OhjCF0BY/k6SKDESHwcXHNSifVjh4FgUeqaViSnjH4hDCwYivNTNL0MZDC2Ir/khhaRHMA+logkhB/Vo6YgysmgIporKlooxl0iKMwvj2noEcjGEVgj7QqszZdSoUbz88stMnTqV888/nz/84Q8sW7astVO6LZ7rcfYJQxgxMPpQ95mSy759MIE2GJuFUlx2xiEM7JUDQNBvcvX542KD7HmQk+nj/KnDMI1o6+iDCjnt6MFEQqnb3DGBGy46goLcqBUyL9vPDRcfgdkNK551BCHlp+D0H2H26AmADGZRfOZPCXldZ5UAEAq5TBk8ibElowAwpMGM4SdRGMiP5SQSjsGPj7qUDCsIQJ/snvzgsO8hnBQ+KB2Di8ecS//cPgAEzQBXTrg49h1KQZaRxXkHnx5TUIcUD2fa4ONw7OgcTMWYG7aPaybOJD8YfdHKDeRw7aTLMezUbcUeiCQVvGbbNosXL2b+/Pl88sknlJaWMnHiRH7zm990hIx7JNXBa1IKPCFwvej+vyGIJZDbnT0FhxiGwEXguArDEBhEFU7cZywDx4uWyDSkQHoq5b7ohiFxiG5fBfwmOE6XN751ZmCQYQiCMox0bZQ0CSkftpt4ZdWZckoJ0mfjCg8QmMrADsUHlRkWuIaNkgrhSgzHl3IHA8MQuKaNi4MhDKRr4e22lS8thWc4eHgYyoSIya6Pm13H3PD5qLfNPY75HuUwBa4ZwVEupjAwbF9a57kOXmvGsiyGDBnCtm3bqK6uprKykhUrVuz9xG5GVME010J2aZd3dnRCNu/duyphH25zfIAB4LXve/Yuh4cALKJbR91hIncmrquod32ALz0XJEV4HnihnW/CiUyqrg3YFsXNDwY3DT/IdRW4ZmzvPtE3eLYA20Kyo5J1/MN61zEvys7GbsccdR0FjoVJdExcvRreZ1pVCuvXr+fdd9/lnXfeYfXq1Rx11FFMmTKFK664goKClvvvmiimZRBxPQwhEEq1azVjmAa2qzANUK5i9wWdaUocL3qb+QyhcxYdQFiWJCIiSATSTY+bsc9nIIwwrufgN7JoaGz79qbfb+IYIVzPI8PIoLFB+791B1pVCtOnT+eEE07gRz/6EYcffjimqQ04e0NaBm8t2sSczzfTuzCLi08dSYYpk75xd9RGfvmjtXyytIxBvXP4r2+NxCdFrA9hSDZXNvHX15ZhOx5nHz+E4f06N2+RpmOQfpcvK5byyqp3ybACfHf0DAqsglh8QCoIBAS1bjV//ewFtjdWcVz/wzl2wJFEGpO//31BxbZwGX//9N/URxo46aCjObz3mLTmT9KkBuNXv/rVr/Z0MBwO8+WXX/Luu+9SWlqKZVn06tUr5hPd2TQ1RRLWuO0IMjP9NDbG+1QbpsGr89bzjzdXUlMf4Ztt9Xy0eDPTJg6M+okmgTQN/vr6cl6Zu57ahggbttbx6bIyjj+8HygV9QF3FD994AO2VTVRWRvio8VbOPKQnuRm+lqsKPYka1dEy9k6pilZW7+e++b/iepQDeUNFcxZP4+pQ49Bui0f2O2VU/lsfv72HWys2UJNuI4vt60g15fJ8KJB2HZy89ixIlz/1u1srS+nOlTLZ6VLGNCjLwN79E24qtXXPrXsSU4hBBkZrRvzW326X3vttbz88sv8+c9/pk+fPvzxj3/k2GOP5eqrr+all17aN6n3Q2xP8e6nm+LaauojVNWGk06J7wFzPt8c17ZlewORZoO3ZRnM/WpLCx3z2rwNiDSkM9B0HTzp8vbaD+PaXOWxuGwplpWaN3DLMiit3Uqj3RTX/t6GBYTdhqT6CAYtvt62EtuL3y56f908mrzk+tB0Hkm98vfp04cLL7yQe++9l+uvv561a9fy85//PN2ydTskUJAbbNGemWElvaJRSsVcSWP9SoHV7BrreYpe+ZktzutdmJmy4DdN10RiUJzZ0pZXlFGQMi88z3PJCeS0aM/PyMMQySke1/UozMxv2UewB5bUW9BdnVaVQm1tLe+88w633347Z555JtOmTeOdd97hoosu4oMPPugoGbsNhoDLZ4zGt0tsw7Qj++NrQ9CYJeC/zz4UY5e3/vNOHBq7UI7jMWpwPoN677xxC/MCnHzUAG1s3s9xbY/Thk8ld5eH9rCCQfTP7ZsyY7PrQo6VwcQ+42JtftPPxWPOQqgE2fcSEIm49M4qYVTR0Fhbti+Tcw8+BSfUNbaeNXum1TiFgw8+mOHDh3PMMcdwzDHHMG7cOAyj6xiKumKRHWlIPAQby+ooyAuQ6TehjTesMASuEmwqq6OkIJOAJeP6kFKgpKSiJkTEcelVkIlUao8Phu7uW93V6Ox4CuVz2FJfRtD0k+fPg7CZ0Ja0L3L6gjY1kQaqmqrol9cXy7MItcy+3oqcoPwOVaEa6iMN9MvtjeX6CIf0HO0I0han8P7771NUVLRv0h1g7AhUG1iciVKqXR5BylVIFINKsqJKb7c+PE+B51KQ5UMI8By3w8o1ajoX11XQZNDb6oNSoELRuJhUE2myyDLyyM0qINLY9vnlukCjSb5RQGGwGLvB1XUZugmtKoVLL7201ZNffvnllArTEViWgZRRv/5EqwwhBD6fgeepfdqO2dcVjGVJfD4Lx3EJhxP7d+8wLtq2k9BmYRgSsx01IVLN3sY8GYJBCykl4bAdS+mwK1IKLGvfrlsyY+7zRW8ZIUg45j6fgWWZrfaRCvY2jpmZvpg80YI58SQzz32+6Jh7nmr3mLsuuO6+vbLsGPO9zXPX9RLK2VF01LMl3bSqFPY1O+oDDzzAG2+8gRCCc845h0suuSTu+LJly/jFL35BQ0MDhx9+OL/+9a/TFgshhECYkkUry1n9TTXHjO1DcV4w/i3ckNQ1Obz10Tp65mcwaXQvpPI6PD2EMA3Wbatn7pdbGN6/B+OGF4PjsCMTxo4UFq8v2Ego4jB1wgD8UsSlyhCmweotNSxato3xI4o5qE8uyunYiSiEINuyCa1bSHjrGjJHHY3K7kljG/LwmKYkywjRsHQOTmUpWYceDxk9qLN3utVlmA6iYTsNC+dgFQ8g56Dx1Du+NikgK+iwua6Ueau/YHj+QEaXjMINGTvH3BS4VoS3N3xIeEOE4wdOwucG2dXBxgo6rKlaw6dbv2Zs8QiGFg7GboNvfyowDAkBm7mlC1hX9Q2T+o+nZ2YR3i5yBAwXK1JN/WfvYuaVkDv8KOpdf2yem6YEX4SPNy1gc0M5xw88ih7+XOzwzj6Ez6PSruTDVZ/QP7cv43oeAmEzpdu5cWPuRDh+UMsxlwGXdTXr+WzrEsaUjOSgHgPxmjp2i1sIgQg4fL5tCWurNjCp3+EU+QtRkZ1yCMujXtXx/uq5FGcWcmSfwxARs0umnkkq9xFAdXU1TU1NKKVwXZeNGzcyefLkPX5+wYIF3Hffffz1r3/FcRxOOeUU/vSnPzF48ODYZ0477TRuvfVWxo4dy4033sghhxzC9773vaSFb5NNwTC45++fsnRdZazpynPGcOTIIhzbwzQlm7Y3ctOjc2NvIz0LMrj9iskJH6ZpK97uN3lz4SaefHVn4sHDhhfzP+eOgR1ymAZX/e596puiSQ4sU/LANVMIGgKlFNKQPP/hWl76YG2sj2kT+vPdqcM6NMAty7KpfPFewt/s/C0F35oFgycRSVD/M9GY5vkjbP3HLdjbv4m1lZz7c9yehxIO21iWxPjmc8pfvC923NdzMIVn30BdkonRAkHJexs+5G9LXoy1jSsZxazxF+A0p5QQQZvr3votDZFGACxpcte0X+C3s1BK4Qt4vLDyNV5dPSfWxwkDj+K7o84gEuq4h5TIdHhg3p9ZsX1NrO0H489ncq8JNDU6mKbEX7OOrU/9ih3bTmZeCSUX3kptJPpbzQyHWz54kM21O+uwXz/5hwzJGUE4bGNakq9rlvLg/Mdjxwf16M91E69AtfO3Jrr2extz6VO8vOYNXl31buycEwZN5tzhp+NF0rNCTihnwOXBBX9m+fbVsbbLx1/A+MJxsWfL5vA3/GbOA6jmMS/JLORXU65NmwLbF5tCUiP3wAMPMHnyZE466SSmT5/OtGnTuOOOO1o9Z8KECTz55JOYpklFRQWu65KRsTOd8+bNmwmFQowdOxaAs846i9dffz0ZcdpFxHHjFALA02+vZMcqzlHwj7dWxC1Pt1Y0snl7fVrKGe5RTlfx3Hur49o+W7ENt1n5WZbB/CWlMYUA0VoQL324BrM5/bYLvPrxurg+3l64scN3dKUbilMIANUfP4tPNe3hjN3OlwKnpjxOIQBUf/Qsfi864X1eiOqPno47Htm6FsLx9SRawybMCyveimv7vGwpdvNOumUZLNzyZezhBGB7Dq+sfAfTF50bjnB4Y+1HcX28t+ETbNGxq7OIE4lTCAD/XvYGTSoqu48I1R8+za52CKe6DKdyM1IKpBRUNFbGKQSAZ5a9jiOiMQa2DPPs16/EHV9XtZEGp5FUkcyYe9LhjTVz4s57b/1cPKODx9wLxykEgOeWvYojo1W2HWnz7NJXYgoBoKxhO1vqyzr02ZIsSa1tX3zxRd577z3uuOMOfvaznzF//nzmzJmz1/Msy+LBBx/k8ccfZ/r06ZSUlMSObdu2Lc6IXVRURFlZywI0rbE3jbcrpdtbPiQUCssy6JGTSV1jJKG9Tgq5x+8pKspO+vuTZXt14htLqZ3flyhITSnIygpgGpKKmsQPXSklhQliHNKFXZkgUElFx7yoR+Kx231MQwl+igIMISgqysZtqE7Yj5Qi6etT1VC5x2OxMW9ZfwmArCw/Zo5JZUNFwuMCKEzDPNkTW2oTzB8VFaSoKBu3qZ6aBBN91zfIqvKW96FSCkMI8ouyqQnVJvxuKfftntj93L2OeeOer31BGsd8dzm31iVwy1JgWSZ5OdnUhxsSugJI2bZnWFtp77VISink5+dTXFzM4MGDWb58OTNmzOCxxx5L6guuuuoqZs6cyaxZs3j66af5zne+A4DneYhdwnyVUnF/J0Nbto9MQzJyYA+Wra+KtZ134jBwXcrL6zBNyflTh3PTo3Njx0vyM+hZkJFwGZau7SPLb3LmlIP422s7K2yNG1aEaYjY900Y2ZO/BZfT0LxaMA3JGccMprqqMTqOpuRbEwfy8kc7VwsnHN4fPK9D3emyLB/+PiMIb975W/Imn0ODYxFJckzzcoqwCvpgV+yM8u4x+WyaZBah8josyyJ38rlsf+n+2HFfySA8KzPp3+oP+pgx/CSeWrIzSn9syUgsjFgf43sdyr+sl2mwow9dU5qcMuxEqitDzdtHJtMGTea1NTvjd6b0n4ChjA4dc1+Gj2EFg1lZsXPr8IyR0wiQEZvnuUefR+jvv4odN/NKkHm9YnIWZOTTO7uELbtUNTt35HSkau7DMjh71Ck89Mns2PGBeX0JysT3SjIkuvZ7G3Ppk0w76FheW/Ve7JwpA45C2TJtY55ITjNgMbzgIFZU7FyhnTlyOipsUF4fHfNzRp3Cb+Y8EDtenFlISUZxh8oJyW0fJWVTOP/887nrrrtYsmQJS5cu5Sc/+QnTp0/n7bff3uM5a9asIRKJMHLkSACeeuop1qxZw8033wxEt4++//3v89Zb0WX7okWLePDBB3nyySf3Jk6MtigFIQSYkk+XbWPVN9VMOawvPXsE4/bYhZTUNNm8tWAjJfkZHDOm9x79/9Pprywsg41ldXy8eAvDB+QzfkS8oVk212x4e+EmQhGXk48cQMAUcXUbhGmwenMNi5aWcdjIYob1zeskQ3OE0NrPiJSuJvPgY1G5vWi0E7+LJLzhYobmj7GrSsk+9ATILIizF2SYDqJ+Gw1fvY9VPJDg0CPaZWjeVLuFuZs/Z0SPgYzpdUicoVka4PoizFk/j5Ab4cRBR+P3gnE1BKwMh9UV6/i0bCljioYzomhIpxmaF25ezPrqTUzufwS9sorjDc2mixmqon7xO5h5xWSOnEy9G4jN8x2G5vkbF7G5oZwTBk4k35+HHYo3NFdEKvhgw3wG5PZlfO9DIdR+Q3Oiax835k6EEwe3HHMZcFlbs4HPSpdwaMlIhvUYjJdGG05Cm0KzofmLrUtYU72Bo/tNoDhYhArHG5rrvFreW/cxRZmFTOo3HhFOfZ2L1uSEFCqF9957jyeeeII//OEPzJgxg8bGRqZMmcKtt966x3PmzJnDgw8+yD/+8Q8AZs2axdlnn82pp54a+8xpp53Gr3/9a8aPH88vf/lLBgwYwGWXXbY3cWK0J3hth9uY47gJLf873OyUUgld+XaQ7iAWyzLw+01s2yMcTlyI3OczEQIikcSueqYpMQxJTk6wUwNu9jbmO2htTINBC8OQhELpdElNbsxzc4Ns316XcMz9fhPLMrDt9Lqk7o2sLD/BoI/a2kbC4Zbjkcw874gx30Fr1z7Zed4RLqmtyZmqZ0s65UyZUtiVpqYmNmzYwPDhw/e63fPQQw/x2muvYRgG06ZN40c/+hEzZ87kqquuYvTo0SxfvpybbrqJ+vp6Dj74YG6//XZ8vuTL8aUrolnKqBdPayPTXSIbofvI2pqcQkTfyFq73slct31FCEFhYVar4yll63LuuG9au/X21kcq5mhHjFeypHuOJjPmydDd76WUKYWGhgZ+//vf89FHH2EYBieccAI//OEP2/QATwcpL8dpCDwh2VJeT3amj5ygDzw34U3TXSYHdB9Z9yRnpuUgw7W4DTVYBX1o9HzYu8YHGIqgjOBUbMbIzsezsmhIZU1iojdTphlB1VciPAeRU0yD649b/vtNF78KY1eWYuX3JIKf0C4prQ1DkGmEcWvLQUhkViENbvw2l/B52CJMWf12emeXIF0LtUuthB19ONVlSMuPyOhBvZM4ZfqextM0IMOI4FRuQQazIZBDg5N80sZ0kK45ahgCz+ewvbECKQT5gR6IiJXSba6uSNrLcd50001IKbnhhhtQSvH0009z6623csstt7RP4i6IlIImW/HTh96PGXAnjCrhirMO3RkfoOlwMi2buvf+QsPSqLun8AXpddFtuGY+nqcwDIk/tJUtf70J5UTzx2eNm0bmxHNpdFJXwD3LjFD+7G1EtkaN92ZOET0vupUa1w+AzwQ2f8XmFx9ghxtbwbdm4R90JGEn+lDPMiJsfep/capKAbAK+1F8/s2x+ABpKeaVLuTJxc9F/xaSayddzpDMg3CcaC2NLNlE6RM34NZHHSb8fUZQcOa11EWS+61SCjK8Gkpn34AXinqHBYceTt60