ML__KNN.ipynb

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        "<a href=\"https://colab.research.google.com/github/naidu199/Machine_Learn/blob/main/ML__KNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_breast_cancer\n",
        "cancer=load_breast_cancer()\n",
        "# dataset=pd.DataFrame(np.c_[cancer['data'],cancer['target']],colums=np.append(cancer['feature_names'],['target']))"
      ],
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        "id": "Jmi94M7ohJE8"
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      "execution_count": 142,
      "outputs": []
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      "source": [
        "cancer.keys()"
      ],
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        "outputId": "2e86b749-81df-4d32-cbe3-40c4b9b928e2"
      },
      "execution_count": 143,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "dataset=pd.DataFrame(np.c_[cancer['data'],cancer['target']],columns=np.append(cancer['feature_names'],['target']))"
      ],
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        "id": "fHh6zn40jEWC"
      },
      "execution_count": 144,
      "outputs": []
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      "source": [
        "dataset.head()"
      ],
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        },
        "id": "SHL8S3ulkmf4",
        "outputId": "81d7b09d-0c87-4a29-df04-66d4161256e0"
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      "execution_count": 145,
      "outputs": [
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          "data": {
            "text/plain": [
              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760          0.3001              0.14710         0.2419   \n",
              "1           0.07864          0.0869              0.07017         0.1812   \n",
              "2           0.15990          0.1974              0.12790         0.2069   \n",
              "3           0.28390          0.2414              0.10520         0.2597   \n",
              "4           0.13280          0.1980              0.10430         0.1809   \n",
              "\n",
              "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "0                 0.07871  ...          17.33           184.60      2019.0   \n",
              "1                 0.05667  ...          23.41           158.80      1956.0   \n",
              "2                 0.05999  ...          25.53           152.50      1709.0   \n",
              "3                 0.09744  ...          26.50            98.87       567.7   \n",
              "4                 0.05883  ...          16.67           152.20      1575.0   \n",
              "\n",
              "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
              "0            0.1622             0.6656           0.7119                0.2654   \n",
              "1            0.1238             0.1866           0.2416                0.1860   \n",
              "2            0.1444             0.4245           0.4504                0.2430   \n",
              "3            0.2098             0.8663           0.6869                0.2575   \n",
              "4            0.1374             0.2050           0.4000                0.1625   \n",
              "\n",
              "   worst symmetry  worst fractal dimension  target  \n",
              "0          0.4601                  0.11890     0.0  \n",
              "1          0.2750                  0.08902     0.0  \n",
              "2          0.3613                  0.08758     0.0  \n",
              "3          0.6638                  0.17300     0.0  \n",
              "4          0.2364                  0.07678     0.0  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
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              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
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              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
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              "      <td>0.4000</td>\n",
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          "metadata": {},
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    {
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        "X=dataset.iloc[:,:-1].values\n",
        "Y=dataset.iloc[:,-1].values"
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      "metadata": {
        "id": "QyWzOHiplVbd"
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      "execution_count": 146,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "#splitting the dat for the training  and testing\n",
        "from sklearn.model_selection import train_test_split\n",
        "xtrain,xtest,ytrain,ytest=train_test_split(X,Y,test_size=0.2)"
      ],
      "metadata": {
        "id": "akq3S7Cgkzes"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#feature scaling\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc=StandardScaler()\n",
        "xtrain=sc.fit_transform(xtrain)\n",
        "xtest=sc.transform(xtest)"
      ],
      "metadata": {
        "id": "p6TzYz-llSQT"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training the model\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier=KNeighborsClassifier(n_neighbors=25, metric=\"minkowski\")\n",
        "classifier.fit(xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "WoDsQ8_Lmt8n",
        "outputId": "bb8b5459-ceb7-4e3a-c1e3-f87290562a74"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=25)"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=25)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=25)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred=classifier.predict(xtest)\n",
        "ypred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nEvNTGL0n267",
        "outputId": "492ef61b-6370-465b-f81e-0bd366beebcd"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1., 0., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 1., 1., 0., 1.,\n",
              "       1., 1., 0., 1., 0., 0., 1., 0., 1., 0., 1., 1., 0., 0., 1., 0., 0.,\n",
              "       1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1.,\n",
              "       0., 0., 0., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 1., 1., 0., 0.,\n",
              "       0., 1., 1., 1., 0., 0., 0., 1., 1., 1., 0., 1., 1., 0., 1., 1., 1.,\n",
              "       0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 1., 1., 1.,\n",
              "       1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm=confusion_matrix(ytest,ypred)"
      ],
      "metadata": {
        "id": "udJmyUtNoRLm"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "pyW_qnDJofME",
        "outputId": "0dc1cf62-45d9-45af-8f87-3d315e4433f1"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 152
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(cm,annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "0UrSFLsrokmu",
        "outputId": "77c44269-7201-4e2b-d143-df342837555c"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 153
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(ytest,ypred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L2GM3LhyorX9",
        "outputId": "77a44289-7cbf-475d-e423-922f746aed88"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.956140350877193"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shirts=pd.read_csv(\"/content/TShirt_size(KNN).csv\")"
      ],
      "metadata": {
        "id": "KY64esQSo-7q"
      },
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " len(shirts.index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "inlOXiKErod-",
        "outputId": "e8d98871-fd3c-46c9-b98e-50c361183ba0"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X1=shirts.iloc[:,:-1].values\n",
        "Y1=shirts.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "7RLLowxbrprn"
      },
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x1train,x1test,y1train,y1test = train_test_split(X,Y,test_size=0.2)"
      ],
      "metadata": {
        "id": "7_AlHL7-r05E"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#feature scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc=StandardScaler()\n",
        "x1train=sc.fit_transform(x1train)\n",
        "x1test=sc.transform(x1test)"
      ],
      "metadata": {
        "id": "HjvpfNMJsDFs"
      },
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training the model\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier1=KNeighborsClassifier(n_neighbors=5, metric=\"minkowski\")\n",
        "classifier1.fit(x1train,y1train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "u-WFapn-s8iq",
        "outputId": "c286547c-43f7-4a32-9993-eaf083003dc0"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" 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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y1pred=classifier1.predict(x1test)"
      ],
      "metadata": {
        "id": "vy-SSGKcs_9D"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm1=confusion_matrix(y1test,y1pred)\n",
        "sns.heatmap(cm1,annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "DCE0v4IltlGe",
        "outputId": "ccbfec93-f17e-40e4-9127-41d697d6e32e"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 162
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y1test,y1pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k6yymdU7tytL",
        "outputId": "98cfe7ec-f5d9-4acc-e81d-3f5f21768527"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.956140350877193"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZDszyIXLu_1S"
      },
      "execution_count": 163,
      "outputs": []
    }
  ]
}