Iris_Flower_pred.ipynb

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       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
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       "      <td>Iris-setosa</td>\n",
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       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
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     "execution_count": 297,
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   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objs as go\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score,ConfusionMatrixDisplay,classification_report\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "df=pd.read_csv(\"D:/dataset/Iris.csv\")\n",
    "df.head()"
   ]
  },
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   "execution_count": 298,
   "id": "e7b9e8db",
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "(150, 6)"
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     "execution_count": 298,
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   "source": [
    "df.shape"
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   "execution_count": 299,
   "id": "33e16fe6",
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       "900"
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   "source": [
    "df.size"
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   "execution_count": 300,
   "id": "32ed09fc",
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      "text/plain": [
       "Id               0\n",
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
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   "source": [
    "df.isna().sum()"
   ]
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   "cell_type": "code",
   "execution_count": 301,
   "id": "28b240bd",
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       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "      <td>112.75</td>\n",
       "      <td>150.0</td>\n",
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       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>150.0</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>4.3</td>\n",
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       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
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       "      <td>5.10</td>\n",
       "      <td>6.9</td>\n",
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       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>150.0</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.80</td>\n",
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       "               count       mean        std  min    25%    50%     75%    max\n",
       "Id             150.0  75.500000  43.445368  1.0  38.25  75.50  112.75  150.0\n",
       "SepalLengthCm  150.0   5.843333   0.828066  4.3   5.10   5.80    6.40    7.9\n",
       "SepalWidthCm   150.0   3.054000   0.433594  2.0   2.80   3.00    3.30    4.4\n",
       "PetalLengthCm  150.0   3.758667   1.764420  1.0   1.60   4.35    5.10    6.9\n",
       "PetalWidthCm   150.0   1.198667   0.763161  0.1   0.30   1.30    1.80    2.5"
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    "df.describe().T"
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     "metadata": {},
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   ],
   "source": [
    "fig= px.line(df['PetalLengthCm'],color=df['Species'])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "24b433fe",
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "fig =px.bar(df,x='SepalWidthCm',y='PetalLengthCm',color='Species') #,animation_frame='species')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "aba931b2",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "fig=px.histogram(df['SepalWidthCm'],color=df['Species'])\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "5c3d1bc4",
   "metadata": {},
   "outputs": [
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   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "7dde2d96",
   "metadata": {},
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   "source": [
    "fig=px.violin(df.drop(['Species'],axis=1))\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "2c888672",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
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       "      <th>size</th>\n",
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       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
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       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
