(09-10-2021)Random Forest and SVM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "07a28776",
   "metadata": {},
   "source": [
    "#### Decision Tree\n",
    "- chances of getting the problem of overfitting\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1369629e",
   "metadata": {},
   "source": [
    "### Random Forest \n",
    "- ensembling classifier\n",
    "- group of decision trees will be formed using random data,so it can be called as random forest\n",
    "- we will consider the output of each decision tree and define the highest repeated times output as the predicted output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "65e4280d",
   "metadata": {
    "collapsed": 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></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"C:/Users/prabhu/Downloads/archive(3)/diabetes.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43e12b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Outcome\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c1f2901",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "936cb607",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(\"Outcome\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e7ffbceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7e3dc0ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_t,x_te,y_t,y_te=train_test_split(x,y,train_size=0.9,random_state=1)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier()\n",
    "model.fit(x_t,y_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c10b43b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(x_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "980e1d20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8181818181818182\n",
      "[[41  7]\n",
      " [ 7 22]]\n",
      "0.7586206896551724\n",
      "0.7586206896551724\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,precision_score,recall_score\n",
    "print(accuracy_score(y_te,y_pred))\n",
    "print(confusion_matrix(y_te,y_pred))\n",
    "print(precision_score(y_te,y_pred))\n",
    "print(recall_score(y_te,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "24e958ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Support Vector Machine\n",
    "from sklearn.datasets import load_wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c13cae6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "wine=load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9455a35d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6aa1f567",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _wine_dataset:\n",
      "\n",
      "Wine recognition dataset\n",
      "------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 178 (50 in each of three classes)\n",
      "    :Number of Attributes: 13 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      " \t\t- Alcohol\n",
      " \t\t- Malic acid\n",
      " \t\t- Ash\n",
      "\t\t- Alcalinity of ash  \n",
      " \t\t- Magnesium\n",
      "\t\t- Total phenols\n",
      " \t\t- Flavanoids\n",
      " \t\t- Nonflavanoid phenols\n",
      " \t\t- Proanthocyanins\n",
      "\t\t- Color intensity\n",
      " \t\t- Hue\n",
      " \t\t- OD280/OD315 of diluted wines\n",
      " \t\t- Proline\n",
      "\n",
      "    - class:\n",
      "            - class_0\n",
      "            - class_1\n",
      "            - class_2\n",
      "\t\t\n",
      "    :Summary Statistics:\n",
      "    \n",
      "    ============================= ==== ===== ======= =====\n",
      "                                   Min   Max   Mean     SD\n",
      "    ============================= ==== ===== ======= =====\n",
      "    Alcohol:                      11.0  14.8    13.0   0.8\n",
      "    Malic Acid:                   0.74  5.80    2.34  1.12\n",
      "    Ash:                          1.36  3.23    2.36  0.27\n",
      "    Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
      "    Magnesium:                    70.0 162.0    99.7  14.3\n",
      "    Total Phenols:                0.98  3.88    2.29  0.63\n",
      "    Flavanoids:                   0.34  5.08    2.03  1.00\n",
      "    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
      "    Proanthocyanins:              0.41  3.58    1.59  0.57\n",
      "    Colour Intensity:              1.3  13.0     5.1   2.3\n",
      "    Hue:                          0.48  1.71    0.96  0.23\n",
      "    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
      "    Proline:                       278  1680     746   315\n",
      "    ============================= ==== ===== ======= =====\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML Wine recognition datasets.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
      "\n",
      "The data is the results of a chemical analysis of wines grown in the same\n",
      "region in Italy by three different cultivators. There are thirteen different\n",
      "measurements taken for different constituents found in the three types of\n",
      "wine.\n",
      "\n",
      "Original Owners: \n",
      "\n",
      "Forina, M. et al, PARVUS - \n",
      "An Extendible Package for Data Exploration, Classification and Correlation. \n",
      "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
      "Via Brigata Salerno, 16147 Genoa, Italy.\n",
      "\n",
      "Citation:\n",
      "\n",
      "Lichman, M. (2013). UCI Machine Learning Repository\n",
      "[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
      "School of Information and Computer Science. \n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "  (1) S. Aeberhard, D. Coomans and O. de Vel, \n",
      "  Comparison of Classifiers in High Dimensional Settings, \n",
      "  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \n",
      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
      "  (Also submitted to Technometrics). \n",
      "\n",
      "  The data was used with many others for comparing various \n",
      "  classifiers. The classes are separable, though only RDA \n",
      "  has achieved 100% correct classification. \n",
      "  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n",
      "  (All results using the leave-one-out technique) \n",
      "\n",
      "  (2) S. Aeberhard, D. Coomans and O. de Vel, \n",
      "  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \n",
      "  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n",
      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
      "  (Also submitted to Journal of Chemometrics).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(wine.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7234c053",
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_df=pd.DataFrame(wine.data,columns=wine.feature_names)\n",
    "wine[\"Target\"]=wine.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8c8b6cef",
   "metadata": {
    "collapsed": 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></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "..       ...         ...   ...                ...        ...            ...   \n",
       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0          3.06                  0.28             2.29             5.64  1.04   \n",
       "1          2.76                  0.26             1.28             4.38  1.05   \n",
       "2          3.24                  0.30             2.81             5.68  1.03   \n",
       "3          3.49                  0.24             2.18             7.80  0.86   \n",
       "4          2.69                  0.39             1.82             4.32  1.04   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "173        0.61                  0.52             1.06             7.70  0.64   \n",
