SVM(Support Vector Machine) on Iris flower analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'filename', 'target', 'target_names']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir (iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
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       "      <td>3</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(iris.data,columns= iris.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target']= iris.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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       "      <td>6.3</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
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       "      <td>104</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "100                6.3               3.3                6.0               2.5   \n",
       "101                5.8               2.7                5.1               1.9   \n",
       "102                7.1               3.0                5.9               2.1   \n",
       "103                6.3               2.9                5.6               1.8   \n",
       "104                6.5               3.0                5.8               2.2   \n",
       "\n",
       "     target  \n",
       "100       2  \n",
       "101       2  \n",
       "102       2  \n",
       "103       2  \n",
       "104       2  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.target==2].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "      <th>flower_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target flower_name  \n",
       "0       0      setosa  \n",
       "1       0      setosa  \n",
       "2       0      setosa  \n",
       "3       0      setosa  \n",
       "4       0      setosa  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['flower_name']= df.target.apply(lambda x: iris.target_names[x])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = df[df.target==0]\n",
    "df1 = df[df.target==1]\n",
    "df2 = df[df.target==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "      <th>flower_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target flower_name  \n",
       "0       0      setosa  \n",
       "1       0      setosa  \n",
       "2       0      setosa  \n",
       "3       0      setosa  \n",
       "4       0      setosa  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17f4e912898>"
      ]
     },
     "execution_count": 26,
     "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": [
    "plt.xlabel('sepal length (cm)')\n",
    "plt.ylabel('sepal width (cm)')\n",
    "\n",
    "plt.scatter(df0['sepal length (cm)'],df0['sepal width (cm)'], color='green',marker='+')\n",
    "plt.scatter(df1['sepal length (cm)'],df1['sepal width (cm)'], color='red',marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17f4e981630>"
      ]
     },
     "execution_count": 27,
     "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": [
    "plt.xlabel('petal length (cm)')\n",
    "plt.ylabel('petal width (cm)')\n",
    "\n",
    "plt.scatter(df0['petal length (cm)'],df0['petal width (cm)'], color='green',marker='+')\n",
    "plt.scatter(df1['petal length (cm)'],df1['petal width (cm)'], color='red',marker='.')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "NOW LET'S TRAIN OUR MODEL USING SKLEARN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= df.drop(['target','flower_name'],axis='columns')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: target, dtype: int32"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.target\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test , y_train, y_test = train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "model= SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "c is regularization parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test) \n",
    "#X_text will generate value that is X_predicted and compare it with y_test to predict how accurate the model is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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