Iris_flower.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "58682bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Topic : Predict the species of flower based its features\n",
    "# Features include : Petals length and width and sepals length and width\n",
    "# Begin by importing necessary libraries to load and visualise the data\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "flowers_data = pd.read_csv(r'C:\\Users\\imane\\OneDrive\\Desktop\\Iris\\Iris.csv', sep =\",\", encoding = \"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "89497cd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    float64\n",
      " 2   SepalWidthCm   150 non-null    float64\n",
      " 3   PetalLengthCm  150 non-null    float64\n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        150 non-null    object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Qick look at the data frame structure (no missing values)\n",
    "flowers_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5d3b95d",
   "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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm         Species\n",
       "0              5.1           3.5            1.4           0.2     Iris-setosa\n",
       "1              4.9           3.0            1.4           0.2     Iris-setosa\n",
       "2              4.7           3.2            1.3           0.2     Iris-setosa\n",
       "3              4.6           3.1            1.5           0.2     Iris-setosa\n",
       "4              5.0           3.6            1.4           0.2     Iris-setosa\n",
       "..             ...           ...            ...           ...             ...\n",
       "145            6.7           3.0            5.2           2.3  Iris-virginica\n",
       "146            6.3           2.5            5.0           1.9  Iris-virginica\n",
       "147            6.5           3.0            5.2           2.0  Iris-virginica\n",
       "148            6.2           3.4            5.4           2.3  Iris-virginica\n",
       "149            5.9           3.0            5.1           1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete irrelevant column (Id)\n",
    "flowers_data = flowers_data.drop(\"Id\", axis = 1)\n",
    "flowers_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "43ec70d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'SepalLengthCm'}>,\n",
       "        <AxesSubplot:title={'center':'SepalWidthCm'}>],\n",
       "       [<AxesSubplot:title={'center':'PetalLengthCm'}>,\n",
       "        <AxesSubplot:title={'center':'PetalWidthCm'}>]], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram to get an idea about the distribution of values\n",
    "flowers_data.hist(bins=70, figsize=(20, 15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7891c1e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap to see correlation between different features \n",
    "# Petal length and width are the most correlated \n",
    "sns.heatmap(flowers_data.corr(),annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a203616",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ddaa7fd220>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 463.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot to see the distribution of species based on their sepal width and length\n",
    "# Species are not well grouped by their sepal info\n",
    "sns.lmplot( x=\"SepalLengthCm\", y=\"SepalWidthCm\", data= flowers_data, fit_reg=False, hue='Species', legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "06cd473c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1dda8382be0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 463.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot to see the distribution of species based on their pedal width and length\n",
    "# Species are better grouped by the pedals info\n",
    "sns.lmplot( x=\"PetalLengthCm\", y=\"PetalWidthCm\", data= flowers_data, fit_reg=False, hue='Species', legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb5215ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Species', ylabel='SepalLengthCm'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6R1/r/aBWIE2Et95+K+pb8cdM8IFx7NdjTVZ6O3C7wBhznTHmQPjDUyq0Nm/ezNwf5zK4lfvgYtqhlu6Cc1u6+eGHaezevTs8B4lDBQUF1qzVLj5w2R3NMQh4u3vZvm07U6ZMsTuaYwrqA6oxZh2QDxQDeSJyjoicE9bIlAqDr776CgHOO8pqTaFyfl4lPp+fCRMmhPU48eTdd9+1+t7rOWs1olqCZEf/7NZgF/x4HpgF/B74beD2cBjjUirkPB4PX0+cwCk5VTRJCW8SaZHup8cJXsZ/9WVUJ4BosWHDBubOnWuNe4/m1nu1wOzWzUWbmT17tt3RHFWwl5iuALoaY4YZYy4N3C4LY1xKhdycOXMo2befga3D23qvNrBVBTt27tIhk0H497//jbhipPUeYPIMkiF8/En0XmsJNsGvIzqvaSsVtEmTviY7FXqdEJkLY6fmekhPEr7++uuIHC9WlZWVMfnbyfjaRMms1WA5wNfRx5LFS6J2Wb+6ShX8TURexVpPdZGIjBaRV6tvkQlRqYbbt28fc+fM5axmFSEfGnk0yU44o1kFM6ZPj9kVgSJhypQp1nJ8HWOn9V7NtLeW9Rs/frzdodSqrj/1BcBC4Evg/4DZge8XBh5TKiZ8//33eH0++od4YlNd+reooqKykpkzZ0b0uLFkyndTkCw59rTKaJUK/hZ+vpv6XVRea6lrmOQ7xph3gOzq+zW2xeLpUAnq28nf0LqRCXo5vlDpku0lJ81aNFodqaSkhCWLl+Br7YN6lGqPBibPULy7OContgX7YfWWWrbdGsI4lAqbrVu3srRgGf2bVyARTiIOgbOau1mwYAF79uyJ7MFjQH5+PsYYTMswdc94IC0tjeHDh5OWlgae0B/CtLBiX7Ag+jo16uqDv15EvgI6iMiXNW7fA/rXqmJC9ZTyfi0jM3rmcGe3qMLv90f9pBg7LFmyBEkKY/eMB4YNG8aIESMYNmxYWBI8KdaY+MWLF4dh5w1T14jT2cA2rMqRL9bYXgrUuaKTUnbz+/18PWE83Zt4yUm15yJe60Z+Omb5mfT1RK655hpbYohWa9euxWSZ4PsSjlcSByebTZgwIWyjdHyNfaxZuyY8O2+AYyb4QFngjcBZkQlHqdBavHgxW7fv4LIe9rTeqw1oWcE7hesoLCyka9eutsYSTTYVbcKfFcaLk0ngLnEzbtw46/tGYTpOFuzduBe32211BUWJYGeylorI/sNuRSLymYh0DHeQStXXV199RUaScHqzyI6eOVy/FlUkOa14lMUYQ+n+0tga+340gZ9h377oWn462A9Gf8UqT9AayMMqU/Ah8DHwZlgiU6qBSkpK+GHa9/RvEb7CYsHKSDKc2aySbyd/Q1lZmb3BRAm/329VY4yF0gR1Cfx9VVQ0bG3fUAs2wV9mjBltjCk1xuw3xrwOXGiM+QQdLqmi1FdffYXH62NwmAuLBev8vErcFZVMmjTJ7lCigtPpREQg+oaPH7/Az5CcHE1LUAWf4MtF5BoRcQRu1wDV/6pib/qZinsej4fP/vspPU/w0jojOjJIp8Y+OjX28em4/0TlpBg7ZDTKgOj4/9swgR7AzMxMe+M4TLAJ/gbgJmAnsCNw/0YRSQPuC1NsStXb999/z+7iPQxt627Qft4rTGNjqZONpU7+vKAR7xU27ALa0DZuNm/ZyqxZsxq0n3jRqlUrpCxGZzjVdADS0tNiM8EbY9YFKkjmGGNyA/fXGGPcxhidg62iijGGjz78gFaNDL2aNqyw2MZSJ26fA7fPwcqSJDaWNqwz/7RmHnLS4OOPP2rQfuJF506dcZQ4Yr4fwFHioFOnTnaHcYRgR9HkisjjIvK6iLxZfQt3cErVx/z581m7bj3D2pTjiLLGodMBF7UpZ+nSAgoKCuwOx3Y9e/bEVBrYb3ckDeAF2Suc3OtkuyM5QrBdNF8AjYEpwIQaN6WizgcfvE+TVOgXpjVXG2pg60oaJQsffPC+3aHY7owzzgBAtkbZf+LjsQ2M33DmmWfaHckRgh2glG6MeTSskSgVAsuXL+ennxbxyxPLSYpQWeDjleqEIa3L+WzWbNavX0+HDh3sDsk2ubm5nNT9JFYWrcTbzRuTBcccRQ6ysrPo1auX3aEcIdi3wHgRuTiskSgVAh9++CEZScKgCK3aVF8XtKkkxSl89JH2xV8y7BLMPmOt+Bxr3Nanj2FDh+F02jzZohbBJvj7sZJ8RWAWa6mIxHKvmYpDRUVFzJgxncGty0mL8skzmcmGc1u5+fbbyezcudPucGw1ePBg0tLTkNWx13yXtYIgXHrppXaHUqtgR9FkGmMcxphUY0xW4PuscAen1PH4+OOPcQlc2Ca6W+/VhratxPj9P9dJSVDp6elcfdXVODY7Qn6x1WQbTFLglmsw2SEcruMB51onA84ZQF5eXuj2G0LBjqIREblRRJ4IfN9GRE4Pb2hKBa+kpIRvJk3i7JaVNE6JjTF3uWl+Tm9WxZdffJHw5QuuueYaklOSkeWhbcWbPgaygWzwD/Rb34eIFAqmynDTjTeFbJ+hFmwXzT+xKkr+MvD9AeAfYYkoDlVVVfHpp58ydepUu0OJW1988QVVHg8XtY2uWiB1Gdq2gnK3m4kTJ9odiq2ys7O57trrcBQ5YmOliQpwrnYyaNCgqK4OGmyCP8MYcy+B8gTGmL1AdBVdiGI//vgjr7zyCk8++SSlpaV2hxN3PB4Pn3/2X05u6omasgTB6tTYx4nZWr4A4PrrryercRbOxc6on/gkSwWHcXDXXXfZHcoxBZvgPSLiJPBrF5Fc4qNEUERs3Ljx4P2ioiIbI4lPM2bMoHjPXi5oE1ut92oX5LnZum078+bNszsUW2VkZPCr//cr2A1SFMUXXPeAY4ODa665Jmr73qsFm+BfBT4DmonI08BM4Jm6XiQi2SIyTkRWisgKEUnIhUNWr1598P6qVatsjCQ+ff75Z+Smw8kNLEtgl9OaechKgc8//9zuUGw3dOhQunTtgnOJMzzL6zWUAWe+kyYnNOHmm2+2O5o6BTuK5gPgEeBZrCX8rgCCqZb0CjDJGNMN6A2sqF+YscsYw6JFi/Ge0BFJTmfJEl3pMJSKiopYtGgxg1q6o64sQbBcDji3pZu5c+awa9cuu8OxldPp5OGRD0MFSEH0nVBZK7AXRvxmBBkZGXaHU6eg5/oZY1YaY/5hjPm7MWYFMPdYzxeRxsA5wBuB11cZY0oaEmwsWrNmDXv37sHXuDWezFbM/fFHa5EDFRITJkzAITCgVWwMjTyaga2q8BujteKBbt26ccUVV+BYE2UXXN3gLHByat9TOe+88+yOJigNmcxd17/XDsAu4C0R+UlExorIEf/yRORuEVkgIgvisfXy3XffgQjeJm3xntCeA6Wl5Ofn2x1WXPD5fHwz6Wt6N/XQJEaGRh5N83Q/3Zp4+XriBIyJ7Z8lFO6++26aNG2Ca6Eraq72OX5y4MLFwyMfthYqiQENSfB1/RW6gFOAfxljfgGUAY8dsRNjXjfG9DXG9M3NzW1AONHH4/EwYeLX+BrnQVIavuw2SFIqX375pd2hxYX8/HyK9+xlQMvYbr1XG9Cyks1btrJ8+XK7Q7FdRkYGIx8ciSkxyKooSKZbQLYIt99+O61bt7Y7mqAdc0K3iPyN2hO5YE0fOJbNwGZjzI+B78dRS4KPZ1OmTGFfyV48Xa2KeTicVOZ0YcaMGWzZsiWm/lCi0ZQpU0hLEvrkROPVuON3WrMq3l6ZwZQpU+jRo4fd4djunHPO4eyzz2bWnFl487zQyKZAPOBa5KJDpw5ce+21NgVRP3W14BcAC2u5LQB+c6wXGmO2A0UiUj0LYDCQME0Tr9fL2++8g8loiq/xz4nc26IHiIN33nnHxuhin8fjYcb0H+ibU2H7gtqhku6C3k2r+H7qdwk/Jr7agw8+SEpyCs58+8bGy1IBNzz6yKO4XFFe5Ogwx0zwxph3jnULYv+/AT4QkSVAH4IYWhkvxo8fz7atW6lsfQrU6K8zyRlUNTuJb775hnXr1tkYYWz76aefOFBWzunNwtt6d3uFtLQ0hg8fTlpaGm5veLsLTm9exZ69JSxbtiysx4kVubm5/PpXv4YdIJts6KrZDY61DoYPH85JJ50U+eM30DETvIh8JSJfHu1W186NMYsC/esnG2OuCMyAjXulpaWMGTsWf1ZLfNltj3i8qlUfcCXz6t/+phfU6mn27NkkO4UeJ4Q3wZd7hWHDhjFixAiGDRtGeZgTfO+mXhwCc+cec5BaQrn88svp3r27NcM1kpdb/ODKd5GTm8Mdd9wRwQOHTl2fN16ISBRxZsyYMZSWllLZY/AhrfeDklKpaHUK+Qvn8MMPPzBw4MCIxxjr5v84l5Oyq8LePZPuMkyYYC1eNmHCBJq5wvsPOSPJ0Lmxlx9/nBv10+AjxeFw8Mgjj3D7HbcjSwRzWmQaRVIomH2Gkc+OJD09PSLHDLW6umh+ONYtUkHGkuXLl/P5F1/gadYdf0bToz7P2/wkTEZTXv7rKxw4cCCCEca+4uJiirZspXuYW+8AaS6D2+1m3LhxuN1u0sKc4AF6NPGwZvUaysvLw36sWNGxY0erGNkGhzX4OtwOgHOFVQq4f//+EThgeARbLvjEQMmB5SKyrvoW7uBijdfr5flRo5DkdKryTj24PXnjHJI3zjn0yeKgov3Z7N1TzOuvvx7hSGNbYWEhAJ0bx+eEsc6NvfiN0bIWh7n11ltp1rwZrp/CPDbegGORg+SkZO4fcX8YDxR+wY6Dfwv4F+AFBgHvArpi8GE+/vhj1q9bh7vtWeD6udimo6wYR9mR65H5G+Xiad6Dz7/4gqVLl0Yy1JhWXbwtL8YqRwYrr5EPOLRInYLU1FQeevAhzL4wj43fCrJNuOvOu2jWrFn4jhMBwSb4NGPMd4AYYzYaY54EhoUvrNizadMm3nzrLbxN2uM7oX3Qr6tq0xdSGvHsc89RWRkfE3bCbefOnaQlCRlJ8XmBukmKwSEk/FJ+tenXrx/9+vXDucIJ7jAcwAuuxS7atW/HVVddFYYDRFawCb5SRBzAahG5T0SuxL5pB1HH7/fz7HPP4cNBVft+x/diZxIV7fqzuahIx8YHqaysjPTYGo58XBwCaS7RPvijGDFiBE5xIotD34qXQsGUGUY+NDLmxrzX5ngW3U4HRgCnAjcBt4QrqFgzbtw4lhUUUNHmTEzy8V9t92Xn4cntwocffsiKFQlXcPO4OZ1O/PHZeD/Ia6yfUx2pVatW3PDLG6zVn3aHcMdl4Cx0Mui8QfTp0yeEO7ZPsOWC5xtjDmAtiTvCGHOVMUYH6gLr16/ntdGj8TVpizenc733U9X2DExSOv/7f3+moiI2F66IlCZNmrC/0uCLzy543F6o9Bqys7PtDiVq/fKXv6RpTlNci10hm+EqSwWX08U9v74nNDuMAsGOoukrIkuBJcBSEVksIqfW9bp4V1lZyZ+efBKfuKjocHbtY96D5UqhvMMAtmwu4tVXXw1dkHGobdu2+AxsLW9IrbzoVXTAarm3bXvkJDllSUtL49e/+jVmjwnNDNdicBQ5uOGXN9C8efOG7y9KBPsOeRO4xxjT3hjTHrgXa2RNwjLG8PLLL7Nh/XrcHc6BpIZPhPA3bk1Vy96MHz+eb775JgRRxqfqj89LipPsDSRMlhYnISL07t3b7lCi2vnnn2+t/lTgBF8DdmTAudhJdpNsrrvuupDFFw2CTfA+Y8yM6m+MMTOxhkwmrM8++4yJEydS1aoPvuw2Iduvp82p+LNaMmrUX7Q//ihatGjBSd26MmNbGvFW6cFvYOaONE75xS9o3Lix3eFENYfDwX333ocpN8jqBrTitwLFcNedd8XsjNWjCTbB/yAio0VkoIicKyL/BKaJyCkicko4A4xGc+bM4ZVXXsHXpC2evBD3VIkDd+fz8DhTePSxx9i2bVto9x8nrrjyKjYfEPJ3xVcrfvb2ZHaVwxVXXml3KDGhT58+nHHmGThX1XMNVwOuZS7y8vIYOnRoyOOzW7AJvjfQBfgT8CRwEvAL4EUSrF7N0qVL+cMTT+DPaEpFp0EN63c/mqQ0yk+8gH0HynnwoYfYuzcharQdlyFDhtCmdSs+WptBVUM+nkcRtxf+vS6DEzt3YsCAAXaHEzPuvONOTOXRJz+ZbIPJrv2jnhRZ9WbuvPPOuBgWebhgR9EMOsYtNhYnDIGVK1fy8MO/xeNMo7zLheAMX+vRpDeh/MQhbNu+gwcefJB9+/aF7VixyOVy8cBDI9leJoxbm2Z3OCHx4ep09lbAgw+NxOGIzwvI4dC1a1cGDBiAc03trXjTx2D61JLgjVVvpn2H9nFb8C/YUTTNReQNEfk68H13EYnN+pn1tGLFCh548EHcxkl516GQFP6k4s9sQfmJ57Nhw0buf+ABSkpKwn7MWHLaaadx+eWXM3FTKgvD1FXTLtNHmtNPmtNPt2wP7TLD83Fh9vYkvt+SwnXXXU/Pnj3Dcox4dvPNN2OqDLLmOD5RbwGz33DLzbfE7T/UYH+qt4FvgFaB71cBD4Qhnqi0dOlSHnjgQcp9Tsq7XYxJidwkXn/jPNwnDmH9+o38ZsQIiouPrGmTyO677z66nNiZfy3LZFNp6CcG3dTVTbtMH+0yffyh7wFu6hr6+fFr9jkZsyKTXr16aongeuratSunnXYazrVBjqgx1qSmFi1bxG3rHYJP8DnGmH8TqOFmjPHSsIFJMWPBggU8+NBDuEkKJPfMiMfgy86jvMsQNhVt4d777mPHjh0RjyFapaSk8Oxzz5ORlc1fFmexyx1bLbGtZQ5eXJxFTm4znn76mbjsB46U66+/HuMOclx8MbAHrr/u+rieMRzsu6FMRJoSmDMmImcCcd8pPH36dH77yCNUuTIo7zYsoi33w/kbt6a860Vs27GbX//6HjZt2mRbLNEmNzeXF158CY8znecWZbG30oal3ephp9vB84sa40rL4oUXX9KZqw106qmn0rZdWxzr6k5rslZIS0/jwgsvjEBk9gk2wT8EfAl0EpFZWOWCj7nodqwbP348TzzxBN60EyjrdnG9asyEmj+zOeXdLqZ4fxm/vuceVq5caXdIUaNTp0785YUX2e9L5Zn8xpREeZLfXSE8+1NjqpwZvPjyX2nTJnRzKRKViHDlFVfCHuBYA8+qwLnZydCLhsbduPfD1bUm62ki0sIYkw+cCzyOtSriZGBzBOKLOGMM7733HqNGjcKb1cq6oOpKtTusg/wZTSk7aRilVYbfjBjBvHnz7A4pavTo0YNRf3mBvd5UnvmpcdS25He5HTydn005abz08l/p3Ln+NYzUoYYMGYLT5UQ2Hv3cS5Fg/IZhw+K/4nldLfjRQFXgfj/g98A/sP4/xt0yRF6vlxdffJExY8bgbdqJii4XhHUoZH2Z1MaUn3QJFc4MHnn0USZOnGh3SFGjd+/e/OWFQJLPj74kv9Pt4On8xrglnZf/+gpdu3a1O6S4kpWVRf9+/XFudh61CJljk4N27dslxD/WuhK80xizJ3D/WuB1Y8ynxpgngLj67ZSWlvLII4/y5ZdfUtXyZCo7DQRH9F58McnWdQFPoxY899xzvPbaa/j9cVpe8Tj17t2bF158iRJfKk9HUZLf6XbwTH5jKp0ZvPzXV+jWrZvdIcWlgQMHYtzGupB6uApgNww+bzASjkmKUabOBC8i1Zf1BwNTazwWN5f7161bx5133cWC/IVUdhiAp+3p4ZmhGmquZCq6XIinWTc+/PBDHn30MUpLS+2OKir06tWLF158iX2+6GjJ7z4suWvLPXzOOussHE4HsvXIcy7brG2JMlO4rgT/EVYdmi+wFsiaASAinYmDUTTGGMaPH89dd9/N9t0luLtdjLdZjL3xHA6q2vensn0/fpw/j1tvu42CggK7o4oK1Ul+rzeV5xc1prTKniRfUik8uyiLCkc6L738V7p06WJLHIkiIyODnj164txZyyfwHdDkhCZ07Ngx8oHZ4JgJ3hjzNDASa6LT2cYcrN3nIMZH0ezZs4ffPf44o0aNoiIth7IeV+DPbGF3WPUjgrd5d9wnXcKu/W7uvfdexowZQ1VVVd2vjXO9evXi2eeeY0dFEi8uzqQywrM3yr0walEW+7ypjPrLC9pyj5C+ffti9pqfryCCVVhst4u+p/ZNiO4ZCGKYpDFmrjHmM2NMWY1tqwIja2KOMYaJEydyw403MmfOXCrbnk5F14uiYhhkQ/kbNaOsx5VUNe3Me++9x+2338HSpUvtDst2p556Kn/605Os3e/knwUZEVvuz+eHV5dmsqXcxZ+fflpLEETQwd/1nhoby8Hv9tOrVy9bYrJDbE37a6DVq1dz77338dxzz1EqjSjreSXelieDxNGvwZVMVadzqeh6IZt2FHPvvffy7LPPsmfPnrpfG8fOOecc7rvvNyzclcyn6yIz7PWj1WkUFLt4+OHfcvrpp0fkmMpSfQFb9tZoqe899LFEEDcXSo+luLiYsWPHWsMJXalUdjwHb86JEbmQmrxxDo5y63J+6vLx+DOaUtXurLAf15fdhrJeV5O05Se+nvQN30+bxi0338zw4cNJSUkJ+/Gj0fDhw1mzZg1ffP01XbO9nNw0fGvWzNuRxKSiVK6++uqEGG8dbRo1akSz5s3Yvm/7wW2yTxAROnToYGNkkRVHTdcjud1u3n77ba677nomTPyaquY9OHDycLy5XSI2SsZRVoz4PIjPg7N0O46yCBYLcybhaXs65b2upiwll9GjR/PLG27g22+/TcghlSLCgw8+SIf27Ri9PDNsF133VgpvrGxEt65duOee+FnAOdZ0aN8B54EaF1pLoXmL5gnVwInLBO/z+Rg/fjzXXncdb775JmXpLSg/+Wqq2p0JrsQ5udVMWmMqul6Au9vF7Czz83//93/cddfd5OfH5GWUBklNTeWJP/6JA14HH6wKT8nnt1dm4CGJJ/74J5KSom+iXKLIy8uDAz9/7yhz0LZNYi1kHncJfv78+dx6222MGjWKPd5k3N0vpbLL+ZhUXd/S37gV5T0up6LTuawu2sYDDzzAo489RlFRkd2hRVTnzp355S9vYOb2FFbuDW0v5eLdLhbuSuK222/X+jI2a9asGcZjDi4C4nA7aN68ub1BRVjcJPgdO3bwu9/9jpEjR7Jx+x4qThyM+6RL8Gcm1gmtkwi+nBMp63U1VW1OY+68Bdx888289tprVFRU2B1dxNx0003k5jTlozXpIVu422/go7WNyGvdimuuuSY0O1X1lpOTY91xA35rBE3Tpk1tjSnSYj7BG2P44osvuPGmm5g9dx5VeX0p63U1vhM6xMZsVLs4XHha9aas13AqmnTkww8/5Kabb2HJkiV2RxYRqamp3Hb7Hazd52RRcWha8T/uSGJzqXDHnXdp10wUOFh+uZKD4+ETrSRzTCf4iooK/vjHP/Liiy9SntKUsp5X4WndJ6pryEQbk5xOVadzcZ80jB37yvjNb37DBx98gAlVszaKXXTRRTTLzeGrDQ2fA2EMjN+YTts2eQwaNCgE0amGysrKsu5UcTDBH9yWIGI2wbvdbu5/4AF++GE6VW1OsyYrpUZ+taV44c9qSVmPK/A06cDo0aP529/+FvdJ3uVycc2117GqxMn6/Q1rFKwscbGx1MF11/8ybtf3jDUZGRkAiFcO9sNXb0sUYf1LFJENIrJURBaJyIJQ7nvUqFGsWLGCis7n4WnVW7tjQsGZTGXnQXia92DcuHGMHz/e7ojCbujQoaSmJPPd5oaNrpqyOYXMRhkMGTIkRJGphkpLC4yS8gZuNbcliEg0NQYZY/oYY/qGaoeFhYV89913VLXqg69p4kxaiAgRqtqdiS+rJa+NHh339WwyMzMZdN5g5uxMpaKe855Kq4QFu5K58KKhCTXGOtodPBc+Dq4gnZoaPYv3REJMfpZcuHAhAJ7mPWyOJAi+KtLS0hg+fLjVevDFQMIUwdO8O6X797N27Vq7owm7Sy65hEqv4cedyfV6/eztyfj86IzVKFMzwYtPDt2WIMKd4A0wWUQWisjdtT1BRO4WkQUismDXrl1B7fTnPs7on40p3iqGDRvGiBEjGDZsGOKNgQQPYKzfbSL0J/fs2ZO81q2Ysa1+b/4Z21PpcmJnOnXqFOLIVEO4XIHRUT4OpopEG90U7nfv2caYU4ChwL0ics7hTzDGvG6M6WuM6ZubmxvUTqsLNyVtXxbKWMPCuJKZMGECr776KhMmTMC46tdKjChjSN6xnOwmTRIiaYkIFw29mJV7Xex0H99bouiAgw37HVw09OIwRafqS0RwJbms5K4JPvSMMVsCX3cCnwEhKanXsWNHLrjgApK3LcG5d2Modhk+zmTcbjfjxo3D7XaDM8oTvDEkFc3DUbqDX/2///dzKyjOXXDBBYjArG3Hd35mbEvB6XRw/vnnhyky1RAu16EJPlH+nquFLcGLSIaIZFbfBy4AQrbU0EMPPUSXLl1IWzMV5+41odptYjN+kjfNJXnbUi6//HKGDh1qd0QR06JFC/r06cPM7alBz2z1+WH29lTOOuushJtAEysOT/Dagg+d5sBMEVkMzAMmGGMmhWrn6enpvPzSS/Tq1ZPUtdNI3jgX/BFerieeeMpJLZxE0vZlXHPNNTz44IMJs+pNtYsuGsqOcqGw5NBWXrtMH+0yj/zbWrLHRUml9ToVnbQFHybGmHXGmN6BW4/A8n8hlZmZycsvvcRVV11F0vYC0pd/iZQn9sIW9eHcs4FGBZ+R6t7NY489xn333ZcQF1cPN3DgQNJSU5i+9dBumpu6urmpq/uI58/YmkLjrEzOOiv89f1V/SQlJWkLPpYlJSXxwAMP8PTTT9PY6SV92eckbflJW/PB8LhJWTOV1NVT6Ni2NWPHjOHiixP3YmFaWhrnDT6fH3fVPSa+tEpYuNsa+55oSSOWHEzwgXSgLfgYNWDAAN5//z0GnTuQ5M0LyVj2BY7SnXaHFZ2MwbVrFY2WfkpKyUbuuOMOXh89OqFWujmaiy++2BoTv+PYF1tnBca+J9J1iliUnJJsjYH3Q1JyUsJ1O8ZNggerUtxTTz3Js88+S9N0B2nLvyJ5w2yIlbHnESAV+0gt/JqUddM5qUsn3nzzTW655RZthQb07NmTNnmt+WHb0Wc8GgPTt6XStcuJCTGMNJalJqcenMmaiH/jcZXgq/Xv358P3n+fq6++iqSdK2hU8CnOvZvsDstexk/S1sVkLP2MRlUlPPTQQ/zzH//QVvthRIShFw9jVYmT7eW1vz02ljrZVOrg4mGXRDg6dbxSU39O8IlWpgDiNMGDNcrm/vvv57V//Yu2LXNJXTWZlDXfgzdxFrWoJuV7SV/+FclF8+l31hm8//57XHHFFQl5ITUYF154ISLCzKOMiZ+5LZkkl5PBgwdHODJ1vFJTU3H4HeC1umsSTdy/w7t3786bb7zBrbfeSnKJNVrEWRK5Jer8GU0xziSMMwlfZgv8GRFcUcb4Sdq6hIxln5PlqOSpp57imWeeIdgZw4kqNzeXU075BbN3pB0xJt7nhzk7UznzrH4JV1s8FqWlpSFeQbxCelrD6/7HmrhP8GD1vd1+++28Pnq01Zov/Mbqm4/ASJuqdmfhT2+KP70pFd0voapdZIbUSVUZaSsnkVw0j7P79+P9995j0KBBCXeRqb6GDLmAneWw7rA68StKXOyrRMsCxwirwB/gQxN8vOvSpQtjx4xh+PDhJO1YTvryr5CKUrvDCjnHvi1kLPuc1MpiHn30Uf785z/TpEkTu8OKKQMGDMDldDLvsAqT83ckk5qSomPfY0RaWhp4rEU/Em2xD0iwBA9WudARI0bwzDPPkIGbjOWf49i32e6wQsMYkrYuIa1wEm1bNueNsWOtCpbaaj9umZmZ/OKUX5C/++cKk8bAwuIUzjjzzIQrOxur0tPT8Xv8OLwO0tO1BZ8wzj77bN4YO5a2rVuSVvgNrh3L7Q6pYfw+ktdNJ7loHgPPPZfRo1+jXbt2dkcV0/r168+2MmFHYDTNxlInJRXQr18/myNTwUpPTwcDpsIk3GpOkMAJHqB169aMfu01zjzzTFI2zCZp0zyCrjQVTXxVpK76hqTdq7ntttt46qmnErK1EmrVZakL9rgO+XraaafZFpM6PtXdMqbKaBdNIkpPT+fZZ57hsssuI3nbEpLXzzy42EVM8FSQvvJrkkq38/jjj3Pbbbdpl0yI5OXl0fSEJqzYa02QWbE3iTZ5rcnJybE5MhWsmg2dRGz0JHyCB3A6nYwcOZKbbrqJpF2FpKydHhtJ3uMmo3AiSZUlPPPMM1x00UV2RxRXRIReJ/dmzf5kjIE1pcmc3LuP3WGp41AzqWsLPoGJCHfddRd33nknruI1pKz9IbqTvMdNxsqvSfIcYNTzz2u/cJicdNJJ7HZbwyXLqgzdunWzOyR1HBK9BZ9YpdWCcPPNN+NwOHj99dcx4qCq4zkQbV0e3grSCydZyX3U85x66ql2RxS3OnfuDFgLa9f8XsUGTfDqCDfeeCMej4e33noLHC6q2veLniTvrSK98Btclft49rnnNLmHWfv27QFYuMvqh9eRSbGlZlJPxFE0muCP4tZbb6WiooKPPvrISvJtT7c/yfs8pK2ejNO9hz//+c8HR3mo8MnJySE5KYndFZCdlUmjRo3sDkkdh5pJPRFb8NoHfxQiwq9+9SuuvPJKkrYvJWlLvr0B+b2krp6Cs3QHT/zhD/Tv39/eeBKEiNC8eTMAmrdsaXM06njVrCCp1STVIUSE+++/n2HDhpG85SdrpSg7+H2krv4O574tPPbYY1rFMMJycq0Enxv4qmJHzRa8dtGoIzgcDh5++GE8Hg+TJ08GBE/rPpELoDq5lxTx8MMP6wpCNqiuvqnj32NPzSX6ErG8hCb4IDidTn73u99hjOHbb78FDJ7Wvwj/gf3eg8l95MiRXHbZZeE/pjrCnXfeSe/evTnzzDPtDkU1gCZ4dVROp5PHH38ch8PBN998A34vnry+4bvw6vOQuvpbnPu28tvf/pZLL700PMdRdWrRooX+/uOAJnh1TNUt+ZSUFL788kvE57Hqu4c6yXsrSVs1GeeBnfzu8cd1hqpSIZCIa7Jqgj9ODoeDkSNHkpaWxieffAI+D1UdB4CE6Hq1x0164SRcFSX86amnGDhwYGj2q1SCS8QEr6No6kFEuOeee7jjjjtI2r2alNXfhWR1KKk8QMaKCSR7Snn++ec1uSsVAi1atgBIyCJ82oKvJxHhlltuISMjg1dffRVZNZmKE4eA88hfaTDrsErFftILvyZNfLzw8sv06tUrHGErlXDGjhlLVVWV3WHYQhN8Aw0fPpyMjAyee+450lZ9g7vLBeA89KNgXeuwinsfGYUTyUh28vJLr9C1a9dwhqxUQknkxdG1iyYEhg4dyhNPPIGzdDtpqyaD3xv0a6ViPxmFE8lMcfH3v72qyV0pFTKa4EPk/PPP5w9/+AOO0u2krv4uqFLDUlVOeuHXZCQ7efXVV+jYsWMEIlVKJQpN8CE0ZMgQRj70EM6SIpI3zD72k31e0lZNJsV4eOnFFzS5K6VCThN8iF1++eVcf/31JO1ciWvnytqfZAwp62cg5cX87/8+pYtIKKXCQhN8GNx9992ccuqppG6ci7hLjnjctXsNruK13HH77Zx11rEvwCqlVH1pgg8Dp9PJH37/e9LSUkjdMAuM+flBTwWpRXPp0bMnN954o31BKqXinib4MMnJyeHXv/oVjv3bcO7ddHB78tafwFvFI7/9LU6n08YIlVLxThN8GA0bNowWLVuRsm2xtcFTQfLOQi688EI6dOhgb3BKqbgX9gQvIk4R+UlExof7WNHG5XJx7TX/gxzYiaOsGNfuNRi/l2uvvdbu0JRSCSASLfj7gRUROE5UGjx4MA6HA+ee9STtXU+nzp3p1KmT3WEppRJAWBO8iOQBw4Cx4TxONMvOzqZ7jx4k7d2A48BO+vfrZ3dISqkEEe4W/F+BR4CjTusUkbtFZIGILNi1a1eYw7HHyb16WcMljeHkk0+2OxylVIIIW4IXkUuAncaYhcd6njHmdWNMX2NM3+q1L+NNz549D97v0qWLjZEopRJJOKtJ9gcuE5GLgVQgS0TeN8Yk3ODv/v378+GHH5Kamkp2drbd4SilEkTYWvDGmN8ZY/KMMe2B64CpiZjcwaodn5eXR05Ojt2hKKUSiI6DV0qpOBWRBT+MMdOAaZE4llJKKYu24JVSKk5pgldKqTilCV4ppeKUJnillIpTmuCVUipOiam5GIXNRGQXsNHuOMIkB9htdxCq3vT8xbZ4Pn/tjDG1lgGIqgQfz0RkgTGmr91xqPrR8xfbEvX8aReNUkrFKU3wSikVpzTBR87rdgegGkTPX2xLyPOnffBKKRWntAWvlFJxShO8UkrFKU3wASJy4BiPzQ7jcR8P177jhV3nJhgi0kpExtXztdNEJKGG7oX7XIrI/4rI+cf5mstE5LE6nlPv82wn7YMPEJEDxphGh21zGWO8kT6uOpRd5ybcxxORacDDxpgFQT7faYzxhTKGSLPxfRbzv7v60Bb8YURkoIjMEJEvgeWBbQcCX1uKyHQRWSQiBSIyoJbX9xCReYHnLBGREwPbb6yxfbSIOEXkOSAtsO2DwPMeCuy7QEQeCGzLEJEJIrI4sP3awPY/isj8wLbXRUQi8kuySUPOjYg0FpGNIuIIfJ8hIkUikiQinURkkogsDOy/W+A5b4vIayLyIzBKRM4N7H+RiPwkIpki0l5ECgLPd4rIC4HjLxGR3wS2Dw48f6mIvCkiKbX8bNcHHi8QkedrbD8gIi+KyGLgrLD8Ym0QxnP5togMD2zfICLPi0g+8D8icrGIrAyc51dFZHzgebeKyN8D998OPDZbRNbV2Fcw5zn63o/GGL1Zn2IOBL4OBMqADrU8NhL4feC+E8isZT9/A24I3E8G0oCTgK+ApMD2fwI319x34P6pwFIgA2gELAN+AVwNjKnxvMaBryfU2PYecKndv8coPzdfAIMC968FxgbufwecGLh/BtbykgBvA+MBZ+D7r4D+gfuNsBbMaQ8UBLb9GhgHuKrPD9Z6xEVAl8C2d4EHAvenAX2BVsAmIDewz6nAFYHnGOAau89BDJ3Lt4HhgfsbgEcC96vPQ4fA9x8B4wP3bwX+XuP1/8Fq/HYH1gS2H/M81/wauB8V70dtwddunjFmfS3b5wO3iciTQC9jTGktz5kDPC4ij2LViHADg7GS93wRWRT4vmMtrz0b+MwYU2aMOQD8FxiAlfSHBFojA4wx+wLPHyQiP4rIUuA8oEd9f+AY0pBz8wlWMgBrneBPRKQR0A/4T+DcjAZa1njNf8zPH+1nAS+JyAgg2xzZrXA+MLp6uzFmD9AVWG+MWRV4zjvAOYe97jRgmjFmV+C1H9R4jg/4tJafJR6E9Fwe5RjV27sB62oc76NjxPW5McZvjFkONK/l8drOM0Th+1ETfO3KattojJmO9cbbArwtIjeLyJU1Prb3NcZ8CFwGuIGJInIeIMA7xpg+gVtXY8yTwQYTSA6nYCX6Pwc+CqZifRIYbozpBYzBaqXEu3qfG+BL4CIROQHrH+5UrPdASY1z08cYc1JtxzPGPAfcifWpbFZ1V06YVZj47TsO9bkM+hh1qKxxP6hulmh9P2qCPw4i0g7YYYwZA4wFTjHGfFYjMSwQkY5YLYVXsT5GnozVBTBcRJoF9nNCYF8AHhFJCtyfAVwhIukikgFcCcwQkVZAuTHmfeAvWMm++o9nd6AVOjzsv4AoFsy5CXwqmg+8gvXx3GeM2Q+sF5H/CexHRKT3UY7RyRiz1BjzfGA/hyf4b4H/JyKuwPNPAAqB9iLSOfCcm4AfDnvdPOBcEckRESdwfS3PSRj1PZd17LYQ6Cgi7QPfX3uM59altvMcle/HiCy6HUcGAr8VEQ9wALi5ludcA9wUeM524BljzB4R+QMwOXBhyAPci1Ua+XVgiYjkG2NuEJG3sd7wYPUr/iQiFwJ/ERF/4LW/NsaUiMgYoCBwnPlh+pljxUDqPjdgfWT/T+D51W4A/hU4R0nAx8DiWl77gIgMAvxY10e+5tDunLFAF6zz6cG6bvJ3EbkNqwvIhXWeXqu5U2PMNrGG6X2P1WKcYIz5ItgfPA4NpP7nslbGGLeI3ANMEpEyGvZ+Odp5jrr3ow6TVEolBBFpZIw5EBjd8g9gtTHmZbvjCiftolFKJYq7AhfSlwGNsS6oxzVtwSulVJzSFrxSSsUpTfBKKRWnNMErpVSc0gSv4pqI/F5ElgVqhiwSkTNCuO+JIpIdqv0pFWo6Dl7FLRE5C7gEa6JMpYjkYNUHCgljzMWh2pdS4aAteBXPWgK7jTGVAMaY3caYrWJVGRwlVvXGedWzTEUkV0Q+DVQEnC8i/QPbG4nIW4HnLxGRqwPbNwT+aRytWqhTrOqEBYHXPmjT70ElKE3wKp5NBtqIyCoR+aeInFvjsX2BmiF/B/4a2PYK8LIx5jSsCp5jA9ufqH6+MeZkDqt7IiInYU1972+M6YNVIOwGoA/Q2hjTM3Cst8LwMyp1VNpFo+JWYNbiqVgVOQdhVY+sXrnnoxpfq2czng90l5/LeGcF6oqcj1WxsHq/ew87VM1qoWAVI9uJVV64o4j8DZiA9Q9HqYjRBK/iWqAI1TRgWqCM6y3VD9V8WuCrAzjTGFNRcx9S97oN1dVCf3fEA1bhsguBX2HVKbr9OH8EpepNu2hU3BKRrhJYUSugD1aBN/i5muC1WDX8wWph/6bG6/sE7n6LVRyuenuTww5Va7XQQP+8wxjzKfAHrCqgSkWMtuBVPGsE/C0wlNELrAHuxhpZ00RElmDV/r4+8PwRwD8C213AdKyW958D2wuw+tefwlqMBQBjzPKjVAt1A28FtgEc0cJXKpy0Fo1KOCKyAehrjNltdyxKhZN20SilVJzSFrxSSsUpbcErpVSc0gSvlFJxShO8UkrFKU3wSikVpzTBK6VUnPr/eY88XYEQdscAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Violin plot to see the distribution of species by their sepal length\n",
    "sns.violinplot(y = flowers_data[\"SepalLengthCm\"], x = flowers_data[\"Species\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1cbf45ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Species', ylabel='SepalWidthCm'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Violin plot to see the distribution of species by their sepal width\n",
    "sns.violinplot(y = flowers_data[\"SepalWidthCm\"], x = flowers_data[\"Species\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a2bbcc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Species', ylabel='PetalLengthCm'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Violin plot to see the distribution of species by their pedal length\n",