       "174        0.75                  0.43             1.41             7.30  0.70   \n",
       "175        0.69                  0.43             1.35            10.20  0.59   \n",
       "176        0.68                  0.53             1.46             9.30  0.60   \n",
       "177        0.76                  0.56             1.35             9.20  0.61   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  \n",
       "0                            3.92   1065.0  \n",
       "1                            3.40   1050.0  \n",
       "2                            3.17   1185.0  \n",
       "3                            3.45   1480.0  \n",
       "4                            2.93    735.0  \n",
       "..                            ...      ...  \n",
       "173                          1.74    740.0  \n",
       "174                          1.56    750.0  \n",
       "175                          1.56    835.0  \n",
       "176                          1.62    840.0  \n",
       "177                          1.60    560.0  \n",
       "\n",
       "[178 rows x 13 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "419e0235",
   "metadata": {
    "collapsed": true
   },
   "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"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD6CAYAAACrklzBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAARCUlEQVR4nO3df2xd5X3H8c/XiVkgoQJyvRblknqdAWmq+CWv2oSEKGALM2BU2h+r1OpuA8V/bEk6Nk10ylAbRdOkadVm/zElK6UXjbWCFrYJ4mCDyKpJFcNJaAoJi+8qQ++A4hsWSEgCxv7uD9vBTs31vcl9zuPz+P2SovjY1+d8FMcfPz7nOecxdxcAID1tsQMAAMKg4AEgURQ8ACSKggeARFHwAJAoCh4AEhW04M3sEjP7gZm9amaHzey3Qx4PAPCx1YH3/w+S9rj775nZBZIuqvfiQqHgnZ2dgSMBQDr27dtXc/eOxT4WrODN7FOSbpL0B5Lk7h9K+rDe53R2dmp0dDRUJABIjpm99kkfC3mK5nOSJiQ9bGYHzOzbZrZ2kXCbzGzUzEYnJiYCxgGAlSVkwa+WdIOkf3T36yW9L+mBs1/k7rvcvdvduzs6Fv0tAwBwDkIWfFVS1d1fmN3+gWYKHwCQgWAF7+5vSfq5mV09+65bJR0KdTwAwEKh58FvlvSomR2UdJ2kvw58PKDlarWaNm/erKNHj8aOAjQlaMG7+0uz59evcfd73P3/Qh4PCKFcLuvgwYMql8uxowBN4U5WoI5araahoSG5u4aGhhjFI1coeKCOcrmsuUVxpqenGcUjVyh4oI6RkRFNTk5KkiYnJzU8PBw5EdA4Ch6oo6enR+3t7ZKk9vZ29fb2Rk4ENI6CB+oolUoyM0lSW1ubSqVS5ERA4yh4oI5CoaC+vj6Zmfr6+rR+/frYkYCGhX6aJJB7pVJJ4+PjjN6ROxQ8sIRCoaDBwcHYMZI2MDCgSqUSZN/ValWSVCwWW77vrq4ubdmypeX7bRUKHkDSTp06FTtCNBQ8gOhCjoLn9j0wMBDsGMsVF1kBIFEUPAAkioIHgERR8ACQKAoeABJFwQNAoih4AEgUBQ8AiaLgASBRFDwAJIqCB4BEUfAAkCgKHgASRcEDQKIoeABIFAUPAImi4AEgURQ8ACSKggeARFHwAJCooItum9m4pOOSpiR95O7dIY8HAPhY0IKf9UV3r2VwHADAPJyiAYBEhS54lzRsZvvMbNNiLzCzTWY2amajExMTgeMAwMoRuuBvdPcbJPVJ+mMzu+nsF7j7Lnfvdvfujo6OwHEAYOUIWvDu/sbs329LelLSF0IeDwDwsWAFb2Zrzeziubcl9Up6OdTxAAALhZxF82lJT5rZ3HH+xd33BDweAGCeYAXv7j+TdG2o/QMA6mOaJAAkioIHgERR8ACQKAoeABJFwQNAoih4AEgUBQ8AiaLgASBRFDwAJIqCB4BEUfAAkCgKHgASRcEDQKIoeGAJtVpNmzdv1tGjR2NHAZpCwQNLKJfLOnjwoMrlcuwoQFMoeKCOWq2moaEhubuGhoYYxSNXKHigjnK5LHeXJE1PTzOKR65Q8EAdIyMjmpyclCRNTk5qeHg4ciKgcRQ8UEdPT8+C7d7e3khJgOZR8EAdd91114Ltu+++O1ISoHkUPFDH448/vmD7sccei5QEaB4FD9Tx7LPPLtgeGRmJlARoHgUP1GFmdbeB5YyCB+q49dZbF2zfdtttkZIAzaPggTr6+/vV1jbzbdLW1qb+/v7IiYDGUfBAHYVC4cxUyd7eXq1fvz5yIqBxq2MHAJa7/v5+vfXWW4zekTsUPLCEQqGgwcHB2DGApnGKJgM8bhZADBR8BnjcLIAYghe8ma0yswNm9lToYy1HtVpNu3fvlrvr6aefZhQPIDNZjOC3SjqcwXGWpXK5vOBphIziAWQlaMGbWVHS70j6dsjjLGfPPPPMgu09e/ZESgJgpQk9gv97SX8haTrwcZat1atX190GgFCCtY2Z3SnpbXffZ2Y313ndJkmbJGnjxo2h4kRz4sSJuttAngwMDKhSqcSO0ZSxsTFJ0pYtWyInaVxXV1dL8oYcTt4o6W4zu0PSGkmfMrN/dvevzH+Ru++StEuSuru7PWCeKDo7OzU+Pr5gG/ly5MgRbd26VYODg+rq6oodJ6pKpaIDrxyQLomdpAmz5w8O/O+BuDkadax1uwpW8O7+dUlfl6TZEfyfn13uK8G2bdt03333ndl+8MEHI6bBudixY4fef/99bd++XY888kjsOPFdIk3fvGLPugbXtrd1Z86ZBx/YVVddpbVr10qS1q5du+JHgHlz5MiRM7+BjY+P5+70BFa2TAre3fe6+51ZHGu5qdVqOnnypCTp5MmTzIPPmR07dizY3r59e6QkQPMYwQe2c+dOuc9cWnB37dy5M3IiNGP+9ZPFtoHljIIP7LnnnluwffYScFjezr4ozkVy5AkFH9jc6P2TtrG8bdu2bcE2F8mRJxR8YGcv8Ta3eATy4bLLLjuzDquZ6dJLL42cCGgcBR8YS77lW7lcXvD141lCyBMKPjCWfMu3kZERTU1NSZKmpqY0PDwcORHQOAo+A/39/br22msZvedQT0+P2tvbJUnt7e3q7e2NnAhoHAUP1FEqlc6cg29ra1OpVIqcCGgcBZ8BVnTKr0KhoL6+PpmZ+vr6OMWGXKHgA6vVahoaGpK7a2hoiDtZc6hUKumaa65h9I7coeADK5fLZ+a+T09PM4rPoUKhoMHBQUbvyB0KPrCRkZEFS/YxCwNAVij4wJiFASAWCj4wZmEAiIWCD4xZGABiYQXoDJRKJY2PjzN6B5ApCj4Dc7MwEFaoBaGr1aokqVgstnzfrVpcGVgMBQ8s4dSpU7EjAOekoYI3sxslfUPSZ2c/xyS5u38uXDSgOaFGwnP7HRgYCLJ/IJRGR/APSfpTSfskTYWLAwBolUYL/l13HwqaBADQUnUL3sxumH3zeTP7W0lPSPpg7uPuvj9gNgDAeVhqBP93Z213z3vbJd3S2jgAlrNqtSq9K7Xt5RaaYI5JVa+2ZFd1C97dv9iSowAAMtfoLJqtkh6WdFzSP0m6QdID7s6Ts4AVpFgsasImNH3zdOwoyWrb26bihtbcc9Ho71l/5O7vSeqV9KuS/lDS37QkAQAgiEYL3mb/vkPSw+7+k3nvwxJqtZo2b97MYh8AMtVowe8zs2HNFPwzZnaxJH5HaxBL9gGIodGCv1fSA5K+opmZND2SvhsoU1JYsg9ALA2fg5dUlrRH0jclPSrpS6FCpYQl+wDE0mjBb5X0m5Jem506eb2kiXqfYGZrzOy/zOwnZvaKmX3zPLPmEkv2AYil0YI/7e6nJcnMfsXdX5V09RKf84GkW9z9WknXSbrdzH7rnJPmFEv2AYil0YKvmtklkv5V0oiZ/ZukN+p9gs84MbvZPvvHzzFnbrFkH4BYGip4d/+Sux9z929I+ivNPF3ynqU+z8xWmdlLkt6WNOLuL5x71HxiyT4AsTS94Ie7/0cTr52SdN3s6P9JM/u8u788/zVmtknSJknauHFjs3FygSX7AMSQyROD3P2YpL2Sbl/kY7vcvdvduzs6OrKIk7m5JfsYvQPIUrCCN7OO2ZG7zOxCSbdJejXU8QAAC4Vck/VySWUzW6WZHySPuftTAY8HAJgnWMG7+0HNzJcHAETAU/sBIFEUPAAkioIHgERR8ACQKAoeABJFwQNAoih4AEgUBZ8B1mQFEAMFnwHWZAUQAwUfGGuyAogl5LNooMXXZL3//vsjpwLOwzGpbW+OxoZzyw6ti5qiccckbWjNrij4wBZbk5WCR151dXXFjtC0sbExSdKVG66MnKRBG1r370zBB9bT06Pdu3drcnKSNVmRe1u2bIkdoWlzmQcGBiInyV6Ofs/KJ9ZkBRALBR8Ya7ICiIVTNBlgTVYAMTCCB4BEUfAZ4EYnADFQ8IFxoxOAWCj4wBa70QkAskDBB7bYjU4AkAUKPrCenh61t7dLEjc6AcgUBR8YNzoBiIWCD4wbnQDEwo1OGeBGJwAxUPAZKBQKGhwcjB0DwApDwc8zMDCgSqXS8v1Wq1VJUrFYbPm+pZlHi+bxKX8AwqLgM3Dq1KnYEZaNUD9EQ5p7nnjefojygx8U/DyhvhlW8vOoz1apVHTk5f3auG4qdpSGXTA5Mxfh9PiLkZM07vUTq2JHwDJAwSNzG9dNaVv3iaVfiHO2YzQv69MhpGDTJM3sCjN73swOm9krZrY11LEAAL8s5Aj+I0l/5u77zexiSfvMbMTdDwU8JgBgVrARvLu/6e77Z98+LumwWrZWOABgKZncyWpmnZKul/RCFscDAGRQ8Ga2TtIPJX3N3d9b5OObzGzUzEYnJiZCxwGAFSPoLBoza9dMuT/q7k8s9hp33yVplyR1d3d7I/vN21xq5lEDiCFYwdvMIxQfknTY3b/Vyn1XKhUd+OkhTV90WSt3G4x9OPNza9//vBU5SePaTr4TOwKA8xRyBH+jpK9K+qmZvTT7vr90992t2Pn0RZfp9G/c2YpdYRFrDj0VOwKA8xSs4N39PyVZqP0DAOrjefAAkCgKHgASRcEDQKJy+bCxarWqtpPvciEwoLaTR1WtfhQ7BoDzwAgeABKVyxF8sVjULz5YzTTJgNYcekrF4mdixwBwHhjBA0CicjmCl2butMzLOXg7PfMIHl/zqchJGjdzJysjeCDPclnwXV1dsSM0ZWzsuCTpyl/PU2F+Jnf/zgAWymXB5+0BWKzJ+rFqtar3j69iSbnAXju+Smur1dgxEBnn4AEgUbkcwSO/isWiTn/0JotuB7ZjdJ3WFIuxYyAyRvAAkCgKHgASRcEDQKIoeABIFAUPAImi4AEgURQ8ACSKefDzDAwMqFKptHy/Y2NjksLdgdvV1ZW7u3sBhEfBZ+DCCy+MHQHACkTBz8MoGEBKKHhk7vUT+XrY2C9Ozlyq+vRF05GTNO71E6t0VewQiI6CR6by+AjiD2evoazpvDJyksZdpXz9W4e6/iWFvQa23K9/UfDI1HL+ZvgkPO4531byNTAKHkB0efzBnwfMgweARFHwAJAoCh4AEkXBA0CiKHgASFSwgjez75jZ22b2cqhjAAA+WcgR/Hcl3R5w/wCAOoIVvLv/SNI7ofYPAKgv+jl4M9tkZqNmNjoxMRE7DgAkI3rBu/sud+929+6Ojo7YcQAgGdELHgAQBgUPAIkKOU3ye5J+LOlqM6ua2b2hjgUA+GXBnibp7l8OtW8AwNI4RQMAiaLgASBRFDwAJIoVnZCMUOt6ruQ1PZFvFDywhJW8pifyjYJHMhgJAwtxDh4AEkXBA0CiKHgASBQFDwCJouABIFEUPAAkioIHgERR8ACQKHP32BnOMLMJSa/FzhFIQVItdgicM75++Zby1++z7r7oeqfLquBTZmaj7t4dOwfODV+/fFupXz9O0QBAoih4AEgUBZ+dXbED4Lzw9cu3Ffn14xw8ACSKETwAJIqCB4BEUfAZMLPbzey/zaxiZg/EzoPGmdl3zOxtM3s5dhY0x8yuMLPnzeywmb1iZltjZ8oa5+ADM7NVko5I6pFUlfSipC+7+6GowdAQM7tJ0glJj7j752PnQePM7HJJl7v7fjO7WNI+SfespO89RvDhfUFSxd1/5u4fSvq+pN+NnAkNcvcfSXondg40z93fdPf9s28fl3RY0oa4qbJFwYe3QdLP521XtcL+kwGxmVmnpOslvRA5SqYo+PBskfdxXgzIiJmtk/RDSV9z9/di58kSBR9eVdIV87aLkt6IlAVYUcysXTPl/qi7PxE7T9Yo+PBelHSlmf2amV0g6fcl/XvkTEDyzMwkPSTpsLt/K3aeGCj4wNz9I0l/IukZzVzkeczdX4mbCo0ys+9J+rGkq82samb3xs6Eht0o6auSbjGzl2b/3BE7VJaYJgkAiWIEDwCJouABIFEUPAAkioIHgERR8ACQKAoeABJFwQNAov4fNiIc6Gh4nDoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD6CAYAAABEUDf/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUgklEQVR4nO3dfZBddX3H8c9nYTEQQCCbqsOaBl1gpuOA2NXW4jA8JWN8QG2djs5oty1M0pmaxGKnox2KCWY6tQrV3XY6RB5cFO0ggqWYhawUcCxK3QSMQDTZxkVvedrLY0ISWbLf/nHvxt1tNveczT337Dn7fs3cuffch3O+k5v97G9/53d+P0eEAADl1JZ3AQCA7BDyAFBihDwAlBghDwAlRsgDQIkR8gBQYpmHvO2TbN9q+2e2t9t+Z9bHBADUHN2CY3xZ0l0R8WHbx0g6bqY3dnR0xNKlS1tQEgCUx5YtW6oRsfhQr2Ua8rZPlHSepD+VpIh4RdIrM71/6dKlGhoayrIkACgd24/P9FrW3TVvkjQq6UbbD9m+zvbCacWttD1ke2h0dDTjcgBgfsk65I+W9DZJ/xoR50h6WdKnJ78hIjZGRHdEdC9efMi/NgAAs5R1yFckVSLiwfr2raqFPgCgBTIN+Yh4StKvbJ9Zf+oiSY9leUwAwG+0Ypz8akk3294m6a2S/r4FxwSaqlqtavXq1Xr22WfzLgVIJfOQj4iH633uZ0XEByPi+ayPCTRbf3+/tm3bpv7+/rxLAVLhileggWq1qoGBAUWEBgYGaM2jUAh5oIH+/n5NLK4zPj5Oax6FQsgDDQwODmpsbEySNDY2ps2bN+dcEZAcIQ80sGzZMrW3t0uS2tvbtXz58pwrApIj5IEGenp6ZFuS1NbWpp6enpwrApIj5IEGOjo6tGLFCtnWihUrtGjRorxLAhJrxSyUQOH19PRoZGSEVjwKh5AHEujo6FBfX1/eZZRab2+vhoeHm77fSqUiSers7Gz6viWpq6tLa9asyWTfzUDIAyi1ffv25V1Crgh5AHNCVq3hif329vZmsv+5jhOvAFBihDwAlBghDwAlRsgDQIkR8gBQYoQ8AJQYIQ8AJUbIA0CJEfIAUGKEPACUGCEPACVGyANAiRHyAFBihDwAlBghDwAlRsgDQIkR8gBQYoQ8AJQYIQ8AJUbIA0CJZb6Qt+0RSbslHZD0akR0Z31MAEBN5iFfd0FEVFt0LABAHd01AFBirQj5kLTZ9hbbK6e/aHul7SHbQ6Ojoy0oBwDmj1aE/LkR8TZJKyT9pe3zJr8YERsjojsiuhcvXtyCcgBg/sg85CPiifr9M5Jul/SOrI8JAKjJNORtL7R9wsRjScslPZLlMQEAv5H16JrXSbrd9sSxvhERd2V8TABAXaYhHxG7JJ2d5TEAADNjCCUAlBghDwAlRsgDQIkR8gBQYoQ8AJQYIQ8AJUbIA0CJEfIAUGKEPACUGCEPACVGyANAiRHyAFBihDwAlBghDyRQrVa1evVqPfvss3mXAqRCyAMJ9Pf3a9u2berv78+7FCAVQh5ooFqtamBgQBGhgYEBWvMoFEIeaKC/v18RIUkaHx+nNY9CIeSBBgYHBzU2NiZJGhsb0+bNm3OuCEiOkAcaWLZs2ZTt5cuX51QJkB4hDzTw/ve/f8r2JZdcklMlQHqEPNDAt771rSnbt9xyS06VAOkR8kAD3/ve96ZsDw4O5lQJkB4hDzRg+7DbwFxGyAMNXHTRRVO2L7744pwqAdJLHPK2z7U9aHuH7V22f2F7V5bFAXPBqlWr1NZW+1Fpa2vTqlWrcq4ISC5NS/56SddIepekt0vqrt8DpdbR0XFwGOXy5cu1aNGinCsCkjs6xXtfjIiBzCoB5rBVq1bpqaeeohWPwmkY8rbfVn94r+0vSLpN0q8nXo+IrRnVBswZHR0d6uvry7sMILUkLfmrp213T3ocki5sXjnlVa1WtX79eq1bt44/9wG0TMOQj4gLWlFI2U2eqvbyyy/PuxwA80Sa0TVrbZ/omutsb7WdaBIP20fZfsj2nbMvtbiq1ao2bdqkiNB3v/tdpqoF0DJpRtf8eUS8JGm5pN+S9GeS/iHhZ9dK2p6yttLo7++fMoshU9UCaJU0IT9xmd97JN0YET+Z9NzMH7I7Jb1X0nXpyyuHu+++e8r2XXfdlVMlAOabNCG/xfZm1UL+btsnSBpP8LkvSfqbhO8tpaOPPvqw2wCQlTRpc6mkt0raFRF7bS9SrctmRrbfJ+mZiNhi+/wZ3rNS0kpJWrJkSYpyimPPnj2H3QaKpLe3V8PDw3mXkdjOnTslSWvWrMm5knS6urqaUnPikI+Icdu/kHSG7QUJP3aupEtsv0fSAkkn2v56RHxs0n43StooSd3d3ZG89OJYunSpRkZGpmyjWHbs2KG1a9eqr69PXV1deZeTq+HhYT306EPSSXlXklC9D+Gh/30o3zrSeKF5u0oc8rYvU+0EaqekhyX9vqQf6jDj5CPiM5I+U//8+ZL+enLAzxdXXHGFLrvssoPbV155ZY7VYDY2bNigl19+WVdddZVuuummvMvJ30nS+Pnztgc2c233NW/uyDR7WqvaXDWP18fOnyNptGmVlNgZZ5yhhQsXSpIWLlw471uCRbNjx46Df4mNjIwUqqsCSBPy+yNivyTZfk1E/EzSmUk/HBH3RcT70hZYBtVqVXv37pUk7d27l3HyBbNhw4Yp21dddVVOlQDppQn5iu2TJH1H0qDtf5f0RBZFlc21116riNrphojQtddem3NFSGPy+ZRDbQNzWeKQj4gPRcQLEbFO0t+pNvXwBydet31y06sriXvuuWfK9vTl5DC3TT9RzolzFMmsevcj4v6IuCMiXpn09D0zfmCem2jFz7SNue2KK66Yss2JcxRJM5f/Y+HLGUxfLm5iAQoUwymnnHJwXVfbOvlk/mhFcTQz5GmezoDl44qtv79/yvfH3EMoEhbybgGWjyu2wcFBHThwQJJ04MABbd68OeeKgOQahrzt0xLui+6aw1i1apXOPvtsWvEFtGzZMrW3t0uS2tvbtXx5ohm2gTkhSUv+Vkmy3ejE6kVHXg4w9/T09Bzsk29ra1NPT0/OFQHJJQn5NtufVW3Omsun3ybeFBHPZVdm8U1eGQrF0tHRoRUrVsi2VqxYQXcbCiVJyH9E0n7V5rk54RA3NFCtVjUwMKCI0MDAAFe8FlBPT4/OOussWvEonCRrvP5c0udtb4uIgRbUVDr9/f0Hx8aPj4+zzmsBdXR0qK+vL+8ygNTSjK55wPY1tofqt6ttvzazykpkcHBwyvJ/jM4A0CppQv4GSbsl/XH99pKkG7MoqmwYnQEgL2lC/s0R8dmI2FW/rZf0pqwKKxNGZwDIS5qQ32f7XRMbts+VtK/5JZUPozMA5CXNGq9/IemmSf3wz0uiSZpQT0+PRkZGaMUDaKk0a7z+RNLZtk+sb780+XXbPRHBIPAZMDqjNbJaZLpSqUiSOjs7m77vZi3YDBxK6rlrIuKl6QFft7YJ9QBz0r59+7RvH72TKJ403TWNMHcNcpdVi3hiv729vZnsH8gKUw0DQImxaAgAlFjikLd9VIO3/NcR1gIAaLI0ffLDtm+VdGNEPDb9xYj4RPPKAjBXVSoV6UWp7T7WHMrMC1IlKk3ZVZpv6SxJOyRdZ/tHtldODKcEAMxNacbJ75b0FUlfsX2epG9K+qd66/5zEdH8wckA5pzOzk6NelTj54/nXUpptd3Xps5Tm3NNRqo+eduX2L5d0pclXa3a3DX/IWlTU6oBADRVmu6anZI+IOkLEXFORFwTEU9HxK2S7sqmvPKoVqtavXo1C4YAaKk0If8nEXFpRDww8UR9kjJFBNdkN8DyfwDykCbkD3WpH5OxJMDyfwDy0vDEq+13SvoDSYsnL9wt6URJjcbOQyz/ByA/SVryx0g6Xv9/Ie+XJH34cB+0vcD2f9v+ie1Hba8/0oKLiOX/AOQlyULe90u63/ZXI+LxlPv/taQLI2KP7XZJP7A9EBE/mk2xRbVs2TJt2rRJY2NjLP8HoKUatuRtf6n+8J9t3zH9drjPRs2e+mZ7/TbvJjJj+T8AeUlyMdTX6vdfnM0B6nPebJHUJelfIuLB2eynyCaW/7vjjjtY/g9ASyXprtlSv79/NgeIiAOS3mr7JEm3235LRDwy8brtlZJWStKSJUtmc4hCYPk/AHlIc8XrubYHbe+wvcv2L2zvSvr5iHhB0n2S3j3t+Y0R0R0R3YsXL066u8KZWP6PVjyAVkozC+X1kv5Kta6XA0k+YHuxpLGIeMH2sZIulvT51FUCAGYlTci/GBEDKff/Bkn99X75Nkm3RMSdKfcBAJilNCF/r+0vSLpNtaGRkqSI2DrTByJim6RzZl8eAOBIpAn536vfd096LiRd2LxyAADNlGY++QuyLAQA0HxJ5q75WER8fdq8NQdFxDXNLwsA0AxJWvIL6/cnZFkIAKD5klwMdW39fl5OLgYARZaku+ZQ88gfxIIhADB3Jemu2ZJ5FQCATCTprmG9uiaoVqtav3691q1bx9QGAFomzdw1i21/0fYm2/85ccuyuDJhjVcAeUizxuvNkrZLOk3Sekkjkn6cQU2lwxqvAPKS5orXRRFxve21k1aLmtX0w/MNa7yidF6Q2u5L00bM0cSyRcfnWkU6L0g6tTm7ShPyY/X7J22/V9ITkjqbU0a5HWqNV0IeRdXV1ZV3Cans3LlTknT6qafnXEkKpzbv3zlNyG+w/VpJn5LUJ+lE1aYeRgOs8YoyWbOmWKOmJ+rt7T3saPDSSvz3VkTcGREvRsQjEXFBRPxuRBx2jVfUsMYrgLykGV3TX1/Cb2L7ZNs3ZFJVyUys8WqbNV4BtFSa7pqz6kv4SZIi4nnbzBWfEGu8AshDmtPjbbZPntiwfYrS/ZIAALRYmpC/WtIDtj9n+3OSHpD0j9mUVT5cDAUgD2lOvN4k6Y8kPS3pGUl/GBFfy6qwMuFiKAB5aRjytk+ZuEl6StI3VLv69an6c2jgUBdDAUArJGnJb5E0VL+feDw06TEaONTFUADQCklmoTxt4nG95X66pAVZFlU2XAwFIC9pxslfJul+SXdJWle/vzKbssqFi6EA5CXN6Jq1kt4u6fGIuEDSOZKqmVRVMlwMBSAvaca574+I/bZl+zUR8TPbZ2ZWWclwMRSAPKQJ+Up9WoPvSBq0/bxqM1EigY6ODvX19eVdBoB5JnHIR8SH6g/X2b5X0mtV65cvld7eXg0PDzd9v5VKRZLU2ZnN7MxdXV2Fmx0QQPZmNS1BfdEQpLBv3768S5gzsvpFmqWJOcmL9ouUX/5g7plpsvqBmO9zWk82PDysHY9s1ZLjD+RdSmLHjNXGKOwfKc6Kl7/cc1TeJWAOIOSRiyXHH9AV3XsavxGztmGoSOvdISuZLtJo+42277W93fajttdmeTwAwFRZt+RflfSpiNhq+wRJW2wPRsRjGR8XAKCMW/IR8WREbK0/3i1pu5q2BjkAoJFMQ34y20tVu0r2wVYdEwDmu5aEvO3jJX1b0icj4qVpr620PWR7aHR0tBXlAMC8kfnoGtvtqgX8zRFx2/TXI2KjpI2S1N3dHUn3W7Sx1oyzBpCHTEPetakXr5e0PSKuaea+h4eH9dBPH9P4ccVYt8Sv1H5/bfmfp3KuJLm2vc/lXQKAI5R1S/5cSR+X9FPbD9ef+9uI2NSMnY8fd4r2/877mrErHMKCx+7MuwQARyjTkI+IH0hylscAAMysZaNrAACtR8gDQIkR8gBQYoWdoKxSqaht74ucHMxQ295nVam8mncZAI4ALXkAKLHCtuQ7Ozv19K+PZghlhhY8dqc6O1+fdxkAjgAteQAoscK25KXaFZlF6ZP3/tqUPbHgxJwrSa52xSsteaDIChvyXV1deZeQys6duyVJp7+5SKH5+sL9OwOYqrAhX7RJs1jj9TcqlYpe3n0Uy9Nl7PHdR2lhpZJ3GcgZffIAUGKFbcmjuDo7O7X/1SdZyDtjG4aO14LOzrzLQM5oyQNAiRHyAFBihDwAlBghDwAlRsgDQIkR8gBQYoQ8AJQY4+Sn6e3t1fDwcNP3u3PnTknZXanb1dVVuKuAAWSPkG+RY489Nu8SAMxDhPw0tIYBlAkhj1z8ck+xJih7em/t9NXrjhvPuZLkfrnnKJ2RdxHIHSGPlivi9MWv1M+pLFh6es6VJHeGivVvzfmwbBDyaLm5/AMxE6aKLq75fj6MkAcwJxTxl38RME4eAEqMkAeAEiPkAaDECHkAKDFCHgBKLNOQt32D7WdsP5LlcQAAh5Z1S/6rkt6d8TEAADPINOQj4vuSnsvyGACAmeXeJ297pe0h20Ojo6N5lwMApZJ7yEfExojojojuxYsX510OAJRK7iEPAMgOIQ8AJZb1EMpvSvqhpDNtV2xfmuXxAABTZToLZUR8NMv9AwAOj+4aACgxQh4ASoyQB4ASY2UolEoR1wmd62uEotgIeSCB+b5OKIqLkEep0CIGpqJPHgBKjJAHgBIj5AGgxAh5ACgxQh4ASoyQB4ASI+QBoMQIeQAoMUdE3jUcZHtU0uN515GhDknVvIvArPH9FVfZv7vfjohDrp86p0K+7GwPRUR33nVgdvj+ims+f3d01wBAiRHyAFBihHxrbcy7ABwRvr/imrffHX3yAFBitOQBoMQIeQAoMUK+RWy/2/bPbQ/b/nTe9SA52zfYfsb2I3nXgnRsv9H2vba3237U9tq8a2o1+uRbwPZRknZIWiapIunHkj4aEY/lWhgSsX2epD2SboqIt+RdD5Kz/QZJb4iIrbZPkLRF0gfn088eLfnWeIek4YjYFRGvSPo3SR/IuSYkFBHfl/Rc3nUgvYh4MiK21h/vlrRd0qn5VtVahHxrnCrpV5O2K5pn/9GAvNleKukcSQ/mXEpLEfKt4UM8Rz8Z0CK2j5f0bUmfjIiX8q6nlQj51qhIeuOk7U5JT+RUCzCv2G5XLeBvjojb8q6n1Qj51vixpNNtn2b7GEkfkXRHzjUBpWfbkq6XtD0irsm7njwQ8i0QEa9K+oSku1U78XNLRDyab1VIyvY3Jf1Q0pm2K7YvzbsmJHaupI9LutD2w/Xbe/IuqpUYQgkAJUZLHgBKjJAHgBIj5AGgxAh5ACgxQh4ASoyQB4ASI+QBoMT+D5MoeBm/8YzKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD6CAYAAABEUDf/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAATwElEQVR4nO3dfWxd9X3H8c/HiWkgwCg4ahGGhdbANLVp6QxqRVWxQiLSFdbRBxWt3aUqSja1CR1b13bLqIqyaaq2qrMrTWR9uqgPG4VW6yCGGNSo6tpRHEDhIV3igmlvC8VOB+QJcOLv/rjX1M6c3HOTe+7x+fn9kq7w8fU956MYf/zzuef8fo4IAQDS1FV0AABAfih5AEgYJQ8ACaPkASBhlDwAJIySB4CE5V7ytk+zfZvtn9jeYfsteR8TAFC3uAPH+GdJd0XEe2yfIOmkI31hT09PLF++vAORACAd27Ztm4iIZXM9l2vJ2z5V0tskXStJEfGSpJeO9PXLly/XyMhInpEAIDm2nzzSc3mfrnmNpHFJX7H9oO0v2l56WLg1tkdsj4yPj+ccBwAWlrxLfrGkN0n6l4i4UNI+SZ+c+QURsSki+iOif9myOf/aAAAco7xLviapFhH3NbZvU730AQAdkGvJR8TTkn5u+4LGpy6T9FiexwQA/EYnrpNfJ+nrtrdLeqOkv+/AMYG2mpiY0Lp167R79+6iowAtyb3kI+Khxjn3FRHxroj437yPCbRbtVrV9u3bVa1Wi44CtIQ7XoEmJiYmNDQ0pIjQ0NAQo3mUCiUPNFGtVjW9uM7U1BSjeZQKJQ80MTw8rMnJSUnS5OSktmzZUnAiIDtKHmhi5cqV6u7uliR1d3dr1apVBScCsqPkgSYqlYpsS5K6urpUqVQKTgRkR8kDTfT09Gj16tWyrdWrV+uMM84oOhKQWSdmoQRKr1KpaGxsjFE8SoeSBzLo6enR4OBg0TGSNjAwoNHR0bbvt1arSZJ6e3vbvm9J6uvr0/r163PZdztQ8gCSduDAgaIjFIqSBzAv5DUant7vwMBALvuf73jjFQASRskDQMIoeQBIGCUPAAmj5AEgYZQ8ACSMkgeAhFHyAJAwSh4AEkbJA0DCKHkASBglDwAJo+QBIGGUPAAkjJIHgIRR8gCQMEoeABJGyQNAwih5AEgYJQ8ACct9IW/bY5L2SDok6WBE9Od9TABAXe4l3/D7ETHRoWMBABo4XQMACetEyYekLba32V5z+JO219gesT0yPj