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       "    <tr>\n",
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       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>239</th>\n",
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       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
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       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
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       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
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       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
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       "      <td>No</td>\n",
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       "  </tbody>\n",
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       "<p>244 rows × 7 columns</p>\n",
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       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
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     "execution_count": 309,
     "metadata": {},
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   "source": [
    "fig=px.scatter(df1,x = 'tip',y='total_bill',color=df1['sex'],size=df1['size'],animation_frame=df1['size'])\n",
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   "id": "cfa3a17f",
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   "cell_type": "code",
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   "source": [
    "fig=px.scatter(df,x='SepalWidthCm',y='SepalLengthCm',color='Species',marginal_x='box',marginal_y='histogram',trendline='ols',)\n",
    "fig.show()"
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  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "2ebd2ddc",
   "metadata": {},
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trendline</b><br>SepalLengthCm = 0.865078 * SepalWidthCm + 3.53973<br>R<sup>2</sup>=0.276582<br><br>Species=Iris-versicolor<br>SepalWidthCm=%{x}<br>SepalLengthCm=%{y} <b>(trend)</b><extra></extra>\",\"legendgroup\":\"Iris-versicolor\",\"marker\":{\"color\":\"#EF553B\",\"symbol\":\"circle\"},\"mode\":\"lines\",\"name\":\"Iris-versicolor\",\"showlegend\":false,\"x\":[2.0,2.2,2.2,2.3,2.3,2.3,2.4,2.4,2.4,2.5,2.5,2.5,2.5,2.6,2.6,2.6,2.7,2.7,2.7,2.7,2.7,2.8,2.8,2.8,2.8,2.8,2.8,2.9,2.9,2.9,2.9,2.9,2.9,2.9,3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.1,3.1,3.1,3.2,3.2,3.2,3.3,3.4],\"xaxis\":\"x\",\"y\":[5.269890155440409,5.4429056994818605,5.4429056994818605,5.529413471502585,5.529413471502585,5.529413471502585,5.61592124352331,5.61592124352331,5.61592124352331,5.702429015544036,5.702429015544036,5.702429015544036,5.702429015544036,5.7889367875647615,5.7889367875647615,5.7889367875647615,5.875444559585487,5.875444559585487,5.875444559585487,5.875444559585487,5.875444559585487,5.961952331606212,5.961952331606212,5.961952331606212,5.961952331606212,5.961952331606212,5.961952331606212,6.048460103626937,6.048460103626937,6.048460103626937,6.048460103626937,6.048460103626937,6.048460103626937,6.048460103626937,6.134967875647662,6.134967875647662,6.134967875647662,6.134967875647662,6.134967875647662,6.134967875647662,6.134967875647662,6.134967875647662,6.221475647668388,6.221475647668388,6.221475647668388,6.307983419689114,6.307983419689114,6.307983419689114,6.394491191709839,6.480998963730563],\"yaxis\":\"y\",\"type\":\"scatter\"},{\"hovertemplate\":\"Species=Iris-virginica<br>SepalWidthCm=%{x}<br>SepalLengthCm=%{y}<extra></extra>\",\"legendgroup\":\"Iris-virginica\",\"marker\":{\"color\":\"#00cc96\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"Iris-virginica\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[3.3,2.7,3.0,2.9,3.0,3.0,2.5,2.9,2.5,3.6,3.2,2.7,3.0,2.5,2.8,3.2,3.0,3.8,2.6,2.2,3.2,2.8,2.8,2.7,3.3,3.2,2.8,3.0,2.8,3.0,2.8,3.8,2.8,2.8,2.6,3.0,3.4,3.1,3.0,3.1,3.1,3.1,2.7,3.2,3.3,3.0,2.5,3.0,3.4,3.0],\"xaxis\":\"x\",\"y\":[6.3,5.8,7.1,6.3,6.5,7.6,4.9,7.3,6.7,7.2,6.5,6.4,6.8,5.7,5.8,6.4,6.5,7.7,7.7,6.0,6.9,5.6,7.7,6.3,6.7,7.2,6.2,6.1,6.4,7.2,7.4,7.9,6.4,6.3,6.1,7.7,6.3,6.4,6.0,6.9,6.7,6.9,5.8,6.8,6.7,6.7,6.3,6.5,6.2,5.9],\"yaxis\":\"y\",\"type\":\"scatter\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Species=Iris-virginica<br>SepalWidthCm=%{x}<extra></extra>\",\"legendgroup\":\"Iris-virginica\",\"marker\":{\"color\":\"#00cc96\",\"symbol\":\"circle\"},\"name\":\"Iris-virginica\",\"notched\":true,\"offsetgroup\":\"Iris-virginica\",\"showlegend\":false,\"x\":[3.3,2.7,3.0,2.9,3.0,3.0,2.5,2.9,2.5,3.6,3.2,2.7,3.0,2.5,2.8,3.2,3.0,3.8,2.6,2.2,3.2,2.8,2.8,2.7,3.3,3.2,2.8,3.0,2.8,3.0,2.8,3.8,2.8,2.8,2.6,3.0,3.4