Machine_Learning/Iris Classification using KNN/irisClassifier.ipynb

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  "cells": [
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      "metadata": {
        "id": "-V-YfY0M5e_r"
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      "source": [
        "# **IRIS DATASET CLASSIFICATION USING KNN**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e3qJFX0Ydekn"
      },
      "source": [
        "Importing necessary libraries"
      ]
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      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "yrFAli-R_XvI"
      },
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      "source": [
        "import pandas as pd\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "import numpy as np\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yVC9-r-bdeko"
      },
      "source": [
        "Load the sklearn iris dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
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        "id": "3SQicCk4_bjh",
        "outputId": "a62c044f-1de8-4906-8853-e89a814f1579"
      },
      "outputs": [
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            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                  5.1               3.5                1.4               0.2   \n",
              "1                  4.9               3.0                1.4               0.2   \n",
              "2                  4.7               3.2                1.3               0.2   \n",
              "3                  4.6               3.1                1.5               0.2   \n",
              "4                  5.0               3.6                1.4               0.2   \n",
              "..                 ...               ...                ...               ...   \n",
              "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 flower_name  \n",
              "0         0      setosa  \n",
              "1         0      setosa  \n",
              "2         0      setosa  \n",
              "3         0      setosa  \n",
              "4         0      setosa  \n",
              "..      ...         ...  \n",
              "145       2   virginica  \n",
              "146       2   virginica  \n",
              "147       2   virginica  \n",
              "148       2   virginica  \n",
              "149       2   virginica  \n",
              "\n",
              "[150 rows x 6 columns]"
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              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
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              "      <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",
              "      <td>setosa</td>\n",