7egTgAsHB0ouQviYg3SVot6SO23zbzyYjYFBH9EdG/bNmyDsQBgIUj95KPiF82/vuMpO9IujjvYwIA6nItedtLbZ8y/bGkVZIeyfOYAIDfyPvqmldJ+o7t6WN9IyLuyvmYAICGXEs+Ih6X9IY8jwEAODIuoQSAhFHyAJAwSh4AEkbJA0DCKHkASBglDwAJo+QBIGGUPAAkjJIHgIRR8gCQMEoeABJGyQNAwih5AEgYJQ9kMDExoXXr1mn37t1FRwFaQskDGVSrVW3fvl3VarXoKEBLKHmgiYmJCQ0NDSkiNDQ0xGgepULJA01Uq1VFhCRpamqK0TxKhZIHmhgeHtbk5KQkaXJyUlu2bCk4EZAdJQ80sXLlylnbq1atKigJ0DpKHmjiyiuvnLV91VVXFZQEaB0lDzTxrW99a9b2rbfeWlASoHWUPNDEPffcM2t7eHi4oCRA6yh5oAnbR90G5jNKHmjisssum7V9+eWXF5QEaB0lDzSxdu1adXXVf1S6urq0du3aghMB2VHyQBM9PT0vX0a5atUqnXHGGQUnArJbXHQAoAzWrl2rp59+mlE8SoeSBzLo6enR4OBg0TGAlnG6pkOYqhZAESj5DmGqWgBF6EjJ215k+0Hbd3TiePPNxMSENm/erIjQnXfeyWgeQMdkKnnbr7X9isbHl9peb/u0Fo5zvaQdx5AvCdVqddYshozmAXRK1pH87ZIO2e6T9CVJ50r6RpYX2u6V9AeSvnhMCRNw9913z9q+6667CkoCYKHJWvJTEXFQ0h9J+nxE/LmkMzO+9vOS/krSVOvx0rB48eKjbgNAXrK2zaTtayRVJE3Pu9rd7EW23ynpmYjYZvvSI3zNGklrJOmcc87JGKdc9u7de9RtoEwGBgY0OjpadIzMdu3aJUlav359wUla09fX15bMWUv+Q5L+VNLfRcQTts+V9LUMr7tE0lW23yFpiaRTbX8tIj4w/QURsUnSJknq7++PltKXxPLlyzU2NjZrG+Wyc+dOXX/99RocHFRfX1/RcQo1OjqqBx99UDqt6CQZNc4hPPiLB4vN0Ypn27erTCUfEY9JWj9j+wlJ/5DhdZ+S9Cmp/oatpL+cWfALxYYNG3Tddde9vH3jjTcWmAbHYuPGjdq3b59uuukm3XLLLUXHKd5p0tSlC/YMbO66trbvwsejlrzthyXNNbq2pIiIFW1LkrDzzz9fS5cu1b59+7R06dIFPxIsm507d778l9jY2JhGR0f5HqI0mo3k39muA0XEVklb27W/MpmYmND+/fslSfv379fu3buZ5KpENm7cOGub0TzK5Kh/E0TEk9MPSS9Ien3jcaDxOWRw8803K6L+B1FE6Oabby44EVox8/2UubaB+SzrzVDvk/RjSe+V9D5J99l+T57BUnLvvffO2j58OTnMb4e/Uc4b5yiTrGf3/0bSRRFRiYg/kXSxpL/NL1ZapkfxR9rG/LZhw4ZZ27xxjjLJWvJdEfHMjO3dLbx2wTt8ubjpBShQDqeffvrL67ra1itf+cqCEwHZZS3qu2zfbfta29dKulPS5vxipYXl48qtWq3O+v4x9xDKJFPJR8THVb9haYWkN0jaFBGfyDNYSlg+rtyGh4d16NAhSdKhQ4e0ZcuWghMB2WWeRCUibld9ojIcA5aPK6+VK1dq8+bNmpycVHd3t1atWlV0JCCzrFfXXG17l+3nbD9ve4/t5/MOB8wHlUrl5XPyXV1dqlQqBScCsst6Tv6zkq6KiN+KiFMj4pSIODXPYKlhZajy6unp0erVq2Vbq1ev5nQbSiVryf8qIhbsoh/Ha2JiQkNDQ4oIDQ0NsTJUCVUqFa1YsYJRPEona8mP2P5329c0Tt1cbfvqXJMlpFqtvnxt/NTUFKP5Eurp6dHg4CCjeJRO1pI/VdJ+SatUn0/+SrVxXpvUDQ8Pz1r+j6szAHRK1qmGP5R3kJRxdQaAomS9uuZ82/fafqSxvcL2hmavQx1XZwAoStbTNf+q+uIfk5IUEdslvT+vUKnh6gwARcl6M9RJEfHj6dFow8Ec8iSrUqlobGyMUTyAjspa8hO2X6vGKlGNaYafyi1VgqavzkC+8lpkularSZJ6e3vbvu92LdgMzCVryX9E9blrfsf2LyQ9IWnBrdWKhevAgQNFRwCOSdarax6XdLntpapPO7wn31jAsclrRDy934GBgVz2D+QlU8nbfoWkd0taLmnx9Ln5iLgpt2QAgOOW9XTNf0h6TtI2SS/mFwcA0E5ZS743Iq7INQkAoO2ylvwPbb8+Ih7ONQ2Aea9Wq0nPSV1bWQE0N89Ktai1ZVdHLXnbD6t+2eRiSR+y/bjqp2ssKSJiRVtSAABy0WwkzyRkAGbp7e3VuMc1delU0VGS1bW1S71nteeejKOWfEQ8Of2x7TdJeqvqI/v/iogH2pIAAJCbrBOU3SipKukMST2SvsIEZa2ZmJjQunXrWDAEQEdlfefkGkkXRcSnI+LTkt4s6Y/zi5Uelv8DUISsJT8macmM7VdI+mnb0ySK5f8AFCVryb8o6VHbX7X9FUmPSNpre8A293k3wfJ/AIqS9Tr57zQe07ZmeZHtJZK+r/rIf7Gk2xqnexaUuZb/u+GGGwpOBWAhyDpB2VGHnrZvj4h3z/HUi5LeHhF7bXdL+oHtoYj472PIWlos/wegKO26Ze01c30y6vY2Nrsbj2jTMUuD5f8AFKVdJX/E4ra9yPZDkp6RNBwR97XpmKXB8n8AipL75BMRcSgi3iipV9LFtl8383nba2yP2B4ZHx/PO05hKpWKVqxYwSgeQEe1q+Td7Asi4lnV37C94rDPb4qI/ojoX7ZsWZvizD/Ty/8xigfQSe0q+U/M9Unby2yf1vj4REmXS/pJm44JAGgi6yyU/+8pzZiFMiK2HGEXZ0qq2l6k+i+UWyPijuPICwBoQa6zUEbEdkkXHs8+AADHLvMslACA8sk6C+Wbbd9ve6/tl2wfsv183uEAAMcn6xuvX1B9Jspdkk6UdJ2kwbxCAQDaI+vcNYqIUduLIuKQ6vPJ/zDHXACANsha8vttnyDpIduflfSUpKX5xQIAtEPW0zUfbHztRyXtk3S2pKvzCgUAaI+sJf+uiHghIp6PiM9ExA1ikW8AmPeylvxcE65c28YcyWONVwBFOGrJ277G9n9KOtf2d2c8tkqirVrAGq8AitDsjdcfqv4ma4+kf5rx+T2StucVKjWHr/FaqVSYqAxAR2S54/VJSW+x/SpJFzWe2hERB/MOl4q51nhl+T+U2rNS19bcZypvj+lli04uNEVrnpV0Vnt2lekSStvvlfSPqk8VbEmDtj8eEbe1J0baWOMVKenr6ys6Qkt27dolSTrvrPMKTtKCs9r375z1OvkNki6KiGek+hTCku6RRMlnwBqvSMn69euLjtCS6bwDAwMFJylG1r+3uqYLvmF3C69d8FjjFUBRshb1kO27bV9r+1pJd0ranF+stLDGK4CiZD1dE5JulvRW1c/Jb5L05rxCpahSqWhsbIxRPICOylryKyPiE5K+Pf0J25/REZb9AwDMD81uhvqzxhKAF9jePuPxhLhOviXcDAWgCM3OyX9D0pWSvtv47/Tj9yLiAzlnS8bhN0MxtQGATjlqyUfEcxExFhHXRMSTMx6/7lTAFMx1MxQAdAKXQXbAXDdDAUAnUPIdsHLlSnV3d0sSN0MB6ChKvgO4GQpAUSj5DuBmKABFybyQN44PN0MBKAIl3yE9PT0aHBwsOgaABYaSP8zAwIBGR0fbvt9arSZJ6u3tbfu+pfq0pGWbHRBA/ij5Djlw4EDREeaNvH6R5ml6TvKy/SLllz8o+cPk9QOx0Oe0nml0dFQ7H3lA55x8qOgomZ0wWb9G4YWx+wtOkt3P9i4qOgLmAUoehTjn5EPa0L+3+RfimG0cKdN6d8hLrpdQ2j7b9vds77D9qO3r8zweAGC2vEfyByX9RUQ8YPsUSdtsD0fEYzkfFwCgnEfyEfFURDzQ+HiPpB1q2xrkAIBmOnbHq+3lki6UdF+njgkAC11HSt72yZJul/SxiHj+sOfW2B6xPTI+Pt6JOACwYOR+dY3tbtUL/usR8e3Dn4+ITaqvGav+/v7Iut+yXWvNddYAipBrybs+9eKXJO2IiM+1c9+jo6N68OHHNHXS6e3cbW78Uv3317afPl1wkuy69rM2DFB2eY/kL5H0QUkP236o8bm/jojN7dj51Emn64XffWc7doU5LHnsjqIjADhOuZZ8RPxAkvM8BgDgyJhPHgASRskDQMIoeQBIWGknKKvVaura/xxvDuaoa/9u1WoHi44B4DgwkgeAhJV2JN/b26tfvbiYSyhztOSxO9Tb++qiYwA4DozkASBhpR3JS/U7MstyTt4v1KfsiSWnFpwku/odr4zkgTIrbcn39fUVHaElu3btkSSd99oylearS/fvDGC20pZ82SbNYo3X36jVatq3ZxHL0+XsyT2LtLRWKzoGCsY5eQBIWGlH8iiv3t5evXDwKRbyztnGkZO1pLe36BgoGCN5AEgYJQ8ACaPkASBhlDwAJIySB4CEUfIAkDBKHgASxnXyhxkYGNDo6Gjb97tr1y5J+d2p29fXV7q7gAHkj5LvkBNPPLHoCAAWIEr+MIyGAaSEkkchfra3XBOU/Wp//e2rV500VXCS7H62d5HOLzoECkfJo+PKOH3xS433VJYsP6/gJNmdr3L9W/N+WD4oeXTcfP6BOBKmii6vhf5+GCUPYF4o4y//MuA6eQBIGCUPAAmj5AEgYZQ8ACSMkgeAhOVa8ra/bPsZ24/keRwAwNzyHsl/VdIVOR8DAHAEuZZ8RHxf0q/zPAYA4MgKPydve43tEdsj4+PjRccBgKQUXvIRsSki+iOif9myZUXHAYCkFF7yAID8UPIAkLC8L6H8pqQfSbrAds32h/M8HgBgtlxnoYyIa/LcPwDg6DhdAwAJo+QBIGGUPAAkjJWhkJQyrhM639cIRblR8kAGC32dUJQXJY+kMCIGZuOcPAAkjJIHgIRR8gCQMEoeABJGyQNAwih5AEgYJQ8ACaPkASBhjoiiM7zM9rikJ4vOkaMeSRNFh8Ax4/tXXql/7347IuZcP3VelXzqbI9ERH/ROXBs+P6V10L+3nG6BgASRskDQMIo+c7aVHQAHBe+f+W1YL93nJMHgIQxkgeAhFHyAJAwSr5DbF9h+39sj9r+ZNF5kJ3tL9t+xvYjRWdBa2yfbft7tnfYftT29UVn6jTOyXeA7UWSdkpaKakm6X5J10TEY4UGQya23yZpr6RbIuJ1RedBdrbPlHRmRDxg+xRJ2yS9ayH97DGS74yLJY1GxOMR8ZKkf5P0hwVnQkYR8X1Jvy46B1oXEU9FxAONj/dI2iHprGJTdRYl3xlnSfr5jO2aFtj/aEDRbC+XdKGk+wqO0lGUfGd4js9xngzoENsnS7pd0sci4vmi83QSJd8ZNUlnz9julfTLgrIAC4rtbtUL/usR8e2i83QaJd8Z90s6z/a5tk+Q9H5J3y04E5A825b0JUk7IuJzRecpAiXfARFxUNJHJd2t+hs/t0bEo8WmQla2vynpR5IusF2z/eGiMyGzSyR9UNLbbT/UeLyj6FCdxCWUAJAwRvIAkDBKHgASRskDQMIoeQBIGCUPAAmj5AEgYZQ8ACTs/wAV+j9iRNJcrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD6CAYAAACrklzBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAU/klEQVR4nO3dfXBddZ3H8c8nJVAoVB7SUSTU6gZYH7YIEx8Qh1FpKkVgHQddnVmNq0w7o7a4rLqiXXbEjru6ruMms4N0BQ3r04LAqtBAAws46zhISrECddssWzAqkBQLfQJC890/7g0kNdyc2+Z3T87J+zVzJznJved+htBPfjkPv58jQgCA8mnKOwAAIA0KHgBKioIHgJKi4AGgpCh4ACgpCh4ASippwds+2vYPbf/a9mbbZ6R8PwDACw5JvP9/kXRLRFxo+1BJR9R6cktLSyxatChxJAAojw0bNgxHxILJvpes4G3Pl3SWpA9LUkQ8K+nZWq9ZtGiR+vv7U0UCgNKx/fCLfS/lIZpXSRqS9C3bG21/0/a8ScItt91vu39oaChhHACYXVIW/CGSTpd0RUScJmm3pM/u/6SIWBsR7RHRvmDBpH9lAAAOQMqCH5Q0GBF3V7d/qErhAwAaIFnBR8Sjkn5j+5Tql86W9GCq9wMATJT6OviVkr5re5Ok10v6UuL3A6bd8PCwVq5cqe3bt+cdBahL0oKPiPuqx9cXR8S7I+IPKd8PSKGnp0ebNm1ST09P3lGAunAnK1DD8PCwent7FRHq7e1lFI9CoeCBGnp6ejS2KM7o6CijeBQKBQ/U0NfXp5GREUnSyMiI1q9fn3MiIDsKHqiho6NDzc3NkqTm5mYtXbo050RAdhQ8UENnZ6dsS5KamprU2dmZcyIgOwoeqKGlpUXLli2TbS1btkzHHXdc3pGAzFLPJgkUXmdnp7Zt28boHYVDwQNTaGlpUXd3d94xSq2rq0sDAwNJ9j04OChJam1tnfZ9t7W1adWqVdO+3+lCwQMotb179+YdITcUPIDcpRwFj+27q6sr2XvMVJxkBYCSouABoKQoeAAoKQoeAEqKggeAkqLgAaCkKHgAKCkKHgBKioIHgJKi4AGgpCh4ACgpCh4ASoqCB4CSouABoKQoeAAoKQoeAEqKggeAkqLgAaCkKHgAKCkKHgBKKumi27a3SdopaZ+k5yKiPeX7AQBekLTgq94eEcMNeB8AwDgcogGAkkpd8CFpve0NtpdP9gTby2332+4fGhpKHAcAZo/UBX9mRJwuaZmkj9s+a/8nRMTaiGiPiPYFCxYkjgMAs0fSgo+I31U/Pi7pRklvTPl+AIAXJCt42/NsHzX2uaSlku5P9X4AgIlSXkXzUkk32h57n+9FxC0J3w8AME6ygo+IhySdmmr/AIDauEwSAEqKggeAkqLgAaCkKHgAKCkKHgBKioIHgJKi4AGgpCh4ACgpCh4ASoqCB4CSouABoKQoeAAoKQoeAEqKggemMDw8rJUrV2r79u15RwHqQsEDU+jp6dGmTZvU09OTdxSgLhQ8UMPw8LB6e3sVEert7WUUj0Kh4IEaenp6FBGSpNHRUUbxKBQKHqihr69PIyMjkqSRkRGtX78+50RAdhQ8UENHR8eE7aVLl+aUBKgfBQ/UcP7550/YvuCCC3JKAtSPggdquO666yZsX3vttTklAepHwQM13HbbbRO2+/r6ckoC1I+CB2qwXXMbmMkyFbzt99o+qvr5ats32D49bTQgf2efffaE7SVLluSUBKhf1hH830XETttvlfROST2SrkgXC5gZVqxYoaamyj+TpqYmrVixIudEQHZZC35f9eO7JF0RET+SdGiaSMDM0dLS8vylkkuXLtVxxx2XcyIgu0MyPu+3tq+UtETSl20fJo7fY5ZYsWKFHn30UUbvKJysBf8+SedI+mpE7LB9vKRPp4sFzBwtLS3q7u7OOwZQt0yj8IjYI+lHknbbXiipWdKvUwYrE6abBZCHrFfRrJT0mKQ+STdXHzclzFUqTDcLIA9Zj6NfLOmUiHhtRPxZ9bE4ywttz7G90fas/IUwPDysdevWKSJ08803M4oH0DBZC/43kp48wPe4WNLmA3xt4fX09EyYjZBRPIBGyVrwD0m60/alti8Ze0z1Itutqlxa+c2DCVlkt95664TtW265JackAGabrFfRPFJ9HKr6rn//uqTPSDqqvljlccghh9TcBoBUMrVNRHyh3h3bPk/S4xGxwfbbajxvuaTlkrRw4cJ632bG27VrV81toEi6uro0MDCQd4y6bN26VZK0atWqnJNk19bWNi15axa87a9HxCdt/0RS7P/9iKg1OfaZki6wfa6kuZLm2/5ORPzlfvtYK2mtJLW3t//RexTdokWLtG3btgnbKJYtW7bo4osvVnd3t9ra2vKOk6uBgQFtfGCjdHTeSeowWvmw8bcb882R1Y7p29VUI/h/r378ar07johLJV0qSdUR/Kf2L/fZYPXq1broooue377ssstyTIMDsWbNGu3evVuXX365rrnmmrzj5O9oafRto3mnKK2mO6dvkoCaBR8RG6of75q2d5xlTj75ZM2bN0+7d+/WvHnzZv0IsGi2bNny/F9g27Zt08DAAD9DFEbWG53OtN1ne4vth2z/n+2Hsr5JRNwZEecdeMziGh4e1p49eyRJe/bs4Tr4glmzZs2E7csvvzynJED9sv4tcJWkr0l6q6Q3SGqvfsQUrrzySkVUTi1EhK688sqcE6Ee48+fTLYNzGRZC/7JiOiNiMcjYvvYI2mykrj99tsnbO+/BBxmtv1PinOSHEWSteDvsP1Pts+wffrYI2mykhgbvb/YNma21atXT9jmJDmKJOtdN2+qfmwf97WQ9I7pjVM+S5YsmXA369jiESiGY489VrYVEbKtY445Ju9IQGZZpwt++yQPyj0Dlnwrtp6engk/P+YSQpFkvuDS9rtsf8b2ZWOPlMHKgiXfiq2vr0/79lVWrNy3b5/Wr1+fcyIgu6yXSX5D0l9IWinJkt4r6RUJc5XKihUrdOqppzJ6L6COjg41NzdLkpqbm7V06dKcEwHZZR3BvyUiPiTpD9V5ac6QdGK6WMDM0NnZKduSKodoOjs7c04EZJe14PdWP+6x/XJJI5JemSZS+bCiU3G1tLRo2bJlsq1ly5ZxiA2FkrXgb7J9tKR/knSvpG2Svp8oU6kMDw+rt7dXEaHe3l7uZC2gzs5OLV68mNE7CifrVTRfjIgdEXG9Ksfe/zQiOMmaQU9Pz/PXvo+OjjKKL6CWlhZ1d3czekfhZD3J2m/747aPiYhnIuJAl++bdfr6+iYs2cdVGAAaJeshmvdLermke2z/wPY7PXbmCTVxFQaAvGQ9RDMQEZ+XdLKk70m6WtIjtr9g+9iUAYuOqzAA5KWeG50WS/pnVU60Xi/pQklPSfqvNNHKgaswAOQl01w0tjeospDUVZI+GxHPVL91t+0zE2Urjc7OTm3bto3RO4CGyjrZ2HsjYtIFPiLiPdOYp5TGrsJAWqkWhB4cHJQktba2Tvu+p2txZWAyWQ/RXFS9Dl6SZPsY22tqPB8ojb1792rv3r1TPxGYYbKO4JdFxOfGNiLiD7bPlbS6xmuAhko1Eh7bb1dXV5L9A6lkHcHPsX3Y2IbtwyUdVuP5AICcZR3Bf0fS7ba/pcpCHx+RxC2ZADCDZSr4iPiK7U2SllS/9MWIuLXWawAA+co6gpekjZKaVRnBb0wTB8BMNjg4KD0pNd2Z+RYa1GuHNBiD07KrrHPRvE/SL1S5uel9qlz/fuG0JAAAJJF1BP95SW+IiMclyfYCSbdJ+mGqYABmntbWVg15SKNvG807Smk13dmk1hOm556LrH9nNY2Ve9X2Ol4LAMhB1pK+xfattj9s+8OSbpa0Ll2schkeHtbKlStZ7ANAQ2WdTfLTktZKWizpVElrI+JvUwYrE5bsA5CHzIdZIuL6iLgkIv46Im5MGapMWLIPQF6yXkXzHttbbT9p+ynbO20/lTpcGbBkH4C8ZB3Bf0XSBRHxkoiYHxFHRcT8Wi+wPdf2L2z/0vYDtr9w8HGLhyX7AOQla8E/FhGb69z3M5LeERGnSnq9pHNsv7nOfRQeS/YByEvN6+Btj8313m/7PyT9pyrFLUmKiBte7LVROS6xq7rZrBfugp1VOjs71dvbK4kl+wA01lQj+POrj/mS9khaOu5r5021c9tzbN8n6XFJfRFx90GlLSCW7AOQl5oj+Ij4K0myfWZE/Gz897Is1RcR+yS9vrpYyI22XxcR9++3n+WSlkvSwoUL60tfECzZByAPWY/BT7beXOY16CJih6Q7JZ0zyffWRkR7RLQvWLAg6y4LZWzJPkbvABppqmPwZ0h6i6QFti8Z9635kuZM8doFkkYiYkd1gZAlkr58kHkBABlNNdnYoZKOrD7vqHFff0qVmSVrOV5Sj+05qvylcG1E3HSgQQEA9ZnqGPxdku6y/e2IeLieHUfEJkmnHUw4AMCByzpd8GG210paNP41EfGOFKEAAAcva8FfJ+kbkr4paV+6OACA6ZK14J+LiCuSJgEATKusl0n+xPbHbB9v+9ixR9JkAICDknUEP3aHzqfHfS0kvWp64wAApkumgo+IV6YOAgCYXllH8LL9OkmvkTR37GsRcU2KUACAg5d1wY+/V2Vqgm5Jb1d1fviEuUqFNVkB5CHrSdYLJZ0t6dHqBGSnSjosWaqSYU1WAHnIWvB7I2JU0nO256sy/S8nWDNgTVYAecl6DL6/OuXvv0naoMpCHr9IFapMJluT9ZJLLpniVcAMtkNqujPr2HAGGFt26MhcU2S3Q9IJ07OrrFfRfKz66Tds3yJpfnWuGUxhsjVZKXgUVVtbW94R6rZ161ZJ0kknnJRzkoxOmL7/zvVcRXOBpLOqm3dJouAz6Ojo0Lp16zQyMsKarCi8VatW5R2hbmOZu7q6ck7SeFmvovlHSRdLerD6WGX7H1IGK4vOzk7ZlsSarAAaK+uBtHMldUTE1RFxtSorM70rXazyYE1WAHnJfIhG0tGSnqh+/pLpj1JerMkKIA9ZC/5LkjbavkOSVTkWf2myVACAgzblIRrbTZJGJb1Z0g3VxxkR8YPE2UqDG50A5GHKgq/e4PSJiPh9RPw4In4UEY82IFspcKMTgLxkPcnaZ/tTtk9kPvj6THajEwA0QtaC/4ikj6ly/Xv/uAemMNmNTgDQCFkL/jWS/lXSLyXdp8qskq9NlKlUOjo61NzcLEnc6ASgobIWfI+kV0vqUqXcX139GqbAjU4A8pK14E+JiIsi4o7qY7mkU1IGKwtudAKQl6wFv9H2m8c2bL9J0s/SRCqfzs5OLV68mNE7gIbKeqPTmyR9yPYj1e2Fkjbb/pWkiIjFSdKVREtLi7q7u/OOAWCWyVrw5yRNMUN0dXVpYGBg2vc7ODgoSWptbZ32fUuVqUWLOMsfgLSyzgf/cOogZbZ37968I8wYqX6JpjQ2n3jRfonyix/1TDZWeqn+Mczm+aj3NzAwoC3336uFR+7LO0pmh45UTlU9ve2enJNk98iuOXlHwAxAwaPhFh65T6vbd039RBywNf1FWZ8OKSVbWLE6rcEdtjfbfsD2xaneCwDwx1KO4J+T9DcRca/toyRtsN0XEQ8mfE8AQFWyEXx19sl7q5/vlLRZ07ZWOABgKskKfjzbiySdJunuRrwfAKABBW/7SEnXS/pkRDw1yfeX2+633T80NJQ6DgDMGkmvorHdrEq5fzcibpjsORGxVtJaSWpvb48s+y3atdRcRw0gD8kK3pUpFK+StDkivjad+x4YGNDGXz2o0SOKseaIn6383trwv8VZCKtpzxNTPwnAjJZyBH+mpA9K+pXt+6pf+1xErJuOnY8ecayefs1507ErTGLugzflHQHAQUpW8BHx35Kcav8AgNoachUNAKDxKHgAKCkKHgBKqpCTjQ0ODqppz5OcCEyoac92DQ4+l3cMAAeBETwAlFQhR/Ctra167JlDuEwyobkP3qTW1pflHQPAQWAEDwAlVcgRvFS507Iox+D9dGUKnpg7P+ck2VXuZGUEDxRZIQu+ra0t7wh12bp1pyTppD8pUmG+rHD/nQFMVMiCL9oEWKzJ+oLBwUHt3jmHJeUSe3jnHM0bHMw7BnLGMXgAKKlCjuBRXK2trXr6ud+z6HZia/qP1NzW1rxjIGeM4AGgpCh4ACgpCh4ASoqCB4CSouABoKQoeAAoKQoeAEqK6+DH6erq0sDAwLTvd+vWrZLS3YHb1tZWuLt7AaRHwTfA4YcfnncEALMQBT8Oo2AAZULBo+Ee2VWsycYe21M5VfXSI0ZzTpLdI7vm6OS8QyB3FDwaqohTED9bPYcyd9FJOSfJ7mQV6791qvNfUtpzYDP9/BcFj4aayf8YXgzTPRfbbD4HRsEDyF0Rf/EXAdfBA0BJUfAAUFIUPACUFAUPACVFwQNASSUreNtX237c9v2p3gMA8OJSjuC/LemchPsHANSQrOAj4qeSnki1fwBAbbkfg7e93Ha/7f6hoaG84wBAaeRe8BGxNiLaI6J9wYIFeccBgNLIveABAGlQ8ABQUikvk/y+pJ9LOsX2oO2PpnovAMAfSzabZER8INW+AQBT4xANAJQUBQ8AJUXBA0BJsaITSiPVup6zeU1PFBsFD0xhNq/piWKj4FEajISBiTgGDwAlRcEDQElR8ABQUhQ8AJQUBQ8AJUXBA0BJUfAAUFIUPACUlCMi7wzPsz0k6eG8cyTSImk47xA4YPz8iq3MP79XRMSk653OqIIvM9v9EdGedw4cGH5+xTZbf34cogGAkqLgAaCkKPjGWZt3ABwUfn7FNit/fhyDB4CSYgQPACVFwQNASVHwDWD7HNv/Y3vA9mfzzoPsbF9t+3Hb9+edBfWxfaLtO2xvtv2A7YvzztRoHINPzPYcSVskdUgalHSPpA9ExIO5BkMmts+StEvSNRHxurzzIDvbx0s6PiLutX2UpA2S3j2b/u0xgk/vjZIGIuKhiHhW0g8k/XnOmZBRRPxU0hN550D9IuL3EXFv9fOdkjZLOiHfVI1Fwad3gqTfjNse1Cz7nwzIm+1Fkk6TdHfOURqKgk/Pk3yN42JAg9g+UtL1kj4ZEU/lnaeRKPj0BiWdOG67VdLvcsoCzCq2m1Up9+9GxA1552k0Cj69eySdZPuVtg+V9H5JP845E1B6ti3pKkmbI+JreefJAwWfWEQ8J+kTkm5V5STPtRHxQL6pkJXt70v6uaRTbA/a/mjemZDZmZI+KOkdtu+rPs7NO1QjcZkkAJQUI3gAKCkKHgBKioIHgJKi4AGgpCh4ACgpCh4ASoqCB4CS+n/j45VuU8f3sAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD6CAYAAACrklzBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAARI0lEQVR4nO3dfWyd5X3G8evnxCyQUEFyvBblkHqdA9LU8SYLbWJCDLCFW2D9Y5NaaZUnlcWTuiQVmyZaRUyKov23qrM1TclKt4PGOgEtFYMYYlAztK1COAkKkLTxWWfooVB8wmjzYogT//aH7WAHc/Kc5NzP7ef29yNZ+HFO7udSDr58+3m7zd0FAEhPW+wAAIAwKHgASBQFDwCJouABIFEUPAAkioIHgEQFLXgzu8LMHjezH5vZYTP73ZD7AwB8aGXg8f9O0jPu/odmdomkyxq9uFQqeWdnZ+BIAJCOffv21d29Y7E/C1bwZvYJSbdK+hNJcvdTkk41+judnZ0aHR0NFQkAkmNmr3/cn4U8RPMZSROS/snMDpjZt81s9SLhNpnZqJmNTkxMBIwDAMtLyIJfKekmSf/g7jdKOiHpgXNf5O673L3b3bs7Ohb9LQMAcAFCFnxNUs3dX5zdflwzhQ8AyEGwgnf3tyX9zMyunf3SHZIOhdofAGCh0NfBb5b0iJkdlHSDpL8JvD+g5er1ujZv3qyjR4/GjgI0JWjBu/vLs8fXr3P3L7j7/4XcHxBCpVLRwYMHValUYkcBmsKdrEAD9Xpdw8PDcncNDw8zi0ehUPBAA5VKRXOL4kxPTzOLR6FQ8EADIyMjmpqakiRNTU1pz549kRMB2VHwQAM9PT1qb2+XJLW3t6u3tzdyIiA7Ch5ooL+/X2YmSWpra1N/f3/kREB2FDzQQKlUUl9fn8xMfX19WrduXexIQGahnyYJFF5/f7/Gx8eZvaNwKHjgPEqlkoaGhmLHSNrg4KCq1WqQsWu1miSpXC63fOyuri5t2bKl5eO2CgUPIGmTk5OxI0RDwQOILuQseG7swcHBYPtYqjjJCgCJouABIFEUPAAkioIHgERR8ACQKAoeABJFwQNAoih4AEgUBQ8AiaLgASBRFDwAJIqCB4BEUfAAkCgKHgASRcEDQKIoeABIFAUPAImi4AEgURQ8ACSKggeARAVddNvMxiUdk3RG0ml37w65PwDAh4IW/Kzfd/d6DvsBAMzDIRoASFTogndJe8xsn5ltWuwFZrbJzEbNbHRiYiJwHABYPkIX/C3ufpOkPklfNbNbz32Bu+9y92537+7o6AgcBwCWj6AF7+4/n/3vO5KekHRzyP0BAD4UrODNbLWZXT73uaReSa+G2h8AYKGQV9F8UtITZja3n39192cC7g8AME+wgnf3n0q6PtT4AIDGuEwSABJFwQNAoih4AEgUBQ8AiaLgASBRFDwAJIqCB4BEUfAAkCgKHgASRcEDQKIoeABIFAUPAImi4AEgURQ8cB71el2bN2/W0aNHY0cBmkLBA+dRqVR08OBBVSqV2FGAplDwQAP1el3Dw8Nydw0PDzOLR6FQ8EADlUpF7i5Jmp6eZhaPQqHggQZGRkY0NTUlSZqamtKePXsiJwKyo+CBBnp6ehZs9/b2RkoCNI+CBxq45557Fmzfe++9kZIAzaPggQYee+yxBduPPvpopCRA8yh4oIHnnntuwfbIyEikJEDzKHigATNruA0sZRQ80MAdd9yxYPvOO++MlARoHgUPNDAwMKC2tplvk7a2Ng0MDEROBGRHwQMNlEqls5dK9vb2at26dZETAdmtjB0AWOoGBgb09ttvM3tH4VDwwHmUSiUNDQ3FjgE0jUM0OeBxswBioOBzwONmAcQQvODNbIWZHTCzp0Lvaymq1+vavXu33F1PP/00s3gAucljBr9V0uEc9rMkVSqVBU8jZBYPIC9BC97MypI+L+nbIfezlD377LMLtp955plISQAsN6Fn8N+S9FeSpgPvZ8lauXJlw20ACCVY25jZ3ZLecfd9ZnZbg9dtkrRJkjZs2BAqTjTHjx9vuA0UyeDgoKrVauwYTRkbG5MkbdmyJXKS7Lq6ulqSN+R08hZJ95rZ5yStkvQJM/sXd//j+S9y912SdklSd3e3B8wTRWdnp8bHxxdso1iOHDmirVu3amhoSF1dXbHjRFWtVnXgtQPSFbGTNGH2+MGBNw/EzZHVe60bKljBu/vXJX1dkmZn8H95brkvB9u2bdN99913dvvBBx+MmAYXYseOHTpx4oS2b9+uhx9+OHac+K6Qpm9btkddg2vb27oj51wHH9g111yj1atXS5JWr1697GeARXPkyJGzv4GNj48X7vAElrdcCt7d97r73Xnsa6mp1+s6efKkJOnkyZNcB18wO3bsWLC9ffv2SEmA5jGDD2znzp1ynzm14O7auXNn5ERoxvzzJ4ttA0sZBR/Y888/v2D73CXgsLSde1Kck+QoEgo+sLnZ+8dtY2nbtm3bgm1OkqNIKPjAzl3ibW7xCBTD2rVrz67Dama68sorIycCsqPgA2PJt2KrVCoL3j+eJYQioeADY8m3YhsZGdGZM2ckSWfOnNGePXsiJwKyo+BzMDAwoOuvv57ZewH19PSovb1dktTe3q7e3t7IiYDsKHiggf7+/rPH4Nva2tTf3x85EZAdBZ8DVnQqrlKppL6+PpmZ+vr6OMSGQqHgA6vX6xoeHpa7a3h4mDtZC6i/v1/XXXcds3cUDgUfWKVSOXvt+/T0NLP4AiqVShoaGmL2jsKh4AMbGRlZsGQfV2EAyAsFHxhXYQCIhYIPjKswAMRCwQfGVRgAYmEF6Bz09/drfHyc2TuAXGUueDP7tKSN7v6cmV0qaaW7HwsXLR1zV2EgrFALQtdqNUlSuVxu+ditWlwZWEymQzRm9qeSHpc0t1pFWdIPAmUClpTJyUlNTk7GjgE0LesM/quSbpb0oiS5+5iZ/XqwVMAFCDUTnht3cHAwyPhAKFlPsn7g7qfmNsxspSRWrgCAJSxrwf+HmX1D0qVm1iPpMUn/Hi4WAOBiZS34ByRNSHpF0oCk3ZK2NfwbAICoMh2Dd/dpSf84+wFgmarVatIvpba93EITzHtSzWstGSpTwZvZ/2qRY+7u/pmWpAAAtFzWq2i6532+StIfSVrb+jgAlrJyuawJm9D0bdOxoySrbW+byutbc89Fpt+z3P3ovI833f1bkm5vSQIAQBBZb3S6ad5Ht5n9maTLA2dLRr1e1+bNm1nsA0Cush6i+Vt9eAz+tKRxzRymQQbzl+y7//77Y8cBsExkPRXeJ+khSc9L+i9Jb0r6YqhQKWHJPgCxZC34H0i6R9KUpOOzHycCZUoKS/YBiCXrIZqyu9/VzMBmtkrSC5J+bXY/j7v7XzeZr/AWW7KPwzQA8pB1Bv/fZvbbTY79gaTb3f16STdIusvMfqfJMQqPJfsAxNKw4M3sFTM7KOn3JO03s5+Y2cF5X/9YPuP47Gb77Meye0AZS/YBiOV8h2juvpjBzWyFpH2SuiT9vbu/eDHjFdHckn1PPvkkS/YByFXDgnf31y9mcHc/I+kGM7tC0hNm9ll3f3X+a8xsk6RNkrRhw4aL2d2SxZJ9AGLI5YlB7v6epL2SPnKi1t13uXu3u3d3dHTkESd3c0v2MXsHkKdgBW9mHbMzd82u4XqnpB+H2h8AYKHMi25fgKskVWaPw7dJetTdnwq4PwDAPMEK3t0PSrox1PgAgMZ4aj8AJIqCB4BEUfAAkCgKHgASRcEDQKIoeABIFAUPAImi4HPAmqwAYqDgczB/TVYAyAsFHxhrsgKIJeSzaKDF12RlyT4U2ntS294CzQ3nlh1aEzVFdu9JWt+aoSj4wFiTFSnp6uqKHaFpY2NjkqSN6zdGTpLR+tb9O1PwgfX09Gj37t2amppiTVYU3pYtW2JHaNpc5sHBwchJ8leg37OKiTVZAcRCwQc2tyarmbEmK4BccYgmB6zJCiAGZvAAkCgKPgfc6AQgBgo+MG50AhALBR/YYjc6AUAeKPjAFrvRCQDyQMEH1tPTo/b2dkniRicAuaLgA+NGJwCxUPCBcaMTgFi40SkH3OgEIAYKPgelUklDQ0OxYwBYZij4eQYHB1WtVls+bq1WkySVy+WWjy3NPFq0iE/5AxAWBZ+DycnJ2BGWjFA/REOae5540X6I8oMfFPw8ob4ZlvPzqM9VrVZ15NX92rDmTOwomV0yNXMtwvvjL0VOkt0bx1fEjoAlgIJH7jasOaNt3cfP/0JcsB2jRVmfDiEFu0zSzK42sx+a2WEze83MtobaFwDgo0LO4E9L+gt3329ml0vaZ2Yj7n4o4D4BALOCzeDd/S133z/7+TFJh9WytcIBAOeTy52sZtYp6UZJL+axPwBADgVvZmskfU/S19z9V4v8+SYzGzWz0YmJidBxAGDZCHoVjZm1a6bcH3H37y/2GnffJWmXJHV3d3uWcYt2LTXXUQOIIVjB28wjFB+SdNjdv9nKsavVqg68ckjTl61t5bDB2KmZn1v7/uftyEmyazv5buwIAC5SyBn8LZK+LOkVM3t59mvfcPfdrRh8+rK1ev+37m7FUFjEqkNPxY4A4CIFK3h3/09JFmp8AEBjPA8eABJFwQNAoih4AEhUIR82VqvV1Hbyl5wIDKjt5FHVaqdjxwBwEZjBA0CiCjmDL5fL+sUHK7lMMqBVh55Sufyp2DEAXARm8ACQqELO4KWZOy2Lcgze3p95BI+v+kTkJNnN3MnKDB4oskIWfFdXV+wITRkbOyZJ2vibRSrMTxXu3xnAQoUs+KI9AIs1WT9Uq9V04tgKlpQL7PVjK7S6VosdA5FxDB4AElXIGTyKq1wu6/3Tb7HodmA7RtdoVbkcOwYiYwYPAImi4AEgURQ8ACSKggeARFHwAJAoCh4AEkXBA0CiuA5+nsHBQVWr1ZaPOzY2JincHbhdXV2Fu7sXQHgUfA4uvfTS2BEALEMU/DzMggGkhIJH7t44XqyHjf3i5Mypqk9eNh05SXZvHF+ha2KHQHQUPHJVxEcQn5o9h7Kqc2PkJNldo2L9W4c6/yWFPQe21M9/UfDI1VL+Zvg4PO652JbzOTAKHkB0RfzBXwRcBw8AiaLgASBRFDwAJIqCB4BEUfAAkKhgBW9m3zGzd8zs1VD7AAB8vJAz+H+WdFfA8QEADQQreHd/QdK7ocYHADQW/Ri8mW0ys1EzG52YmIgdBwCSEb3g3X2Xu3e7e3dHR0fsOACQjOgFDwAIg4IHgESFvEzyu5J+JOlaM6uZ2VdC7QsA8FHBnibp7l8KNTYA4Pw4RAMAiaLgASBRFDwAJIoVnZCMUOt6Luc1PVFsFDxwHst5TU8UGwWPZDATBhbiGDwAJIqCB4BEUfAAkCgKHgASRcEDQKIoeABIFAUPAImi4AEgUebusTOcZWYTkl6PnSOQkqR67BC4YLx/xZby+/dpd190vdMlVfApM7NRd++OnQMXhvev2Jbr+8chGgBIFAUPAImi4POzK3YAXBTev2Jblu8fx+ABIFHM4AEgURQ8ACSKgs+Bmd1lZj8xs6qZPRA7D7Izs++Y2Ttm9mrsLGiOmV1tZj80s8Nm9pqZbY2dKW8cgw/MzFZIOiKpR1JN0kuSvuTuh6IGQyZmdquk45IedvfPxs6D7MzsKklXuft+M7tc0j5JX1hO33vM4MO7WVLV3X/q7qck/ZukP4icCRm5+wuS3o2dA81z97fcff/s58ckHZa0Pm6qfFHw4a2X9LN52zUts//JgNjMrFPSjZJejBwlVxR8eLbI1zguBuTEzNZI+p6kr7n7r2LnyRMFH15N0tXztsuSfh4pC7CsmFm7Zsr9EXf/fuw8eaPgw3tJ0kYz+w0zu0TSFyU9GTkTkDwzM0kPSTrs7t+MnScGCj4wdz8t6c8lPauZkzyPuvtrcVMhKzP7rqQfSbrWzGpm9pXYmZDZLZK+LOl2M3t59uNzsUPlicskASBRzOABIFEUPAAkioIHgERR8ACQKAoeABJFwQNAoih4AEjU/wNCiCal7rkFeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "import matplotlib.pyplot  as plt\n",
    "import seaborn as sns\n",
    "x1=wine.target\n",
    "y1=wine.data\n",
    "features=wine.feature_names\n",
    "for i in range(13):\n",
    "    sns.boxplot(x=x1,y=y1[:,1])\n",
    "    plt.ylabel(features[i])\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f51104e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_=wine.data\n",
    "y_=wine.target\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_t,x_te,y_t,y_te=train_test_split(x_,y_,test_size=0.2,random_state=25)\n",
    "from sklearn.svm import SVC\n",
    "model=SVC()\n",
    "model.fit(x_t,y_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "daf62ebb",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on SVC in module sklearn.svm._classes object:\n",
      "\n",
      "class SVC(sklearn.svm._base.BaseSVC)\n",
      " |  SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)\n",
      " |  \n",
      " |  C-Support Vector Classification.\n",
      " |  \n",
      " |  The implementation is based on libsvm. The fit time scales at least\n",
      " |  quadratically with the number of samples and may be impractical\n",
      " |  beyond tens of thousands of samples. For large datasets\n",
      " |  consider using :class:`~sklearn.svm.LinearSVC` or\n",
      " |  :class:`~sklearn.linear_model.SGDClassifier` instead, possibly after a\n",
      " |  :class:`~sklearn.kernel_approximation.Nystroem` transformer.\n",
      " |  \n",
      " |  The multiclass support is handled according to a one-vs-one scheme.\n",
      " |  \n",
      " |  For details on the precise mathematical formulation of the provided\n",
      " |  kernel functions and how `gamma`, `coef0` and `degree` affect each\n",
      " |  other, see the corresponding section in the narrative documentation:\n",
      " |  :ref:`svm_kernels`.\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <svm_classification>`.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  C : float, default=1.0\n",
      " |      Regularization parameter. The strength of the regularization is\n",
      " |      inversely proportional to C. Must be strictly positive. The penalty\n",
      " |      is a squared l2 penalty.\n",
      " |  \n",
      " |  kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'\n",
      " |      Specifies the kernel type to be used in the algorithm.\n",
      " |      It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or\n",
      " |      a callable.\n",
      " |      If none is given, 'rbf' will be used. If a callable is given it is\n",
      " |      used to pre-compute the kernel matrix from data matrices; that matrix\n",
      " |      should be an array of shape ``(n_samples, n_samples)``.\n",
      " |  \n",
      " |  degree : int, default=3\n",
      " |      Degree of the polynomial kernel function ('poly').\n",
      " |      Ignored by all other kernels.\n",
      " |  \n",
      " |  gamma : {'scale', 'auto'} or float, default='scale'\n",
      " |      Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.\n",
      " |  \n",
      " |      - if ``gamma='scale'`` (default) is passed then it uses\n",
      " |        1 / (n_features * X.var()) as value of gamma,\n",
      " |      - if 'auto', uses 1 / n_features.\n",
      " |  \n",
      " |      .. versionchanged:: 0.22\n",
      " |         The default value of ``gamma`` changed from 'auto' to 'scale'.\n",
      " |  \n",
      " |  coef0 : float, default=0.0\n",
      " |      Independent term in kernel function.\n",
      " |      It is only significant in 'poly' and 'sigmoid'.\n",
      " |  \n",
      " |  shrinking : bool, default=True\n",
      " |      Whether to use the shrinking heuristic.\n",
      " |      See the :ref:`User Guide <shrinking_svm>`.\n",
      " |  \n",
      " |  probability : bool, default=False\n",
      " |      Whether to enable probability estimates. This must be enabled prior\n",
      " |      to calling `fit`, will slow down that method as it internally uses\n",
      " |      5-fold cross-validation, and `predict_proba` may be inconsistent with\n",
      " |      `predict`. Read more in the :ref:`User Guide <scores_probabilities>`.\n",
      " |  \n",
      " |  tol : float, default=1e-3\n",
      " |      Tolerance for stopping criterion.\n",