,3.1,3.0,3.1,3.1,3.1,2.7,3.2,3.3,3.0,2.5,3.0,3.4,3.0],\"xaxis\":\"x3\",\"yaxis\":\"y3\",\"type\":\"box\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"y\",\"hovertemplate\":\"Species=Iris-virginica<br>SepalLengthCm=%{y}<br>count=%{x}<extra></extra>\",\"legendgroup\":\"Iris-virginica\",\"marker\":{\"color\":\"#00cc96\"},\"name\":\"Iris-virginica\",\"offsetgroup\":\"Iris-virginica\",\"opacity\":0.5,\"showlegend\":false,\"xaxis\":\"x2\",\"y\":[6.3,5.8,7.1,6.3,6.5,7.6,4.9,7.3,6.7,7.2,6.5,6.4,6.8,5.7,5.8,6.4,6.5,7.7,7.7,6.0,6.9,5.6,7.7,6.3,6.7,7.2,6.2,6.1,6.4,7.2,7.4,7.9,6.4,6.3,6.1,7.7,6.3,6.4,6.0,6.9,6.7,6.9,5.8,6.8,6.7,6.7,6.3,6.5,6.2,5.9],\"yaxis\":\"y2\",\"type\":\"histogram\"},{\"hovertemplate\":\"<b>OLS trendline</b><br>SepalLengthCm = 0.901534 * SepalWidthCm + 3.90684<br>R<sup>2</sup>=0.209057<br><br>Species=Iris-virginica<br>SepalWidthCm=%{x}<br>SepalLengthCm=%{y} <b>(trend)</b><extra></extra>\",\"legendgroup\":\"Iris-virginica\",\"marker\":{\"color\":\"#00cc96\",\"symbol\":\"circle\"},\"mode\":\"lines\",\"name\":\"Iris-virginica\",\"showlegend\":false,\"x\":[2.2,2.5,2.5,2.5,2.5,2.6,2.6,2.7,2.7,2.7,2.7,2.8,2.8,2.8,2.8,2.8,2.8,2.8,2.8,2.9,2.9,3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.1,3.1,3.1,3.1,3.2,3.2,3.2,3.2,3.2,3.3,3.3,3.3,3.4,3.4,3.6,3.8,3.8],\"xaxis\":\"x\",\"y\":[5.8902123150582755,6.160672658058942,6.160672658058942,6.160672658058942,6.160672658058942,6.250826105725832,6.250826105725832,6.340979553392721,6.340979553392721,6.340979553392721,6.340979553392721,6.43113300105961,6.43113300105961,6.43113300105961,6.43113300105961,6.43113300105961,6.43113300105961,6.43113300105961,6.43113300105961,6.5212864487264985,6.5212864487264985,6.611439896393388,6.611439896393388,6.611439896393388,6.611439896393388,6.611439896393388,6.611439896393388,6.611439896393388,6.611439896393388,6.611439896393388,6.611439896393388,6.611439896393388,6.611439896393388,6.701593344060277,6.701593344060277,6.701593344060277,6.701593344060277,6.7917467917271654,6.7917467917271654,6.7917467917271654,6.7917467917271654,6.7917467917271654,6.881900239394055,6.881900239394055,6.881900239394055,6.972053687060944,6.972053687060944,7.1523605823947225,7.3326674777285,7.3326674777285],\"yaxis\":\"y\",\"type\":\"scatter\"}], 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   "source": [
    "fig=px.scatter(df,x='SepalWidthCm',y='SepalLengthCm',color='Species',marginal_x='box',marginal_y='histogram',trendline='ols')\n",
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       "                            \n",
       "var gd = document.getElementById('042c96a0-0812-4375-9d1d-68c64b606173');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=px.imshow(df.drop(['Species'],axis=1),text_auto=True, aspect=\"auto\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "ef5be433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00e+00, 5.10e+00, 3.50e+00, 1.40e+00, 2.00e-01],\n",
       "       [2.00e+00, 4.90e+00, 3.00e+00, 1.40e+00, 2.00e-01],\n",
       "       [3.00e+00, 4.70e+00, 3.20e+00, 1.30e+00, 2.00e-01],\n",
       "       [4.00e+00, 4.60e+00, 3.10e+00, 1.50e+00, 2.00e-01],\n",
       "       [5.00e+00, 5.00e+00, 3.60e+00, 1.40e+00, 2.00e-01],\n",
       "       [6.00e+00, 5.40e+00, 3.90e+00, 1.70e+00, 4.00e-01],\n",
       "       [7.00e+00, 4.60e+00, 3.40e+00, 1.40e+00, 3.00e-01],\n",
       "       [8.00e+00, 5.00e+00, 3.40e+00, 1.50e+00, 2.00e-01],\n",
       "       [9.00e+00, 4.40e+00, 2.90e+00, 1.40e+00, 2.00e-01],\n",
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       "       [1.10e+01, 5.40e+00, 3.70e+00, 1.50e+00, 2.00e-01],\n",
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       "       [1.40e+01, 4.30e+00, 3.00e+00, 1.10e+00, 1.00e-01],\n",
       "       [1.50e+01, 5.80e+00, 4.00e+00, 1.20e+00, 2.00e-01],\n",
       "       [1.60e+01, 5.70e+00, 4.40e+00, 1.50e+00, 4.00e-01],\n",
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       "       [1.90e+01, 5.70e+00, 3.80e+00, 1.70e+00, 3.00e-01],\n",
       "       [2.00e+01, 5.10e+00, 3.80e+00, 1.50e+00, 3.00e-01],\n",
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       "       [2.60e+01, 5.00e+00, 3.00e+00, 1.60e+00, 2.00e-01],\n",
       "       [2.70e+01, 5.00e+00, 3.40e+00, 1.60e+00, 4.00e-01],\n",
       "       [2.80e+01, 5.20e+00, 3.50e+00, 1.50e+00, 2.00e-01],\n",
       "       [2.90e+01, 5.20e+00, 3.40e+00, 1.40e+00, 2.00e-01],\n",
       "       [3.00e+01, 4.70e+00, 3.20e+00, 1.60e+00, 2.00e-01],\n",
       "       [3.10e+01, 4.80e+00, 3.10e+00, 1.60e+00, 2.00e-01],\n",
       "       [3.20e+01, 5.40e+00, 3.40e+00, 1.50e+00, 4.00e-01],\n",