H1K/Hxa8V36HW96/l631UVemvjm9+MWxV0Fj18nh1tVIKk5h6dKl/O53v2PixIlMmjSJe++9l4ULF6Zbto5FCJ58dVlMIQAsWFpGVV24zV5RmtQgBMhwfUwhAKhIE1Xv/AW/jF4nvwhR+eafYgoBoP7zNzG9cMrkME1J+JulMYUA4NSWU/fF2/iaU434CVP5xmPs6tdc+fYT+EVULp/PoGHphzGFAGBv30Ro7WexdCWe6fD3r3YG0HnK47FP/45jNvdhCmo+eSmmEADCm5fjlG+IGpiTwCcdqt5/KqYQAJpWLYLGqv1unvt8Bh9vXBBTCADf1JbyRemSlNWf2B9JaiYVFxdTWbnTl7uxsZEePXqkTajOwFGK8uqWTvEVtSG6SKG5Aw4hBG5jS594p7YcqaKrN4nCqW0ZI+CFGpIubLQ3pBQ4VVtbtDuVW5CiuUSqUHEPWgBlh9jhviQEOJWbW/RhV26JPYwdz8HZrTBNTbiOHT9E4OJWt4whcKqTD4KSysWp3d6i3a2r3P/muYAtddtaNG+p37bfKcBUktQ06NmzJ2effTZ33nkn99xzD+eeey6GYXDrrbe26oHUnfAZkpMm9I9r8/sMBvfO7ZL5SQ4EPE9h5vdC+DPi2rMOPR5bRnP728JP1ujj4o7LjBxkVo+U7ZHbtkvG8Ikg4m+XrLHTsN3mB7oyCAwcHXfc32cYrtiRzM0jc8xJu/UsyDr42JjnjiUs+uX2jvvE4b3HIFzZ/B0mWWOnxnchDYKDxybt/WPLAFmHnhAvheXHKhm0381zx/Y4YXB8Kh6B4JgBE7p0QrrOptUazTtYuXIlo0aNwufzYZomw4YNo2/fvgQCAQKBABMmTOgAUVuSyhrNnqfoW5JNcY8Mttc0Mah3Lj+7cDxBS3brusfQfWRNJKcrDHqMnoxTXYYwLHKPPIPgwVNosptTeniQ1fcgjEAGbkM1gT7DKJpxDY0iEy+F3onCNMkZNh67agtGRg75J12KKDqISLNScDHpMWI8yg6jIk1kDD2C/JMvp9HzR1NcKzADGWQOGIlTtRUzp5DCU/8bN7snjhftw1AGkwcdRl24HttzOLr/EXx39BnQ7O/ueQp/Th7+kv64Nduw8ntTeMZPsP09cL2Wb76JxtPzFBnFvfHlFuLUbcdfMpCiGdcSMrJTOl5tJR1zVCnI8AUYVTKEsoZyCjPy+eERF1JoFaLc9q0UuvO9BMnVaE7aJTUUCrFhwwaGDh1KOBwmGGxZdrKjSYdLqmlJIg4YMro1sae3p+7ihQDdR9bWPCb80kEqB1sGiCQoHu+zBJYXwhMmYS+12Tp3YJoSH2ECPoPaiNnCJ14I8JsKw4vgCh9hV7R4abEsA0s1AYII/gR9CITl4eJgYuEmeP5YlsTnhVBCEFb+dhVXivbRhBIGYWV1+iohnXPUsgxsEbUxmZ5vn2IZ9od7KSUJ8RYvXsxJJ53ED3/4Q7Zt28aUKVP47LPP2idtF8exPaTyUG7Hp8zWJMbzFE2OQYPrT6gQACK2osH10+QYaavG5zgejY6FkZmb8MGiFIRsQYPrJ+S0VAgQ3YpqdHw0OtYe+lB4EYGIJFYI0T48GtxoH+2NiI324afR6Zrpm1OJbbsQMSGBIte0JCmlcOedd/LEE0+Ql5dHz549ueuuu/jtb3+bbtk0GgAMS0HAxguEEX6nhVHVNCW5Vphco4E8X4hEq2PpUxC0UYEIwu+mxdDo80nyrBC5Rj25vhB+X8vbS/g8VMCGoI30tXxAGaaAgI3rD0HAwditvreUIIIOKhiGjAhGsGUfPhNyrDB2zTayzEjSnkltQUpBlhkhx2gkxwrj69hsHillb2OeCqQU4HdRgUj02ltdVxEndSlDoRBDhgyJ/X3cccdx3333tXKGRpMahOXxecVXPPHFs4SdMEPyB3LNxJmIUDTYyjAkWaKBsmfvIlK2DuHPoPDky8jqP4b65iInwu8yZ9M8nlv6KrbncGjJCK444mJUCn3VfT6DQONWtjx7J27tdozsfErO+ilk9SXcXDtCBl3+seTffLhhAUIIThg0mbNHnBLL1SNN2Bop5b45j1ETrqM4s5CfH30FmeTGVgQi6PLHRX/n89IlGNLgtGEncvJBU1BN0VvZb3rIbSso/c/DeKEGrKJ+FJ9zA/UyM2UrKCkFWbKJ8ufujI15wfQfEugzmpDTvVyYkhnzfUUIUH6bhz55nOXbV+MzLC449EyOKDkMlaa6D/tCUhKZpklNTU3s7Wrt2rV7OUOjSQ2uYfPHRX8n7ET3hFdXrudfX7+MaH7TyjDCVLw1m0hZNIZAhRspf/lhTKJxDEJAg9vAP5e8hN3s7vll2XLeWvMBZoI3+fYSVA2UPX8PbrO7p1tXSdnz9xAQzRk+TcnS7av4YMMnKBSe8nh77Yesr90Ue5P3TId75j4adUMFtjVs5/75f8ZtjlMIBE3eXzePz0uXRL/Dc3lx+ZtUhqpifQSkQ/kL98bcY+3yTVS+9n8EZOJcTu3BLx2q3okf8+0vPYBfpO47Ooq9jXkqkBa8tOLNWM2FiGsz+/OnCZMg5XYXIKm7YtasWVx44YWUlpZyzTXX8N3vfpcrrrgi3bJpDnCkFGytL48rTgKwYvsaXKIPeKkcwltWxp+oPJza7UgZXUmsrdzYou+l5atwSN1DTCgPZ7cYAreuEtxmOQ3Bl2VLW5z3VdnyWB1t24tQH4mPddhUswVE9PfbKsyy3Yq5QHQ8YgFwTfUoN/53hbaswiB1LpgGDuHNK+IblYdTV5my2JCOYm9jngocnITX7Zua0rRsVe0re1UKSimOPfZYHn74Yf77v/+boUOH8uSTT3LyySd3hHyaAxjPU/TMKm6x/39w8XDM5p1PT/oI9B0Zf6I0MXMK8TxwXY8hBQNa9H1oyUhMlbqNcCUMrPz4GAMjpxAhm+V0FYf1Gt3ivLE9D44ZPy3pI8cfXxhlUI9+oKK/3xQ+RpcMb9HHyKKhsaybMpiFMOONKoF+I3FJ3VaZKywC/UbFN0oTIzu/SyTXawt7G/NUYGJxSHHL69Yvr3eXNPK3qhRWr17NiSeeyIcffkhJSQl///vfeemll7jsssv4+OOPO0pGzQGM4ZpcdeQlZPqiAWyHFA/n7JGn4DUniWuwTfJPuhh/3xFANHCt+MyrsVU0j49SEBSZXDLuPAKmH4HgiD5jOWHQZJw9eDK1h5DMpPjsn8YUg5lXQs9zfk6jiMrtOB7D8gcz7aBjMYTElCanD59Kv+zesb1rw7H4+dFXUJiRD0C/3N78+MgfYDjRh3y4yeXo/hOY2G88iqdmKQAAIABJREFUQgj8ho/zR3+bPH8uXnOQQZNrUXzO9RiZeQD4ew8h/+SZhLzU5TUKOQZ5J+w25mf9lHAKv6Oj2NuYpwI3ojht6EmM63kwABlWkFmH/xc+5U/Zd6SSVuMULr/8ck477TS+/e1v89xzz/GnP/2JV155hbKyMq6++mr++c9/dqSsLUhX6uxk6C7+ytB9ZN2TnIYlcI0ICoVUBsKOj0MwTUmGbEIqDxCEZZCmsNqtD3ANB1AIZUBEtvutdk9y+v0mAa8eoTyUkDSJzBZ586VP4UkHBEjXxIvEv5EapsA1I3gopJIYTnwhFikBv4snXEBgKhOnKb4Py4QAYUwDIq6kyWtbwaFkkFIQlBEMXJQQhFQA22n/d3TmHN3bmO9Ke+UUQoDPQTWnRTFcCzeNJpi0ZUktLS3l29/+NgCffPIJJ554IlJKevXqRX198oXRNZp9wbUV2BaCaLq53W0MjuNRy65vXS0fTq4NNFd8S9drRDjsECaw67e2+ExUCUTfqBM9dlxHwf+3d97hUVRdA//N7GxJD4HQERRpIkpVSiBKkQ6ivKgoVfywgYDSFHwpgoqF90UEUUFQRKSIBcUXEURCkI4IgiDSlIQkJCHZZOvM/f6ILMQACWGTbOD+nifPk713Zu7ZM7Nzbjn3HK/ZN4TX/3GUYQAOE/w9HXSxND4eL3iwEl0mjLTkTIriGxuGIMswc+67FJ1Wi578dO4PhBB/70w3/d1G4HJZo6BeECFr9+7dTJgwwffZ5fJfFEpJYHJuJ7FJePGqZlzewveurwaTScWquFCFgUe14SrEtE9QkBmvyMLAwGYKISvryn+WZrMJi+FAz0rHbDbj8fj/5aGqCmg6uqKjCQ3Dk3cTnGZW8Sg50Xs1/ep26BYWIQRpacm43U78YRCSklTfFFggUzrkVMjODiUoqEyh9uNc1ihERERw8OBB7HY7ycnJNGvWDIBdu3ZRoUKFwskrKRWYTCoh2En77gNciUcIuvE2Itv0JcNjversVVeCWQObM4nU7+bjzUgh5NY2hDXqTKa74IvEVqvOn1kJLNr7GXZXFh1ubElsjea4ryB/sk3TUZIPkfrDxwivh/Dm9xJyY2OyLpFvujCYTCoes4NFe5bzR9oJGlSoy4P1e4JT8+lctRn8lLCDNYc3EGQO4pHbelHJVgnhKV5/d7s9x0W9QoWqKMrVt61paqnYbVwa5BTCICMjFV0/S1hY5BWff9knetSoUQwcOBC73c5zzz1HcHAw8+fP55133uHtt98utNCSwCdIcZL06TQ8Z3LCPdv3/oCelUF4xyfJ9nNWs8sRrLj466MJOWGogbObV6KYzFgadL5kyIt/4lQ8TN74X3SR82NevO9LwqwhNKt4Bw5H/hO7igJmTyanlr3MuV7xmW/mUOGBiZiiavltk5OuuXl502xOZea4tm489hMZLjuP3f4wuE1omsrBtEN8sHuZ75yXfpzFzE6TsChBxTqKczjsREVV8ItBkPgXRVGJiChDUlJCoYzCZe9ow4YN+fHHH4mPj2fgwIEANGrUiOXLl5dYZFRJ8aAYbp9BOIfjyG40pfhmQ1VVwZOW4DMI58javwmzUbCNP0FBZg4m/eYzCOf44cQOdJFdoGuYzSayf9vKP6dJ7Hu/x2zyoz+78PgMwjn2JOyHv9swVJ0fjv2Uq14Iwd7EX9G04k0aYxg6JlMpjm1xjWMyaRhG4X6r+Zp5i8VCeHi473Pjxo2pUaNGoRqTlB4UkzmPv3tx+6EbhsAUmjeZkxZZAUMp2EvQ6zWoEFo+T3nlkHJoasFcKA1DYC5XJU+5OfoGDH/6s5s0zKbcMkUFRZ6fOkKlSljeadtKYeVLZJ5bJqoJXK7m3sixn+SiuISZqPaDgL8fLpNGua5P4aB4Q6YbWgihDc8np1GDQinTbkCBfeI9Hp0KIeVoWulWX1mZoAjuq9+ZgvpKeL0Glsp1fH75AOaoSoTe1ha3HxebVa/GgNt7o/ytc03VeLzZI2jeHM8qr1vQpVZbov/2qYecTXhVQgNzE5SkdFLgfAqBiNynUDAKK6tN07HgRs9MxRReDqdhxq0XXT/iUnIGa140w4HusGMKK0u2bsV7BS9BVQXN6sHudeL0OokKisQsggu0nnAORVEI0dwoLjsKOsIaQZbuf/9/xWxgmLykOtIoGxyF4tUQnvO9PpNJxbC4SXdmYNHMBJuCwWW66AiuKJ/RxMTjVKyYd6d4YfHHAu6GDev46KOF6LqOEAadOnWlb9/+fpEvJSWZV16Zyn/+MzvgF5ohR59//nk0zz266n0Kkusbp9eEkyCU4CqIEox1lrOwHYZiC0O44UpdIA0D3A4zFswEmcLwZnPFcY+EENg9ZlDLXPCy9X+HRHhUFI+FcmoFhCNvC7pugEMjUolCeCjR+xJIJCcnMXv2f1iwYDEREZFkZ2fz9NP/xw03VCcmJjb/C+RDuXLRvP76LD9IGvhIoyDJl6IeS6qqgllTEOLyPbDLyWEyqWgmMIRyyfy7FosJRRE5iWyKaISpaSYUVcBl5CgI+em89I7vi4b09HS8Xi9Op5OICAgODmbChElYLFZ69+5O27bt2b59KwDjx79I7dp1+fPPk7z++stkZJzFarUxcuRoateuS2JiAtOnTyYtLRWbzcbYsRMJCQlh2LChfP7516SmnuG116Zz+vRpVFVl6NCnaNbsTnbs2MacObNQFIWwsDAmTZpOZGRu759zU/2BfP+kUZCUKEGaFzUrGfvOb/GWqUDE7e2w60FX5OYZbPZC6kmy9q7HHF2diFtjyfSen9oxmXLi/2fuWotuTyWsSRe8QWVxev3rsaMGGfyS/Cu7EvZxe8V63F6+PoazZDb8XW/UqlWb1q1j6dOnJ7Vr16FRo6Z06NCJqlWrAWCzBfHBB0uIi/uRadMmsWjRUqZN+zcjR46hdu26HD36B88//xyffPIZb7zxCrGxbbn//j5s2RLHokXzefLJ4b62/vvf1+natQcxMbGkpKTw5JOPsnDhEhYtms/o0eOpV68+H3+8iEOHDnLHHc1zTlIEXuElw2FHM2mEW0L9GnTPn0ijICkxNE2FM0dJ/PR8Fr/MPd9TccArZOgFW0g2m1W8R3dx5ps5f5dswr5vI9EPvEjm34vRISYXpxaMwcjOAMC+dyMV+03FFHqD3/YYqBaDFQdW8/0fcQDEn9xBq2pNebh+bwjARCrXIs89N54BAx5l27af2LZtC0OHDuLf/54KQI8e9wEQE9OGadMmkZR0mgMHfmX69Cm+8x0OB2fPprNnzy4mTcp5Jlu0iKFFixgSEk75jtuxYxvHjx/n/ffnAeD1evnrrz+JiWnD88+PpnXrWFq3jqVZs+a+c9y6mwR7su9zpstOlbCKAWkYpFGQlBgW4eJs/Ge5yvTMM3hTTqJG1izQFI9FOEjZsipXmSflJMKZAaaymEwq7lOHfQYhB8HZ+JWEdXwah59CShsmnQ1H43OVxZ/cyUMN7kXBfxE3JRcnPj4OhyObdu3uoWvXHnTt2oMvv1zF6tVfAGAynb/PQhgYhoHFYmXhwiW+8qSk04SHR+TafyGE4Nixo9hs52Na6brBrFlzCQ+PACAlJYUyZcpQq1YdWrVqQ3z8JubMmcVdd+1nwIBHQRGkOS98/sBr6LgNDxYl8J4N2YWRlByKAupFXspXtClKQbnI8coF171ovcmMwL+9NNM/dvcqiuJzL5UULTabjXfeedvXoxdCcPjwIWrVyslj8P33/wNg48YNVK9+IxUrVqJq1Wr873/fALB9+0889dT/AdCwYSPWrVsLwI4dW5kxI3c++iZNmvLZZ8sBOHr0D/r3fwCXy8ljjw0gOzuLPn360qdPXw4dOug752LbBgL1yZAjBUmJ4RIWIts8ROLiifD3IrO5bBVMZSphuAs2Ee/CRpnYh0haOcNXZq1SF2EJAU9Or85c4Ua0yArnM6OpJiJi/kW2YcJfHkSqrtG1Tns+P/Ctr+yemm1QDFMpjh9aemjcuCmDBz/GmDEj8HpzYsfeeWcLBg4cwtq1a/jll59ZvfpLgoJsvPDCJAD+/e+XeO216SxZ8iGaZmbKlOkoisLIkWN49dWXWLVqxd8LzRNytTVy5BhmzJjGgAEPIoRg4sQpBAeHMHToU0ybNhmTyURwcPD584RCVFAkpzynfc+CxaRhVs0BGVxW7lMoJNfDPoXiwGoysOiZZB+IxxxZHkv127B7r8z/P8jkRXOfJevgFszRN2CtWo9Mj8W3E9hkUggxuXAc2Y2RlUbILa1wKaG49ML11S6lT8Wmk5iVxN6kA9SPrk3V0EoYzuINP3Eh19s+hUvRu3d33nprHpUqVc7/4HwotJyKwMAgy52NpmoEabYiXU+Q+xQkpRaXruJWIjDf3o2wiOBC+f87dA1FK4e50b3ohkGG28h1DV0XZOgWzDe1xKRApkcnH+/XQiGcJiqZK1OtejV0XUd3ltr+lsTfCAUVE+GWsBxvtAB+NKRRKMWoqoKicFUhDkymnHj9JTXighyf7fy+g6IoqKpySW8hIQS6blz2e1zNvoGCYhgCt/tiqW8Kjqrm7Hf4Z9a24iY/nZcWVqz4qqRF8FEa5mWK1CjMnj2bNWvWABAbG8uYMWPy1K9cudIXcK9Pnz48/PDDRSnSNYGqKmD1ciozEbfhoUZEVRS3+YqMg8mkEmJy4kk+kZOspdwNZOmWYo+hYzZBsMmF69TvOB1RhIWUw+41+348igIhmgfsKXgzUwmtdDMOYeXC966qCXSzh9/OHCc6pCyRtghECU7bXA1mm5dMbxYnU//kprI3YsOKx1W830VRAKtOqjONVHs6NaOqo3rMGFdn6ySlhCIzCvHx8cTFxbFq1SoURWHIkCF89913dOjQwXfMvn37ePPNN2nUqFFRiXFtYvXy0o//5a/MRAAibOFMbzcWHAVPnB5qcpCw6Hn0zDPA34nmH3mJswXcH+APVFXB5k3jr/fH+cJj22o0oEzX4TkhJcgxCOn/ewfH7zsBUDQLFftPQ7dWQNcNNE3ltCeRyetmov8dKrjVDU15uP79iGJ+mV4tZpvOd0d/ZPkFi9XD7xjA7eUa4HQWY2/dovP+7iXsSvglRy6Tmal3P0ekqWypHzVI8qfIXFKjo6MZN24cFosFs9lMzZo1OXXqVK5j9u3bx7x58+jevTtTpkyRKT4LgKap7E/+zWcQAM46M1h7ZCNmS8Fup8WiYf9lo88gAHjTT5N9eBtmc/G9SK2ql/QNi3PlS3Ae+wXsZ3LcORVQnJk+gwAgvG7Svl+EVXHnyK26WfTzcp9BANh8YgcuUfqeJa+i89nBtbnKFv78GR6lYLkj/IGiQJZu9xkEAI/u4aO9n2GYZKCl64EiGynUqlXL9/+xY8dYs2YNn3zyia8sKyuLevXqMXr0aKpXr864ceOYM2cOI0eOLHAb+a2iFzXR0WEl0m5qYnqesjPZaYSEWjFfwsf/QlmFEKRkJOc5Rs88Q9nIYP8Jmg96dgZns/N+F+HMpFzVnOfHceJk3vOyzmK1KISUCSPNcZazzrweNm7DTZXooksZWxT3/nRmUp5kQHZ3FihKodsrzHnJyYl5ys46MzBbVSIicq6XlKTm7Ej3I/6+XlFRWuRUVbVQ97/IF5oPHz7M0KFDGTNmTK7kPCEhIbz33nu+z4MHD+b555+/IqNwPbqkKgrcWaURn/7yZa4XSMeb7yIj3XlRffxTVlVVCG10D5m71+Fzg1BUQuq3ISXFXmw5mM1mE6GNOuE6NdtXpliD0aJv8MkbHlEBNSgMw3Fe/rBG92D3mPEkZ6KZTbS7qRWf7ju/mFjGFkGYObTI7k9R3XtLkMZNZW7gj7QTvrKWVRtjMtRCtVdYOaODyhFmCSHTneUra39TawynieTMnOsZhuFXF9KrdUmNiWlKXNyOPOWff74CgHvv7V3oa1/IhXKeOvUXixbNZ/z4F/1ybX+iaSqGYeS5/yXukrpz506GDx/O888/T9euXXPVnTp1ivj4eHr3zrlZQgg0TTpD5YcQYDFsTG07mmX7V+PW3dxbtxNlLWULvOHLMAReWxQVH55E+uYVOTldWz+ARwtHFONCs8djEFqjIWW7D8O+ay1aWBQRbR4iy7BxzlhlG1YqDXiZ9B+X4j2bROhtbbHUbIrdnfPD9HoEd1VvRbAWRNzJ7VQKLU/v+t1Q3Wb0QPb7uxgeK2NaDeXzA//jcNpxGpWvyz217sLrKN61EdVjZmq7MSzb9xXJ2We4u0ZLGlVogNcP6xpb9ify2cYjnMlwUTbcyn2xNWlRv6IfpL44/jIGFyMxMYG//vqzyK5fUhTZ5rWEhAR69erFzJkzadGiRZ761NRUOnfuzIoVK6hatSovvPAC1atXZ+jQoQVu43ocKZzDZFIRJi8CgckwX7aXdSlZTSYVi+IGFNzCXGKLiJqmYhYugkODScv05rmnqqpgMRmohhePasVzkWxnZrOKR3FjQsPwKEU62inKe6+qYLaAV7gxqzacjsLfk6uRU1UVFE2g48UsLHl0XpjNa1v2J7JozUHcFzyrFk1lQOe6tL69sl9GCrt27WDu3FnousFNN9X0bVgbMOBRXn55Mn/8cQSAXr3+RY8evfJcZ/bs/7B9+1ZUVaF167sYPPj/yM7O5s03X+WPP44ghEHfvv3p0KETAwY8yKlTf9GpUzeefXYsH364gLVr16CqKs2aNefJJ4fjdDqYNOkFzpzJWb8bPPgxYmJi2b17J+++OweXy0lmpp3hw0fSuvVdhf7+/yQgN6/Nnz8fl8vFK6+84it78MEHWb9+PcOHD6dBgwZMmTKFJ554Ao/HQ+PGjRk0aFBRiXPNoesG/J0FzUvhfky6buDwPQIl51Xi9Rp4MRNqsWEYeV9ihiFwGgpghksYrpyXlkbOcnMpGyFcgGGAywlgQS/Be2IYAtwAGh4/yfHZxiO5DAKA22vw2cYjtL796ncbn+PkyROsWLGa0NBQ5s/PiWT6yy8/k5GRwQcfLCElJZm5c9/KYxQSExP46ad4Fi9ehtPpZPr0ybhcLhYtmk+dOvWYMGEyLlc2Q4YM5JZbbuWZZ55jwYJ3efbZsWzZspm4uB95//2P0DSNCRPG8PnnKwkODqZixcq89tp/OXz4N9au/ZaYmFhWrvyUceMmUr16DXbu3M5///u6X43C1VBkRmHChAlMmDAhT/lDDz3k+79jx4507NixqES4pgnWvGiKF4RARyNbtxTbWoA/0TSVENUBXjees05CLRp2t5xGvBY5k3Fxj7BLlReWatWqExqauzd80001OXHiOKNGPU3z5q146qln8pxXrlw0VquVJ54YTMuWrXniiWFYrVZ27NiGy+Xk66+/RFHA6XRy9OgfBAefd8rYuXM77dt39EVT7dq1B2vWfM0TTwxj3ry3SUlJokWLGAYOfBSAiROnEh+/iQ0b1rF//y84HA6/6uBqkL++Ukio2UPmpk+w790ACIJuvJ2obsPIcJWu26mqEKpmc3r5K7hPHwPVRGTL+whv2IEMd+CFFJZcHWXDrRc1AGXDrX5tx2rNe72IiEg++mgZ27dvZcuWzQwe/AgffbSMYcPOT1cvXLiEd99dyJ49u9iyZTOPPz6It956F8PQmThxKnXq1EXTVJKSkgkPj2Dv3j2+c/+ZNTBnl76XatVuYMmSFfz00xY2b/6RpUsXs3jxcp566jEaN25Co0ZNaNKkGZMn5+1AlxSlw7dK4sNkUjHOnMS+dz3npkkcR3/GcXBLse4x8AfBZkHapmU5BgHA0EmPW47izkKVT+Y1x32xNbH8w53ToqncF1uzyNuOi9vI1Kkv0rJlDCNGPEdQUBBJSadZuHCJ7+/QoYM8/fT/cfvtjXj66RHUqHETJ04cp3HjZj4vppSUZAYMeIjTpxMxmTR0PWfCsnHjZqxb9z9cLider5dvvvmSxo2bsnLlp8yfP4+2bdvz7LPjSEtLw27P5OTJ4zz66OM0b96KTZs2YhiBsymwdHUtJZhMCq6/DuYpd53cT3Cd1leYjr5kUXUn7sSjecrdyScxVS4bUD8UydVzzsuoOL2PztG8eSt++GE9/fr1wWKx0LFjF2rWvDnXMbVr1+XWW2+jf/8HsNlsNGhwO82bt6RRo8a88car9OvXB8MwePLJ4VSpUpXQ0FDs9kymTp3IxIlTOXz4Nx59tD+67uWOO5pz//05eRYmTXqB/v0fwGQy8dRTwwkPj6Bbt57069cHTdNo3LgZTqcTh8NBUFBQkesiP2To7EJSUt5HqqoQ7EggYdG4XOXleo5Ar9IErzdvELWS9pS6FCE2Bce2zzj70xcXlCpUe+It0vWS3Zh4OQJVn/9Ehs72P6VJzsJ6H8lBeinDMARGSDmi2g1EsQajaBbC7+iOpdqtFzUIgUyWUxB+RzdC6rUCRcUUEkn5e0fgUW35nyyRSIoEOX1UCsn2aljr3kWlui1Q/t5jkOkO1OR+lyfDbSai3SCi2vUHBC4liCxXqR28SiSlHmkUSikuD7jwr9dGSWAYkOnWAI3o6DDSS8G0jERyLSONwhWiaSoeA1IznJg0FT1A5xdVVcGmejChoztykqWUZCKdy2Ezg1m40LMMNM1c6qbB/EnOfXNjUgy8QsOpa6Vy/4mk9CKNwhWgqiqJ6U7mrNxLSrqD2MZV+Ffb2hiewMo+oqoKoWo2qf97D+fJA9iq1Caq01CyTKEBFw8/3OIlc9sXnPllA6aQMkR1HIIpvBou/fpb7jKZFEKMDM6sfgdX4h8E1WhAmQ6PYtdtAWvQJdce198v7yrQFYUX3onnWEIGdoeHrzcfY/Xmo5i0wNofEKS6SPn8DRxHdiHcDhxHfyZpxcsEFWNc/oJgMStk79tAxravMBx2PCknOf3JFKxK6cuF4A+CFRenP30J54n9CLeD7EPbOPP1bGyqu6RFk1xHSKNQQFRV4a9kO55/TBdt+SUBT4D1vk3ouE79nqvMk3wSVQTWiEYzXGQdjM9daOi4Tx/DZCqdC+dXheHGm346V5Hz2C9oSmA9X5JrG2kUCohhCMpF5t1YUq18KCY1sF5gghz3zgtRbSGgBtaIxlDNmMtVy1OuRZa/LqdLFJMZxZzbeUCLKI8hAuv5CkRiYppetPzzz1f4diOXBCkpyTz33PBCnXup71TUSKNwBQSZVe676/wuyKhwGwO710cJsIVAJ1bKdR+OouXED1JMZsp1exqnCCxvJZdXJbLNQ2jh0b6ysMYdEdZwAkylxYJLWCjb+QlQc5b6FLONct2H4bwGvMzO4T4cj33Js2S+OxD7kmdxH47P/6Sr4N57exdpToX8KFcumtdfn1Vi7RcGudB8BQjdoHurGnRpWQO318BmMWESBnoxJqYpCB4vKGVupPLQ2QinHS0kjGyvhjuwZo8QQpAlQijfbxq4szFZbbgNjSzv9flYurwK1iq3UeWJtxHOLJSgUBy6Ga83sJ6vwuI+HI9r00Lw5qyRCPuZnM+AVi/GL21cTT6FuLiNfPnl58yYMROAFSuW8ueffzJs2EjmzPkvu3fvxDAMOnfuxgMPPJynrU6dujJnziwURSEsLIxJk6bjcGQzbNhQVqz4isTEBKZPn0xaWio2m42xYydy8821+PrrL1m6dDGKolCnTj1GjhyTKwKr0+nk1Vdf4vffD6GqKg8++AidO3fjm2++Ys2a1Zw9m06rVm0YOvQpv+jw+vz1XQVCN1CBahVyQggEqvOkW1dw6xYwRREdFoYrQP3/dd0gU7eAYiE6Qu5TcOkqLt0KJuvf+QyuHdzbV/oMgg+vG/f2lQT7yShA4fMpNG/eitdee5mMjAzCw8P5/vu1DB/+LF99tQqABQs+xjC8DB/+JHXr3pKnrWHDhjJ69Hjq1avPxx8v4tChg1SrdoPv+m+88QqxsW25//4+bNkSx6JF8xk4cAgffriAd99dSEREJG+88SoffPBertDeCxbMIyIigo8+WkZ6ejqPPTaAWrXqAJCcnMTixcv9mrVSTh9JJAVEVVWCglRcbkehk7erqoLNnLM3Qw2wtaiiRtjPXFF5Yckvn8L69esumk9B0zTatLmbjRvXk5iYyNmzZ6lXrz47dmwjLu5HBg7sy6OP9ic5OYkjR37P01ZMTBuef340b775KrVr1+GOO5rnuv6ePbvo1KkLAC1axDB16ivs2bOTVq1aExGRswbYo0cvdu7cluu8nTt30LVrTwAiIyNp3boNu3fvBHKC+Pk7jbEcKUgkBcBsEWSTwaf71+LS3fSs3Z4oaxk8roIv3ltNBhZ3Gmc3rUJRTIS36IVLi8CtXx/GQQkte1EDoISW9Ws7V5NPoWPHLrz//lwyMzO4557OQM5o9sknhxMb2xZNU0lJSSUoKIj9+3/J1dYDDzxMq1ZtiI/fxJw5s7jrrv2+awCYTOdft0IIjh07ehGHCuELx33+2IvnarjUd71a5EhBIikAHpObMd+9zA/HfmLLyV2M//410j1nCzxiUBQFizeDUwtGk7U/Dvu+jZxa8Bw2kYVyfdgELM3uB+0fyZM0S055EVOQfAoAt97agJSUFP73v2/o0KETAE2aNOXLLz/H6/WSnZ3Nk08+yv79v+Rp47HHBpCdnUWfPn3p06cvhw7lDnHfsGEj1q1bC8COHVuZMWMajRo1IS7uRzIyzgLw5Zef06hRbq+jxo2b8fXXOZGE09PT2bTphzzH+BM5UpBI8iEoyMz643G49fPZKgSCr3/fwMBb/4W3AAv4FouJzPhv4cJen+7Fvm8j5oY9cQeaF0ARYKnVEshZWxD2MyihZbE0u99XXpQUJJ/COdq168C2bVuoUqUqkOPB9OefJxk0qC+GYdClS3caN27Krl07cp03dOhTTJs2GZPJRHBwMGPH5s6mNnLkGF599SVWrVrx90LzBG688Sb69RvE00//H16vlzp16jF69Phc5w0aNIQ33niV/v1xREFEAAATBElEQVQfwDAM+vcfTJ06dTly5LAfNXQemU+hkJSWmPpQemQNVDltNjPbErYyb/fSXOVdbo6ld92eOLLzf6FbLBre3Z9zdnNun/ky7Qai1G2Hx+N/lwWZT8H/lCY5ZT4FiaSIcDo9NKpyG2WDyvjKgsw2utRuh8tZsB6+2+0lrGF71KDzP0hTSCQh9VoUiUGQSAqLnD6SSAqAyWtlervR7Dt9ALfupmGlBmi6FfcVjFTtRhCVH30Dx9G9KIqKrUYD7LqVc7m2JZJAQBoFiaQAuN06uDVuL9eI8PAgkpMzcV/hy1zXBWd1K1r1OwE46zaQBkESaEijIJFcAS7X1S8Il4Y5acn1i1xTkEgkEokPaRQkEolE4kMaBYlEUuopytDZcXEbef/9d67onIED+/r9msWFXFOQSCTFxrbEXXx55FvSXOmUsUbSo2Yn7qjYuMja80fY7JiYWGJiYq/onHM7pP15zeJCGgWJRFIsbEvcxZKDK/EYOTvD01zpLDm4EoCWVf0TtqEoQmfXrl2H3bt38sILk7j33q7ccsutHD78G3PmvM+6dWtZufJTQkPDqF69OpUrV+XRR4cSE9OUuLgdzJ8/j5SUZE6ePMHp04l069aTAQMe5ZtvvvJdc/v2rcye/R+EMKhYsRL//vdLALz88lSSk5NISUmmadM7GDduIkoxxESR00cSiaRY+PLItz6DcA6P4eHLI9/6tZ2TJ08wa9Y7TJgw2Vd2Yejs1177Lz//vDvPec2bt+K33w6QkZEBwPffr6Vjx84XOa4ln3zyGWfOnOGzz5Yxf/5HvP32e5w8efKi8vz++2Fmznybd99dyOLFi8jMPL/L3O12M2XKRCZMmMSHH37KTTfdzJo1q4mPj6NWrdrMm/cBS5euYs+eXfz228GLXt/fyJGCRCIpFtJc6VdUXljyC53dvHmrfENnN2vW3Bc6++jRP3Idd8sttwI5Qe1atmxNSEhOW+3bdyQzMyPPdRs3borZbKZMmSjCw8PJyrL76v7443eio6N9+REef/xpX92vv+5j2bIlHDt2lLNnz+JwZBdSI1eGNAoSiaRYKGONvKgBKGONvMjRhcffobMvdX1VNeUJa30xLJbzkWEVReHCcHM54bTPTwnZ7Xays7P48ccN/PDDenr06EXv3ndw9OgRiitMXZFOH82ePZuuXbvStWtXZsyYkaf+wIED3HfffXTs2JEXXngBb0HCTUoKhNkE4WYXYZzFm5mKxSR3zkpKlh41O2FWzbnKzKqZHjU7FXnbVxM6+1I0bdqMLVs2k5Vlx+PxsHHj+iue87/hhuqkp6f5RiMff7yIzz9fyfbtW+nR4z7uuaczbrebw4cPYRjFs+mxyEYK8fHxxMXFsWrVKhRFYciQIXz33Xd06NDBd8zo0aN56aWXaNiwIc8//zzLli2jb9/Lu3JJ8kfTFMxnj3Fq+csItxPFbCX6vucwl62FR9pdSQlxzsuoOL2PznE1obMvxU033Uzv3g8ydOhggoKCiIyMvOKkN1arlYkTp/DSS//G6/VQuXJVJk6cwoED+3n99ZdZvPgDQkJCufXW20hIOHVF1y4sRRY6+/Dhw2RlZdGwYUMApkyZQvXq1RkwYAAAf/31FwMGDGDdunUA7Nixg1mzZvHhhx8WuA0ZOvvihJldnF40Dt2e5itTg0KpOPgNMj3+z9TkLwJZpxci5ZShswFOnDjOli1xPPDAwwCMGzeKbt3uJSamTUmKCFxd6OwiGynUqlXL9/+xY8dYs2YNn3zyia8sKSmJ6Oho3+fo6GhOnz59RW3k9+WKmujosBJt/1J4M1y5DAKA4bBjwghYmc8R6PKd43qXMylJLXSe6kvh7+sVFefkrFq1Cr/9doD+/R9AURTuvLMFsbGxxeI2WhBUVS3U/S/yhebDhw8zdOhQxowZQ40aNXzlhmHkUp4Q4oqVKUcKFydEU7BUvBF34lFfmblsFbyYSA9QmSGwdXohUs6c368/e/alcaSgqhovvvhSrnpdFwRC5FtNUzEMI8/9L/EkOzt37mTgwIE8++yz9OqVe6NIxYoVSU5O9n1OSUmhfPnyRSnOdYNTWInuNRpb9VvBpGGrVo/y/xqPw7CVtGgSiSTAKbKRQkJCAk899RQzZ86kRYsWeeqrVKmC1Wpl586dNGnShC+++II2bUp+Lu5aQNcFdjWEiK4jiFJ0zBYr6Q4FQw/8npik9FCY0b2keLiapeIiMwrz58/H5XLxyiuv+MoefPBB1q9fz/Dhw2nQoAGvv/46EyZMwG63U79+ffr3719U4lx3GIYg29AAjejIMIyswJ/ukJQeNM1CVlYGISHh0jAEGEIIMjMz0DRL/gdfhCLzPioO5JpCwSgtsko5/UtRyqnrXtLSkvF63X65nqqqxeaHfzWUFjlDQoIJDi7z9+a485So95FEIrl2MZk0ypWr5LfrSUPrX65GztLhAyaRSCSSYkEaBYlEIpH4KNXTR6pasgtcJd3+lVBaZJVy+pfSIieUHllLs5wFkb1ULzRLJBKJxL/I6SOJRCKR+JBGQSKRSCQ+pFGQSCQSiQ9pFCQSiUTiQxoFiUQikfiQRkEikUgkPqRRkEgkEokPaRQkEolE4kMaBYlEIpH4KNVhLoqTV199lbS0tFz5IQBmz57NypUrCQ8PB6BPnz48/PDDxS5fv379SE1NRdNybumUKVO4/fbbffUHDhzghRdeICsri6ZNmzJ58mTfsYEma6DodP369cyePRuHw0GrVq2YMGFCrvpA0Wl+cgaCPpcvX87ixYt9n//880969uzJiy++6CsLFH0WRNZA0CnAF198wbvvvgtAmzZtGDt2bK76QulUSPIlPj5e3HnnnWLs2LF56oYOHSp27dpVAlKdxzAMERMTIzwezyWP6dq1q9i9e7cQQojx48eLjz/+uLjEy0VBZA0EnZ44cULExMSIhIQE4Xa7xUMPPSR++OGHXMcEgk4LImcg6PNCDh06JDp06CDOnDmTqzwQ9PlPLiVrIOg0OztbNGvWTJw5c0Z4PB7Ru3dvsXnz5lzHFEancvooH9LT05k5cyaPP/74Rev37dvHvHnz6N69O1OmTMHlchWzhPDHH38AMHjwYHr06JGrlwPw119/4XQ6adiwIQD33Xcf3377bbHLCfnLCoGh0++++44uXbpQsWJFzGYzM2fOzDWaCRSd5icnBIY+L2TSpEmMHDmSqKgoX1mg6POfXExWCAyd6rqOYRg4HA68Xi9erxer1eqrL6xOpVHIhxdffJGRI0f6hokXkpWVRb169Rg9ejSrVq0iIyODOXPmFLuMGRkZtGjRgrfffpuFCxeydOlSNm/e7KtPSkoiOjra9zk6OprTp08Xu5yQv6yBotPjx4+j6zqPP/44PXv2ZMmSJURERPjqA0Wn+ckZKPo8R3x8PE6nk86dO+cqDxR9XsilZA0UnYaGhvLMM8/QuXNnYmNjqVKlCo0bN/bVF1an0ihchuXLl1OpUiVatGhx0fqQkBDee+89atasiaZpDB48mI0bNxazlNCoUSNmzJhBWFgYUVFR9O7dO5cchmHkyqMrSjDhen6yBopOdV1ny5YtTJ8+nU8//ZS9e/eyatUqX32g6DQ/OQNFn+dYunQpgwYNylMeKPq8kEvJGig6PXjwICtXrmTDhg1s2rQJVVWZP3++r76wOpVG4TJ88803bN68mZ49ezJr1izWr1/P9OnTffWnTp1ixYoVvs9CiBJZGNuxYwdbtmy5pBwVK1YkOTnZ9zklJYXy5csXq4znyE/WQNFpuXLlaNGiBVFRUdhsNtq3b8/evXt99YGi0/zkDBR9ArjdbrZv307btm3z1AWKPs9xOVkDRadxcXG0aNGCsmXLYrFYuO+++9i2bZuvvrA6lUbhMnzwwQesXr2aL774guHDh9O2bVuef/55X73NZuO1117j5MmTCCH4+OOP6dChQ7HLmZmZyYwZM3C5XNjtdlatWpVLjipVqmC1Wtm5cyeQ47HQpk2bYpezILIGik7vvvtu4uLiyMjIQNd1Nm3aRP369X31gaLT/OQMFH0C/Pbbb9SoUYPg4OA8dYGiz3NcTtZA0WndunWJj48nOzsbIQTr16+nQYMGvvpC69Q/6+DXPitXrvR5Hw0ZMkTs3btXCCHEt99+K7p27SruueceMW7cOOFyuUpEvpkzZ4pOnTqJe+65RyxcuDCPnAcOHBD333+/6Nixoxg1alSJyVkQWQNFp8uXL/fJMXnyZKHrekDqND85A0WfX3/9tRgxYkSuskDUpxD5yxooOp03b57o2LGj6Natmxg/frxwOp1XrVOZeU0ikUgkPuT0kUQikUh8SKMgkUgkEh/SKEgkEonEhzQKEolEIvEhjYJEIpFIfEijIJFIJBIf0ihIShV79uyhX79+dO/enW7dujFkyBAOHz7s93a2bt1Kt27dAHjrrbeYMmWK39u4GLNnz2bdunUAjBs3LlfYgn+yYcMG+vXrR8+ePenatSsjRowgISGhWOSUXLvIfAqSUoPb7Wbo0KEsWLDAt2v3iy++4LHHHuP777/HZDKVsIRXz9atW7n55pvzPe6rr75i7ty5zJ07l+rVqyOE4N1336V///58/fXXWCyWYpBWci0ijYKk1OBwOMjMzCQ7O9tX1qNHD0JDQ9F1nY0bNzJ37lw8Hg82m42xY8fSqFEj3nrrLY4fP05iYiLJycnUrVuXadOmERoayoYNG5g3bx5ut5vU1FTuvfdeRowYUWCZjhw5wrRp00hPT0fXdfr160fv3r3ZunUrM2fOpFq1ahw+fBiv18vkyZNp0qQJqampjB8/nhMnThAZGUl0dDS1atUiKiqKffv2MWPGDJ+B2717Nw8++CApKSnUqlWLN954g+DgYGbOnMnUqVOpXr06AIqi8H//939UqlQJt9vN6tWrWbt2LYZhcOrUKSpUqECfPn1YvHgxx44dY9CgQQwePNi/N0hybVCke7AlEj+zYMECcdttt4m2bduK5557TixfvlxkZ2eLo0ePim7duonU1FQhRE5ylFatWomsrCwxa9Ys0aZNG5GcnCx0XRejRo0Sr7zyijAMQzzyyCPi6NGjQgghEhMTRb169cSZM2fETz/9JLp27SqEEGLWrFli8uTJeWTxeDyiS5cuYt++fUIIITIyMkTnzp3F7t27xU8//STq1asnfv31VyGEEPPnzxcPP/ywEEKIkSNHihkzZgghhDh9+rRo1aqVmDVrlhBCiEceeUSsWbNGCCHE2LFjRe/evUV2drbwer2iV69eYtWqVSI1NVXUrl1bZGdnX1JPK1euFE2aNBGnTp0Suq6LLl26iGHDhgld18WBAwdEgwYNhK7rV3s7JNcgcqQgKVUMGjSIf/3rX2zfvp3t27fz3nvv8d5779G3b1+SkpIYOHCg71hFUThx4gQAnTp1oly5cgD07t2b6dOnM3bsWN555x1++OEHVq9ezZEjRxBC4HA4CiTLsWPHOHHiRK4giU6nk19//ZWaNWtSuXJl6tWrB8Att9ziC2m9ceNG3//ly5enU6dOl2yjffv2BAUFAVCrVi1SU1NR1ZylQMMwLitfgwYNqFSpEgBVq1YlJiYGVVWpVq0aLpcLh8NBSEhIgb6r5PpBGgVJqWHnzp3s3r2bIUOGcPfdd3P33XczatQounXrht1up0WLFvznP//xHZ+QkED58uX57rvvcq03GIaBqqpkZ2fTq1cv2rdvT9OmTbn//vtZt24dooDhwHRdJywsjC+++MJXlpKSQlhYGHv27MFms/nKFUXxXVfTtFxtnHvJX4wLQzKfu0ZERAQ1atTg559/pmXLlrmOf+aZZ3jiiScA8qwrlFTIbEnpQnofSUoNUVFRzJ07lx07dvjKkpOTsdvttGvXjs2bN3PkyBEgpzfeo0cPnE4nAN9//z2ZmZkYhsGyZcu4++67OX78OHa7nREjRtC2bVu2bt2K2+3Otwd+jhtvvBGbzeYzCgkJCXTr1o19+/Zd9rzY2FhfPP60tDTWrVvnS35iMpnwer35tv30008zbdo0jh8/DuQYqDlz5nDw4EFuuummAskvkVwM2XWQlBpuvPFG3n77bWbOnEliYiJWq5WwsDCmT59O3bp1mTJlCqNGjfIlPZk7d65veqRcuXI89thjpKWl0axZMx5//HEsFgt33XUXnTt3xmKxULt2bW6++WaOHz+ep5e9bNmyXBnN6tSpw9KlS5kzZw7Tpk3j/fffx+v18swzz9CkSRO2bt16ye8xfvx4JkyYQPfu3YmMjKRy5cq+UUXbtm1588038Xg8l9VF9+7dEUIwatQovF4vLpeL+vXrs2jRIul5JLkqZOhsyTXPW2+9RVpaGi+++GJJiwLAxx9/zC233EKjRo1wu9307duXYcOGERsbW9KiSSRypCCRFDc333wzU6dOxTAMPB4PnTp1kgZBEjDIkYJEIpFIfMiFZolEIpH4kEZBIpFIJD6kUZBIJBKJD2kUJBKJROJDGgWJRCKR+Ph/tGZVMrSftX8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(df['SepalLengthCm'],df['SepalWidthCm'], hue = df['Species'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Setosa has the lowest Sepal length but the largest Sepal Width between the three species. While versicolor has average dimensions of sepals, virginica has the longest sepals\n",
    "\n",
    "- Thus we can easily differentiate between Setosa and other varieties based on Sepal features. However, Virginica and Versicolor do not have a strong demarcation between each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d397beaac0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(df['PetalLengthCm'],df['PetalWidthCm'], hue = df['Species'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The Setosa species has the smallest petals by far between these species, followed by versicolor. Virginica has the largest petals \n",
    "\n",
    "- In this figure, we see a distinction between the versicolor and virginica species as well. We can easily observe 3 seperate species based on these features\n",