      " |  \n",
      " |  cache_size : float, default=200\n",
      " |      Specify the size of the kernel cache (in MB).\n",
      " |  \n",
      " |  class_weight : dict or 'balanced', default=None\n",
      " |      Set the parameter C of class i to class_weight[i]*C for\n",
      " |      SVC. If not given, all classes are supposed to have\n",
      " |      weight one.\n",
      " |      The \"balanced\" mode uses the values of y to automatically adjust\n",
      " |      weights inversely proportional to class frequencies in the input data\n",
      " |      as ``n_samples / (n_classes * np.bincount(y))``\n",
      " |  \n",
      " |  verbose : bool, default=False\n",
      " |      Enable verbose output. Note that this setting takes advantage of a\n",
      " |      per-process runtime setting in libsvm that, if enabled, may not work\n",
      " |      properly in a multithreaded context.\n",
      " |  \n",
      " |  max_iter : int, default=-1\n",
      " |      Hard limit on iterations within solver, or -1 for no limit.\n",
      " |  \n",
      " |  decision_function_shape : {'ovo', 'ovr'}, default='ovr'\n",
      " |      Whether to return a one-vs-rest ('ovr') decision function of shape\n",
      " |      (n_samples, n_classes) as all other classifiers, or the original\n",
      " |      one-vs-one ('ovo') decision function of libsvm which has shape\n",
      " |      (n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one\n",
      " |      ('ovo') is always used as multi-class strategy. The parameter is\n",
      " |      ignored for binary classification.\n",
      " |  \n",
      " |      .. versionchanged:: 0.19\n",
      " |          decision_function_shape is 'ovr' by default.\n",
      " |  \n",
      " |      .. versionadded:: 0.17\n",
      " |         *decision_function_shape='ovr'* is recommended.\n",
      " |  \n",
      " |      .. versionchanged:: 0.17\n",
      " |         Deprecated *decision_function_shape='ovo' and None*.\n",
      " |  \n",
      " |  break_ties : bool, default=False\n",
      " |      If true, ``decision_function_shape='ovr'``, and number of classes > 2,\n",
      " |      :term:`predict` will break ties according to the confidence values of\n",
      " |      :term:`decision_function`; otherwise the first class among the tied\n",
      " |      classes is returned. Please note that breaking ties comes at a\n",
      " |      relatively high computational cost compared to a simple predict.\n",
      " |  \n",
      " |      .. versionadded:: 0.22\n",
      " |  \n",
      " |  random_state : int, RandomState instance or None, default=None\n",
      " |      Controls the pseudo random number generation for shuffling the data for\n",
      " |      probability estimates. Ignored when `probability` is False.\n",
      " |      Pass an int for reproducible output across multiple function calls.\n",
      " |      See :term:`Glossary <random_state>`.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  class_weight_ : ndarray of shape (n_classes,)\n",
      " |      Multipliers of parameter C for each class.\n",
      " |      Computed based on the ``class_weight`` parameter.\n",
      " |  \n",
      " |  classes_ : ndarray of shape (n_classes,)\n",
      " |      The classes labels.\n",
      " |  \n",
      " |  coef_ : ndarray of shape (n_classes * (n_classes - 1) / 2, n_features)\n",
      " |      Weights assigned to the features (coefficients in the primal\n",
      " |      problem). This is only available in the case of a linear kernel.\n",
      " |  \n",
      " |      `coef_` is a readonly property derived from `dual_coef_` and\n",
      " |      `support_vectors_`.\n",
      " |  \n",
      " |  dual_coef_ : ndarray of shape (n_classes -1, n_SV)\n",
      " |      Dual coefficients of the support vector in the decision\n",
      " |      function (see :ref:`sgd_mathematical_formulation`), multiplied by\n",
      " |      their targets.\n",
      " |      For multiclass, coefficient for all 1-vs-1 classifiers.\n",
      " |      The layout of the coefficients in the multiclass case is somewhat\n",
      " |      non-trivial. See the :ref:`multi-class section of the User Guide\n",
      " |      <svm_multi_class>` for details.\n",
      " |  \n",
      " |  fit_status_ : int\n",
      " |      0 if correctly fitted, 1 otherwise (will raise warning)\n",
      " |  \n",
      " |  intercept_ : ndarray of shape (n_classes * (n_classes - 1) / 2,)\n",
      " |      Constants in decision function.\n",
      " |  \n",
      " |  support_ : ndarray of shape (n_SV)\n",
      " |      Indices of support vectors.\n",
      " |  \n",
      " |  support_vectors_ : ndarray of shape (n_SV, n_features)\n",
      " |      Support vectors.\n",
      " |  \n",
      " |  n_support_ : ndarray of shape (n_classes,), dtype=int32\n",
      " |      Number of support vectors for each class.\n",
      " |  \n",
      " |  probA_ : ndarray of shape (n_classes * (n_classes - 1) / 2)\n",
      " |  probB_ : ndarray of shape (n_classes * (n_classes - 1) / 2)\n",
      " |      If `probability=True`, it corresponds to the parameters learned in\n",
      " |      Platt scaling to produce probability estimates from decision values.\n",
      " |      If `probability=False`, it's an empty array. Platt scaling uses the\n",
      " |      logistic function\n",
      " |      ``1 / (1 + exp(decision_value * probA_ + probB_))``\n",
      " |      where ``probA_`` and ``probB_`` are learned from the dataset [2]_. For\n",
      " |      more information on the multiclass case and training procedure see\n",
      " |      section 8 of [1]_.\n",
      " |  \n",
      " |  shape_fit_ : tuple of int of shape (n_dimensions_of_X,)\n",
      " |      Array dimensions of training vector ``X``.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> import numpy as np\n",
      " |  >>> from sklearn.pipeline import make_pipeline\n",
      " |  >>> from sklearn.preprocessing import StandardScaler\n",
      " |  >>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])\n",
      " |  >>> y = np.array([1, 1, 2, 2])\n",
      " |  >>> from sklearn.svm import SVC\n",
      " |  >>> clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))\n",
      " |  >>> clf.fit(X, y)\n",
      " |  Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      " |                  ('svc', SVC(gamma='auto'))])\n",
      " |  \n",
      " |  >>> print(clf.predict([[-0.8, -1]]))\n",
      " |  [1]\n",
      " |  \n",
      " |  See Also\n",
      " |  --------\n",
      " |  SVR : Support Vector Machine for Regression implemented using libsvm.\n",
      " |  \n",
      " |  LinearSVC : Scalable Linear Support Vector Machine for classification\n",
      " |      implemented using liblinear. Check the See Also section of\n",
      " |      LinearSVC for more comparison element.\n",
      " |  \n",
      " |  References\n",
      " |  ----------\n",
      " |  .. [1] `LIBSVM: A Library for Support Vector Machines\n",
      " |      <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_\n",
      " |  \n",
      " |  .. [2] `Platt, John (1999). \"Probabilistic outputs for support vector\n",
      " |      machines and comparison to regularizedlikelihood methods.\"\n",
      " |      <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      SVC\n",
      " |      sklearn.svm._base.BaseSVC\n",
      " |      sklearn.base.ClassifierMixin\n",
      " |      sklearn.svm._base.BaseLibSVM\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, *, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.svm._base.BaseSVC:\n",
      " |  \n",
      " |  decision_function(self, X)\n",