       "       [3.30e+01, 5.20e+00, 4.10e+00, 1.50e+00, 1.00e-01],\n",
       "       [3.40e+01, 5.50e+00, 4.20e+00, 1.40e+00, 2.00e-01],\n",
       "       [3.50e+01, 4.90e+00, 3.10e+00, 1.50e+00, 1.00e-01],\n",
       "       [3.60e+01, 5.00e+00, 3.20e+00, 1.20e+00, 2.00e-01],\n",
       "       [3.70e+01, 5.50e+00, 3.50e+00, 1.30e+00, 2.00e-01],\n",
       "       [3.80e+01, 4.90e+00, 3.10e+00, 1.50e+00, 1.00e-01],\n",
       "       [3.90e+01, 4.40e+00, 3.00e+00, 1.30e+00, 2.00e-01],\n",
       "       [4.00e+01, 5.10e+00, 3.40e+00, 1.50e+00, 2.00e-01],\n",
       "       [4.10e+01, 5.00e+00, 3.50e+00, 1.30e+00, 3.00e-01],\n",
       "       [4.20e+01, 4.50e+00, 2.30e+00, 1.30e+00, 3.00e-01],\n",
       "       [4.30e+01, 4.40e+00, 3.20e+00, 1.30e+00, 2.00e-01],\n",
       "       [4.40e+01, 5.00e+00, 3.50e+00, 1.60e+00, 6.00e-01],\n",
       "       [4.50e+01, 5.10e+00, 3.80e+00, 1.90e+00, 4.00e-01],\n",
       "       [4.60e+01, 4.80e+00, 3.00e+00, 1.40e+00, 3.00e-01],\n",
       "       [4.70e+01, 5.10e+00, 3.80e+00, 1.60e+00, 2.00e-01],\n",
       "       [4.80e+01, 4.60e+00, 3.20e+00, 1.40e+00, 2.00e-01],\n",
       "       [4.90e+01, 5.30e+00, 3.70e+00, 1.50e+00, 2.00e-01],\n",
       "       [5.00e+01, 5.00e+00, 3.30e+00, 1.40e+00, 2.00e-01],\n",
       "       [5.10e+01, 7.00e+00, 3.20e+00, 4.70e+00, 1.40e+00],\n",
       "       [5.20e+01, 6.40e+00, 3.20e+00, 4.50e+00, 1.50e+00],\n",
       "       [5.30e+01, 6.90e+00, 3.10e+00, 4.90e+00, 1.50e+00],\n",
       "       [5.40e+01, 5.50e+00, 2.30e+00, 4.00e+00, 1.30e+00],\n",
       "       [5.50e+01, 6.50e+00, 2.80e+00, 4.60e+00, 1.50e+00],\n",
       "       [5.60e+01, 5.70e+00, 2.80e+00, 4.50e+00, 1.30e+00],\n",
       "       [5.70e+01, 6.30e+00, 3.30e+00, 4.70e+00, 1.60e+00],\n",
       "       [5.80e+01, 4.90e+00, 2.40e+00, 3.30e+00, 1.00e+00],\n",
       "       [5.90e+01, 6.60e+00, 2.90e+00, 4.60e+00, 1.30e+00],\n",
       "       [6.00e+01, 5.20e+00, 2.70e+00, 3.90e+00, 1.40e+00],\n",
       "       [6.10e+01, 5.00e+00, 2.00e+00, 3.50e+00, 1.00e+00],\n",
       "       [6.20e+01, 5.90e+00, 3.00e+00, 4.20e+00, 1.50e+00],\n",
       "       [6.30e+01, 6.00e+00, 2.20e+00, 4.00e+00, 1.00e+00],\n",
       "       [6.40e+01, 6.10e+00, 2.90e+00, 4.70e+00, 1.40e+00],\n",
       "       [6.50e+01, 5.60e+00, 2.90e+00, 3.60e+00, 1.30e+00],\n",
       "       [6.60e+01, 6.70e+00, 3.10e+00, 4.40e+00, 1.40e+00],\n",
       "       [6.70e+01, 5.60e+00, 3.00e+00, 4.50e+00, 1.50e+00],\n",
       "       [6.80e+01, 5.80e+00, 2.70e+00, 4.10e+00, 1.00e+00],\n",
       "       [6.90e+01, 6.20e+00, 2.20e+00, 4.50e+00, 1.50e+00],\n",
       "       [7.00e+01, 5.60e+00, 2.50e+00, 3.90e+00, 1.10e+00],\n",
       "       [7.10e+01, 5.90e+00, 3.20e+00, 4.80e+00, 1.80e+00],\n",
       "       [7.20e+01, 6.10e+00, 2.80e+00, 4.00e+00, 1.30e+00],\n",
       "       [7.30e+01, 6.30e+00, 2.50e+00, 4.90e+00, 1.50e+00],\n",
       "       [7.40e+01, 6.10e+00, 2.80e+00, 4.70e+00, 1.20e+00],\n",
       "       [7.50e+01, 6.40e+00, 2.90e+00, 4.30e+00, 1.30e+00],\n",
       "       [7.60e+01, 6.60e+00, 3.00e+00, 4.40e+00, 1.40e+00],\n",
       "       [7.70e+01, 6.80e+00, 2.80e+00, 4.80e+00, 1.40e+00],\n",
       "       [7.80e+01, 6.70e+00, 3.00e+00, 5.00e+00, 1.70e+00],\n",
       "       [7.90e+01, 6.00e+00, 2.90e+00, 4.50e+00, 1.50e+00],\n",
       "       [8.00e+01, 5.70e+00, 2.60e+00, 3.50e+00, 1.00e+00],\n",
       "       [8.10e+01, 5.50e+00, 2.40e+00, 3.80e+00, 1.10e+00],\n",
       "       [8.20e+01, 5.50e+00, 2.40e+00, 3.70e+00, 1.00e+00],\n",
       "       [8.30e+01, 5.80e+00, 2.70e+00, 3.90e+00, 1.20e+00],\n",
       "       [8.40e+01, 6.00e+00, 2.70e+00, 5.10e+00, 1.60e+00],\n",
       "       [8.50e+01, 5.40e+00, 3.00e+00, 4.50e+00, 1.50e+00],\n",
       "       [8.60e+01, 6.00e+00, 3.40e+00, 4.50e+00, 1.60e+00],\n",
       "       [8.70e+01, 6.70e+00, 3.10e+00, 4.70e+00, 1.50e+00],\n",
       "       [8.80e+01, 6.30e+00, 2.30e+00, 4.40e+00, 1.30e+00],\n",
       "       [8.90e+01, 5.60e+00, 3.00e+00, 4.10e+00, 1.30e+00],\n",
       "       [9.00e+01, 5.50e+00, 2.50e+00, 4.00e+00, 1.30e+00],\n",
       "       [9.10e+01, 5.50e+00, 2.60e+00, 4.40e+00, 1.20e+00],\n",
       "       [9.20e+01, 6.10e+00, 3.00e+00, 4.60e+00, 1.40e+00],\n",
       "       [9.30e+01, 5.80e+00, 2.60e+00, 4.00e+00, 1.20e+00],\n",
       "       [9.40e+01, 5.00e+00, 2.30e+00, 3.30e+00, 1.00e+00],\n",
       "       [9.50e+01, 5.60e+00, 2.70e+00, 4.20e+00, 1.30e+00],\n",
       "       [9.60e+01, 5.70e+00, 3.00e+00, 4.20e+00, 1.20e+00],\n",
       "       [9.70e+01, 5.70e+00, 2.90e+00, 4.20e+00, 1.30e+00],\n",
       "       [9.80e+01, 6.20e+00, 2.90e+00, 4.30e+00, 1.30e+00],\n",
