Notebooks/Day_43_Unsupervised_Learning(Kmeans algorithm).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aeb9b615",
   "metadata": {},
   "source": [
    "***Unsupervised Learning***\n",
    "- training the machine with unlabelled data\n",
    "- we provide only input \n",
    "- two types\n",
    "    - Clustering\n",
    "    - Association"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff73d479",
   "metadata": {},
   "source": [
    "***Clustering Algorithm***\n",
    "- group the similar datapoints to clusters\n",
    "- kmeans()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a0006f",
   "metadata": {},
   "source": [
    "Kmeans Clustering\n",
    "- K refers to no of clusters\n",
    "- To select the optimal value of K,use elbow method\n",
    "Steps involved:\n",
    "    - choose the value of k\n",
    "    - choose the centroids\n",
    "    - calculate the distance and mean \n",
    "    - repeat the steps until you reach the point of convergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef9c6d24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "iris=load_iris()\n",
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "355d62df",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_df=pd.DataFrame(iris.data,columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2169872c",
   "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>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",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78b9a1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d18fbf22",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the value of sse for k=1 is 681.3705999999996\n",
      "the value of sse for k=2 is 152.34795176035797\n",
      "the value of sse for k=3 is 78.851441426146\n",
      "the value of sse for k=4 is 57.22847321428572\n",
      "the value of sse for k=5 is 46.44618205128204\n",
      "the value of sse for k=6 is 39.03998724608725\n",
      "the value of sse for k=7 is 34.46949589883803\n",
      "the value of sse for k=8 is 30.186555194805198\n",
      "the value of sse for k=9 is 28.28937085137087\n",
      "the value of sse for k=10 is 26.552373141564328\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "k=range(1,11)\n",
    "l=[]\n",
    "for i in k:\n",
    "    model=KMeans(n_clusters=i,random_state=42)\n",
    "    model.fit(x)\n",
    "    sse=model.inertia_\n",
    "    l.append(sse) \n",
    "    print(\"the value of sse for k={} is {}\".format(i,sse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "600dd5f0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(k,l)\n",
    "plt.xlabel(\"no of clusters\")\n",
    "plt.ylabel(\"sse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2ba8b89f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3, random_state=42)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "model=KMeans(n_clusters=3,random_state=42)\n",
    "model.fit(x)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8045514b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.9016129 , 2.7483871 , 4.39354839, 1.43387097],\n",
       "       [5.006     , 3.428     , 1.462     , 0.246     ],\n",
       "       [6.85      , 3.07368421, 5.74210526, 2.07105263]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers=model.cluster_centers_\n",
    "centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "397ddf20",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_df[\"target\"]=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9e8d6c47",
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\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",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "..      ...  \n",
       "145       2  \n",
       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5e9c985e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50\n",
       "1    50\n",
       "2    50\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "443f10c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_df[\"pred_target\"]=model.predict(x)\n",
    "y=model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5bf391d2",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>pred_target</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.2</td>\n",
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       "      <td>0.2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
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       "      <td>0.2</td>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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  pred_target  \n",
       "0       0            1  \n",
       "1       0            1  \n",
       "2       0            1  \n",
       "3       0            1  \n",
       "4       0            1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4cbdd063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    62\n",
       "1    50\n",
       "2    38\n",
       "Name: pred_target, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df[\"pred_target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4db99524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5528190123564091"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "silhouette_score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9fbe8e11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#silhouette_score\n",
    "# +1----> incorrectly\n",
    "# -1----->clustered\n",
    "# 0------>overlapping\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "957b46e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=iris_df[iris_df.pred_target==0]\n",
    "df2=iris_df[iris_df.pred_target==1]\n",
    "df3=iris_df[iris_df.pred_target==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cf005466",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>target</th>\n",
       "      <th>pred_target</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
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       "      <td>1.4</td>\n",
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       "      <td>0</td>\n",
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       "      <th>51</th>\n",
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       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
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       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
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       "      <th>55</th>\n",
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       "      <td>1.5</td>\n",
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       "      <th>138</th>\n",
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       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "50                 7.0               3.2                4.7               1.4   \n",
       "51                 6.4               3.2                4.5               1.5   \n",
       "53                 5.5               2.3                4.0               1.3   \n",
       "54                 6.5               2.8                4.6               1.5   \n",
       "55                 5.7               2.8                4.5               1.3   \n",
       "..                 ...               ...                ...               ...   \n",
       "133                6.3               2.8                5.1               1.5   \n",
       "138                6.0               3.0                4.8               1.8   \n",
       "142                5.8               2.7                5.1               1.9   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  pred_target  \n",
       "50        1            0  \n",
       "51        1            0  \n",
       "53        1            0  \n",
       "54        1            0  \n",
       "55        1            0  \n",
       "..      ...          ...  \n",
       "133       2            0  \n",
       "138       2            0  \n",
       "142       2            0  \n",
       "146       2            0  \n",
       "149       2            0  \n",
       "\n",
       "[62 rows x 6 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a3116208",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "      <td>0.2</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.1</td>\n",
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       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>5.4</td>\n",
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       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "5                 5.4               3.9                1.7               0.4   \n",
       "6                 4.6               3.4                1.4               0.3   \n",
       "7                 5.0               3.4                1.5               0.2   \n",
       "8                 4.4               2.9                1.4               0.2   \n",
       "9                 4.9               3.1                1.5               0.1   \n",
       "10                5.4               3.7                1.5               0.2   \n",
       "11                4.8               3.4                1.6               0.2   \n",
       "12                4.8               3.0                1.4               0.1   \n",
       "13                4.3               3.0                1.1               0.1   \n",
       "14                5.8               4.0                1.2               0.2   \n",
       "15                5.7               4.4                1.5               0.4   \n",
       "16                5.4               3.9                1.3               0.4   \n",
       "17                5.1               3.5                1.4               0.3   \n",
       "18                5.7               3.8                1.7               0.3   \n",
       "19                5.1               3.8                1.5               0.3   \n",
       "20                5.4               3.4                1.7               0.2   \n",
       "21                5.1               3.7                1.5               0.4   \n",
       "22                4.6               3.6                1.0               0.2   \n",
       "23                5.1               3.3                1.7               0.5   \n",
       "24                4.8               3.4                1.9               0.2   \n",
       "25                5.0               3.0                1.6               0.2   \n",
       "26                5.0               3.4                1.6               0.4   \n",
       "27                5.2               3.5                1.5               0.2   \n",
       "28                5.2               3.4                1.4               0.2   \n",
       "29                4.7               3.2                1.6               0.2   \n",
       "30                4.8               3.1                1.6               0.2   \n",
       "31                5.4               3.4                1.5               0.4   \n",
       "32                5.2               4.1                1.5               0.1   \n",
       "33                5.5               4.2                1.4               0.2   \n",
       "34                4.9               3.1                1.5               0.2   \n",
       "35                5.0               3.2                1.2               0.2   \n",
       "36                5.5               3.5                1.3               0.2   \n",
       "37                4.9               3.6                1.4               0.1   \n",
       "38                4.4               3.0                1.3               0.2   \n",
       "39                5.1               3.4                1.5               0.2   \n",
       "40                5.0               3.5                1.3               0.3   \n",
       "41                4.5               2.3                1.3               0.3   \n",
       "42                4.4               3.2                1.3               0.2   \n",
       "43                5.0               3.5                1.6               0.6   \n",
       "44                5.1               3.8                1.9               0.4   \n",
       "45                4.8               3.0                1.4               0.3   \n",
       "46                5.1               3.8                1.6               0.2   \n",
       "47                4.6               3.2                1.4               0.2   \n",
       "48                5.3               3.7                1.5               0.2   \n",
       "49                5.0               3.3                1.4               0.2   \n",
       "\n",
       "    target  pred_target  \n",
       "0        0            1  \n",
       "1        0            1  \n",
       "2        0            1  \n",
       "3        0            1  \n",
       "4        0            1  \n",
       "5        0            1  \n",
       "6        0            1  \n",
       "7        0            1  \n",
       "8        0            1  \n",
       "9        0            1  \n",
       "10       0            1  \n",
       "11       0            1  \n",
       "12       0            1  \n",
       "13       0            1  \n",
       "14       0            1  \n",
       "15       0            1  \n",
       "16       0            1  \n",
       "17       0            1  \n",
       "18       0            1  \n",
       "19       0            1  \n",
       "20       0            1  \n",
       "21       0            1  \n",
       "22       0            1  \n",
       "23       0            1  \n",
       "24       0            1  \n",
       "25       0            1  \n",
       "26       0            1  \n",
       "27       0            1  \n",
       "28       0            1  \n",
       "29       0            1  \n",
       "30       0            1  \n",
       "31       0            1  \n",
       "32       0            1  \n",