      " |      Evaluates the decision function for the samples in X.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X : ndarray of shape (n_samples, n_classes * (n_classes-1) / 2)\n",
      " |          Returns the decision function of the sample for each class\n",
      " |          in the model.\n",
      " |          If decision_function_shape='ovr', the shape is (n_samples,\n",
      " |          n_classes).\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      If decision_function_shape='ovo', the function values are proportional\n",
      " |      to the distance of the samples X to the separating hyperplane. If the\n",
      " |      exact distances are required, divide the function values by the norm of\n",
      " |      the weight vector (``coef_``). See also `this question\n",
      " |      <https://stats.stackexchange.com/questions/14876/\n",
      " |      interpreting-distance-from-hyperplane-in-svm>`_ for further details.\n",
      " |      If decision_function_shape='ovr', the decision function is a monotonic\n",
      " |      transformation of ovo decision function.\n",
      " |  \n",
      " |  predict(self, X)\n",
      " |      Perform classification on samples in X.\n",
      " |      \n",
      " |      For an one-class model, +1 or -1 is returned.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features) or                 (n_samples_test, n_samples_train)\n",
      " |          For kernel=\"precomputed\", the expected shape of X is\n",
      " |          (n_samples_test, n_samples_train).\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y_pred : ndarray of shape (n_samples,)\n",
      " |          Class labels for samples in X.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties inherited from sklearn.svm._base.BaseSVC:\n",
      " |  \n",
      " |  predict_log_proba\n",
      " |      Compute log probabilities of possible outcomes for samples in X.\n",
      " |      \n",
      " |      The model need to have probability information computed at training\n",
      " |      time: fit with attribute `probability` set to True.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features) or                 (n_samples_test, n_samples_train)\n",
      " |          For kernel=\"precomputed\", the expected shape of X is\n",
      " |          (n_samples_test, n_samples_train).\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      T : ndarray of shape (n_samples, n_classes)\n",
      " |          Returns the log-probabilities of the sample for each class in\n",
      " |          the model. The columns correspond to the classes in sorted\n",
      " |          order, as they appear in the attribute :term:`classes_`.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The probability model is created using cross validation, so\n",
      " |      the results can be slightly different than those obtained by\n",
      " |      predict. Also, it will produce meaningless results on very small\n",
      " |      datasets.\n",
      " |  \n",
      " |  predict_proba\n",
      " |      Compute probabilities of possible outcomes for samples in X.\n",
      " |      \n",
      " |      The model need to have probability information computed at training\n",
      " |      time: fit with attribute `probability` set to True.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          For kernel=\"precomputed\", the expected shape of X is\n",
      " |          (n_samples_test, n_samples_train).\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      T : ndarray of shape (n_samples, n_classes)\n",
      " |          Returns the probability of the sample for each class in\n",
      " |          the model. The columns correspond to the classes in sorted\n",
      " |          order, as they appear in the attribute :term:`classes_`.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The probability model is created using cross validation, so\n",
      " |      the results can be slightly different than those obtained by\n",
      " |      predict. Also, it will produce meaningless results on very small\n",
      " |      datasets.\n",
      " |  \n",
      " |  probA_\n",
      " |  \n",
      " |  probB_\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  score(self, X, y, sample_weight=None)\n",
      " |      Return the mean accuracy on the given test data and labels.\n",
      " |      \n",
      " |      In multi-label classification, this is the subset accuracy\n",
      " |      which is a harsh metric since you require for each sample that\n",
      " |      each label set be correctly predicted.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Test samples.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
      " |          True labels for `X`.\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Sample weights.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          Mean accuracy of ``self.predict(X)`` wrt. `y`.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.svm._base.BaseLibSVM:\n",
      " |  \n",
      " |  fit(self, X, y, sample_weight=None)\n",
      " |      Fit the SVM model according to the given training data.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)                 or (n_samples, n_samples)\n",
      " |          Training vectors, where n_samples is the number of samples\n",
      " |          and n_features is the number of features.\n",
      " |          For kernel=\"precomputed\", the expected shape of X is\n",
      " |          (n_samples, n_samples).\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,)\n",
      " |          Target values (class labels in classification, real numbers in\n",
      " |          regression).\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Per-sample weights. Rescale C per sample. Higher weights\n",
      " |          force the classifier to put more emphasis on these points.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      If X and y are not C-ordered and contiguous arrays of np.float64 and\n",
      " |      X is not a scipy.sparse.csr_matrix, X and/or y may be copied.\n",
      " |      \n",
      " |      If X is a dense array, then the other methods will not support sparse\n",
      " |      matrices as input.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties inherited from sklearn.svm._base.BaseLibSVM:\n",
      " |  \n",
      " |  coef_\n",
      " |  \n",
      " |  n_support_\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self, N_CHAR_MAX=700)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : bool, default=True\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : dict\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n",
      " |      parameters of the form ``<component>__<parameter>`` so that it's\n",
      " |      possible to update each component of a nested object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      **params : dict\n",
      " |          Estimator parameters.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : estimator instance\n",
      " |          Estimator instance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(SVC())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a385d146",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7222222222222222\n"
     ]
    }
   ],
   "source": [
    "y_pred=model.predict(x_te)\n",
    "print(accuracy_score(y_te,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "34727a79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9722222222222222\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model=SVC(kernel=\"linear\")\n",
    "model.fit(x_t,y_t)\n",
    "y_pred=model.predict(x_te)\n",
    "print(accuracy_score(y_te,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f719ab5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}