    "\n",
    "- For better clarity we can run a pairplot to merge all these figures into one large diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1d397b61f10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 825.225x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, hue=\"Species\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It is difficult to observe strong demarcations between Virginica and Versicolor but Setosa is distinct in nature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109321</td>\n",
       "      <td>0.871305</td>\n",
       "      <td>0.817058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <td>-0.109321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.421057</td>\n",
       "      <td>-0.356376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <td>0.871305</td>\n",
       "      <td>-0.421057</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.961883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>0.817058</td>\n",
       "      <td>-0.356376</td>\n",
       "      <td>0.961883</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "SepalLengthCm       1.000000     -0.109321       0.871305      0.817058\n",
       "SepalWidthCm       -0.109321      1.000000      -0.421057     -0.356376\n",
       "PetalLengthCm       0.871305     -0.421057       1.000000      0.961883\n",
       "PetalWidthCm        0.817058     -0.356376       0.961883      1.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), cmap='Blues', annot = True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We see that there is a high correlation between Petal Length and Petal Width, as well as between Petal Length and Sepal Length. Even Petal Width and Sepal Length show a high correlation. Thus it may be a case of collinearity which will make Linear Regression difficult on this dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Preparing the data (OHE, Train Test Split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species_Iris-versicolor</th>\n",
       "      <th>Species_Iris-virginica</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>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</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",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0              5.1           3.5            1.4           0.2   \n",
       "1              4.9           3.0            1.4           0.2   \n",
       "2              4.7           3.2            1.3           0.2   \n",
       "3              4.6           3.1            1.5           0.2   \n",
       "4              5.0           3.6            1.4           0.2   \n",
       "..             ...           ...            ...           ...   \n",
       "145            6.7           3.0            5.2           2.3   \n",
       "146            6.3           2.5            5.0           1.9   \n",
       "147            6.5           3.0            5.2           2.0   \n",
       "148            6.2           3.4            5.4           2.3   \n",
       "149            5.9           3.0            5.1           1.8   \n",
       "\n",
       "     Species_Iris-versicolor  Species_Iris-virginica  \n",
       "0                          0                       0  \n",
       "1                          0                       0  \n",
       "2                          0                       0  \n",
       "3                          0                       0  \n",
       "4                          0                       0  \n",
       "..                       ...                     ...  \n",
       "145                        0                       1  \n",
       "146                        0                       1  \n",
       "147                        0                       1  \n",
       "148                        0                       1  \n",
       "149                        0                       1  \n",
       "\n",
       "[147 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.get_dummies(data=df, columns=['Species'],drop_first=True )\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "0              5.1           3.5            1.4           0.2\n",
       "1              4.9           3.0            1.4           0.2\n",
       "2              4.7           3.2            1.3           0.2\n",
       "3              4.6           3.1            1.5           0.2\n",
       "4              5.0           3.6            1.4           0.2\n",
       "..             ...           ...            ...           ...\n",
       "145            6.7           3.0            5.2           2.3\n",
       "146            6.3           2.5            5.0           1.9\n",
       "147            6.5           3.0            5.2           2.0\n",
       "148            6.2           3.4            5.4           2.3\n",
       "149            5.9           3.0            5.1           1.8\n",
       "\n",
       "[147 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(['Species'], axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species_Iris-versicolor</th>\n",
       "      <th>Species_Iris-virginica</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Species_Iris-versicolor  Species_Iris-virginica\n",
       "0                          0                       0\n",
       "1                          0                       0\n",
       "2                          0                       0\n",
       "3                          0                       0\n",
       "4                          0                       0\n",
       "..                       ...                     ...\n",
       "145                        0                       1\n",
       "146                        0                       1\n",
       "147                        0                       1\n",
       "148                        0                       1\n",
       "149                        0                       1\n",
       "\n",
       "[147 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df1.drop(X, axis=1)\n",
    "\n",
    "import copy\n",
    "\n",
    "ohe_y=copy.copy(y)\n",
    "y "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species_Iris-versicolor</th>\n",
       "      <th>Species_Iris-virginica</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Species_Iris-versicolor  Species_Iris-virginica\n",
       "0                          0                       0\n",
       "1                          0                       0\n",
       "2                          0                       0\n",
       "3                          0                       0\n",
       "4                          0                       0\n",
       "..                       ...                     ...\n",
       "145                        0                       1\n",
       "146                        0                       1\n",
       "147                        0                       1\n",
       "148                        0                       1\n",
       "149                        0                       1\n",
       "\n",
       "[147 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "def split(X,y):\n",