       "       [9.90e+01, 5.10e+00, 2.50e+00, 3.00e+00, 1.10e+00],\n",
       "       [1.00e+02, 5.70e+00, 2.80e+00, 4.10e+00, 1.30e+00],\n",
       "       [1.01e+02, 6.30e+00, 3.30e+00, 6.00e+00, 2.50e+00],\n",
       "       [1.02e+02, 5.80e+00, 2.70e+00, 5.10e+00, 1.90e+00],\n",
       "       [1.03e+02, 7.10e+00, 3.00e+00, 5.90e+00, 2.10e+00],\n",
       "       [1.04e+02, 6.30e+00, 2.90e+00, 5.60e+00, 1.80e+00],\n",
       "       [1.05e+02, 6.50e+00, 3.00e+00, 5.80e+00, 2.20e+00],\n",
       "       [1.06e+02, 7.60e+00, 3.00e+00, 6.60e+00, 2.10e+00],\n",
       "       [1.07e+02, 4.90e+00, 2.50e+00, 4.50e+00, 1.70e+00],\n",
       "       [1.08e+02, 7.30e+00, 2.90e+00, 6.30e+00, 1.80e+00],\n",
       "       [1.09e+02, 6.70e+00, 2.50e+00, 5.80e+00, 1.80e+00],\n",
       "       [1.10e+02, 7.20e+00, 3.60e+00, 6.10e+00, 2.50e+00],\n",
       "       [1.11e+02, 6.50e+00, 3.20e+00, 5.10e+00, 2.00e+00],\n",
       "       [1.12e+02, 6.40e+00, 2.70e+00, 5.30e+00, 1.90e+00],\n",
       "       [1.13e+02, 6.80e+00, 3.00e+00, 5.50e+00, 2.10e+00],\n",
       "       [1.14e+02, 5.70e+00, 2.50e+00, 5.00e+00, 2.00e+00],\n",
       "       [1.15e+02, 5.80e+00, 2.80e+00, 5.10e+00, 2.40e+00],\n",
       "       [1.16e+02, 6.40e+00, 3.20e+00, 5.30e+00, 2.30e+00],\n",
       "       [1.17e+02, 6.50e+00, 3.00e+00, 5.50e+00, 1.80e+00],\n",
       "       [1.18e+02, 7.70e+00, 3.80e+00, 6.70e+00, 2.20e+00],\n",
       "       [1.19e+02, 7.70e+00, 2.60e+00, 6.90e+00, 2.30e+00],\n",
       "       [1.20e+02, 6.00e+00, 2.20e+00, 5.00e+00, 1.50e+00],\n",
       "       [1.21e+02, 6.90e+00, 3.20e+00, 5.70e+00, 2.30e+00],\n",
       "       [1.22e+02, 5.60e+00, 2.80e+00, 4.90e+00, 2.00e+00],\n",
       "       [1.23e+02, 7.70e+00, 2.80e+00, 6.70e+00, 2.00e+00],\n",
       "       [1.24e+02, 6.30e+00, 2.70e+00, 4.90e+00, 1.80e+00],\n",
       "       [1.25e+02, 6.70e+00, 3.30e+00, 5.70e+00, 2.10e+00],\n",
       "       [1.26e+02, 7.20e+00, 3.20e+00, 6.00e+00, 1.80e+00],\n",
       "       [1.27e+02, 6.20e+00, 2.80e+00, 4.80e+00, 1.80e+00],\n",
       "       [1.28e+02, 6.10e+00, 3.00e+00, 4.90e+00, 1.80e+00],\n",
       "       [1.29e+02, 6.40e+00, 2.80e+00, 5.60e+00, 2.10e+00],\n",
       "       [1.30e+02, 7.20e+00, 3.00e+00, 5.80e+00, 1.60e+00],\n",
       "       [1.31e+02, 7.40e+00, 2.80e+00, 6.10e+00, 1.90e+00],\n",
       "       [1.32e+02, 7.90e+00, 3.80e+00, 6.40e+00, 2.00e+00],\n",
       "       [1.33e+02, 6.40e+00, 2.80e+00, 5.60e+00, 2.20e+00],\n",
       "       [1.34e+02, 6.30e+00, 2.80e+00, 5.10e+00, 1.50e+00],\n",
       "       [1.35e+02, 6.10e+00, 2.60e+00, 5.60e+00, 1.40e+00],\n",
       "       [1.36e+02, 7.70e+00, 3.00e+00, 6.10e+00, 2.30e+00],\n",
       "       [1.37e+02, 6.30e+00, 3.40e+00, 5.60e+00, 2.40e+00],\n",
       "       [1.38e+02, 6.40e+00, 3.10e+00, 5.50e+00, 1.80e+00],\n",
       "       [1.39e+02, 6.00e+00, 3.00e+00, 4.80e+00, 1.80e+00],\n",
       "       [1.40e+02, 6.90e+00, 3.10e+00, 5.40e+00, 2.10e+00],\n",
       "       [1.41e+02, 6.70e+00, 3.10e+00, 5.60e+00, 2.40e+00],\n",
       "       [1.42e+02, 6.90e+00, 3.10e+00, 5.10e+00, 2.30e+00],\n",
       "       [1.43e+02, 5.80e+00, 2.70e+00, 5.10e+00, 1.90e+00],\n",
       "       [1.44e+02, 6.80e+00, 3.20e+00, 5.90e+00, 2.30e+00],\n",
       "       [1.45e+02, 6.70e+00, 3.30e+00, 5.70e+00, 2.50e+00],\n",
       "       [1.46e+02, 6.70e+00, 3.00e+00, 5.20e+00, 2.30e+00],\n",
       "       [1.47e+02, 6.30e+00, 2.50e+00, 5.00e+00, 1.90e+00],\n",
       "       [1.48e+02, 6.50e+00, 3.00e+00, 5.20e+00, 2.00e+00],\n",
       "       [1.49e+02, 6.20e+00, 3.40e+00, 5.40e+00, 2.30e+00],\n",
       "       [1.50e+02, 5.90e+00, 3.00e+00, 5.10e+00, 1.80e+00]])"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting to inputs and outputs\n",
    "x=df.iloc[:,:-1].values # input data\n",
    "y=df.iloc[:,-1].values # output data\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "fafe991f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.00e+01, 5.20e+00, 2.70e+00, 3.90e+00, 1.40e+00],\n",
       "       [1.26e+02, 7.20e+00, 3.20e+00, 6.00e+00, 1.80e+00],\n",
       "       [1.20e+02, 6.00e+00, 2.20e+00, 5.00e+00, 1.50e+00],\n",
       "       [5.60e+01, 5.70e+00, 2.80e+00, 4.50e+00, 1.30e+00],\n",
       "       [1.15e+02, 5.80e+00, 2.80e+00, 5.10e+00, 2.40e+00],\n",
       "       [7.90e+01, 6.00e+00, 2.90e+00, 4.50e+00, 1.50e+00],\n",
       "       [3.60e+01, 5.00e+00, 3.20e+00, 1.20e+00, 2.00e-01],\n",
       "       [2.80e+01, 5.20e+00, 3.50e+00, 1.50e+00, 2.00e-01],\n",
       "       [1.23e+02, 7.70e+00, 2.80e+00, 6.70e+00, 2.00e+00],\n",
       "       [8.30e+01, 5.80e+00, 2.70e+00, 3.90e+00, 1.20e+00],\n",
