Priyanshu_Agarwal_(KNN).ipynb

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        "<a href=\"https://colab.research.google.com/github/priyanshu-101/Assignment-1-2025/blob/main/Priyanshu_Agarwal_(KNN).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": 5,
      "metadata": {
        "id": "DCQhSHWcVv_I"
      },
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      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "3UgXk3HrWndJ"
      },
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      "source": [
        "can = pd.read_csv('breast-cancer.csv',index_col=0)"
      ]
    },
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      "source": [
        "can.head()"
      ],
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              "         diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "id                                                                         \n",
              "842302           M        17.99         10.38          122.80     1001.0   \n",
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              "84348301         M        11.42         20.38           77.58      386.1   \n",
              "84358402         M        20.29         14.34          135.10     1297.0   \n",
              "\n",
              "          smoothness_mean  compactness_mean  concavity_mean  \\\n",
              "id                                                            \n",
              "842302            0.11840           0.27760          0.3001   \n",
              "842517            0.08474           0.07864          0.0869   \n",
              "84300903          0.10960           0.15990          0.1974   \n",
              "84348301          0.14250           0.28390          0.2414   \n",
              "84358402          0.10030           0.13280          0.1980   \n",
              "\n",
              "          concave points_mean  symmetry_mean  ...  radius_worst  \\\n",
              "id                                            ...                 \n",
              "842302                0.14710         0.2419  ...         25.38   \n",
              "842517                0.07017         0.1812  ...         24.99   \n",
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              "84348301              0.10520         0.2597  ...         14.91   \n",
              "84358402              0.10430         0.1809  ...         22.54   \n",
              "\n",
              "          texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
              "id                                                                       \n",
              "842302            17.33           184.60      2019.0            0.1622   \n",
              "842517            23.41           158.80      1956.0            0.1238   \n",
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              "\n",
              "          compactness_worst  concavity_worst  concave points_worst  \\\n",
              "id                                                                   \n",
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              "\n",
              "          symmetry_worst  fractal_dimension_worst  \n",
              "id                                                 \n",
              "842302            0.4601                  0.11890  \n",
              "842517            0.2750                  0.08902  \n",
              "84300903          0.3613                  0.08758  \n",
              "84348301          0.6638                  0.17300  \n",
              "84358402          0.2364                  0.07678  \n",
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              "(569, 31)"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 569 entries, 842302 to 92751\n",
            "Data columns (total 31 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   diagnosis                569 non-null    object \n",
            " 1   radius_mean              569 non-null    float64\n",
            " 2   texture_mean             569 non-null    float64\n",
            " 3   perimeter_mean           569 non-null    float64\n",