    "    return train_test_split(X,y, test_size=0.2, random_state=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test=split(X,y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train is : (117, 4)\n",
      "Shape of y_train is : (117, 2)\n",
      "Shape of X_test is : (30, 4)\n",
      "Shape of y_test is : (30, 2)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of X_train is :', X_train.shape)\n",
    "print('Shape of y_train is :',y_train.shape)\n",
    "print('Shape of X_test is :', X_test.shape)\n",
    "print('Shape of y_test is :',y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_y_train = copy.copy(y_train)\n",
    "ohe_y_test = copy.copy(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Even though the data has low variance, we should scale the data as it is a best practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scalar=MinMaxScaler() # defining object\n",
    "scalar.fit(X_train) #defining the scal\n",
    "X_train_s=scalar.transform(X_train)#actual scaling\n",
    "X_test_s=scalar.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linreg=LinearRegression()\n",
    "linreg.fit(X_train_s, y_train) #calculates B0 and B1\n",
    "y_pred_train_lg=linreg.predict(X_train_s)\n",
    "y_pred_test_lg=linreg.predict(X_test_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the Root Mean Squared Error for Train data is  : 0.34180506865802174\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "RMSE_train_lg=np.sqrt(metrics.mean_squared_error(y_train,y_pred_train_lg))\n",
    "print('The Root Mean Squared Error for Train data is  :',RMSE_train_lg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the Root Mean Squared Error for Test data is  : 0.4041998239161507\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "RMSE_test_lg=np.sqrt(metrics.mean_squared_error(ohe_y_test,y_pred_test_lg))\n",
    "print('The Root Mean Squared Error for Test data is  :',RMSE_test_lg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-square value for Train dataset is  : 0.46893765516327857\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_train_lg=r2_score(y_train, y_pred_train_lg)\n",
    "print('The R-square value for Train dataset is  :', r2_train_lg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-square value for Test dataset is  : 0.2774207618960332\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_test_lg=r2_score(y_test, y_pred_test_lg)\n",
    "print('The R-square value for Test dataset is  :', r2_test_lg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Adjusted_r2(r_square,N): #creating function so that we can call the function again and again\n",
    "    return(1-(((1-r_square)*(N-1))/(N-4)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Adjusted R-square of Linear Regression Train Dataset is : 0.4548386548578789\n",
      "The Adjusted R-square of Linear Regression Test Dataset is : 0.19404623442249858\n"
     ]
    }
   ],
   "source": [
    "adj_r2_train=Adjusted_r2(r2_train_lg,len(X_train))\n",
    "print('The Adjusted R-square of Linear Regression Train Dataset is :', adj_r2_train)\n",
    "adj_r2_test=Adjusted_r2(r2_test_lg,len(X_test))\n",
    "print('The Adjusted R-square of Linear Regression Test Dataset is :', adj_r2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Linear Regression - results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Root Mean Squared Error for Train data is  : 0.34180506865802174\n",
      "The Root Mean Squared Error for Test data is  : 0.4041998239161507\n",
      "The R-square value for Train dataset is  : 0.46893765516327857\n",
      "The R-square value for Test dataset is  : 0.2774207618960332\n",
      "The Adjusted R-square of Linear Regression Train Dataset is : 0.9703294716240318\n",
      "The Adjusted R-square of Linear Regression Test Dataset is : 0.8185610963555675\n"
     ]
    }
   ],
   "source": [
    "print('The Root Mean Squared Error for Train data is  :',RMSE_train_lg)\n",
    "print('The Root Mean Squared Error for Test data is  :',RMSE_test_lg)\n",
    "print('The R-square value for Train dataset is  :', r2_train_lg)\n",
    "print('The R-square value for Test dataset is  :', r2_test_lg)\n",
    "print('The Adjusted R-square of Linear Regression Train Dataset is :', adj_r2_train)\n",
    "print('The Adjusted R-square of Linear Regression Test Dataset is :', adj_r2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Thus we can conclude that using Linear Regression is not effective and yields poor results. This may be due to high correlation between features and strong collinearity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "2         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: Species, Length: 147, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "y=df['Species']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score for test data is: 0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train,y_train)\n",
    "\n",
    "y_pred_train = logreg.predict(X_train)  \n",
    "y_pred_test = logreg.predict(X_test)  \n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy score for test data is:', accuracy_score(y_test,y_pred_test))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              P-Setosa  P-Versicolor  P-Virginica\n",
      "A-Setosa             9             0            0\n",
      "A-Versicolor         0            12            1\n",
      "A-Virginica          0             1            7\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         9\n",
      "Iris-versicolor       0.92      0.92      0.92        13\n",
      " Iris-virginica       0.88      0.88      0.88         8\n",
      "\n",
      "       accuracy                           0.93        30\n",
      "      macro avg       0.93      0.93      0.93        30\n",
      "   weighted avg       0.93      0.93      0.93        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "confusion_matrix = pd.DataFrame(confusion_matrix(y_test, y_pred_test))\n",
    "classification_report = classification_report(y_test,y_pred_test)\n",
    "\n",
    "confusion_matrix.index = ['A-Setosa','A-Versicolor','A-Virginica']\n",
    "confusion_matrix.columns = ['P-Setosa','P-Versicolor','P-Virginica']\n",
    "print(confusion_matrix)\n",
    "print(classification_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Here we can see that our model is 93% accurate\n",
    "- According to the confusion matrix, there are 2 instances where the prediction was incorrect. We have one False Positive and one False Negative. \n",
    "- We can further see that all setosa flowers were correctly identified. The problem arises while distinguishing between Virginica and Versicolor flowers.\n",
    "- Logistic Regression is very effective in predicting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification with Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtree = DecisionTreeClassifier()\n",
    "dtree.fit(X_train,y_train)\n",
    "predictions = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9  0  0]\n",
      " [ 0 12  1]\n",
      " [ 0  1  7]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall score for test data is: 0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "recall1 = recall_score(y_test,predictions,average=\"weighted\")\n",