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              "      <td>1.3</td>\n",
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              "      <td>0</td>\n",
              "      <td>setosa</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>3.1</td>\n",
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              "      <td>...</td>\n",
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              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
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              "      <td>virginica</td>\n",
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              "      <td>2</td>\n",
              "      <td>virginica</td>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.435866284936698,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594667,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flower_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"setosa\",\n          \"versicolor\",\n          \"virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "iris = load_iris()\n",
        "df = pd.DataFrame(iris.data,columns=iris.feature_names)\n",
        "df['target'] = iris.target\n",
        "df['flower_name'] =df.target.apply(lambda x: iris.target_names[x])\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Overall Scatter Plot"
      ],
      "metadata": {
        "id": "a2KFm3Elep7D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('Petal Length')\n",
        "plt.ylabel('Petal Width')\n",
        "plt.scatter(df['petal length (cm)'], df['petal width (cm)'],color=\"blue\",marker='.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "911pl6wEeg2M",
        "outputId": "03af00d3-b29e-49c2-a71d-7a80a6b6281d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f45f2ddecb0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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fd0v17NlT3333nRm1AAAABMyncFNWVuZ6/fGPf9Tvf/97FRYW6vvvv3ebV1ZWZna9AAAAHvkUblq1aqXWrVurdevW+tWvfqVPPvlEw4YNU3x8vGt6zXf88dFHH2nMmDHq0KGDHA6HVq1a5fH7hYWFcjgcF71KSkr8Wi8AALAvn6652bhxoykrr6ioUL9+/TRp0iSNGzfO53EHDhxwuw0sPj7ejPIANGJHjjgfz9+tW/CffZKXJ739tvSb30jTpvm/7kBqKyqSNm+WBg+WUlP9W7aZ+wQIKsNPBw8eNKqrqy+aXl1dbRw8eNDfxblIMlauXOnxOxs3bjQkGSdPnqz3ekpLSw1JRmlpab2XAcDeliwxjIgIw5Cc70uWBG/Zbds6l1vzatvWv3UHUltWlvu6s7J8X7aZ+wTwhT+/334/xC8yMlLFxcUXHS35/vvvFR8fX+/n3DgcDq1cuVKZmZl1fqewsFAZGRlKTk5WZWWlevfurccff1zp6el1jqmsrFRlZaXrc1lZmRITE3mIH4BamdkpOi9Pmj794unz5jmP4HhbdyC1FRVJAwZcPH37ducRHE/LluieDeuZ2hXc+NfD+i5UXl6u6Ohofxfnl/bt22vhwoV6++239fbbbysxMVHXXXeddu3aVeeY3NxcxcXFuV6JiYmm1gggvJnZKfrtt2uf/s47vq07kNo2b659+pYt3pdN92yEG5+fczPtXyeGHQ6H/t//+39q0aKFa15VVZU+/fRTXXnllUEv8Hzdu3dX9+7dXZ/T0tL01Vdf6bnnntOrr75a65iZM2e6apfOHbkBgNrUdIq+8ChFMDpF/+Y30gUPd5ck1Vxy6G3dgdQ2eHDt02sOfHtbtln7BDCDz0dudu/erd27d8swDO3bt8/1effu3fr888/Vr18/5efnm1hq7QYMGKAvPfzzISoqSrGxsW4