            " 4   area_mean                569 non-null    float64\n",
            " 5   smoothness_mean          569 non-null    float64\n",
            " 6   compactness_mean         569 non-null    float64\n",
            " 7   concavity_mean           569 non-null    float64\n",
            " 8   concave points_mean      569 non-null    float64\n",
            " 9   symmetry_mean            569 non-null    float64\n",
            " 10  fractal_dimension_mean   569 non-null    float64\n",
            " 11  radius_se                569 non-null    float64\n",
            " 12  texture_se               569 non-null    float64\n",
            " 13  perimeter_se             569 non-null    float64\n",
            " 14  area_se                  569 non-null    float64\n",
            " 15  smoothness_se            569 non-null    float64\n",
            " 16  compactness_se           569 non-null    float64\n",
            " 17  concavity_se             569 non-null    float64\n",
            " 18  concave points_se        569 non-null    float64\n",
            " 19  symmetry_se              569 non-null    float64\n",
            " 20  fractal_dimension_se     569 non-null    float64\n",
            " 21  radius_worst             569 non-null    float64\n",
            " 22  texture_worst            569 non-null    float64\n",
            " 23  perimeter_worst          569 non-null    float64\n",
            " 24  area_worst               569 non-null    float64\n",
            " 25  smoothness_worst         569 non-null    float64\n",
            " 26  compactness_worst        569 non-null    float64\n",
            " 27  concavity_worst          569 non-null    float64\n",
            " 28  concave points_worst     569 non-null    float64\n",
            " 29  symmetry_worst           569 non-null    float64\n",
            " 30  fractal_dimension_worst  569 non-null    float64\n",
            "dtypes: float64(30), object(1)\n",
            "memory usage: 142.2+ KB\n"
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              "       radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
              "count   569.000000    569.000000      569.000000   569.000000   \n",
              "mean     14.127292     19.289649       91.969033   654.889104   \n",
              "std       3.524049      4.301036       24.298981   351.914129   \n",
              "min       6.981000      9.710000       43.790000   143.500000   \n",
              "25%      11.700000     16.170000       75.170000   420.300000   \n",
              "50%      13.370000     18.840000       86.240000   551.100000   \n",
              "75%      15.780000     21.800000      104.100000   782.700000   \n",
              "max      28.110000     39.280000      188.500000  2501.000000   \n",
              "\n",
              "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "count       569.000000        569.000000      569.000000           569.000000   \n",
              "mean          0.096360          0.104341        0.088799             0.048919   \n",
              "std           0.014064          0.052813        0.079720             0.038803   \n",
              "min           0.052630          0.019380        0.000000             0.000000   \n",
              "25%           0.086370          0.064920        0.029560             0.020310   \n",
              "50%           0.095870          0.092630        0.061540             0.033500   \n",
              "75%           0.105300          0.130400        0.130700             0.074000   \n",
              "max           0.163400          0.345400        0.426800             0.201200   \n",
              "\n",
              "       symmetry_mean  fractal_dimension_mean  ...  radius_worst  \\\n",
              "count     569.000000              569.000000  ...    569.000000   \n",
              "mean        0.181162                0.062798  ...     16.269190   \n",
              "std         0.027414                0.007060  ...      4.833242   \n",
              "min         0.106000                0.049960  ...      7.930000   \n",