       "       [4.60e+01, 4.80e+00, 3.00e+00, 1.40e+00, 3.00e-01],\n",
       "       [1.17e+02, 6.50e+00, 3.00e+00, 5.50e+00, 1.80e+00],\n",
       "       [7.50e+01, 6.40e+00, 2.90e+00, 4.30e+00, 1.30e+00],\n",
       "       [5.20e+01, 6.40e+00, 3.20e+00, 4.50e+00, 1.50e+00],\n",
       "       [3.10e+01, 4.80e+00, 3.10e+00, 1.60e+00, 2.00e-01],\n",
       "       [3.80e+01, 4.90e+00, 3.10e+00, 1.50e+00, 1.00e-01],\n",
       "       [1.06e+02, 7.60e+00, 3.00e+00, 6.60e+00, 2.10e+00],\n",
       "       [5.80e+01, 4.90e+00, 2.40e+00, 3.30e+00, 1.00e+00],\n",
       "       [6.00e+00, 5.40e+00, 3.90e+00, 1.70e+00, 4.00e-01],\n",
       "       [7.70e+01, 6.80e+00, 2.80e+00, 4.80e+00, 1.40e+00],\n",
       "       [5.70e+01, 6.30e+00, 3.30e+00, 4.70e+00, 1.60e+00],\n",
       "       [1.46e+02, 6.70e+00, 3.00e+00, 5.20e+00, 2.30e+00],\n",
       "       [5.90e+01, 6.60e+00, 2.90e+00, 4.60e+00, 1.30e+00],\n",
       "       [3.40e+01, 5.50e+00, 4.20e+00, 1.40e+00, 2.00e-01],\n",
       "       [2.60e+01, 5.00e+00, 3.00e+00, 1.60e+00, 2.00e-01],\n",
       "       [7.30e+01, 6.30e+00, 2.50e+00, 4.90e+00, 1.50e+00],\n",
       "       [1.18e+02, 7.70e+00, 3.80e+00, 6.70e+00, 2.20e+00],\n",
       "       [1.32e+02, 7.90e+00, 3.80e+00, 6.40e+00, 2.00e+00],\n",
       "       [1.01e+02, 6.30e+00, 3.30e+00, 6.00e+00, 2.50e+00],\n",
       "       [1.49e+02, 6.20e+00, 3.40e+00, 5.40e+00, 2.30e+00],\n",
       "       [1.05e+02, 6.50e+00, 3.00e+00, 5.80e+00, 2.20e+00],\n",
       "       [3.70e+01, 5.50e+00, 3.50e+00, 1.30e+00, 2.00e-01],\n",
       "       [8.10e+01, 5.50e+00, 2.40e+00, 3.80e+00, 1.10e+00],\n",
       "       [9.50e+01, 5.60e+00, 2.70e+00, 4.20e+00, 1.30e+00],\n",
       "       [1.00e+01, 4.90e+00, 3.10e+00, 1.50e+00, 1.00e-01],\n",
       "       [5.40e+01, 5.50e+00, 2.30e+00, 4.00e+00, 1.30e+00],\n",
       "       [3.30e+01, 5.20e+00, 4.10e+00, 1.50e+00, 1.00e-01],\n",
       "       [9.90e+01, 5.10e+00, 2.50e+00, 3.00e+00, 1.10e+00],\n",
       "       [2.70e+01, 5.00e+00, 3.40e+00, 1.60e+00, 4.00e-01],\n",
       "       [1.31e+02, 7.40e+00, 2.80e+00, 6.10e+00, 1.90e+00],\n",
       "       [1.41e+02, 6.70e+00, 3.10e+00, 5.60e+00, 2.40e+00],\n",
       "       [8.80e+01, 6.30e+00, 2.30e+00, 4.40e+00, 1.30e+00],\n",
       "       [8.50e+01, 5.40e+00, 3.00e+00, 4.50e+00, 1.50e+00],\n",
       "       [7.10e+01, 5.90e+00, 3.20e+00, 4.80e+00, 1.80e+00],\n",
       "       [4.10e+01, 5.00e+00, 3.50e+00, 1.30e+00, 3.00e-01],\n",
       "       [1.25e+02, 6.70e+00, 3.30e+00, 5.70e+00, 2.10e+00],\n",
       "       [6.90e+01, 6.20e+00, 2.20e+00, 4.50e+00, 1.50e+00],\n",
       "       [1.19e+02, 7.70e+00, 2.60e+00, 6.90e+00, 2.30e+00],\n",
       "       [1.09e+02, 6.70e+00, 2.50e+00, 5.80e+00, 1.80e+00],\n",
       "       [4.00e+00, 4.60e+00, 3.10e+00, 1.50e+00, 2.00e-01],\n",
       "       [1.03e+02, 7.10e+00, 3.00e+00, 5.90e+00, 2.10e+00],\n",
       "       [4.30e+01, 4.40e+00, 3.20e+00, 1.30e+00, 2.00e-01],\n",
       "       [8.90e+01, 5.60e+00, 3.00e+00, 4.10e+00, 1.30e+00],\n",
       "       [1.11e+02, 6.50e+00, 3.20e+00, 5.10e+00, 2.00e+00],\n",
       "       [7.40e+01, 6.10e+00, 2.80e+00, 4.70e+00, 1.20e+00],\n",
       "       [8.00e+00, 5.00e+00, 3.40e+00, 1.50e+00, 2.00e-01],\n",
       "       [1.36e+02, 7.70e+00, 3.00e+00, 6.10e+00, 2.30e+00],\n",
       "       [8.20e+01, 5.50e+00, 2.40e+00, 3.70e+00, 1.00e+00],\n",
       "       [9.80e+01, 6.20e+00, 2.90e+00, 4.30e+00, 1.30e+00],\n",
       "       [9.60e+01, 5.70e+00, 3.00e+00, 4.20e+00, 1.20e+00],\n",
       "       [2.40e+01, 5.10e+00, 3.30e+00, 1.70e+00, 5.00e-01],\n",
       "       [1.28e+02, 6.10e+00, 3.00e+00, 4.90e+00, 1.80e+00],\n",
       "       [1.70e+01, 5.40e+00, 3.90e+00, 1.30e+00, 4.00e-01],\n",
       "       [9.70e+01, 5.70e+00, 2.90e+00, 4.20e+00, 1.30e+00],\n",
       "       [1.24e+02, 6.30e+00, 2.70e+00, 4.90e+00, 1.80e+00],\n",
       "       [8.40e+01, 6.00e+00, 2.70e+00, 5.10e+00, 1.60e+00],\n",
       "       [2.50e+01, 4.80e+00, 3.40e+00, 1.90e+00, 2.00e-01],\n",
       "       [1.48e+02, 6.50e+00, 3.00e+00, 5.20e+00, 2.00e+00],\n",
       "       [8.70e+01, 6.70e+00, 3.10e+00, 4.70e+00, 1.50e+00],\n",
       "       [6.80e+01, 5.80e+00, 2.70e+00, 4.10e+00, 1.00e+00],\n",
       "       [7.00e+00, 4.60e+00, 3.40e+00, 1.40e+00, 3.00e-01],\n",
       "       [1.35e+02, 6.10e+00, 2.60e+00, 5.60e+00, 1.40e+00],\n",
       "       [1.30e+01, 4.80e+00, 3.00e+00, 1.40e+00, 1.00e-01],\n",
       "       [6.30e+01, 6.00e+00, 2.20e+00, 4.00e+00, 1.00e+00],\n",
       "       [1.43e+02, 5.80e+00, 2.70e+00, 5.10e+00, 1.90e+00],\n",