    "print('Recall score for test data is:', recall1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         9\n",
      "Iris-versicolor       0.92      0.92      0.92        13\n",
      " Iris-virginica       0.88      0.88      0.88         8\n",
      "\n",
      "       accuracy                           0.93        30\n",
      "      macro avg       0.93      0.93      0.93        30\n",
      "   weighted avg       0.93      0.93      0.93        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that we get the same results with our first decision tree as well\n",
    "- Let us try some Hyperparemeter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9  0  0]\n",
      " [ 0 12  1]\n",
      " [ 0  1  7]]\n"
     ]
    }
   ],
   "source": [
    "dtree1 = DecisionTreeClassifier(criterion = 'entropy', max_leaf_nodes=10, max_depth=3, min_samples_split=5, min_samples_leaf=4)\n",
    "dtree1.fit(X_train,y_train)\n",
    "predictions1 = dtree1.predict(X_test)\n",
    "print(confusion_matrix(y_test,predictions1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The results are identical to the previous model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeRegressor(random_state=0),\n",
       "             param_grid=[{'max_depth': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
       "                                        14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n",
       "                                        24, 25, 26, 27, 28, 29, 30, 31, ...],\n",
       "                          'max_features': ['auto', 'log2'],\n",
       "                          'splitter': ['best', 'random']}],\n",
       "             refit='AUC')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "decision_tree_regressor= DecisionTreeRegressor(random_state = 0)\n",
    "\n",
    "max_depth = list(range(2, 60))\n",
    "max_depth.append('None')\n",
    "\n",
    "tree_para = [{'max_depth': max_depth,\n",
    "              'splitter' : [\"best\", \"random\"],\n",
    "              'max_features': [\"auto\", \"log2\"] }]\n",
    "\n",
    "grid_search = GridSearchCV(decision_tree_regressor,tree_para, cv=10, refit='AUC')\n",
    "grid_search.fit(X_train_s, ohe_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = grid_search.best_estimator_\n",
    "y_train_gscv = model.predict(X_train_s)\n",
    "y_test_gscv = model.predict(X_test_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE value of Train dataset for Decision Tree Grid Search Model is : 0.08006407690254357\n",
      "The RMSE value of Test dataset for Decision Tree Grid Search Model is : 0.18819316317727025\n",
      "The R-square value of Train dataset for Decision Tree Grid Search Model is : 0.9710968128751345\n",
      "The R-square value of Test dataset for Decision Tree Grid Search Model is : 0.8373306381118881\n",
      "The Adjusted R-square of Decision Tree GridSearch model Train Dataset for the model is : 0.9703294716240318\n",
      "The Adjusted R-square of Decision Tree Test GridSearch model Test Dataset for the model is : 0.8185610963555675\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "RMSE_train_gscv=np.sqrt(metrics.mean_squared_error(ohe_y_train,y_train_gscv))\n",
    "print('The RMSE value of Train dataset for Decision Tree Grid Search Model is :',RMSE_train_gscv)\n",
    "\n",
    "RMSE_test_gscv=np.sqrt(metrics.mean_squared_error(ohe_y_test,y_test_gscv))\n",
    "print('The RMSE value of Test dataset for Decision Tree Grid Search Model is :',RMSE_test_gscv)\n",
    "\n",
    "r2_m_train=r2_score(ohe_y_train, y_train_gscv)\n",
    "print('The R-square value of Train dataset for Decision Tree Grid Search Model is :',r2_m_train)\n",
    "\n",
    "r2_m_test=r2_score(ohe_y_test, y_test_gscv)\n",
    "print('The R-square value of Test dataset for Decision Tree Grid Search Model is :',r2_m_test)\n",
    "\n",
    "adj_r2_train=Adjusted_r2(r2_m_train,len(X_train))\n",
    "print('The Adjusted R-square of Decision Tree GridSearch model Train Dataset for the model is :', adj_r2_train)\n",
    "adj_r2_test=Adjusted_r2(r2_m_test,len(X_test))\n",
    "print('The Adjusted R-square of Decision Tree Test GridSearch model Test Dataset for the model is :', adj_r2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The results of the first 2 Decision Trees was the same as that of logistic regression in terms of prediction\n",
    "- While the Adjusted R-square of Decision Tree GridSearch model Train Dataset for the model is 97%, the test dataset value is only approx. 82%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model_rf1 = RandomForestClassifier(random_state = 0)\n",
    "model_rf1.fit(X_train_s,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train_rf1 = model_rf1.predict(X_train_s)\n",
    "y_pred_test_rf1 = model_rf1.predict(X_test_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9  0  0]\n",
      " [ 0 12  1]\n",
      " [ 0  1  7]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         9\n",
      "Iris-versicolor       0.92      0.92      0.92        13\n",
      " Iris-virginica       0.88      0.88      0.88         8\n",
      "\n",
      "       accuracy                           0.93        30\n",
      "      macro avg       0.93      0.93      0.93        30\n",
      "   weighted avg       0.93      0.93      0.93        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))\n",
    "print(classification_report(y_test,y_pred_test_rf1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- With Random Forest, the results still show no change. Let us try hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=15, random_state=10)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf2 = RandomForestClassifier(random_state = 10,\n",
    "                                n_estimators = 15,\n",
    "                                min_samples_split = 2,\n",
    "                                min_samples_leaf = 1)\n",
    "\n",
    "model_rf2.fit(X_train_s, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train_rf2= model_rf2.predict(X_train_s)\n",
    "y_pred_test_rf2 = model_rf2.predict(X_test_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9  0  0]\n",
      " [ 0 12  1]\n",
      " [ 0  1  7]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         9\n",
      "Iris-versicolor       0.92      0.92      0.92        13\n",
      " Iris-virginica       0.88      0.88      0.88         8\n",
      "\n",
      "       accuracy                           0.93        30\n",
      "      macro avg       0.93      0.93      0.93        30\n",
      "   weighted avg       0.93      0.93      0.93        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,y_pred_test_rf1))\n",
    "print(classification_report(y_test,y_pred_test_rf1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There is still no change in the prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint as sp_randint\n",
    "max_depth = list(range(2, 60))\n",
    "max_depth.append('None')\n",
    "param_dist = {\"max_depth\": max_depth,\n",
    "              \"min_samples_split\": sp_randint(2, 11),\n",