              "25%         0.161900                0.057700  ...     13.010000   \n",
              "50%         0.179200                0.061540  ...     14.970000   \n",
              "75%         0.195700                0.066120  ...     18.790000   \n",
              "max         0.304000                0.097440  ...     36.040000   \n",
              "\n",
              "       texture_worst  perimeter_worst   area_worst  smoothness_worst  \\\n",
              "count     569.000000       569.000000   569.000000        569.000000   \n",
              "mean       25.677223       107.261213   880.583128          0.132369   \n",
              "std         6.146258        33.602542   569.356993          0.022832   \n",
              "min        12.020000        50.410000   185.200000          0.071170   \n",
              "25%        21.080000        84.110000   515.300000          0.116600   \n",
              "50%        25.410000        97.660000   686.500000          0.131300   \n",
              "75%        29.720000       125.400000  1084.000000          0.146000   \n",
              "max        49.540000       251.200000  4254.000000          0.222600   \n",
              "\n",
              "       compactness_worst  concavity_worst  concave points_worst  \\\n",
              "count         569.000000       569.000000            569.000000   \n",
              "mean            0.254265         0.272188              0.114606   \n",
              "std             0.157336         0.208624              0.065732   \n",
              "min             0.027290         0.000000              0.000000   \n",
              "25%             0.147200         0.114500              0.064930   \n",
              "50%             0.211900         0.226700              0.099930   \n",
              "75%             0.339100         0.382900              0.161400   \n",
              "max             1.058000         1.252000              0.291000   \n",
              "\n",
              "       symmetry_worst  fractal_dimension_worst  \n",
              "count      569.000000               569.000000  \n",
              "mean         0.290076                 0.083946  \n",
              "std          0.061867                 0.018061  \n",
              "min          0.156500                 0.055040  \n",
              "25%          0.250400                 0.071460  \n",
              "50%          0.282200                 0.080040  \n",
              "75%          0.317900                 0.092080  \n",
              "max          0.663800                 0.207500  \n",
              "\n",
              "[8 rows x 30 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>...</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>...</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>...</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>...</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>...</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>...</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "    </tr>\n",
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              "      <th>max</th>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>...</td>\n",
              "      <td>36.040000</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 30 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "can.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KA-fIZ6-VFeZ",
        "outputId": "d451b24d-4d1b-4bbd-b3a7-00f37248ed3a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "diagnosis                  0\n",
              "radius_mean                0\n",
              "texture_mean               0\n",
              "perimeter_mean             0\n",
              "area_mean                  0\n",
              "smoothness_mean            0\n",
              "compactness_mean           0\n",
              "concavity_mean             0\n",
              "concave points_mean        0\n",
              "symmetry_mean              0\n",
              "fractal_dimension_mean     0\n",
              "radius_se                  0\n",