       "       [6.40e+01, 6.10e+00, 2.90e+00, 4.70e+00, 1.40e+00],\n",
       "       [5.30e+01, 6.90e+00, 3.10e+00, 4.90e+00, 1.50e+00],\n",
       "       [5.00e+00, 5.00e+00, 3.60e+00, 1.40e+00, 2.00e-01],\n",
       "       [1.80e+01, 5.10e+00, 3.50e+00, 1.40e+00, 3.00e-01],\n",
       "       [1.27e+02, 6.20e+00, 2.80e+00, 4.80e+00, 1.80e+00],\n",
       "       [2.10e+01, 5.40e+00, 3.40e+00, 1.70e+00, 2.00e-01],\n",
       "       [1.12e+02, 6.40e+00, 2.70e+00, 5.30e+00, 1.90e+00],\n",
       "       [1.16e+02, 6.40e+00, 3.20e+00, 5.30e+00, 2.30e+00],\n",
       "       [6.20e+01, 5.90e+00, 3.00e+00, 4.20e+00, 1.50e+00],\n",
       "       [2.90e+01, 5.20e+00, 3.40e+00, 1.40e+00, 2.00e-01],\n",
       "       [9.10e+01, 5.50e+00, 2.60e+00, 4.40e+00, 1.20e+00],\n",
       "       [9.00e+00, 4.40e+00, 2.90e+00, 1.40e+00, 2.00e-01],\n",
       "       [1.45e+02, 6.70e+00, 3.30e+00, 5.70e+00, 2.50e+00],\n",
       "       [1.14e+02, 5.70e+00, 2.50e+00, 5.00e+00, 2.00e+00],\n",
       "       [4.20e+01, 4.50e+00, 2.30e+00, 1.30e+00, 3.00e-01],\n",
       "       [1.20e+01, 4.80e+00, 3.40e+00, 1.60e+00, 2.00e-01],\n",
       "       [6.70e+01, 5.60e+00, 3.00e+00, 4.50e+00, 1.50e+00],\n",
       "       [1.47e+02, 6.30e+00, 2.50e+00, 5.00e+00, 1.90e+00],\n",
       "       [2.00e+00, 4.90e+00, 3.00e+00, 1.40e+00, 2.00e-01],\n",
       "       [4.70e+01, 5.10e+00, 3.80e+00, 1.60e+00, 2.00e-01],\n",
       "       [7.60e+01, 6.60e+00, 3.00e+00, 4.40e+00, 1.40e+00],\n",
       "       [1.50e+02, 5.90e+00, 3.00e+00, 5.10e+00, 1.80e+00],\n",
       "       [9.30e+01, 5.80e+00, 2.60e+00, 4.00e+00, 1.20e+00],\n",
       "       [1.29e+02, 6.40e+00, 2.80e+00, 5.60e+00, 2.10e+00],\n",
       "       [1.90e+01, 5.70e+00, 3.80e+00, 1.70e+00, 3.00e-01],\n",
       "       [1.37e+02, 6.30e+00, 3.40e+00, 5.60e+00, 2.40e+00],\n",
       "       [1.08e+02, 7.30e+00, 2.90e+00, 6.30e+00, 1.80e+00],\n",
       "       [1.30e+02, 7.20e+00, 3.00e+00, 5.80e+00, 1.60e+00],\n",
       "       [2.00e+01, 5.10e+00, 3.80e+00, 1.50e+00, 3.00e-01],\n",
       "       [8.00e+01, 5.70e+00, 2.60e+00, 3.50e+00, 1.00e+00]])"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.30)\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "7477be23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-7.60673251e-01, -1.08566939e+00,  1.13411653e+00,\n",
       "        -1.31465430e+00, -8.45613560e-01],\n",
       "       [-1.52266597e+00, -6.03659723e-01,  1.60854775e+00,\n",
       "        -1.37190801e+00, -1.37888337e+00],\n",
       "       [-3.68131547e-01, -1.08566939e+00, -2.42411761e+00,\n",
       "        -2.26833757e-01, -3.12343747e-01],\n",
       "       [-1.26866840e+00, -9.65166971e-01,  1.60854775e+00,\n",
       "        -1.37190801e+00, -1.11224847e+00],\n",
       "       [ 1.45603284e+00,  1.20387652e+00,  1.85254095e-01,\n",
       "         8.60986785e-01,  1.15414824e+00],\n",
       "       [-9.68489447e-01, -1.20617180e+00,  1.85254095e-01,\n",
       "        -1.37190801e+00, -1.51220082e+00],\n",
       "       [ 1.31748871e+00,  4.80862022e-01, -5.26392733e-01,\n",
       "         6.89225647e-01,  3.54243518e-01],\n",
       "       [ 7.63312187e-01,  1.56538377e+00,  1.37133214e+00,\n",
       "         1.26176277e+00,  1.68741805e+00],\n",
       "       [ 2.44101564e-02,  9.62871687e-01, -5.19615144e-02,\n",
       "         6.31971934e-01,  6.20878424e-01],\n",
       "       [-1.45339391e+00, -1.92918630e+00, -5.19615144e-02,\n",
       "        -1.60092286e+00, -1.51220082e+00],\n",
       "       [ 1.40985146e+00,  6.01364439e-01,  1.85254095e-01,\n",
       "         9.18240498e-01,  7.54195877e-01],\n",
       "       [-6.45219809e-01, -7.24162139e-01,  1.60854775e+00,\n",
       "        -1.37190801e+00, -1.37888337e+00],\n",
       "       [ 3.47679795e-01,  2.39857190e-01, -5.19615144e-02,\n",
       "         4.02957083e-01,  2.20926065e-01],\n",
       "       [ 1.54839559e+00,  1.08337410e+00,  4.22469704e-01,\n",
       "         1.14725535e+00,  1.42078314e+00],\n",
       "       [-1.03776151e+00, -6.03659723e-01,  8.96900923e-01,\n",
       "        -1.37190801e+00, -1.11224847e+00],\n",
       "       [-1.75357286e+00, -9.65166971e-01,  1.13411653e+00,\n",
       "        -1.42916172e+00, -1.37888337e+00],\n",
       "       [-5.99038432e-01,  1.32437894e+00,  4.22469704e-01,\n",
       "         4.60210796e-01,  2.20926065e-01],\n",
       "       [-8.53036005e-01, -9.65166971e-01,  8.96900923e-01,\n",
       "        -1.37190801e+00, -1.37888337e+00],\n",
       "       [-6.68310497e-01, -1.56767905e+00,  4.22469704e-01,\n",