    "              \"min_samples_leaf\": sp_randint(1, 11),\n",
    "              \"n_estimators\": [100, 400, 700, 1000, 1500],\n",
    "              'max_features': [\"auto\", \"log2\"]\n",
    "             }\n",
    "n_iter_search = 50\n",
    "random_search = RandomizedSearchCV(model_rf1, param_distributions = param_dist,\n",
    "                                   n_iter = n_iter_search,\n",
    "                                   n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(estimator=RandomForestClassifier(random_state=0), n_iter=50,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'max_depth': [2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                      10, 11, 12, 13, 14, 15,\n",
       "                                                      16, 17, 18, 19, 20, 21,\n",
       "                                                      22, 23, 24, 25, 26, 27,\n",
       "                                                      28, 29, 30, 31, ...],\n",
       "                                        'max_features': ['auto', 'log2'],\n",
       "                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001D39A4F0FD0>,\n",
       "                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001D39A4F09D0>,\n",
       "                                        'n_estimators': [100, 400, 700, 1000,\n",
       "                                                         1500]})"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(X_train_s, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9  0  0]\n",
      " [ 0 12  1]\n",
      " [ 0  1  7]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         9\n",
      "Iris-versicolor       0.92      0.92      0.92        13\n",
      " Iris-virginica       0.88      0.88      0.88         8\n",
      "\n",
      "       accuracy                           0.93        30\n",
      "      macro avg       0.93      0.93      0.93        30\n",
      "   weighted avg       0.93      0.93      0.93        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_rf3 = random_search.best_estimator_\n",
    "y_pred_test_rf3 = model_rf3.predict(X_test_s)\n",
    "print(confusion_matrix(y_test,y_pred_test_rf3))\n",
    "print(classification_report(y_test,y_pred_test_rf3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Thus the answer is still the same"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- No Random Search model has performed better or worse than the original Logistic Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB  \n",
    "from sklearn.metrics import plot_confusion_matrix   \n",
    "naive = GaussianNB()\n",
    "naive.fit(X_train, y_train)\n",
    "y_train_pred_count = naive.predict(X_train)\n",
    "y_test_pred_count = naive.predict(X_test)\n",
    "y_train_pred_proba = naive.predict_proba(X_train)\n",
    "y_test_pred_proba = naive.predict_proba(X_test)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols = 2, sharex = False, figsize=(15, 7))\n",
    "plot_confusion_matrix(estimator = naive, X = X_train, y_true = y_train, values_format = '.7g', cmap = 'YlGnBu', ax = ax1)\n",
    "plot_confusion_matrix(estimator = naive, X = X_test, y_true = y_test, values_format = '.7g', cmap = 'YlGnBu', ax = ax2)\n",
    "ax1.set_title(label = 'Train Data', size = 14)\n",
    "ax2.set_title(label = 'Test Data', size = 14)\n",
    "ax1.grid(b = False)\n",
    "ax2.grid(b = False)\n",
    "plt.suptitle(t = 'Confusion Matrix', size = 16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Training Report          \n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        39\n",
      "Iris-versicolor       0.95      0.95      0.95        37\n",
      " Iris-virginica       0.95      0.95      0.95        41\n",
      "\n",
      "       accuracy                           0.97       117\n",
      "      macro avg       0.97      0.97      0.97       117\n",
      "   weighted avg       0.97      0.97      0.97       117\n",
      "\n",
      "                    Testing Report           \n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         9\n",
      "Iris-versicolor       0.92      0.85      0.88        13\n",
      " Iris-virginica       0.78      0.88      0.82         8\n",
      "\n",
      "       accuracy                           0.90        30\n",
      "      macro avg       0.90      0.91      0.90        30\n",
      "   weighted avg       0.90      0.90      0.90        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "logistic_report_train = classification_report(y_train, y_train_pred_count)\n",
    "logistic_report_test = classification_report(y_test, y_test_pred_count)\n",
    "print('                    Training Report          ')\n",
    "print(logistic_report_train)\n",
    "print('                    Testing Report           ')\n",
    "print(logistic_report_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Naive Bayes Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Here we observe that Naive Bayes did not improve the prediction. In fact, the prediction deteriorated. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0             0   1   2\n",
       "Species                    \n",
       "Iris-setosa      48   0   0\n",
       "Iris-versicolor   0  48   2\n",
       "Iris-virginica    0  13  36"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "k=len(y.unique())\n",
    "model=KMeans(n_clusters=k)\n",
    "model.fit(X)\n",
    "model.labels_\n",
    "pd.crosstab(y, model.labels_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K Means Clustering Result\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "- Thus K means clustering is not as accurate as other methods. it has given us 15 incorrect values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model=SVC()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "predSVC=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9  0  0]\n",
      " [ 0 12  1]\n",
      " [ 0  0  8]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         9\n",
      "Iris-versicolor       1.00      0.92      0.96        13\n",
      " Iris-virginica       0.89      1.00      0.94         8\n",
      "\n",
      "       accuracy                           0.97        30\n",
      "      macro avg       0.96      0.97      0.97        30\n",
      "   weighted avg       0.97      0.97      0.97        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(y_test,predSVC))\n",
    "print(classification_report(y_test, predSVC))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVM Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM has given us the best results so far, giving us an accuracy of 97% and a recall of 97% as well. Thus, this is the best choice to classify the flowers!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Thus, the best classification tool that can be used to identify the flowers in the dataset is the SVM Classifier which gives us an accuracy and recall of 97%. It has the least error out of all the models attempted \n",
    "- However, in case of large datasets where SVM might be costly to perform, we can always use Logistic regression which gives us accuracy and recall of 93%"
   ]
  }
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