              "texture_se                 0\n",
              "perimeter_se               0\n",
              "area_se                    0\n",
              "smoothness_se              0\n",
              "compactness_se             0\n",
              "concavity_se               0\n",
              "concave points_se          0\n",
              "symmetry_se                0\n",
              "fractal_dimension_se       0\n",
              "radius_worst               0\n",
              "texture_worst              0\n",
              "perimeter_worst            0\n",
              "area_worst                 0\n",
              "smoothness_worst           0\n",
              "compactness_worst          0\n",
              "concavity_worst            0\n",
              "concave points_worst       0\n",
              "symmetry_worst             0\n",
              "fractal_dimension_worst    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "can['diagnosis'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lE6ZyeWcVSJc",
        "outputId": "e38c782e-1d6e-4e07-87b9-65827c5dcb9c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M', 'B'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "can['diagnosis'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jEGQlKFyVa0h",
        "outputId": "6fb0a358-7a5f-4eaf-fde0-43573e737c52"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='diagnosis',data=can)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "wvUP1CbeVsgm",
        "outputId": "7747bf3d-ca5a-4bc2-c2d5-272a1b6c81c8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1212632040>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "Label_Encoder= LabelEncoder()\n",
        "can['diagnosis'] = Label_Encoder.fit_transform(can['diagnosis'])"
      ],
      "metadata": {
        "id": "9uWJwTbkWekg"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "can['diagnosis'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kEIaL303WtFz",
        "outputId": "3005051e-3cff-4e60-ed12-86b50d910922"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    357\n",
              "1    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X= can.drop('diagnosis',axis= 1)\n",
        "Y = can['diagnosis']"
      ],
      "metadata": {
        "id": "PAlFyeoKXDRD"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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 = 0.3, random_state = 0)"
      ],
      "metadata": {
        "id": "PTyoHzqPXPgY"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "SjXqZV8-XcCb"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "accuracy_rate=[]\n",
        "\n",
        "for i in range(1,40):\n",
        "    classifier= KNeighborsClassifier(n_neighbors =i)\n",
        "    score = cross_val_score(classifier,X,Y,cv=10)\n",
        "    accuracy_rate.append(score.mean())"
      ],
      "metadata": {
        "id": "nasV1sfIXlw-"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1,40),accuracy_rate, color= 'green',linestyle = 'dashed',marker='*', markersize=10)\n",
        "plt.title('Accuracy Rate vs K-Value')\n",
        "plt.xlabel('K-Value')\n",
        "plt.ylabel('Accuracy_Rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "Z6G4ZsBsX2wL",
        "outputId": "72a5eb19-cecb-4293-9372-9fe554a12df1"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Accuracy_Rate')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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yn3e5J/v/r6l7/N0kmAgXzO8CBonIYmAQsBEoA1DV9c7prK7ANSLSKpw3VtUXVLWPqvZp2bJlpOOOilDqQXhLtHoRNa1n8djcx2j/RHu/P2+42y/X8irb9xat/etvepRk//81xiPayWMj0MHrdXunrYKqblLVi1T1eGCs05ZfeRlgGTDQWb99oPdMZqHUgxjaZSguP/918a4XUdN6Frl5uZzY7sSKCnyR3r4gPDH0iWr3B4v/sncv4w+f/qHa8blwcd4R51k9EJPwop08FgDdRKSziKQDw4Fp3guISAuRijkbxgAvO+3tRaSe87wpMABYqaqbgT0icpIzyupqYGqUf46Y8tSDyEzNPKTdUw/ik6s+YeqIqX774/3FEix+f/HtLd7Los2LanR/R7DtTxsxjTv631Ht/kDxl5SVMH3VdIrLin32hxLf1BFTmTZiWrW2b0wsRTV5qGopcAvwCfAD8G9VXS4i40TEUyv0VGCliKwCWgHjnfYjga9E5FtgFvCYqn7n9P0OeAlYDawBPormzxEP+UX5pIj71EeaK61KPYv8onxSXam4xEW91Hoh17uIlfyifFLFHZ/n50iRlIDxzdswjzItq9H9HYdsP8D+CaffM+rLJa6A8S/avIjCksKQ4o/G9o2Jpahf81DV/6rqEap6uKqOd9ruV9VpzvN3VbWbs8