       "        -1.42916172e+00, -1.37888337e+00],\n",
       "       [ 6.94040122e-01, -1.20617180e+00, -1.23803956e+00,\n",
       "         3.45703370e-01,  6.20878424e-01],\n",
       "       [-5.06675678e-01,  7.21866855e-01, -5.26392733e-01,\n",
       "         4.02957083e-01,  3.54243518e-01],\n",
       "       [-1.24557771e+00, -1.56767905e+00,  1.37133214e+00,\n",
       "        -1.65817658e+00, -1.37888337e+00],\n",
       "       [-1.14133974e-01,  2.39857190e-01, -5.26392733e-01,\n",
       "         5.94348066e-02,  8.76086120e-02],\n",
       "       [-1.08394289e+00, -1.44717664e+00,  4.22469704e-01,\n",
       "        -1.31465430e+00, -1.37888337e+00],\n",
       "       [ 2.09135664e-01,  1.19354774e-01,  8.96900923e-01,\n",
       "         3.45703370e-01,  4.87560971e-01],\n",
       "       [ 8.32584252e-01,  1.08337410e+00, -5.19615144e-02,\n",
       "         9.18240498e-01,  1.15414824e+00],\n",
       "       [-1.43030322e+00, -1.21650058e-01,  2.32019458e+00,\n",
       "        -1.54366915e+00, -1.37888337e+00],\n",
       "       [ 3.93861172e-01, -1.08566939e+00, -1.71247078e+00,\n",
       "        -3.41341183e-01, -3.12343747e-01],\n",
       "       [-8.76126694e-01, -1.80868388e+00, -5.19615144e-02,\n",
       "        -1.48641544e+00, -1.37888337e+00],\n",
       "       [-1.40721253e+00, -2.42152474e-01,  3.26905701e+00,\n",
       "        -1.37190801e+00, -1.11224847e+00],\n",
       "       [-1.60315351e-01, -3.62654890e-01, -1.23803956e+00,\n",
       "         2.18109382e-03, -1.79026294e-01],\n",
       "       [-1.70739148e+00, -1.44717664e+00,  4.22469704e-01,\n",
       "        -1.48641544e+00, -1.37888337e+00],\n",
       "       [ 1.43294215e+00,  1.19354774e-01, -5.19615144e-02,\n",
       "         5.17464508e-01,  7.54195877e-01],\n",
       "       [-6.22129121e-01, -1.08566939e+00,  6.59685313e-01,\n",
       "        -1.42916172e+00, -1.37888337e+00],\n",
       "       [-2.75768794e-01, -3.62654890e-01, -2.89177124e-01,\n",
       "        -1.69580044e-01,  8.76086120e-02],\n",
       "       [ 1.04040045e+00, -3.62654890e-01, -5.26392733e-01,\n",
       "         5.74718221e-01,  1.02083078e+00],\n",
       "       [ 5.78586679e-01, -1.21650058e-01, -7.63608342e-01,\n",
       "         6.89225647e-01,  8.87513330e-01],\n",
       "       [ 6.24768056e-01,  4.80862022e-01, -2.89177124e-01,\n",
       "         9.75494210e-01,  7.54195877e-01],\n",
       "       [ 1.50221422e+00,  1.20387652e+00,  1.85254095e-01,\n",
       "         6.89225647e-01,  1.42078314e+00],\n",
       "       [ 1.01730976e+00,  1.20387652e+00,  4.22469704e-01,\n",
       "         1.03274792e+00,  1.42078314e+00],\n",
       "       [ 3.01498418e-01, -4.83157307e-01, -1.23803956e+00,\n",
       "         5.94348066e-02,  8.76086120e-02],\n",
       "       [ 5.32405303e-01, -2.42152474e-01, -5.26392733e-01,\n",
       "         1.16688519e-01,  8.76086120e-02],\n",
       "       [ 1.29439802e+00,  6.01364439e-01, -5.26392733e-01,\n",
       "         9.75494210e-01,  1.28746569e+00],\n",
       "       [-7.37582563e-01, -9.65166971e-01,  1.84576336e+00,\n",
       "        -1.14289316e+00, -1.11224847e+00],\n",
       "       [-2.52678105e-01,  9.62871687e-01,  1.85254095e-01,\n",
       "         2.88449657e-01,  2.20926065e-01]])"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(x_train)\n",
    "x_train = scaler.transform(x_train)\n",
    "x_test = scaler.transform(x_test)\n",
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "b4e7c41e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-setosa', 'Iris-versicolor', 'Iris-setosa', 'Iris-versicolor',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-versicolor', 'Iris-setosa', 'Iris-virginica',\n",
       "       'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
       "       'Iris-virginica', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor'], dtype=object)"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors=7)\n",
    "classifier.fit(x_train, y_train)\n",
    "y_predict = classifier.predict(x_test)\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "e3572d59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score=accuracy_score(y_test, y_predict)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "4bd26df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1d86c545f70>"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = [\"Iris-setosa\", \"Iris-versicolor\", \"Iris-virginica\"]\n",
    "result = confusion_matrix(y_test, y_predict)\n",
    "cmd = ConfusionMatrixDisplay(result, display_labels=label)\n",
    "cmd.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}