       "33       0            1  \n",
       "34       0            1  \n",
       "35       0            1  \n",
       "36       0            1  \n",
       "37       0            1  \n",
       "38       0            1  \n",
       "39       0            1  \n",
       "40       0            1  \n",
       "41       0            1  \n",
       "42       0            1  \n",
       "43       0            1  \n",
       "44       0            1  \n",
       "45       0            1  \n",
       "46       0            1  \n",
       "47       0            1  \n",
       "48       0            1  \n",
       "49       0            1  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5f3e3c84",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (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>pred_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "      <td>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",
       "52                 6.9               3.1                4.9               1.5   \n",
       "77                 6.7               3.0                5.0               1.7   \n",
       "100                6.3               3.3                6.0               2.5   \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",
       "105                7.6               3.0                6.6               2.1   \n",
       "107                7.3               2.9                6.3               1.8   \n",
       "108                6.7               2.5                5.8               1.8   \n",
       "109                7.2               3.6                6.1               2.5   \n",
       "110                6.5               3.2                5.1               2.0   \n",
       "111                6.4               2.7                5.3               1.9   \n",
       "112                6.8               3.0                5.5               2.1   \n",
       "115                6.4               3.2                5.3               2.3   \n",
       "116                6.5               3.0                5.5               1.8   \n",
       "117                7.7               3.8                6.7               2.2   \n",
       "118                7.7               2.6                6.9               2.3   \n",
       "120                6.9               3.2                5.7               2.3   \n",
       "122                7.7               2.8                6.7               2.0   \n",
       "124                6.7               3.3                5.7               2.1   \n",
       "125                7.2               3.2                6.0               1.8   \n",
       "128                6.4               2.8                5.6               2.1   \n",
       "129                7.2               3.0                5.8               1.6   \n",
       "130                7.4               2.8                6.1               1.9   \n",
       "131                7.9               3.8                6.4               2.0   \n",
       "132                6.4               2.8                5.6               2.2   \n",
       "134                6.1               2.6                5.6               1.4   \n",
       "135                7.7               3.0                6.1               2.3   \n",
       "136                6.3               3.4                5.6               2.4   \n",
       "137                6.4               3.1                5.5               1.8   \n",
       "139                6.9               3.1                5.4               2.1   \n",
       "140                6.7               3.1                5.6               2.4   \n",
       "141                6.9               3.1                5.1               2.3   \n",
       "143                6.8               3.2                5.9               2.3   \n",
       "144                6.7               3.3                5.7               2.5   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "\n",
       "     target  pred_target  \n",
       "52        1            2  \n",
       "77        1            2  \n",
       "100       2            2  \n",
       "102       2            2  \n",
       "103       2            2  \n",
       "104       2            2  \n",
       "105       2            2  \n",
       "107       2            2  \n",
       "108       2            2  \n",
       "109       2            2  \n",
       "110       2            2  \n",
       "111       2            2  \n",
       "112       2            2  \n",
       "115       2            2  \n",
       "116       2            2  \n",
       "117       2            2  \n",
       "118       2            2  \n",
       "120       2            2  \n",
       "122       2            2  \n",
       "124       2            2  \n",
       "125       2            2  \n",
       "128       2            2  \n",
       "129       2            2  \n",
       "130       2            2  \n",
       "131       2            2  \n",
       "132       2            2  \n",
       "134       2            2  \n",
       "135       2            2  \n",
       "136       2            2  \n",
       "137       2            2  \n",
       "139       2            2  \n",
       "140       2            2  \n",
       "141       2            2  \n",
       "143       2            2  \n",
       "144       2            2  \n",
       "145       2            2  \n",
       "147       2            2  \n",
       "148       2            2  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9ff2b910",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x22a65faba30>"
      ]
     },
     "execution_count": 59,
     "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.scatter(df1[\"sepal length (cm)\"],df1['sepal width (cm)'],color=\"red\")\n",
    "plt.scatter(df2[\"sepal length (cm)\"],df2['sepal width (cm)'],color=\"green\")\n",
    "plt.scatter(df3[\"sepal length (cm)\"],df3['sepal width (cm)'],color=\"blue\")\n",
    "plt.scatter(model.cluster_centers_[:,0],centers[:,1],color=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f1c20d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x22a65f43820>"
      ]
     },
     "execution_count": 58,
     "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.scatter(df1[\"petal length (cm)\"],df1['petal width (cm)'],color=\"red\")\n",
    "plt.scatter(df2[\"petal length (cm)\"],df2['petal width (cm)'],color=\"green\")\n",
    "plt.scatter(df3[\"petal length (cm)\"],df3['petal width (cm)'],color=\"blue\")\n",
    "plt.scatter(model.cluster_centers_[:,0],centers[:,1],color=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f1bf57b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.9016129, 5.006    , 6.85     ])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.cluster_centers_[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "089d9665",
   "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
}