    "sns.violinplot(y = flowers_data[\"PetalLengthCm\"], x = flowers_data[\"Species\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0c38c73f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Species', ylabel='PetalWidthCm'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ExnZ/WSA7BG1RLrvssjhG540erwhU9R3gVJyVP7/gPgT4qKr+K9bBGZNI9u/fz2233sKA9BA3Tm08oqdQdy4b18zUQUF++cs7qKioiG2QJiKfz8e8j8+DPfS4aI18IBQOLWTGjBlxi80rEdv5VXWPqv6nql7uPm5X1T2R3mdMMmlpaeGWW35AXe1+vjmtjvyM3vcE8vvgpmkNDEwPcssPvm9rGieAc889F3C+9XepDXy7fcyeNRufL/lvh/bqNxSRM0XkBRHZICKVIlIlIpWxDs6YRBAOh/npT37Cuop1fGVqPSUFRz9PQUGG8u0TDxJoOMj3vvsdGhoaYhCp6a2xY8dSNLIIqe4mEewFDStnnHFGfAPzSG9T3YM44wbOAk7BuU9wSqyCMiaR3Hvvvby2dCmfnthE6bA+DEt1FeeF+cYJdXywdSu33/4fNurYY6eUnoJvn6/Lxe1lj5Cens4JJ5wQ/8A80NtEcFBVF7vNRDXtj5hGZkwCeOaZZ3j00UeZW9zMhWOOHDh2tE4YEuS6jzRSVrac3/zmN8ceoOmzadOmoW3a5ZrGvv0+Jk+ZTEZGRvwD80CkKSZmishM4FURuUNETm/f5+7v6b1/EJE9IrKmm9fPFZGDIrLSfdx+DL+HMVG3Zs0afnXnnUwbEuSzk46cRqKvzhnZysVjm1m4cCELFy6MTqXmqE2ePBkAOXDYiVWQg8KUycmzJnEkkbqP3nnY886LdCowu4f3PgQsAP7cQ5nXVfXiCDEYE3d1dXX85+3/weDMEDed0IA/wrXzX9Zns7XeWRv3p2V5jM0Pcc3k7rukXDEhwNaGNO666zdMnTqVCRMmRDN80wvFxcWkpaXRWndYN+BG0KBy3HHHeROYByJ1H52lqrOAL7Zvd9r3pQjvXQrsj2KsxsTNb37zG/bX1HDTCXXkpkfuIbS13k8g5CMQ8rGuNr0jKXTHJ/CV4xvI9QX56U9+TFtb3+89mL5JS0tj5KiRSN1hVwRuU9HYsWPjH5RHenuP4Iku9j0eheOfLiKrRGSxiBzfXSERuV5EykSkbO9eG9BsYqusrIyXXnqJS0sCjO9DD6HeKshQrpvSQGXVFh577LGYHcd0b+yYsfiaDv0YlAYnMRQXF3sRkici3SOYIiKXAwNE5JOdHl8Aso7x2CuAsao6HbgHeKq7gqp6n6qWqmrp0KFDj/GwxnQvHA7zf79dwNAcuHRcc8yPN3NoGzMK2/jLn/9MXZ0tAx5vRUVFzoL0nS/6GiErO4sBAwZ4FVbcRboimAxcjLOi5yWdHjOBLx/LgVW1TlUb3O1ngXQRSa6FQE2/s2zZMjZtruTycY2kx2kc0RUTmmgKBHjiia4uvE0sFRUVoSE9JBFIo1BUVIREq3dAP9DjzWJV/SfwTxE5XVWjuu6eiIwAdququhPb+QDrkmo89cQTjzMkG04bHnkeoWgZnRfmpMI2/vnUP7jmmmtIT0+P27FTXVFRkbMRouNrsS/go2hckWcxeaHHRCAi9+DmShG5+vDXVfXmHt77MHAuUCgi24H/BNLd9/0OmA98RUSCODN+XKW2gofx0O7duykrW84nxwdIi/OsAueNauHOVQdZtmwZZ5/d1VpQJhY61hgI4Xw6KdDUKUGkiEjdR8vcn2cCU4FH3eefAsp7eqOqHpE4Dnt9AU73UmMSwiuvvALAWSPidzXQbtqQNvIz4OWXX7ZEEEcdiSDs7mgDbVWGDx/uWUxeiNQ09CcAEfkKcJaqBt3nvwNej314xsTP0tdeo6QgzLCccOTCUZbmg9KhLbz91pu0tramzIhWr+Xk5JCXn0d9W72zw10/KJlXI+tKby+ABwEFnZ7nufuMSQq1tbWUV1Qws/DYp5Hoq5mFbQSaW1i1apVnMaSioqIi8IEO1I5EkGpNQ71NBD8H3hORh0TkTzhdP/87dmEZE18rVqxAVTlxiHcDuz4yuA2/zxnHYOJn1MhR+NJ86EmKNDo9hZJ9RbLDRbpHAICq/lFEFgMfdXd9X1WrYxeWMfG1evVqMtOEcfmxG0AWSZYfxheEWP2+XRHE0yFjCRohOyeb/Px8r8OKq4gDytyfM4GRwDb3MTLSpHPG9CcbNqxnbF5bxDmFYm1cfhubNm3GOtDFT1FRERpWaP5wDEGqiXRF8G2cgWOHTz4HkSedM6bf2L1rF5OzvbsaaDciO0xzSwsHDx5k4MCBXoeTEjo++BudMQSjJo7yNiAPREoE3wNn8rk4xGKMZwKBAFlZ3n8Lz0xzYggEApYI4qS9h5A0CjSm3o1iiJwI1ovIPuBN4C3gTVXdEPuwjImv7JwcmoIHvA6DQNC5WZmdne1xJKmjY8xArTP99LBhwzyNxwuRpqEeBvwbTiI4HXhSRHaLyD9F5HtxiM+YuCgpGcfWhr733Q8EhezsbObPn092dnbHB/rR2lLvZ0BBfkpNeOa1rKws8vLzkH3OOUu1wWTQi+6jqrpBVR9S1euBTwA/BY4HfhTr4IyJl1NOPZXtDcKOxr7dLW4KCvPmzePmm29m3rx5NPUhEbSG4L2aLE796GkpNeFZIhg6dCiyXzq2U02kXkNniMh3ROTvIvIO8DPAD3wWsK8sJmmcf/75ZKSn8/SWvs2unpOmLFq0iLvvvptFixaRk3b09xte3ZFJY6tyySWX9CkG03fDhn7YHFRYmHqTIEf6+vMGcBXwJHCuql6lqr9R1WWqGv8JWYyJkUGDBnH5/Pm8uSuTDbU9ry7Wlew0JeBOJR0IBMg+ykRQ2yI8uSWXmTNmMH369KM+vjk2gwcP7tgeNCj1Jk2IlAhG4owgngE8JyJvicgCEfmMiIyPfXjGxM/nP/95hg0byu/L8wkE43fcsMIDFbm0qp9v/fu/W7OQB9p7aOXk5qTkNOCRbhZXq+qTqvodVf0YMAdYh3N/YGM8AjQmXnJycvjhf9zO3oCPBypyideYrme3ZrJyXzpf/erXUmqd3ETSnghSdbK/SPcIBojIhSLyYxF5CWdU8WeBp4Er4xGgMfE0ffp0vnz99fxrdwaLP8iM+fHW1KTx2OYcZs2axSc/+cmYH890rX1KCSE1r8YijSPYBLztPn4MvKuqgZhHZYyHPv3pT1NRUc4jr79OSX6IqYNj0060L+Djt2sLGDNmNN///vetSchDubm5ACl7DiI1DQ1V1UtV9X9UdaklAZMKRIRbb72N4uJiFqzNp7Yl+h8OwTDcsyaPkD+Ln/33/5CTkxP1Y5jeS/V//0hLVT7NIcs6H0pVL416RMYkgJycHH7y05/x5S99iQcqcvn29Aai+WXxn1VZbD7o50c/+gGjR4+OXsWmT7KynG7DqXpFEKlp6JdxicKYBDRu3Diuv+EGFixYwDt70vno8O7XKhibH2Jrvb9je2wP01nvbPSxcEs2c+fOZdYsm8YrEWRmxv5+UCKLtFTla/EKxJhEdPnll7P42UU8trmS0qG13U5Tfc3kQEci+GFpQ491Pr45m8ysLG666aZoh2v6qD0RpOrUHr0aTy8iE0XkCREpF5HK9kesgzPGa36/n2uv+yK7m4QV+469f/meJh9lezK4fP6nUnLgUqIqLi7mwgsv5Fvf+pbXoXiiVyuUAX8E/hP4NTALuJbeL3NpTL925plnMmTwIN7a1copw45tKcu3d2egwKWX2u21RJKens6tt97qdRie6e2HebaqvgyIqm5V1f8C5sUuLGMSh9/v54wzz2JNbSbhYxxktmZ/OpMmTkjJGS5N4uptImgRER+wUURuEpHLgLwYxmVMQpk6dSqBNmVPoO8XwqqwpT6dqcefEMXIjDl2vf1f/Q0gB7gZOBlndPHnYhWUMYlmzJgxAFQ39T0R1LUKgaDaNBIm4fT2f3WJqjao6nZVvVZVLwfGxDIwYxJJe1NOTXPfE0FNi/PeVFwByyS23v6vvqWX+4xJSoMHD8YnwoGWvieC/W4SScWFT0xiizSy+CLg48AoEbm700sFQI8TsIjIH4CLgT2qekSjqDhD+O5y628CvqCqK44ufGPiIy0tjcLCIewJNPe5jr3u/YX2xdKNSRSRvt7sBMqAZmB5p8dC4III730IuLCH1y8CJrqP64F7I4drjHdKxo1nW2PfxxJ80OBnYEF+x5THxiSKSCOLVwGrRORvbtkxqrq+NxWr6lIRKemhyCeAP6uqAstEZKCIFKnqrl7GbkxcTZs2jXffeYf6ViE/4+j6karCuoOZnDDTVh8ziae3DZ4XAiuB5wBE5CQRWXiMxx6Fs75Bu+3uviOIyPUiUiYiZXv37j3GwxrTN6eddhoKvLOn66uCnuYYqqr3s7cJzjjjjBhGaEzf9DYR/BdwKlALoKorgXExiagLqnqfqpaqaqndaDNemTRpEseNH8dLO7K7HFh2zeQA10zueqb2F7dlkpWZwTnnnBPjKI05er1NBG2qevCwfce6kN8OoPP8u8XuPmMSkohw1dWfZlu9j3e7uSroyq5GH29WZ3LxJZd2rIRlTCLpbSJYKyKfBvzuBHT3AG8d47EXAp8Tx2nAQbs/YBLdnDlzGFcylkc259Ha/UzTHVThrxtzyMzM5LOf/WzsAzSmD3qbCL4OHA+0AH8DDgLf7OkNIvIwzhKXk0Vku4h8UURuFJEb3SLPApU4y2HeD3z16MM3Jr78fj/f+Oa32NsE/6jKili+bG86K/elc+11X2Tw4MFxiNCYoxdpHEEWcCMwAVgNnK6qvVrAVVWvjvC6Al/rZZzGJIyZM2dy4YUX8uzzz3Ha8LZubxA3tgl/Wp/HhOPGM3/+/DhHaUzvRboi+BNQipMELsJWLDMGgJtuuomCggIeqMgjFO66zN82ZlPX5uMHt9xKWlpvZ3w3Jv4iJYKpqvpZVf09MB/4WBxiMibhFRQUcPM3v0VVnY+Xth+5zOH6Wj+v7czkyiuvZNKkSR5EaEzvRUoEHatw9LZJyJhUMXv2bE495RT+XpVLfeuHi56HFf68Po9hQwu59tprPYzQmN6JlAimi0id+6gHTmzfFpG6eARoTKISEW76+tdpDsLTWz68cbxsdzpb633c+JWvkpUV+YayMV7rMRGoql9VC9xHvqqmddouiFeQxiSqkpISzpszh5d3ZtPYJqjCM1tzKBk7htmzZ3sdnjG9YusOG3OMrrjiClqCylvVGWyu8/NBvY9PXXElPp/9eZn+wboyGHOMJk+ezLiSsby6s5LNB/2kp6fZ1YDpV+wrizFRcNbZH+ODej9vVGcyY8ZMcnNzvQ7JmF6zKwJjouCqq65ixIgRhEIhTj31VK/DMeaoWCIwJgry8/O55JJLvA7DmD6xpiFjjElxlgiMMSbFWSIwxpgUZ4nAGGNSnCUCY4xJcZYIEkhDQwMvvvgiDQ0NXodijEkhlggSyFNPPcVPfvITnnrqKa9DMcakEEsECaSqquqQn8YYEw+WCBLI2rXlzs/yCo8jMcakEksECWLv3r3s3LmDcHo2O3dsZ+/evV6HZIxJEZYIEkRZWRkAbaNPPeS5McbEmiWCBLFy5UokI5tg4XFIRjYrV670OiRjTIqwRJAgyisqaMspBPHRllNIeYXdJzDGxIclggSgquzatQvNGuA8zxrArl27PI7KGJMqLBEkgNbWVlpbWtA0Z6FzTcuitaWFlpYWjyMzxqQCSwQJoP0DX/1ph/y0RGCMiQdLBAkgEAg4G760Q342Nzd7FJExJpXENBGIyIUisl5ENonID7p4/QsisldEVrqPL8UynkS1f/9+ADQt2/3pNBHV1NR4FpMxJnXELBGIiB/4LXARMBW4WkSmdlH0UVU9yX08EKt4EtmGDRsACOcMdH8OOmS/McbEUiyvCE4FNqlqpaq2Ao8An4jh8folVWXx4ucgewCaWeDsyyyA7AEsfu45VNXjCI0xyS6WiWAUsK3T8+3uvsNdLiLvi8gTIjK6q4pE5HoRKRORsmSbemHx4sWUl6+lZfgJIOLsFKFl+AmUr13L4sWLvQ3QGJP0vL5Z/DRQoqonAi8Cf+qqkKrep6qlqlo6dOjQuAYYS0uWLOGOO+4gPGAkwWGTD3ktOGwy4QEjueOOO1iyZIk3ARpjUkIsE8EOoPM3/GJ3XwdVrVHV9j6SDwAnxzCehNHa2sq9997L7bffTltOIYEJ54EcdirER2DCebTlFHL77bdz77330tra6k3AxpikFstE8C4wUUTGiUgGcBWwsHMBESnq9PRSIOnnVVi5ciVfuPZaHn74YdqGTSEw5SJIy+y6cFomgSkX0TZsCg8//DDXXnedzUFkjIm6tFhVrKpBEbkJeB7wA39Q1bUi8mOgTFUXAjeLyKVAENgPfCFW8XittraWBQsW8MILL0BWPs2TLyQ0sDjyG31ptI47i9CgErZtfZObb76Z888/n5tuuomBAwfGPG5jTPKT/tYrpbS0VPvbFM2vv/46P//FL6hvaKB1xDTaRs4Afx9ycChI+s73yKheTX5eHrf84AecddZZ0Q/YGJN0RGS5qpZ29ZrXN4uT3mOPPcZtt91GXSiDpuMvo230Kd0mgYytb5Ox9e3uK/On0Tb6FJqOv4y6UAa33norjz/+eIwiN8akCksEMbRixQoWLFhAcFAJTVMvQd2BYt3xNdbga4w8mlhzBtE09WKCg0q45557WLFiRbRCNsakIEsEMfTwI49AZh4tE84Fnz+6lfvSnHoz85zjGGNMH1kiiKGKinW0FYz8cDK5aPOl0VYwkoqKdbGp3xiTEiwRxEhzczN1B2vRzPyYHkcz86k7WGszlRpj+swSQYy8/bZz0zecWxjT47TX3348Y4w5WpYIYqCyspI7f/VryBlEaEBX0ysdKWPr2/iaavA11ZBV/kzPvYc6CQ0YBTmDuPNXv6aysvJYwjbGpChLBFHU2trKo48+yg033Eh9oJWmrqaO6IavsQYJtSGhNvz11b3qPQSA+GiacB71gVZuuOFGHn30UZuKwhhzVGI2sjiV7N69m+eff56/P/kPDuyvITRwNC3jzkIzcuNyfM0eSOPxnyBY9Qa//e1v+dvDj3D5Jy/jggsuYPjw4XGJwRjTf1ki6INQKMTGjRt59913eeONN6moKHf2DxhJ65QLCQ/oxdQRUaYZuTRPOh9f3Q5Cu97ngQce4MEHH2TKlI9w1llncsoppzBx4kT8/ih3YzXG9HuWCHqhvr6e9evXU15ezpo1a1j1/moCTY0AaG4hbcUnExxyHJpV4G2gIoQHFNM8oBhpriOtZjPlH2yl4v77uf/++8nOyWX6idM44YQTmDp1KpMnTyY/P7a9mowxic8SQSeqSk1NDZs2bWLTpk1s3LiRinXrqN6168NCOYNoyxtNqGgEoQEjIT3Hu4B7oFkFtI2aQduoGdDWhP/gTtrqq1n2/nqWLVvWUW5EUREfmTKFiRMnMmHCBCZMmMCQIUOQ9kVyjDFJL2UTQTAY5IMPPmDjxo1s3LjR+eDftIn6urqOMpJdQFv2EMLFpYTyhjpdNbubMjqRpecQKpxAqHACrQDBFnyN+/A37GV7Uw27ly3n1Vdf7SieX1DARDcpTJw4kYkTJzJmzBjS0lL2v4sxSS1l/rLr6upYtWoVq1atYm15ORs2bKCtvXeNz4/mDCKYPYLw2KmEc4YQzhncPz/0eyMtk/CAUYTdrq0t4CSHpv34mmpoa9rP8g0fsGLlKgiHAEjPyGDy5MlM/chHmD59OtOnT6egwOOmMGNMVCR1IgiHw7zwwgs8s2gRq99/H1VFfGmEcocQGjyRcE4hodxCNHtAr7t5Jq20TMIFRYQLnLWCWgE0jARq8TfW0Na0j/e37GHt2goee+wxRIQTTzyRiy++mLlz5+Lzpfi/nzH9WFIngl/+8pc888wzkDOQ1pEnESoYRThvaPQngIuGUCvZ2dnMmzePRYsW0RBKgLEA4kNzBhPMGQxMdPaFQ/ga9uKv28GqDVWs+tnPWLVqFd/73vc8DdUY03dJnQiqtmwBoC2viFBBUeImAUCCrcy7dB4333wzAI8tfM7jiLrh8zv/joRpa2smPVDb8e9sjOmfkjoR/OTHP+b3v/89L7/yCsE9FYg/nWBuIaHcYYRzCwnnDnEmhUuAHjKalsGiRYsAWLRoEZqW7XFELlWkpd5dK2Ef/sY9pDXuQ0NtpKWnc94FF3DDDTd4HaUx5hikxFKVTU1NrFixguXLl7N6zRo2bdpEOOTcBJW0DELZgwhlDyKcM5hw9mDCOYPifqM4Y+vbpO1ZB6qEswcSLiiidezpcY3BuWF8AF9gP76m/fgDB/AHDqBBp5nK5/czYcIEpp1wAieffDIzZ84kJycxu88aYw7V01KVKZEIDtfS0kJVVRUbNmxwxgxs3szmzZUdg8QAJDOPtqyBhHMGodmDCeUORrMGxrRpKav8GQCap14cs2MAEA4hzbX4G/cjgf34mg6Q3nwAbfnw98/OyeW448Yz4bjjmDBhApMmTWLcuHFkZiZpTypjklxPiSCpm4a6k5mZyZQpU5gyZUrHPlVlz549VFVVUVlZSVVVFRs3beKDD9YRbGtzCvl8aPZggjlDnKalvGHO1UMi9zjSsPMtv2EPvsZ9pDXVIIH9EA4DkJaeztixY5lw3FmMGzeO8ePHM27cOIYNG2aDyoxJESmZCLoiIgwfPpzhw4dz2mmndewPBoNs3769Y6Txhg0bWLduPY171zvv86cTzBtGKL+I0ICRzqAzLxODhp22/IM78dfvIq1hDxpyEllubh5Tpkxm0qS5HSOJi4uLbaCYMSnOPgEiSEtLo6SkhJKSEubMmQM4Vw87duygvLyctWvXsuK999i6pQy2g6Rn0TqgmNCgEkIDi2O3TGVn4SD+2u34D2wh4+B2tM1ZrWxsSQkzZ1/M8ccfz9SpUxk1apR9yzfGHMESQR+ICMXFxRQXF3P++ecDUFtbS1lZGcuWLePNt96mceMmJC2D1kElBIdOIpw3PGLvpHDukN4HoYqvYTdpezeQcWALGmwlNy+fM2d9jNNOO43S0lIGDhx4DL+lMSZVpOTN4lgLBoO89957vPjii7y6ZAktzc2QM4iWYVMJFk4E/zHk31CQtH0bydxTDk0HyMzKYvasWcyZM4cZM2ZYM48xpkvWa8hDTU1NvPLKK/z9ySfZvGkTkp5Fy7CptI04/ui6qAZbSK9eS+aecrStmQkTJ/LJyy5j9uzZ1oXTGBORJYIEoKq8//77/O1vf+Ptt99G0jJpGTGNtqITer6PEA6SvmsNmdWr0WALZ5xxBldffTUnnniitfcbY3rNs+6jInIhcBfgBx5Q1Z8f9nom8GfgZKAGuFJVt8QyJq+ISMesnRs3buTBBx/krbfeImPfBgIlZxEeMPKI9/gO7iB7y5vQXMfpZ57JF6+7jokTJ3oQvTEmmcXsikBE/MAGYC6wHXgXuFpVyzuV+SpwoqreKCJXAZep6pU91dtfrwi6smLFCu6445fs2LGD1uKZtI08ybmhrEr6zpVkbF/BqFGj+O53v8PMmTO9DtcY04/1dEUQyw7vpwKbVLVSVVuBR4BPHFbmE8Cf3O0ngPMkhdo7Zs6cyR//+Afmzp1DxvblZFS9jr+mkoyq18nYvpy5c+fwxz/+wZKAMSamYtk0NArY1un5duCj3ZVR1aCIHASGAPs6FxKR64HrAcaMGROreD2RlZXFbbfdhs/n4/nnnyd97wYALrjgAm655Rab598YE3P9oq+hqt4H3AdO05DH4USdz+fj1ltv5ZprriEUCuH3+xk9erTdDDbGxEUsE8EOYHSn58Xuvq7KbBeRNGAAzk3jlCMiSXe1Y4zpH2LZ7vAuMFFExolIBnAVsPCwMguBz7vb84FXtL/1ZzXGmH4uZlcEbpv/TcDzON1H/6Cqa0Xkx0CZqi4EHgT+IiKbgP04ycIYY0wcxfQegao+Czx72L7bO203A5+KZQzGGGN6Zl1SjDEmxVkiMMaYFGeJwBhjUpwlAmOMSXH9bvZREdkLbPU6jhgq5LCR1aZfsfPXfyX7uRurqkO7eqHfJYJkJyJl3U0MZRKfnb/+K5XPnTUNGWNMirNEYIwxKc4SQeK5z+sAzDGx89d/pey5s3sExhiT4uyKwBhjUpwlAmOMSXGWCI6CiDT08NpbMTzurbGqO5l4dX56Q0RGisgTfXzvEhFJqW6NsT6XIvJjEZlzlO+5VER+EKFMn8+zl+wewVEQkQZVzTtsX5qqBuN9XHMkr85PrI8nIkuA76hqWS/L+1U1FM0Y4s3Dv7V+/2/XF3ZF0Acicq6IvC4iC4Fyd1+D+7NIRJaKyEoRWSMiZ3fx/uNF5B23zPsiMtHd/9lO+38vIn4R+TmQ7e77q1vu392614jIN919uSKySERWufuvdPffLiLvuvvukxRY//JYzo+IDBCRrSLic5/nisg2EUkXkeNE5DkRWe7WP8Ut85CI/E5E/gX8r4ic49a/UkTeE5F8ESkRkTVueb+I/NI9/vsi8nV3/3lu+dUi8gcRyezid7vafX2NiPyi0/4GEblTRFYBp8fkH9YDMTyXD4nIfHf/FhH5hYisAD4lIh8XkXXueb5bRJ5xy31BRBa42w+5r70lIpWd6urNeU68v0lVtUcvH0CD+/NcoBEY18Vr3wZuc7f9QH4X9dwDfMbdzgCygY8ATwPp7v7/Az7XuW53+2RgNZAL5AFrgRnA5cD9ncoNcH8O7rTvL8AlXv879oPz809glrt9JfCAu/0yMNHd/ijOinoADwHPAH73+dPAme52Hs66HyXAGnffV4AngLT2cwRkAduASe6+PwPfdLeXAKXASOADYKhb5yvAv7llFLjC63PQj87lQ8B8d3sL8D13u/08jHOfPww8425/AVjQ6f2P43yZngpscvf3eJ47/3S3E+Jv0q4I+u4dVa3qYv+7wLUi8l/ANFWt76LM28CtIvJ9nPk/AsB5OB/y74rISvf5+C7eexbwD1VtVNUG4EngbJzkMNf9ZnO2qh50y88SkX+JyGpgNnB8X3/hfuZYzs+jOB8a4Kya96iI5AFnAI+75+f3QFGn9zyuHzYpvAn8SkRuBgbqkc0Zc4Dft+9X1f3AZKBKVTe4Zf4EfOyw950CLFHVve57/9qpTAj4exe/SzKI6rns5hjt+6cAlZ2O93APcT2lqmFVLQeGd/F6V+cZEvBv0hJB3zV2tVNVl+L8ce4AHhKRz4nIZZ2aCkpV9W/ApUAAeFZEZgMC/ElVT3Ifk1X1v3objPsBMhMnIfzUvfzMwrmymK+q04D7cb7xpII+nx+ctbQvFJHBOMn5FZy/ldpO5+ckVf1IV8dT1Z8DX8K50nuzvQkpxpo1edu2o30ue32MCFo6bfeqeSdR/yYtEUSZiIwFdqvq/cADwExV/UenD48yERmP863jbpxL1xNxmh3mi8gwt57Bbl0AbSKS7m6/DvybiOSISC5wGfC6iIwEmlT1/wF34CSF9v9g+9xvtPNj/g+Q4HpzftwrrXeBu3CaBUKqWgdUicin3HpERKZ3c4zjVHW1qv7CrefwRPAicIOIpLnlBwPrgRIRmeCWuQZ47bD3vQOcIyKFIuIHru6iTMro67mMUO16YLyIlLjPr+yhbCRdneeE/JuM6ZrFKepc4Lsi0gY0AJ/roswVwDVumWrgv1V1v4j8EHjBvbnVBnwNZ8rt+4D3RWSFqn5GRB7C+VAAp83zPRG5ALhDRMLue7+iqrUicj+wxj3OuzH6nfuTc4l8fsBpKnjcLd/uM8C97nlKBx4BVnXx3m+KyCwgjHMPZzGHNiM9AEzCOadtOPd2FojItThNT2k45+p3nStV1V3idF98Fecb6CJV/Wdvf/EkdC59P5ddUtWAiHwVeE5EGjm2v5nuznPC/U1a91FjjOlERPJUtcHtzfNbYKOq/trruGLJmoaMMeZQX3Y7BKwFBuB0DEhqdkVgjDEpzq4IjDEmxVkiMMaYFGeJwBhjUpwlAmMAEblNRNa6c8KsFJGPRrHuZ0VkYLTqMybabByBSXkicjpwMc6ApBYRKcSZAyoqVPXj0arLmFiwKwJjnMFe+1S1BUBV96nqTnFmpfxfcWb7fKd91K+IDBWRv7szSL4rIme6+/NE5I9u+fdF5HJ3/xY3uXQ3w6xfnNks17jv/ZZH/w4mRVkiMAZeAEaLyAYR+T8ROafTawfdOWEWAL9x990F/FpVT8GZ9fUBd/9/tJdX1RM5bF4bEfkIzpQFZ6rqSTgTxX0GOAkYpaonuMf6Ywx+R2O6ZU1DJuW5o0hPxpnFdRbObKPtK1E93Oln++jSOcBU+XAa+QJ33pg5ODNcttd74LBDdZ5hFpxJ6fbgTFs9XkTuARbhJCZj4sYSgTGAOxnZEmCJOz3w59tf6lzM/ekDTlPV5s51SOT1RdpnmL3liBecCewuAG7EmYvquqP8FYzpM2saMilPRCaLu0qc6yScyf7gw9knr8RZRwKcb+xf7/T+k9zNF3EmCmzfP+iwQ3U5w6x7/8Cnqn8Hfogzc6wxcWNXBMY4q4jd43bxDAKbgOtxehINEpH3ceaev9otfzPwW3d/GrAU55v8T939a3Da/3+Es3AQAKpa3s0MswHgj+4+gCOuGIyJJZtryJhuiMgWoFRV93kdizGxZE1DxhiT4uyKwBhjUpxdERhjTIqzRGCMMSnOEoExxqQ4SwTGGJPiLBEYY0yK+/9GGjLa660GOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Violin plot to see the distribution of species by their pedal width\n",
    "sns.violinplot(y = flowers_data[\"PetalWidthCm\"], x = flowers_data[\"Species\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bafc5cf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set = train_test_split(flowers_data, test_size = 0.2,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5efc182",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate labels from sets\n",
    "train_set_labels = train_set[\"Species\"].copy()\n",
    "train_set = train_set.drop(\"Species\", axis = 1)\n",
    "test_set_labels = test_set[\"Species\"].copy()\n",
    "test_set = test_set.drop(\"Species\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f982bb9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale sets\n",
    "from sklearn.preprocessing import MinMaxScaler as Scaler\n",
    "scaler = Scaler()\n",
    "scaler.fit(train_set)\n",
    "scaled_train_set = scaler.transform(train_set)\n",
    "scaled_test_set = scaler.transform(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "62465050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.088235</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.411765</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.087719</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.596491</td>\n",
       "      <td>0.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.614035</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>0.441176</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.035088</td>\n",
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       "      <th>118</th>\n",
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       "      <td>0.526316</td>\n",
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       "      <th>119</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3\n",
       "0    0.088235  0.666667  0.000000  0.041667\n",
       "1    0.411765  1.000000  0.087719  0.125000\n",
       "2    0.705882  0.458333  0.596491  0.541667\n",
       "3    0.147059  0.583333  0.105263  0.041667\n",
       "4    0.029412  0.500000  0.052632  0.041667\n",
       "..        ...       ...       ...       ...\n",
       "115  0.529412  0.333333  0.526316  0.500000\n",
       "116  0.176471  0.208333  0.614035  0.666667\n",
       "117  0.441176  0.833333  0.035088  0.041667\n",
       "118  0.441176  0.250000  0.526316  0.458333\n",
       "119  0.823529  0.416667  0.859649  0.833333\n",
       "\n",
       "[120 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Have a peak at scaled training set\n",
    "scaled_df = pd.DataFrame(data = scaled_train_set)\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "217da5f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import classification algorithms\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from  sklearn.tree import DecisionTreeClassifier\n",
    "import xgboost as xgb\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bc220ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the utility to compare models\n",
    "from sklearn import model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "03662beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare an array with all algorithms\n",
    "models = []\n",
    "models.append((\"LogReg\", LogisticRegression()))\n",
    "models.append((\"RanFor\", RandomForestClassifier()))\n",
    "models.append((\"DecTree\", DecisionTreeClassifier()))\n",
    "models.append((\"XGB\",OneVsRestClassifier(xgb.XGBClassifier(use_label_encoder=False,eval_metric='mlogloss'))))\n",
    "models.append((\"KNN\",KNeighborsClassifier()))\n",
    "models.append((\"SVC\",SVC()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4df7d5cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare configuration to run the test\n",
    "seed = 7\n",
    "results = []\n",
    "names = []\n",
    "X = scaled_train_set\n",
    "Y = train_set_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "44b83c0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogReg: 0.916667 (0.074536)\n",
      "RanFor: 0.941667 (0.065085)\n",
      "DecTree: 0.941667 (0.053359)\n",
      "XGB: 0.933333 (0.062361)\n",
      "KNN: 0.950000 (0.055277)\n",
      "SVC: 0.941667 (0.065085)\n"
     ]
    }
   ],
   "source": [
    "# Test algorithms and print results \n",
    "for name, model in models:\n",