z1nhFWqvqZqh6nqj2df1/wes+FqnqM8563aC28VXfSokkUlhTSIqsFE86YUKWexaRFkyg8WEjPVj2ZOnwqxx52LPsP7k+YUTmTFk1if8l+yrWcS4+6FID9JYHjy83LJUVS6N/B9/0R4W7fe/8E23+B+t+48A3aNGjDgZIDQeMHGNhxYETjm3zxZFy4KCwJXs/EmFixWXUTVOPMxjw29DFuP+l2XOLi1r63HlLPonFmYyYOnVjR33tZb1btXOX3XHmsNc5szE19buK5hc9x/QnX0yyrGRsKNkCA2zlH9RpFz1Y9aZDeICLb994/gzsPDrj/gvVfctQlQeuJ5K7LpXvz7rRqEHxcR7jb//HWH/nPiv/EtbyuMd6snkcC23dwHxkpGaSlpAVddv6G+fSf1J/HzniMO0++MwbRBffqklcZNXUUq29dzeHNDo93ODX2zvJ32LR3E7eddJvP/nGzxpGRksHoAaOjFsPe4r0V07wbEwuJPFTX+HH/zPtpPqF5SBPondT+JAZ3Hsxj8x6jqLQoBtEFtzZ/LYLQobF7wF1ZeRlvL3ubvcV7qyy7YscKXv/2dfYd3BfrMEP2waoPuHfGvewo3OGz//5B90c1cUxdMZU2j7fhx50/Rm0bxoTKkkcCW5u/lvaN2odcEOneAfeyZd8WXln8SpQjC01eQR5tGrapGHa7eMtiRrw3gucXPl9l2Xe/f5eRU0ZSUlYS6zBDNmbAGA6UHODp+U9X6du6byvFpYFHWdVUv/b9KNMyHv7y4eALGxNlljwSWF5BHtlNskNefnDnwfRr148Pf/wwilGF7opjr+DPp/254nWftn0Y0nkIj897vMrRUW5eLse2Opam9ZrGOsyQHdnySC468iL++vVfq0xUePN/b6bn8z2juv3WDVpzY+8beWPpG6zNXxvVbRkTjCWPBJaXn0d249CTh4gwbcQ0po2YFnzhGDi9y+mMOn7UIW1jB45l6/6th4waKikrYe76uTW+vyMW7h14LwXFBfx9wd8r2lSV2etm07dd36hv/+5T7sYlLh758pGob8uYQCx5JKj9B/ez88DOsJIHwGH1D8MlLvYU76mYmiMeyrWcuevnsvvA7kPaT+10Kv3b92fCnAkVp6gWb1nM/pL9Ebm/I9p6t+nNzSfeTNdmXSvaVu1cxbb922ISf7tG7bi217W89u1rVfatMbFkySNBlWs5jwx5hNO7nB72usu3Lafjkx15/4f3oxBZaDbv3cwpL5/C5OWTD2kXEcYOHEtmaibrCtYB7pvrAAZmB78/IhE8e/azXHL0JRWvPfd3xCr5/XHQH1n8m8U0rde0xvVArJ6IqS5LHgmqYUZDRg8YzYntTgx73R4tenBY/cMYP3s8+Qfy4/Lhzytwz2Ds68jp7G5ns/x3yzm82eEUFBXwyepPWHnLSlo3aB3TGGti38F9PPv1s2zbt41xueNomdWSbs26xWTbbRu2pUeLHgBMXTk1qvVKrJ6I8ceSR4Lasm8Lefl51apzneJK4Z4B97B4y2L+PPvPcfnw5+U7ycPHBX8RIcWVwr6D+/jHN/9gysopfL3x65jGV1Nz18/l1o9u5Z7P72HDng2MOGZEyKPiIqFcy7ns3cu4b4Z7dt1o1SuxeiLGH7tJMEHd8797eGLeExTdV4RLws/xB8sO0vWZrhQUF7CneA+ndTqNGdfMCL5ihDzy5SOM+XwM+8bs81tRb8DLA1iwaQEHyw7Sr10/5l8/P2bx1ZSqcuKLJ7J8+3KKSotitn8r1wPxiHS9kmD9Vk+k7vB3k6BNT5Kg8gry6NC4Q9iJw9+Xy5z1c5AHf/nLONof/rz8PJrXa14lcfiLb9HmRTGNr7rivX/HDhzLvA3zKCwpPKQ9kvVKgvVbPREDdtoqYYU7TNejcrEhj1h/+H974m95ZVjVmxX9xVdS/svNgYn85RTv/RtKvZQLe1zod/2stCzO7np2wP4hnYYE7Ld6IgYseSSscG8Q9KhpMaZIOa7VcZzX/byEja+6EiH+YPVS/nHuP3jqV09VKajl6Z80bFLA/tcvej1gf6L+35jYsuSRgA6WHWTz3s3VOvKA6hdjihRV5Z3l7/i9Czre8dVUIsQfqB5Iy/otaZ7VnPSUdJ/9rRu0DtjftmHbQ/o9/yZSvRgTf5Y8EpCq8s+L/slFR15U7ffw/nIBd3nVWH34dxTu4NJ3L2XKiikhxZeIxayCiXf8kaxXEqx/+ojpVfqNseSRgDJSM7j82Ms5rtVx1X4P7w9/24ZtaZTRKGYffs89Hp2adAopPl9fXoku3vF76n0svHEhZxx+BgtuWMCEMybQKKNRxPvbNmzLeUecxyOnP1LRbwyqWiceJ5xwgiaLNbvW6Jd5X2pJWUm132PYW8P08bmPa1l5mV475Vpt+khTnThnog57a1gEI/Xt3eXvKg+gizYtCik+VdXSslJ9bM5jMYkvEpI9/nC8vuR15QF0yeYl8Q7FxAGwUH18p8b9Sz1Wj2RKHn+a+SeVB0SLS4sj8n6vLn5VeQD9but3EXm/YB6f+7jyALqzcGdMtmeiKy8/T3kAfXr+0/EOxcSBv+Rhp60SUF5BHm0btq0y2qW6crJz6NGih98iRpGWl59Hg/QGNM1M3OnVTeg6Nu5IduPsijm8jAG7STAhrc1fW61huv50btqZH27+IWLvF8w9A+7h8mMvj+l0HSa6BnUaxEc/foSq2v+rAeyCeUKq7g2CwZRruftcZZS1adiGfu37RX07JnZyOuZwoPQAG/ZsiHcoJkFY8kgwZeVlrN+zPuBIper46MePaDGhBWt2r4no+/ryzFfPVEyzbmqHK467gt2jd1fUozfGkkcCmnH1DEb1GhV8wTB0atKJ3UW7o37eem/xXm77+DY+W/NZVLdjYiszNZNUl53lNr+w5BFl4RbTSXGlMDB7IN2aR7Y2RI8WPWiR1SLs5BFu/BV1PCJ4zcYkhte/fZ2hbwyNyalPk/gseURZuMV0vt/+PW999xYHSg5ENA4RISc7J+zkEW78nilJIn3azcTf/oP7+eynz/hp90/xDsUkAEseURZuMZ2pK6Zy+X8uj0r98ZyOOfyc/3NYFz3Djb+iCFQULvib+BrUaRAAs/JmxTkSkwjsJGaEVa734LlXI9R6D3kFebTIauG3gFJN/Krrr7h7790I/odaRiL+9JR0WjVoFcHITSI4ssWRFac+rz3+2niHY+LMjjwirHK9h3CL6eQVRGeYLrivezx6xqO0a9TO7zI1jf+h0x5i5S0rq1X90CQ2z6lPO/IwEGLyEJEjRORzEVnmvD5ORBKzWk+c1bTeQ15+9ep4hKqotChgvfCaxp+RmmHXO2qx8484n37t+lFUWhTvUEychfrn4YvAGKAEQFWXAsOjFVSyq269B1V1310exesFT81/in4v9WPb/m1+l/HEnyIph7SHUq/ivhn32TDdWuyaXtfw9sVvV/ndNnVPqMkjS1Ur/7laGulgahNPvQePUOs9fH/z99zZ/86oxZWTnQPAl+u+DLjczsKdlGs5goRcr6KotIjxs8czf8P8iMZsEk+oQ7dN7RVq8tghIocDCiAiFwOboxZVLeCp9+BxWP3DgtZ7EBE6NekU8JpETfVp24fM1MygQ3ZfXfIqAMccdkzI9SrWF6wH7B6P2u7aqdfS96W+8Q7DxFmoyeNm4B9ADxHZCNwO3BS1qGqBxpmN+cuQv1S87t68+yHFdnxZsmUJE+ZMiGo1uvSUdPq37x8weRSWFNIgvQGPDX2MJTctYcOeDaS6Unn0jEcDxl9xg6AN063Vjmp5FKt2rmLzXvv7sS4LNXmoqp4OtAR6qOqAMNatk6YMn8I1va4B3NcKvtr4Fbf0vYUpw/2XZp3x8wxG/290VO7x8JaTncO3W7/1e+rh6flPs3jLYkb1GlUxamrBpgWc3e3sgPFX3ONhRx61mufU5+x1s+MciYmnUBPAewCqul9V9zpt70YnpNrDcwQx/JjhnNX1LHYd2BVw+bz8PBqmN4x6HYxrel5D7shcn/eSHCg5wFNfPcWxrY6laT13HJ4vi2Cnurbt30aKpNCuYfROu5n4692mN/XT6jNrrQ3ZrcsC3iQoIj2Ao4HGInKRV1cjwIZbBNE0synjB4/nwh4XcmTLI4Mun1fgHqYb7