vAKiLmZ2ip02T2rZ1n9a27bmLir2tO5DaUlOlrCz3aVlZ5y4q9rRsumcj3Ph9zc3EiRP1/PPPBz0k+HLNTW1+9atfKSYmRu/UHNf1gsaZAHxhZqfovDznqahx4+q+W8rTugOprajIeSoqPb3uu6XqWjbds2GlBukKHgzl5eWuoy4pKSnKy8tTRkaG2rRpo6SkJM2cOVNHjx7VK6+8IkmaP3++unTpol69eumnn37SkiVL9MILL+jDDz/UsGHDfFon4QYAgPAT9K7g/jyDxtcjKJK0Y8cOZWRkuD7XXBuTlZWl/Px8FRcX69ChQ675Z86c0fTp03X06FG1aNFCffv21d/+9je3ZQAAgMbNpyM3EydOdP23YRhauXKl4uLi1L9/f0nSzp079eOPP2rcuHFatmyZedUGAUduAAAIP0E/cnN+YJkxY4ZuueUWLVy4UJH/urqsqqpK999/P2EBAABYzu9rbtq2bauPP/7Y7ZZsydkSIS0tTd9//31QCww2jtwAABB+TH2I388//6zPP//8oumff/65qi98yhMAAEAD8/khfjUmTpyoyZMn66uvvtKAfz3L+9NPP9WcOXPcrs0BAACwgt/h5tlnn1VCQoLmzZun4uJiSc62CP/xH/+h6bU1TQEAk4Ryl2pv3bc98bZdnpYdyvvELI1xm+FFoB06w627Nl3BAXsI5S7V3rpve+JtuzwtO5T3iVka4zY3VqZ2BQ93XFAMhD8zO3cHylv3bU+8bZenZbdvH7r7xCyh/PcAwRf0W8GvuuoqbdiwQa1bt1ZKSkqtXcFreOrQDQDB4KlLtdU/ap66b3sLN962y9Oy+/UL3X1illD+ewBr+RRuxo4dq6ioKNd/ewo3AGA2Mzt3B8pb921PvG2Xp2W3bx+6+8Qsofz3ABYz/SRZiOGaG8AeliwxjMhI57UWkZGhda1FoNfceNoub9fchOo+MUtj3ObGypRrbpKTkzV06FANHTpU1113nRITE81NXSbhmhvAPkK5S7W37tueeNsuT8sO5X1ilsa4zY2RKV3BH3/8cRUWFurTTz/VmTNn1KVLF2VkZGjo0KHKyMhQQkJCUIo3G+EGAIDwY0q4qVFZWaktW7Zo06ZNrrBz9uxZXXHFFRo6dKhefPHFgIo3G+EGAIDwY2q4udDJkyc1b948vfDCCyovL1dVVVUgizMd4QYAgPAT9FvBz3fmzBlt27ZNhYWFriM3HTt21E033aQhQ4bUu2gAAIBg8DnczJ492xVmkpOTde211+ruu+/W66+/rg4dOphZIwAAgM98Pi0VERGhpKQkPfzww7r55pt16aWXml2bKTgtBQBA+PHn9zvC14WuXbtWv/3tb5Wfn68OHTqoT58+euCBB/TWW2/pxIkTARcNAAAQDPW6oPjUqVPavHmzNm3apI0bN2rv3r3q2rWrMjIy9Oc//9mMOoOGIzdAw7Oqa3NBgbRmjXTjjdLo0RfP99Rd21tXb2/zPW2zt/1Bl2vgYn79fgfytMCff/7Z2Lp1q/Hwww8bsbGxRkRERCCLaxA8oRhoWFZ1bU5Lc3+Sb1qa+3xPT/r19oRhb/M9bbO3/UGXa6B2pnUFr66u1o4dO7Rx40YVFhZqy5YtqqioUKdOnZSRkaGMjAxlZWUFFs1MxpEboOFY1bW5oEAaM+bi6e+95zyC46m7tuS5q7e3rt+etlnyvD/ocg3UzZRbwW+44QZt3bpVp06dUocOHZSRkaHnnntOGRkZuvzyywMuGoD9WNW1ec2a2qevW+cMN566a19Y7/nzUlO9d/32tM2G4Xl/0OUaCA6fw02rVq00d+5cZWRkqFu3bmbWBMAmrOrafOON0oIFF08fOdL5Xp/O3TXzvI31ts2e5tHlGggOn++W+q//+i/dfffdBBsAPuvUSVq82PkDLTnfFy0y/yjE6NFSWpr7tLS0cxcVp6ZKF55Bz8pyTvc0z9tYyfM2e9sfVu0vwG4Cbr8QbrjmBmh4VnVtLihwnooaObLuu6Xq6q7trau3t/mettnb/qDLNXCxBu0tFW4INwAAhB9THuIHAAAQDgg3AADAVny6W6qsrMznBXKqBwAAWMmncNOqVSs5HA6P3zEMQw6HQ1VVVUEpDAAAoD58CjcbN240uw4AAICg8CncDBkyxOw6AAAAgsLnJxRf6PTp0zp06JDOnDnjNr1v374BFwUAvjCzu3agnbnN7OxN13DAM7/vljpx4oRGjx6tmJgY9erVSykpKW4vAGgIS5c6m0wOHep8X7rUv/mBLNvs8VYtG7ALvx/id/vtt+vgwYOaP3++rrvuOq1cuVLHjh3TU089pXnz5mnUqFFm1RoUPMQPCH/eumcH0l070M7cZnb2pms4GjNTH+L397//XXl5eerfv78iIiKUnJysO+64Q3/605+Um5tb76IBwFeeumf7Mj+QZZs93qplA3bid7ipqKhQfHy8JKl169Y6ceKEJKlPnz7atWtXcKsDgFrUdM8+X23dteuaH8iyzR5v1bIBO/E73HTv3l0HDhyQJPXr10+LFi3S0aNHtXDhQrVv3z7oBQLAhczsrh1oZ24zO3vTNRzwjd/X3Lz22mv6+eeflZ2drZ07d2rkyJH64Ycf1KxZM+Xn52v8+PFm1RoUXHMD2IeZ3bUD7cxtZmdvuoajMWrQruCnT5/W559/rqSkJF122WWBLKpBEG4AAAg/pl5QPHv2bJ0+fdr1uUWLFrrqqqvUsmVLzZ492/9qAQAAgsjvIzeRkZEqLi52XVRc4/vvv1d8fHzI95biyA0AAOHH1CM3NQ0yL7R37161adPG38UBAAAElc/tF1q3bi2HwyGHw6ErrrjCLeBUVVWpvLxc9957rylFAgAA+MrncDN//nwZhqFJkybpiSeeUFxcnGtes2bN1LlzZw0aNMiUIgEAAHzlc7jJysqSJHXp0kXp6elq0qTePTcBAABM4/c1N0OGDNHBgwf16KOP6tZbb9Xx48clSWvXrtVnn30W9AIBnHPkiLRxo/O9sfC0zY1xfwDwzu9ws2nTJvXp00effvqp3nnnHZWXl0tyXlA8a9asoBcIwKkxdoP2tM2NcX8A8I3ft4IPGjRIN998s6ZNm6aYmBjt3btXl19+ubZv365x48bpSIj/E4pbwRGOGmM3aE/bLDW+/QE0dqbeCr5v3z79+te/vmh6fHy8vvvuO38XB8AHjbEbtKdtboz7A4Dv/A43rVq1UnFx8UXTd+/erY4dOwalKADuGmM3aE/b3Bj3BwDf+R1ufvvb32rGjBkqKSmRw+FQdXW1tmzZooceekgTJkwwo0ag0WuM3aA9bXNj3B8AfOf3NTdnzpxRTk6O8vPzVVVVpSZNmqiqqkq33Xab8vPzFVnzf5sQxTU3CGeNsRu0p21ujPsDaKwapCv44cOHtW/fPpWXlyslJUXdunWrV7ENjXADAED48ef32+cn8VVXV2vu3Ll69913debMGQ0bNkyzZs1S8+bNAy4YAAAgWHy+5ubpp5/WI488oksuuUQdO3bU888/r5ycHDNrAwAA8JvP4eaVV17RX/7yF33wwQdatWqV3nvvPb3++uuqvvB+TAAAAAv5HG4OHTqkG2+80fV5+PDhcjgc+vbbb00pDAAAoD58Djc///yzoqOj3aY1bdpUZ8+eDXpRAAAA9eXzBcWGYSg7O1tRUVGuaT/99JPuvfdetWzZ0jXtnXfe8XnlH330kebOnaudO3equLhYK1euVGZmpscxhYWFmjZtmj777DMlJibq0UcfVXZ2ts/rBAAA9ubzkZusrCzFx8crLi7O9brjjjvUoUMHt2n+qKioUL9+/fTiiy/69P2vv/5ao0aNUkZGhvbs2aOpU6fqzjvv1AcffODXegFcrKhIystzvjfkWDM7e3tbNl3FAZsyQoQkY+XKlR6/8/vf/97o1auX27Tx48cbI0aM8Hk9paWlhiSjtLS0PmUCtpSVZRjSuVdWVsOMXbLEMCIinOMiIpyfg8Xbss1cN4Dg8+f32+/2C1batm2bhg8f7jZtxIgR2rZtW51jKisrVVZW5vYCcE5RkfTyy+7TXn7Zt6MwgYw9ckS6++5zDTCrq6V77gnOURRvyzZz3QCsF1bhpqSkRO3atXOb1q5dO5WVlen//u//ah2Tm5vrdtosMTGxIUoFwsbmzbVP37LF3LFmdvb2tmy6igP2Flbhpj5mzpyp0tJS1+vw4cNWlwSElMGDa5+enm7uWDM7e3tbNl3FAXsLq3CTkJCgY8eOuU07duyYYmNj62wDERUVpdjYWLcXgHNSU6WsLPdpWVnO6WaONbOzt7dl01UcsLd6N84MNofD4fVW8BkzZmjNmjXat2+fa9ptt92mH374QevWrfNpPTTOBGpXVOQ8nZSe7ls4CdZYMzt7e1s2XcWB8NEgXcGDoby8XF/+6yR3SkqK8vLylJGRoTZt2igpKUkzZ87U0aNH9corr0hy3greu3dv5eTkaNKkSfr73/+u3/3ud3r//fc1YsQIn9ZJuAEAIPz48/tt6WmpHTt2KCUlRSkpKZKkadOmKSUlRY899pgkqbi4WIcOHXJ9v0uXLnr//fe1fv169evXT/PmzdOSJUt8DjYAAMD+Qua0VEPhyA0AAOEnbI7cAAAABBvhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBjpyRNq40fkOAEC4I9w0ckuXSsnJ0tChzvelS62uCACAwBBuGrEjR6S775aqq52fq6ule+7hCA4AILwRbhqxL744F2xqVFVJX35pTT0AAAQD4aYR69ZNirjgb0BkpNS1qzX1AAAQDISbRqxTJ2nxYmegkZzvixY5pwMAEK6aWF0ArDV5sjRihPNUVNeuBBsAQPgj3ECdOhFqAAD2wWkpAABgK4QbAABgK4QbAABgK4QbAABgK4QbAABgKyERbl588UV17txZ0dHRGjhwoLZv317nd/Pz8+VwONxe0dHRDVgtAAAIZZaHmzfeeEPTpk3TrFmztGvXLvXr108jRozQ8ePH6xwTGxur4uJi1+vgwYMNWHHjQsdwAEC4sTzc5OXl6a677tLEiRPVs2dPLVy4UC1atNBLL71U5xiHw6GEhATXq127dg1YceNBx3AAQDiyNNycOXNGO3fu1PDhw13TIiIiNHz4cG3btq3OceXl5UpOTlZiYqLGjh2rzz77rM7vVlZWqqyszO0F7+gYDgAIV5aGm++++05VVVUXHXlp166dSkpKah3TvXt3vfTSS1q9erVee+01VVdXKy0tTUfq+NXNzc1VXFyc65WYmBj07bAjOoYDAMKV5ael/DVo0CBNmDBBV155pYYMGaJ33nlHbdu21aJFi2r9/syZM1VaWup6HT58uIErDk90DAcAhCtLw81ll12myMhIHTt2zG36sWPHlJCQ4NMymjZtqpSUFH1ZxyGFqKgoxcbGur3gHR3DAQDhytJw06xZM1199dXasGGDa1p1dbU2bNigQYMG+bSMqqoq7du3T+3btzerzEZr8mTpm2+cd0t9843zMwAAoc7yruDTpk1TVlaW+vfvrwEDBmj+/PmqqKjQxIkTJUkTJkxQx44dlZubK0maPXu2rrnmGnXt2lU//vij5s6dq4MHD+rOO++0cjNsi47hAIBwY3m4GT9+vE6cOKHHHntMJSUluvLKK7Vu3TrXRcaHDh1SxHkXf5w8eVJ33XWXSkpK1Lp1a1199dXaunWrevbsadUmAACAEOIwDMOwuoiGVFZWpri4OJWWlnL9DQAAYcKf3++wu1sKAADAE8INAACwFcINAACwFcINAACwFcJNmAikO3dBgXT//c53f5cdaFdwuooDABoa4SYMBNKdOz1dGjNGWrDA+Z6e7vuyA+0KTldxAIAVuBU8xB054gwG5zexjIx0PjHY28P1CgqcgeZC770njR7tedlS/dcbaN0AAFyIW8FtJJDu3GvW1D593Trvyw60KzhdxQEAViHchLhAunPfeGPt00eO9L7sQLuC01UcAGAVwk2IC6Q79+jRUlqa+7S0NOd0b8sOtCs4XcUBAFbhmpswceSI85RO167+B4SCAuepqJEjzwUbX5cdyHqDMR4AAMm/32/CDQAACHlcUAwAABotwg0AALAVwg0AALAVwg0AALAVwg0AALAVwg0AALAVwk2YKCqS8vKc7xcKpOu3N3T1BgCEG8JNGMjOlgYMkKZPd75nZ5+bF0jXb2/o6g0ACEc8xC/EFRU5A82Ftm+Xjh2rf9dvb08Lpqs3ACCU8BA/G9m8ufbpW7YE1vXbG7p6AwDCFeEmxA0eXPv09PTAun57Q1dvAEC4ItyEuNRUKSvLfVpWlnN6IF2/vaGrNwAgXHHNTZgoKnKeikpPdwab8wXS9dsbunoDAEIBXcE9CNdwAwBAY8YFxQAAoNEi3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3ARRIB20PXX9lqT8fGnsWOf7hSZPlhISnO+1mTpV6tLF+V6bvDzn83Py8i6eF2hXcLqKAwAaGs+5CZKlS6W773b2Y4qIcD7dt66wcaHsbOnll899zspyDzFdu0pffXXu8y9+ca7HU9Om0s8/n5vXpIl09uy5z9HRUmXluc9RUdJPP537HB8vnThx7nPbttLx44FvUzDGAwBQg+fcNLAjR879iEvO93vu8e1oRVGRe7CRnJ9rjuDk57sHG8n5OT/fGRTODzaS83NNgJg61T3YSM7PNUdw8vLcg43k/JyXF9g2SYGPBwCgvgg3QRBIB21PXb8laeXK2uevXi29/37t82qmr15d91hJevvt2ue/807gXcHpKg4AsArhJggC6aDtqeu3JP3617XPHztWGjWq9nk108eOrXusJP3mN7XPHzcu8K7gdBUHAFiFa26CZOlS52mXqqpzHbTtcM1NfbcpGOMBAKhB40wPzGycGUgHbU9dvyVn2Fm92nnUJTvbfd7kyc5TUaNGOQPFhaZOPTd2/vyL5+flOU9FjRsnTZsWvG0KxngAACTCjUd0BQcAIPxwtxQAAGi0CDcAAMBWCDcAAMBWCDcAAMBWCDcAAMBWCDcAAMBWCDcNxMzu2N46inubDwCAnRBuGsDSpVJysjR0qPO9tgft1Vd2tjRggDR9uvP9wgf8eZsPAIDd8BA/kx054gw05zeRjIyUvvkm8Cf2FhU5A8uFtm93PuXY23wAAMIFD/ELIWZ2x/bWUdzbfAAA7IhwYzIzu2N76yjubT4AAHZEuDFZp07S4sXOQCOd644djCaSqanODuLny8o6d8rJ23wAAOyIa24aiJndsb11FPc2HwCAUEdXcA/oCg4AQPjhgmIAANBoEW4AAICtEG4AAICtEG4AAICtEG4AAICthES4efHFF9W5c2dFR0dr4MCB2r59u8fvv/nmm+rRo4eio6PVp08frVmzpoEqBQAAoc7ycPPGG29o2rRpmjVrlnbt2qV+/fppxIgROn78eK3f37p1q2699VZNnjxZu3fvVmZmpjIzM7V///4GrhwAAIQiy59zM3DgQKWmpurPf/6zJKm6ulqJiYl64IEH9PDDD1/0/fHjx6uiokIFBQWuaddcc42uvPJKLVy40Ov6eM4NAADhJ2yec3PmzBnt3LlTw4cPd02LiIjQ8OHDtW3btlrHbNu2ze37kjRixIg6v19ZWamysjK3FwAAsC9Lw813332nqqoqtWvXzm16u3btVFJSUuuYkpISv76fm5uruLg41ysxMTE4xQMAgJBk+TU3Zps5c6ZKS0tdr8OHD1tdEgAAMFETK1d+2WWXKTIyUseOHXObfuzYMSUkJNQ6JiEhwa/vR0VFKSoqyvW55hIjTk8BABA+an63fblU2NJw06xZM1199dXasGGDMjMzJTkvKN6wYYOmTJlS65hBgwZpw4YNmjp1qmva+vXrNWjQIJ/WeerUKUni9BQAAGHo1KlTiouL8/gdS8ONJE2bNk1ZWVnq37+/BgwYoPnz56uiokITJ06UJE2YMEEdO3ZUbm6uJOnBBx/UkCFDNG/ePI0aNUorVqzQjh07tHjxYp/W16FDBx0+fFgxMTFyOBxB3ZaysjIlJibq8OHD3InlA/aX/9hn/mF/+Y995h/2l38C2V+GYejUqVPq0KGD1+9aHm7Gjx+vEydO6LHHHlNJSYmuvPJKrVu3znXR8KFDhxQRce7SoLS0NC1fvlyPPvqoHnnkEXXr1k2rVq1S7969fVpfRESEOnXqZMq21IiNjeUvuR/YX/5jn/mH/eU/9pl/2F/+qe/+8nbEpoblz7mxE56h4x/2l//YZ/5hf/mPfeYf9pd/Gmp/2f5uKQAA0LgQboIoKipKs2bNcrs7C3Vjf/mPfeYf9pf/2Gf+YX/5p6H2F6elAACArXDkBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhJgg++ugjjRkzRh06dJDD4dCqVausLimk5ebmKjU1VTExMYqPj1dmZqYOHDhgdVkha8GCBerbt6/roVeDBg3S2rVrrS4rbMyZM0cOh8OtZQvcPf7443I4HG6vHj16WF1WyDt69KjuuOMOXXrppWrevLn69OmjHTt2WF1WSOrcufNFf8ccDodycnJMWR/hJggqKirUr18/vfjii1aXEhY2bdqknJwcffLJJ1q/fr3Onj2r66+/XhUVFVaXFpI6deqkOXPmaOfOndqxY4eGDh2qsWPH6rPPPrO6tJBXVFSkRYsWqW/fvlaXEvJ69eql4uJi1+vjjz+2uqSQdvLkSaWnp6tp06Zau3at/vnPf2revHlq3bq11aWFpKKiIre/X+vXr5ck3Xzzzaasz/L2C3Zwww036IYbbrC6jLCxbt06t8/5+fmKj4/Xzp07de2111pUVegaM2aM2+enn35aCxYs0CeffKJevXpZVFXoKy8v1+23366//vWveuqpp6wuJ+Q1adJECQkJVpcRNv74xz8qMTFRy5Ytc03r0qWLhRWFtrZt27p9njNnjn7xi19oyJAhpqyPIzewXGlpqSSpTZs2FlcS+qqqqrRixQpVVFRo0KBBVpcT0nJycjRq1CgNHz7c6lLCwhdffKEOHTro8ssv1+23365Dhw5ZXVJIe/fdd9W/f3/dfPPNio+PV0pKiv76179aXVZYOHPmjF577TVNmjQp6A2sa3DkBpaqrq7W1KlTlZ6e7nPz08Zo3759GjRokH766SddcsklWrlypXr27Gl1WSFrxYoV2rVrl4qKiqwuJSwMHDhQ+fn56t69u4qLi/XEE09o8ODB2r9/v2JiYqwuLyT97//+rxYsWKBp06bpkUceUVFRkX73u9+pWbNmysrKsrq8kLZq1Sr9+OOPys7ONm0dhBtYKicnR/v37+f8vhfdu3fXnj17VFpaqrfeektZWVnatGkTAacWhw8f1oMPPqj169crOjra6nLCwvmn1fv27auBAwcqOTlZ//3f/63JkydbWFnoqq6uVv/+/fXMM89IklJSUrR//34tXLiQcOPF0qVLdcMNN6hDhw6mrYPTUrDMlClTVFBQoI0bN6pTp05WlxPSmjVrpq5du+rqq69Wbm6u+vXrp+eff97qskLSzp07dfz4cV111VVq0qSJmjRpok2bNuk///M/1aRJE1VVVVldYshr1aqVrrjiCn355ZdWlxKy2rdvf9E/Ln75y19yOs+LgwcP6m9/+5vuvPNOU9fDkRs0OMMw9MADD2jlypUqLCzkIrx6qK6uVmVlpdVlhKRhw4Zp3759btMmTpyoHj16aMaMGYqMjLSosvBRXl6ur776Sv/2b/9mdSkhKz09/aJHWPzP//yPkpOTLaooPCxbtkzx8fEaNWqUqesh3ARBeXm5279wvv76a+3Zs0dt2rRRUlKShZWFppycHC1fvlyrV69WTEyMSkpKJElxcXFq3ry5xdWFnpkzZ+qGG25QUlKSTp06peXLl6uwsFAffPCB1aWFpJiYmIuu32rZsqUuvfRSruuqw0MPPaQxY8YoOTlZ3377rWbNmqXIyEjdeuutVpcWsv793/9daWlpeuaZZ3TLLbdo+/btWrx4sRYvXmx1aSGrurpay5YtU1ZWlpo0MTl+GAjYxo0bDUkXvbKysqwuLSTVtq8kGcuWLbO6tJA0adIkIzk52WjWrJnRtm1bY9iwYcaHH35odVlhZciQIcaDDz5odRkha/z48Ub79u2NZs2aGR07djTGjx9vfPnll1aXFfLee+89o3fv3kZUVJTRo0cPY/HixVaXFNI++OADQ5Jx4MAB09flMAzDMDc+AQAANBwuKAYAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEAALZCuAEQ0goLC+VwOPTjjz9aXUq9dO7cWfPnz7e6DKBRIdwA8El2drYcDoccDoerS/ns2bP1888/+zQ+Pz9frVq1MqW2UAgQZm4fAP/QOBOAz0aOHKlly5apsrJSa9asUU5Ojpo2baqZM2daXRoAuHDkBoDPoqKilJCQoOTkZN13330aPny43n33XUlSZWWlHnroIXXs2FEtW7bUwIEDVVhYKMl5amnixIkqLS11Hf15/PHHJUmvvvqq+vfvr5iYGCUkJOi2227T8ePHg1r36tWrddVVVyk6OlqXX365nnjiCbcjTg6HQ0uWLNGvf/1rtWjRQt26dXNtV413331X3bp1U3R0tDIyMvTyyy+7Tpd52j5JOn36tCZNmqSYmBglJSXRORowGeEGQL01b95cZ86ckSRNmTJF27Zt04oVK/SPf/xDN998s0aOHKkvvvhCaWlpmj9/vmJjY1VcXKzi4mI99NBDkqSzZ8/qySef1N69e7Vq1Sp98803ys7ODlqNmzdv1oQJE/Tggw/qn//8pxYtWqT8/Hw9/fTTbt974okndMstt+gf//iHbrzxRt1+++364YcfJElff/21brrpJmVmZmrv3r2655579Ic//ME11tP2SdK8efPUv39/7d69W/fff7/uu+8+HThwIGjbCOACpvcdB2ALWVlZxtixYw3DMIzq6mpj/fr1RlRUlPHQQw8ZBw8eNCIjI42jR4+6jRk2bJgxc+ZMwzAMY9myZUZcXJzX9RQVFRmSjFOnThmGYRgbN240JBknT56sc0xycrLx3HPP1Tpv2LBhxjPPPOM27dVXXzXat2/v+izJePTRR12fy8vLDUnG2rVrDcMwjBkzZhi9e/d2W8Yf/vAHt7rq2r7k5GTjjjvucH2urq424uPjjQULFtS5PQACwzU3AHxWUFCgSy65RGfPnlV1dbVuu+02Pf744yosLFRVVZWuuOIKt+9XVlbq0ksv9bjMnTt36vHHH9fevXt18uRJVVdXS5IOHTqknj17Blzz3r17tWXLFrcjNVVVVfrpp590+vRptWjRQpLUt29f1/yWLVsqNjbWdXrswIEDSk1NdVvugAEDfK7h/GU7HA4lJCQE/dQbgHMINwB8lpGRoQULFqhZs2bq0KGDmjRx/i+kvLxckZGR2rlzpyIjI93GXHLJJXUur6KiQiNGjNCIESP0+uuvq23btjp06JBGjBjhOt0VqPLycj3xxBMaN27cRfOio6Nd/920aVO3eQ6HwxW0AmXmsgFcjHADwGctW7ZU165dL5qekpKiqqoqHT9+XIMHD651bLNmzVRVVeU27fPPP9f333+vOXPmKDExUZK0Y8eOoNZ81VVX6cCBA7XW7avu3btrzZo1btOKiorcPte2fQCsQbgBELArrrhCt99+uyZMmKB58+YpJSVFJ06c0IYNG9S3b1+NGjVKnTt3Vnl5uTZs2KB+/fqpRYsWSkpKUrNmzfTCCy/o3nvv1f79+/Xkk0/Wq4ajR49qz549btOSk5P12GOPafTo0UpKStJNN92kiIgI7d27V/v379dTTz3l07Lvuece5eXlacaMGZo8ebL27Nmj/Px8Sc6jMJJq3b6aU14AGhZ3SwEIimXLlmnChAmaPn26unfvrszMTBUVFSkpKUmS846ie++9V+PHj1fbtm31pz/9SW3btlV+fr7efPNN9ezZU3PmzNGzzz5br/U/++yzSklJcXu9//77GjFihAoKCvThhx8qNTVV11xzjZ577jklJyf7vOwuXbrorbfe0jvvvKO+fftqwYIFrruloqKi6tw+ANZwGIZhWF0EAISbp59+WgsXLtThw4etLgXABTgtBQA++Mtf/qLU1FRdeuml2rJli+bOnaspU6ZYXRaAWhBuAMAHX3zxhZ566in98MMPSkpK0vTp02k7AYQoTksBAABb4YJiAABgK4QbAABgK4QbAABgK4QbAABgK4QbAABgK4QbAABgK4QbAABgK4QbAABgK/8f7XtTP76ZUtwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UDbOySZOdekp"
      },
      "source": [
        "The dataset has 150 rows out of which first 50 belong to category \"setosa\" next 50 rows belong to category \"Versicolour\" and last 50 belong to category \"Virginica\"\n",
        "\n",
        "\n",
        "We will split the dataset into 3 classes below"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "fcTOn-LoAN6g"
      },
      "outputs": [],
      "source": [
        "df0 = df[:50]\n",
        "df1 = df[50:100]\n",
        "df2 = df[100:150]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87VrYi5xdekp"
      },
      "source": [
        "Plotting the petal dimensions for all classes individually"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "7SARqn6tGmwL",
        "outputId": "70c98609-b77b-4114-e955-06ca7a1ecb4d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f45f3a3b280>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.xlabel('Petal Length')\n",
        "plt.ylabel('Petal Width')\n",
        "plt.scatter(df0['petal length (cm)'], df0['petal width (cm)'],color=\"green\",marker='+')\n",
        "plt.scatter(df1['petal length (cm)'], df1['petal width (cm)'],color=\"blue\",marker='.')\n",
        "plt.scatter(df2['petal length (cm)'], df2['petal width (cm)'],color=\"red\",marker='*')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KTrhg7Aqdekq"
      },
      "source": [
        "Considering Petal length and Petal Width for this classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "E79nIQD8DXN6"
      },
      "outputs": [],
      "source": [
        "X = iris.data[:, 2:4]\n",
        "y = iris.target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "x1-NVnFZDZaf"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "yANvXGnsDdJZ",