    "    kfolds = model_selection.KFold(n_splits=10)\n",
    "    cv_results  = model_selection.cross_val_score(model, X, Y, cv=kfolds, scoring=\"accuracy\")\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "\n",
    "    outcome = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4b99fb47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot results for comparison\n",
    "figure = plt.figure()\n",
    "figure.suptitle(\"Algorithms performance\")\n",
    "ax = figure.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9d23e8dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performance of most algorithms seems somewhat scattered\n",
    "# Since there is high correlation between Petals length and width more than other features\n",
    "# Reduce the dataset to only these two features and perform algorithms once again\n",
    "petals_data = flowers_data[[\"PetalLengthCm\",\"PetalWidthCm\",\"Species\"]]\n",
    "train_set_petals, test_set_petals = train_test_split(petals_data, test_size = 0.2,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e4fa7384",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate labels from sets\n",
    "train_set_labels_petals = train_set_petals[\"Species\"].copy()\n",
    "train_set_petals = train_set_petals.drop(\"Species\", axis = 1)\n",
    "test_set_labels_petals = test_set_petals[\"Species\"].copy()\n",
    "test_set_petals = test_set_petals.drop(\"Species\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cbb641b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale sets\n",
    "scaler_petals = Scaler()\n",
    "scaler_petals.fit(train_set_petals)\n",
    "scaled_train_set_petals = scaler_petals.transform(train_set_petals)\n",
    "scaled_test_set_petals = scaler_petals.transform(test_set_petals)\n",
    "scaled_df_petals = pd.DataFrame(data = scaled_train_set_petals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0baaf112",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare an array with all algorithms\n",
    "models_petals = []\n",
    "models_petals.append((\"LogReg\", LogisticRegression()))\n",
    "models_petals.append((\"RanFor\", RandomForestClassifier()))\n",
    "models_petals.append((\"DecTree\", DecisionTreeClassifier()))\n",
    "models_petals.append((\"KNN\",KNeighborsClassifier()))\n",
    "models_petals.append((\"SVC\",SVC()))\n",
    "models_petals.append((\"XGB\",OneVsRestClassifier(xgb.XGBClassifier(use_label_encoder=False,eval_metric='mlogloss'))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fc8d9477",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare configuration to run the test\n",
    "seed_petals = 7\n",
    "results_petals = []\n",
    "names_petals = []\n",
    "X_petals = scaled_train_set_petals\n",
    "Y_petals = train_set_labels_petals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e56765f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogReg: 0.941667 (0.083749)\n",
      "RanFor: 0.958333 (0.067185)\n",
      "DecTree: 0.941667 (0.053359)\n",
      "KNN: 0.958333 (0.067185)\n",
      "SVC: 0.950000 (0.066667)\n",
      "XGB: 0.941667 (0.065085)\n"
     ]
    }
   ],
   "source": [
    "# Test algorithms and print results \n",
    "for name, model in models_petals:\n",
    "    kfolds = model_selection.KFold(n_splits=10)\n",
    "    cv_results_petals  = model_selection.cross_val_score(model, X_petals, Y_petals, cv=kfolds, scoring=\"accuracy\")\n",
    "    results_petals.append(cv_results_petals)\n",
    "    names_petals.append(name)\n",
    "\n",
    "    outcome_petals = \"%s: %f (%f)\" % (name, cv_results_petals.mean(), cv_results_petals.std())\n",
    "    print(outcome_petals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3d0b066d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot results for comparison\n",
    "figure_petals = plt.figure()\n",
    "figure.suptitle(\"Algorithms performance on Petals data\")\n",
    "ax_petals = figure_petals.add_subplot(111)\n",
    "plt.boxplot(results_petals)\n",
    "ax_petals.set_xticklabels(names_petals)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ad286978",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_features': 'sqrt', 'min_samples_split': 4, 'n_estimators': 50}\n"
     ]
    }
   ],
   "source": [
    "# From the boxplots we see that the Random Forest and KNN had the best performance\n",
    "# I chose Random Forest algorithm to proceed with the hyperparameters tuning and make predictions\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Specify tunable parameters and range of values\n",
    "\n",
    "model_params = {\n",
    "    'n_estimators': [50, 150, 250],\n",
    "    'max_features': ['sqrt', 0.25, 0.5, 0.75, 1.0],\n",
    "    'min_samples_split': [2, 4, 6]\n",
    "}\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Configure the grid search \n",
    "clf = GridSearchCV(rf_model, model_params, cv=5)\n",
    "\n",
    "# Train the data\n",
    "model = clf.fit(X_petals,Y_petals)\n",
    "\n",
    "# Get the best parameters\n",
    "print(model.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7d653d9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  1.0\n"
     ]
    }
   ],
   "source": [
    "# Make predictions using the test set\n",
    "predictions = model.predict(scaled_test_set_petals)\n",
    "\n",
    "# Import accuracy score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Calculate and print accuracy of the model\n",
    "accuracy = accuracy_score(test_set_labels_petals, predictions)\n",
    "print(\"Accuracy: \", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb88e9e5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}