XoJnZt2pnPTzj77Xl78Mtv2b2PMgDEVbV2adqFtw7bk5uVyUx//ZyvHDBzDHf3vIC0lLeIxm8SR6krllI6nkLvOikPVZcHuMO8OnAs0Ac7zat8L3BDKBkTkTOBpIAV4SVUfqdSfDbyM+5TYLuBKVd0gIr2A53AnqjJgvKpOdtZ5FRgEeM67jFTVJaHEE0utGrTi3oH3VrzefWB3xV/zvkR7mK63L9d9yeLNi7m1360VbSVlJUycO5H+7fszsOPAinbvebGCFQOyIZx1w+39bie/KN+KQ9VhAZOHqk4FpopIf1WdF+6bi0gK8DfgDGADsEBEpqnq916LPQa8rqqvichg4GHgKqAQuFpVfxSRtsA3IvKJqnquJv9BVRP61Fl+UT4FRQW0b9Seh3IfYuLcieSPzvf7l/mGPRs4pcMpMYlt+qrpPDHvCa7rfV3FDYH//fG/5BXk8dez/lrlC+GC7heQlZpFUWkR9dLq+XzPm6bfxMCOA7niuCuiHr+Jr7O6nRXvEEychXrNY7GI3CwifxeRlz2PENbrC6xW1Z9U9SDwNjCs0jJHATOc5zM9/aq6SlV/dJ5vArbhPjpJGv9e/m86Pd2JLfu2cOxhx1JYUsjCTQv9Lr/pzk08fPrDMYktJzuHkvISvtrwVUXb+d3PZ+Y1MznniHOqLH/ZMZcxadgkv4mjtLyUlxa9xPfbv/fZb2qfZduWMTvPLprXVaEmjzeA1sCvgFlAe9ynroJpB6z3er3BafP2LeC5nnIh0FBEmnsvICJ9gXTAuwzeeBFZKiJPikhGiD9HTHkumDfJbFJx0TnQvEDpKekBh8JG0ikdTkGQiovgntMPp3Y61e+8VKrK1n1bffZt2ruJMi2zqUnqkN9/9Htu/ejWgPVewq0HY5JHqMmjq6r+Edivqq8B5wCRKlJ9FzBIRBbjvo6xEfc1DgBEpA3u5DVKtWIM6xigB3Ai0AwY7euNReRGEVkoIgu3b98eoXBDV1BUQIqkkJWWRcv6LTmq5VF+k8eCjQv4/Ue/Z8u+LTGJrXFmY3q17sWMtTO44O0LGPL6EB6f+3jAda6ecjWnvOz7tJoN0617crJzWLp1acB6L+HWgzHJI9TkUeL8my8ixwCNgcNCWG8j4F30uL3TVkFVN6nqRap6PDDWacsHEJFGwIfAWFWd77XOZnUrBl7BfXqsClV9QVX7qGqfli1jf8YrvyifJplNKq4fDMoexJfrvqS0vOrMLl9v/Jq/fv3XmFZpy8nOYdnWZUxdOZWZa2f6PSXl0bt1b9bsXnQqwaYAAB+9SURBVMPGPRur9HmKQNkNgnXHoOxBqHvSCb8zD4RbD8Ykj1DrebwgIk2B+4BpQAPgjyGstwDoJiKdcSeN4cDl3guISAtgl3NUMQb3yCtEJB14H/fF9HcrrdNGVTeL+1v5AmBZiD9HTBUUF9A4s3HF65G9RtKvXT/Kysuq1IPOK8gjIyUjpnUwxg8ez5ItS5iVN4s0V1rQuuneN4cNP+bQeTGLy4ppmdWSjo07Ri1eE3+V6714VK734hFuPRiTPEJKHqr6kvM0F+gCICJBvyVUtVREbgE+wT1U92VVXS4i44CFqjoNOBV4WETUef+bndUvBXKA5iIy0mnzDMn9l4i0BARYQoJOlTKy10jO7Hpmxeu+7frSt53vOYHW5q+lY+OOUa+DUfnDn+Zyj/wq13Ky/vLLNOy+Ptw9W/ekYXpDcvNyqySP63tfz/W9r49i5CYRjB04lnkb5lFYUnhIu3e9F1/todaDMclDgp0mEZH+uC9y56rqNhE5DrgHGKiqHQKunED69OmjCxf6H+kUK6t3rebHnT9WGerY76V+NMpoxGdXRXc685k/z+Tct86t8uH3Fqhmx1n/Oov1BetZ9ruEPNgzMRDK75A/werBmMQjIt+oap/K7QH/zBWRibhPI/0a+FBE/gx8CnwFdItGoLXJ0q1Lq9QLnzBnAiPeG0FZedkh7cWlxXRu4vuu70iqabGnu/rfxfjB46u0X/LOJTwx74lIhmoSVKB6NR+M+AD9k/LBiA/Crmdjkkuw01bnAMerapFzzWM9cIyqro16ZLXAOW+ew+ldTueVYa9UtA3KHsSLi15k6dalHN/m+Ir2JTctidnFcs+H/5J3LjmkIlwoH+4hXYZUaVNVpq+aTqfGnaIQrUlEnno1LnGRkZJBcVnxIfVevPvTXGkUlxXjEldUZ4w2sRXsBHuRqhYBqOpu4EdLHKHLL8qnSUaTQ9oGdRoE+L7fI5bTPHh/uEMt9uTx9cavmfHzjIrX2/Zvo6i0yIbp1iGeejU9W/X0We/Fu//VC14F3FP926ir2iNY8ugiItM8D6BzpdfGj9LyUvYd3EeTzEOTR/tG7enStMshyeOrDV9x4eQLWbNrTeW3iZpgH/5A7v7sbu7+7O6K11bHo+5pnNmYiUMnsvDGhZxx+BksuGHBIfVqvPuHHzOcG3rfgEtcFaOvTPILdtqq8lQige8iMxX2FO8BqJI8wH3qatrKaRV3dX+37TumrJjCk796MmbxeT7ct590Oy5xMbjzYJ6a/1RINRpysnMYP3s8e4r30Cijkd0gWAdVruuS4krhzpPv5M6T7/TZP2bAGF5e/DJHtzw6ZjGa6Ap45KGqswI9PMuJyHvRDzW5eKZj8L7Pw+PBUx9k5S0rK05T5eXn4RJXTOtgTBk+hf/r/38VQ4M9H/5AxZ48crJzKNdy5q6fC7jH8vdu09uOPIxfnZt25vJjL+f5b55nZ+HOeIdjIiBSNxV0idD71BrNs5rz1q/fYlD2oCp9HRp3oHnWL9N35RXk0b5R+6Spg9G/fX9SXakV82IN6zGMb278xmeiNMbjjzl/5N1L3qVZvWbxDsVEQKh3mAcTuzk1kkSjjEZVbqTz9tKil9i+fztjBo6JaR2PSKifXp8+bfswZ/2ceIdikki35t3o1txG+NcWVoc8Sjbv3czMn2f6vZEqNy+XJ+c/iarSPKs5x7c+3udyierNi97k4ys+Btx3rf/h0z/EOSKTDMq1nNGfjeaZr56JdyimhiKVPKyUWCWfrvmUwa8P9juF+aDsQWwv3M4PO37g/cve5+mzno5xhDXTuWnniokUF2xacMj9Isb44xIXS7ct5c+5f67WHeomcYSUPETkPJGAky75nBK9LvPcL+HvOoDnRrxZa/3X90hkqsrYz8fy9Pyn2VO8x0ZamZCNHTiW7YXbefGbF+MdiqmBUI88LgN+FJEJItKjcqeqfhrZsJKfJ3n4K+7UpWkX2jVsxyNzHqHJI034Mu/LWIZXYyLChz9+yLjccQC0rJ9URR5NHA3oOICc7Bwmzp3Itn3brJhUkgopeajqlcDxuCv5vSoi85xCSw2jGl0SKyguoGF6wypTr3uICEMPH8q6gnUUFBcwb0PYJeLjLic7h10HdgGQtzsvztGYZHLvgHvZuHcjYz4fY8WkklTI1zxUdQ/wLu465G1wl4xdJCK3Rim2pJZflB906Oqk8ydVlG398McPYxBVZHnqewB89lN0ZwM2tcvQw4dyzyn3sHTbUsCKSSWjoFOyA4jI+cAooCvwOvCaMz17FvC9qnaKapQREOsp2ZdtW8bWfVurTCRYuZ6GS1yUaznpKemH1DxI1GI5/ooBJUv8Jr4q//54fm8q//4E67ffr9ip1pTsXn4NPKmqx6rqRFXdBqCqhcB1EYyz1jjmsGN8zkA7duDYQ6ZDL3fKsidLsZzK8XskS/wmvir//vgqFuXNikklrlCTxwPA154XIlJPRDoBqGrVP0MNH6/+mIWbqh7p1LSeRrwle/wmvoL9/qRICke3PJqMlAyf/fb7lThCTR7vAOVer8ucNuPHLf+9hSfn+57oMFAxnWQolpPs8Zv4CvT7M2X4FJb9bhnvXvqu/X4luFCTR6qqVhw3Os9tbuUAfNXyqNxf3XoaiSDZ4zfxFez3p3IxKe/1TGIINXlsdy6aAyAiw4Ad0Qkp+alq0NFWNamnkQiSPX4TX+EUk5oyfApprjSKSovs9yuBhDox4k3Av0TkWdxTkawHro5aVEmusKSQMi3zWcvDoyb1NBJBssdv4ivY70/l/ufOeY7JyydTL7VenCM3HiEN1a1YWKQBgKrui1pEURLLobob92yk/ZPtef6c5/lNn9/EZJvGGBMN/obqhjwlu4icAxwNZHqKGKnquIhFWIu0yGrB/Ovm23xPxkRQuZYzZcUUujbrynGtjot3OHVeqBMjPo97fqtbcZ+2ugSwb0Y/MlIz6Ne+H60btI53KMbUGvsO7uO6adfxwBcPxDsUQ+gXzE9W1auB3ar6INAfOCJ6YSW3n3f/zKRFkyrmfTLG1FyjjEbc2vdW3l/xPsu3LY93OHVeqMnDU6yhUETaAiW457cyPszfMJ/rP7iebfu3xTsUY2qV3/f7PVlpWTwy55F4h1LnhZo8PhCRJsBEYBGwFngzWkElO89Y9ECjrYwx4WuR1YKbTriJt757i592/xTvcOq0oMnDKQL1uarmq+p7uK919FDV+6MeXZIqKHbXHmicEXhWXWNM+O48+U66t+jOhj0b4h1KnRY0eahqOfA3r9fFqmqVWQLIL8onPSW9yvQKxpiaa9uwLct+u4yc7JygxaKi3R9MbS5mFeppq89F5NfiGaNrAiooKqBxRmNsdxkTHSLCgZIDTJw7sUbFpGraH0xtLmYVavL4De6JEItFZI+I7BWRPVGMK6mNO20cuaNy4x2GMbXaXZ/excOzHwaqX0yqpv3B1OZiVmHdYZ7MYl0MyhgTef6KkaW50igpL/HbXtP+UItRhVrsKpmKWdWoGJSI5Ph6RD7M2uHVJa8ybeW0eIdhTK3jrxiZry9+7/aa9odajCqUYle1pZhVqKet/uD1+CPwAe4CUcaHCXMm8MbSN+IdhjG1TijFyJ4Y+gTpKb4rRkSiP1AxqrpULC2k5KGq53k9zgCOAXZHN7TklV+Ub8N0jYmSYMXI7uh/B+9d+l7U+oN98Xviq5yAalsxq1CPPCrbABwZyUBqk4LiArtB0JgoCqeYVDT6g9myb0vFqSrBPeqythVLC/Wax19F5Bnn8SwwG/ed5qaSkrISCksKLXkYE0XhFJOKVP/+g/t54ZsXQorv4S/do8COaH4EF/a4EEHYf3B/rRp1FeqU7N7DlEqBt1R1ThTiSXp2d7kx0RduMama9rdp0IZjnz+WHYXBC6geKDnAhj0b6NasGz/c/APTVk7j/RXvc1u/2/g5/+fo7ZQYC2morojUB4pUtcx5nQJkqGphCOueCTwNpAAvqeojlfqzgZeBlsAu4EpV3SAivYDngEZAGTBeVSc763QG3gaaA98AV3nXWPclVkN1y7WcnYU7yUzNpGFGw6hvzxgTG2f96ywWblrI2tvWUj+9vt/lnvnqGW77+DZmjZxFTnYORaVFFJcWByxLnchqNFQX+Bzwrv9YDwg6SNlJMn8DzgKOAkaIyFGVFnsMeF1VjwPGAQ877YXA1ap6NHAm8JQzOSPAo8CTqtoV94X760L8OaLOJS5a1m9picOYWuaPOX9kR+EOnl/4fMDlNu/dzJDOQ8jJdt/NkJmambSJI5BQk0emd+lZ57nvsWiH6gusVtWfnCODt4FhlZY5CpjhPJ/p6VfVVar6o/N8E7ANaOlMkTIYeNdZ5zXgghB/jqhbuWMl9824zyZtM6aWObnDyQzuPJiJcydyoOSA3+UePv1hPrnyk0PaPln9Cee+eS4lZb7vJ0lGoSaP/SLS2/NCRE4A/O+9X7QD1nu93uC0efsWuMh5fiHQUESaey8gIn2BdGAN7lNV+apaGuA9PevdKCILRWTh9u3bQwi35pZvX8742eOtEJQxtdD9OfdTUl7Csm3LqvQdLDvIos3ucUQprpRD+vYU7+HDHz9k8ZbFMYkzFkJNHrcD74jIbBH5EpgM3BKhGO4CBonIYmAQsBH3NQ4ARKQN8AYwypnhN2Sq+oKq9lHVPi1btoxQuIF5huLZBXNjap9BnQax/o71nNjuxCp9b3z7Bie8cAJfbfiqSt/A7IEAzFo7K+oxxkqoNwkuAHoAvwVuAo5U1W9CWHUj0MHrdXunzfu9N6nqRap6PDDWacsHEJFGwIfAWFWd76yyE2giIqn+3jOerBCUMbVbVloWZeVl/Lz7l5FTpeWl/OXLv3BCmxPo265vlXVaN2hN9+bdyV1XeyZMDfU+j5uB+qq6TFWXAQ1E5HchrLoA6CYinUUkHRgOHDLpk4i0cApOAYzBPfIKZ/n3cV9M91zfQN3Dw2YCFztN1wBTQ/k5YqGgqABB7IK5MbXY1VOuZvDrg9mxfwcXvn0hkxZN4qfdP/HHnD/6LcWQk53D7LzZlJWX+exPNqGetrrBczQAoKq7gRuCreRcl7gF+AT4Afi3qi4XkXEicr6z2KnAShFZBbQCxjvtlwI5wEgRWeI8ejl9o4H/E5HVuK+BTArx54i6guICGmU0wiXVvXnfGJPoRhwzgrX5axnz+RimrJzCA7Me4LhWx3F+9/P9rnNGlzPo3aY3Ow/sjGGk0RPqfR7fAcc5f/V7huAudYbRJoVY3eehqhwoPeB3YjRjTPJTVU544QR+2PEDRaVFpEgKb/36LS45+pJ4hxZx/u7zCPUO84+BySLyD+f1b4CPIhVcbSIiljiMqaX81RNJcaVw6buXVtxAEKheh6e+R7IL9dzKaNz3YtzkPL7j0JsGjWPCnAn8fcHf4x2GMSYK/NUTCbVex4NfPEj2U9nUhiJ8oY62Kge+AtbivvFvMO5rGKaSt5a9xcerP453GMaYKKhpvY6OjTuyZd8Wvt/+fRSjjI2AyUNEjhCRP4nICuCvwDoAVT1NVZ+NRYDJJr8o34bpGlOLBasnEqheh2fKkty85B+yG+zIYwXuo4xzVXWAqv4Vrxv4TFVWCMqY2q+69T66NO1Cu4btmJWX/DcLBkseFwGbgZki8qKIDAF8D2I2lGs5e4r32JGHMbVcsHog/ogIOdk55OblJv11j4CjrVR1CjDFmZJ9GO5pSg4TkeeA91X10xjEmDQKSwrJSsuy5GFMLResHkggI3uNpG+7vpSWl5KWkhaDaKMjpPs8DllBpClwCXCZqg6JSlRREKv7PMA9BtzfXabGGJNMalrPo4Kq7nYmHEyaxBFrljiMMYFs2bfF5wSKycTm0Iig5duWc/l7l7Nix4p4h2KMSWC3fnQrl717WbzDqBFLHhH0c/7PvLXsLfYW7413KMaYBJbTMYe8gjzy8vPiHUq1WfKIIJuO3RgTikGdBgHJfb+HJY8IKigqAKiV9YqNMZFzzGHH0DSzaVLf72HJI4KsiqAxJhQucTEweyAzfp7BhW9fWPGHZ2UFRQVR7a8JSx4RlOpKpUOjDmSkZsQ7FGNMgvvL4L9w84k3M2XlFD5Y9YHPZaatnBbV/pqw5BFBoweMZt0d6+IdhjEmCRx92NFMXzUdwO+d6Z72aPXXRNg3CSarWN4kaIwxvlSuB5LqSqW0vJT0lPRDpnX38LRHuj9QvZHKInaToPHv7s/u5p7/3RPvMIwxCapyPZDS8lIAn1/83u2R7A9UbyQcljwiaFbeLJZsWRLvMIwxCSpYPRBBeGLoE9Xud4krYH+weiPhsOQRQflF+TZM1xgTkL96IBkpGfztnL9xR/87mHzxZDJSMqr0P3fOcwH7nz/neb/9odQbCYcljwgqKCqgSYbdIGiMCcxXPZC0lDQapjes6E9LSavSXz+9fsD+emn1/PaHUm8kHJY8IsiqCBpjQhGsHki0+yMhYD0PE7rS8lK6t+hOdpPseIdijElwweqBRLs/EmyorjHGGL9sqK4xxpiIseQRId9t/Y7+k/rz9cav4x2KMcZEnSWPCNmybwvzN8ynpKwk3qEYY0zUWfKIkIoZde0+D2NMHWDJI0KsEJQxpi6x5BEhBcXu+fIteRhj6gJLHkGEWkylRVYLTmp/EvXT6scoMmOMiR9LHkGEWkxlZK+RzLtuHiISo8iMMSZ+LHkEEc1iKsYYk6xsepJKKhdrSU9JB2DO+jnIg78cVVQupnLDtBvYe3Avb1/8duyCNcaYOLEjj0oqF2sJtZjKyp0r2bp/a2yCNMaYOLPkUUmwYi3+iqnYjLrGmLrEkocP/oq1BCqmkl+UT+MMu0HQGFM3WPLww7tYC7jLPwYqplJQXGBHHsaYOiPqyUNEzhSRlSKyWkTu8dGfLSKfi8hSEflCRNp79X0sIvkiMr3SOq+KyM8issR59Ip03N7FVFrVb0WjjEZ+i6moKoM7D6ZX64iHYYwxCSmqyUNEUoC/AWcBRwEjROSoSos9BryuqscB44CHvfomAlf5efs/qGov57EkwqFXFFNZeONChh4+lB4tejDhjAk0ymhUZVkR4f3L3mdkr5GRDsMYYxJStIfq9gVWq+pPACLyNjAM+N5rmaOA/3OezwSmeDpU9XMROTXKMfo0ZXhFGLx0/ksVQ3bvPPnOeIRjjDEJJdqnrdoB671eb3DavH0LXOQ8vxBoKCLNQ3jv8c6pridFJMPXAiJyo4gsFJGF27dvDzf2Cp7E4c93W7+j1WOt+HTNp9XehjHGJJNEuGB+FzBIRBYDg4CNQFmQdcYAPYATgWbAaF8LqeoLqtpHVfu0bNmyRkH+dvpvuePjO3z27Tqwi237t5HqsnsujTF1Q7STx0agg9fr9k5bBVXdpKoXqerxwFinzfeQpl/W2axuxcAruE+PRdX2wu28v+J9n302Hbsxpq6JdvJYAHQTkc4ikg4MB6Z5LyAiLUTEE8cYIOgkUiLSxvlXgAuAZRGN2oeBHQeSV5DH+oL1Vfo807HbfR7GmLoiqslDVUuBW4BPgB+Af6vqchEZJyLnO4udCqwUkVVAK2C8Z30RmQ28AwwRkQ0i8iun618i8h3wHdAC+HM0fw6AnOwcAGavm12lz448jDF1TdRP0qvqf4H/Vmq73+v5u8C7ftYd6Kd9cCRjDMVxrY6jUUYjZufN5vJjLz+kr2uzrgw/ZrjPYbzGGFMb2RXeEKW4UhjZcyTtG7Wv0nd2t7M5u9vZcYjKGGPiw5JHGJ4+62mf7apqRaCMMXVKIgzVTSolZSVVStJe8s4l9H0x6gO+jDEmYVjyCENZeRltHm/DuFnjDmnPL8onLSUtTlEZY0zsWfIIQ4orhaMPO7rKiCubjt0YU9dY8gjTwI4DWbR5EfsO7qtos+nYjTF1jSWPMA3sOJAyLWPe+nkVbVZF0BhT11jyCNPJHU7GJS5y83Ir2kb1GsWQzkPiGJUxxsSWDdUNU8OMhvzt7L9xYtsTK9omnDEhjhEZY0zsWfKohpv63FTxvKy8jKLSIrLSsuxeD2NMnWGnraqhuLSYT1Z/ws+7f2bVzlU0eLgBk5dPjndYxhgTM5Y8qmHvwb2c+a8zmbx8csWMunbB3BhTl1jyqIYWWS04ssWR5OblVsyoa/d5GGPqEkse1ZSTncOc9XPYWbgTsCMPY0zdYsmjmgZ2HMie4j0Vd5tb8jDG1CWWPKppYLa71Mj+kv3cO+BemtZrGueIjDEmdmyobjV1bNyRJb9ZQodGHbhu2nUUlxaTmZoZ77CMMSYm7MijBnq27snby99mysopfLDqg3iHY4wxMWPJowbW7FrDzf+9GYCXF78c52iMMSZ27LRVmE5//XQ+//nzKu1z1s9BHvzlDvMhnYfwv6v/F8vQjDEmZuzII0xjB44lKy2rSvvBsoMVz7PSsrgv575YhmWMMTFlySNMp3U+jekjpvtMIOBOHB9e/iGndjo1toEZY0wMWfKohtM6n8bkiydXGV2VmZrJ5IsnW+IwxtR6ljyqKb8on1RXKi5xUS+1Hi5xkepKrZiuxBhjajNLHtU0adEkCg8W0rNVT6YOn0rPVj0pPFhoo66MMXWCjbaqpsaZjZk4dCK3n3Q7LnExuPNgnpr/VMV0JcYYU5uJqsY7hpjo06ePLly4MN5hGGNMUhGRb1S1T+V2O21ljDEmbJY8jDHGhM2ShzHGmLBZ8jDGGBO2OnPBXES2A3l+ulsAO2IYTrgsvpqx+GrG4quZZI8vW1VbVm6sM8kjEBFZ6Gs0QaKw+GrG4qsZi69mamt8dtrKGGNM2Cx5GGOMCZslD7cX4h1AEBZfzVh8NWPx1UytjM+ueRhjjAmbHXkYY4wJmyUPY4wxYavTyUNEzhSRlSKyWkTuiXc8vojIWhH5TkSWiEjcZ3YUkZdFZJuILPNqayYin4nIj86/TRMsvgdEZKOzD5eIyNlxjK+DiMwUke9FZLmI3Oa0J8Q+DBBfQuxDEckUka9F5Fsnvged9s4i8pXzWZ4sIukJFt+rIvKz1/7rFY/4vOJMEZHFIjLdeR3+/lPVOvkAUoA1QBcgHfgWOCrecfmIcy3QIt5xeMWTA/QGlnm1TQDucZ7fAzyaYPE9ANwV733nxNIG6O08bwisAo5KlH0YIL6E2IeAAA2c52nAV8BJwL+B4U7788BvEyy+V4GL473/vOL8P+BNYLrzOuz9V5ePPPoCq1X1J1U9CLwNDItzTAlPVXOBXZWahwGvOc9fAy6IaVBe/MSXMFR1s6oucp7vBX4A2pEg+zBAfAlB3fY5L9OchwKDgXed9njuP3/xJQwRaQ+cA7zkvBaqsf/qcvJoB6z3er2BBPqQeFHgUxH5RkRujHcwfrRS1c3O8y1Aq3gG48ctIrLUOa0Vt9Nq3kSkE3A87r9OE24fVooPEmQfOqdclgDbgM9wn0HIV9VSZ5G4fpYrx6eqnv033tl/T4pIRrziA54C7gbKndfNqcb+q8vJI1kMUNXewFnAzSKSE++AAlH3cW9C/aUFPAccDvQCNgOPxzccEJEGwHvA7aq6x7svEfahj/gSZh+qapmq9gLa4z6D0CNesfhSOT4ROQYYgzvOE4FmwOh4xCYi5wLbVPWbmr5XXU4eG4EOXq/bO20JRVU3Ov9uA97H/WFJNFtFpA2A8++2OMdzCFXd6nygy4EXifM+FJE03F/M/1LV/zjNCbMPfcWXaPvQiSkfmAn0B5qIiKesdkJ8lr3iO9M5HaiqWgy8Qvz23ynA+SKyFvep+sHA01Rj/9Xl5LEA6OaMMkgHhgPT4hzTIUSkvog09DwHhgLLAq8VF9OAa5zn1wBT4xhLFZ4vZceFxHEfOueXJwE/qOoTXl0JsQ/9xZco+1BEWopIE+d5PeAM3NdlZgIXO4vFc//5im+F1x8Ggvt6Qlz2n6qOUdX2qtoJ93feDFW9gursv3hf9Y/nAzgb92iSNcDYeMfjI74uuEeBfQssT4QYgbdwn7YowX1u9Drc50w/B34E/gc0S7D43gC+A5bi/pJuE8f4BuA+JbUUWOI8zk6UfRggvoTYh8BxwGInjmXA/U57F+BrYDXwDpCRYPHNcPbfMuCfOCOy4vkATuWX0VZh7z+bnsQYY0zY6vJpK2OMMdVkycMYY0zYLHkYY4wJmyUPY4wxYbPkYYwxJmyWPIypARHZ5/X8bBFZJSLZzutBIjKv0vKpIrJVRNr6eb9TPTOdGpPILHkYEwEiMgR4BjhLVfOc5tlAe08ycZwOLFfVTbGO0ZhIsuRhTA058429CJyrqms87eqeyuPfuO/k9RgOvCUifUVknlNTYa6IdPfxvg+IyF1er5c5kxUiIlc6dSOWiMg/RCQlOj+dMb5Z8jCmZjKAKcAFqrrCR/9bOMnDmUn1bNzzRq0ABqrq8cD9wF9C3aCIHAlcBpyi7gn4yoAravJDGBOu1OCLGGMCKAHm4p4G5bbKnaq6UEQaOEcWRwJfqeouEekAvCYi3XBPB5IWxjaHACcAC9xTJVGPBJuM0tR+duRhTM2UA5finnr7Xk8tB+cxzlnGc/Qx3HkO8BAwU1WPAc4DMn28dymHfkY9ywjwmqr2ch7dVfWByP5YxgRmRx7G1JCqForIObgvkG91TiV5ewv3ZIKNcR+h4Dz3THs90s9brwXOBRCR3kBnp/1zYKqIPKmq20SkGdDQ60K9MVFnRx7GRICq7gLOBO4TkfMr9f0A7Mc9/fV+p3kC8LCILMb/H3HvAc1EZDlwC+4ZoFHV74H7cFeYXIq7ml4bP+9hTFTYrLrGGGPCZkcexhhjwmbJwxhjTNgseRhjjAmbJQ9jjDFhs+RhjDEmbJY8jDHGhM2ShzHGmLD9P1whJabbb+LmAAAAAElFTkSuQmCC\n"
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            "needs_background": "light"
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        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPOASjlr5bjHDPSAWl6AhaB",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}