        "outputId": "ebb2048f-98dd-40f4-f7f4-8a45924ba3b0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "k = 5\n",
        "knn = KNeighborsClassifier(n_neighbors=k)\n",
        "knn.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1LX34YYddekq"
      },
      "source": [
        "Plotting Resultant Classes Areas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "RGDCGwc6GR83",
        "outputId": "80be6f6c-f338-4d70-de8e-e99d17a1594b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01),\n",
        "                     np.arange(y_min, y_max, 0.01))\n",
        "\n",
        "Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z = Z.reshape(xx.shape)\n",
        "\n",
        "#Plot the decision boundary\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.contourf(xx, yy, Z, alpha=0.3, cmap='coolwarm')\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap='coolwarm', marker='.', s=100)\n",
        "plt.title('k-NN Decision Boundaries for Iris Dataset')\n",
        "plt.xlabel('Sepal Length')\n",
        "plt.ylabel('Sepal Width')\n",
        "plt.xlim(xx.min(), xx.max())\n",
        "plt.ylim(yy.min(), yy.max())\n",
        "plt.grid()\n",
        "plt.colorbar(label='Class')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffOV322Gdekr"
      },
      "source": [
        "Checking Model Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g--3KvV_DjO5",
        "outputId": "a079f4f6-7031-43a4-c96f-91eca2913b89"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "execution_count": 170,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9mAGHSHsdekr"
      },
      "source": [
        "Ploting Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpDuMliED5p4",
        "outputId": "0ac02e05-d1ed-4903-cc55-ea4439dc01bf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[10,  0,  0],\n",
              "       [ 0,  9,  0],\n",
              "       [ 0,  0, 11]])"
            ]
          },
          "execution_count": 171,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "y_pred = knn.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e-7efAwOdekr"
      },
      "source": [
        "Print Datailed Report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RjFhus_rDotw",
        "outputId": "077bde22-dcf1-4ab0-a1d7-76c5b3a0ad7c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        10\n",
            "           1       1.00      1.00      1.00         9\n",
            "           2       1.00      1.00      1.00        11\n",
            "\n",
            "    accuracy                           1.00        30\n",
            "   macro avg       1.00      1.00      1.00        30\n",
            "weighted avg       1.00      1.00      1.00        30\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}