week5-k-nearest-neigbour.ipynb

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   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "data = pd.read_csv(\"/kaggle/input/knearest-neighbour-knn-classification/data.csv\")"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</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>...</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",
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       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
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       "      <td>842302</td>\n",
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       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</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>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\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>...</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>84300903</td>\n",
       "      <td>M</td>\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>...</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>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\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>...</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",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\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>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 33 columns</p>\n",
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      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
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   ],
   "source": [
    "data.head()"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       569 non-null    int64  \n",
      " 1   diagnosis                569 non-null    object \n",
      " 2   radius_mean              569 non-null    float64\n",
      " 3   texture_mean             569 non-null    float64\n",
      " 4   perimeter_mean           569 non-null    float64\n",
      " 5   area_mean                569 non-null    float64\n",
      " 6   smoothness_mean          569 non-null    float64\n",
      " 7   compactness_mean         569 non-null    float64\n",
      " 8   concavity_mean           569 non-null    float64\n",
      " 9   concave points_mean      569 non-null    float64\n",
      " 10  symmetry_mean            569 non-null    float64\n",
      " 11  fractal_dimension_mean   569 non-null    float64\n",
      " 12  radius_se                569 non-null    float64\n",
      " 13  texture_se               569 non-null    float64\n",
      " 14  perimeter_se             569 non-null    float64\n",
      " 15  area_se                  569 non-null    float64\n",
      " 16  smoothness_se            569 non-null    float64\n",
      " 17  compactness_se           569 non-null    float64\n",
      " 18  concavity_se             569 non-null    float64\n",
      " 19  concave points_se        569 non-null    float64\n",
      " 20  symmetry_se              569 non-null    float64\n",
      " 21  fractal_dimension_se     569 non-null    float64\n",
      " 22  radius_worst             569 non-null    float64\n",
      " 23  texture_worst            569 non-null    float64\n",
      " 24  perimeter_worst          569 non-null    float64\n",
      " 25  area_worst               569 non-null    float64\n",
      " 26  smoothness_worst         569 non-null    float64\n",
      " 27  compactness_worst        569 non-null    float64\n",
      " 28  concavity_worst          569 non-null    float64\n",
      " 29  concave points_worst     569 non-null    float64\n",
      " 30  symmetry_worst           569 non-null    float64\n",
      " 31  fractal_dimension_worst  569 non-null    float64\n",
      " 32  Unnamed: 32              0 non-null      float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
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   "outputs": [],
   "source": [
    "data.drop([\"Unnamed: 32\",\"id\"],axis=1, inplace=True)"
   ]
  },
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   "execution_count": 6,
   "id": "3a592a12",
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   "outputs": [],
   "source": [
    "M = data[data.diagnosis==\"M\"]\n",
    "B = data[data.diagnosis==\"B\"]"
   ]
  },
  {
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   "execution_count": 7,
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 212 entries, 0 to 567\n",
      "Data columns (total 31 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   diagnosis                212 non-null    object \n",
      " 1   radius_mean              212 non-null    float64\n",
      " 2   texture_mean             212 non-null    float64\n",
      " 3   perimeter_mean           212 non-null    float64\n",
      " 4   area_mean                212 non-null    float64\n",
      " 5   smoothness_mean          212 non-null    float64\n",
      " 6   compactness_mean         212 non-null    float64\n",
      " 7   concavity_mean           212 non-null    float64\n",
      " 8   concave points_mean      212 non-null    float64\n",
      " 9   symmetry_mean            212 non-null    float64\n",
      " 10  fractal_dimension_mean   212 non-null    float64\n",
      " 11  radius_se                212 non-null    float64\n",
      " 12  texture_se               212 non-null    float64\n",
      " 13  perimeter_se             212 non-null    float64\n",
      " 14  area_se                  212 non-null    float64\n",
      " 15  smoothness_se            212 non-null    float64\n",
      " 16  compactness_se           212 non-null    float64\n",
      " 17  concavity_se             212 non-null    float64\n",
      " 18  concave points_se        212 non-null    float64\n",
      " 19  symmetry_se              212 non-null    float64\n",
      " 20  fractal_dimension_se     212 non-null    float64\n",
      " 21  radius_worst             212 non-null    float64\n",
      " 22  texture_worst            212 non-null    float64\n",
      " 23  perimeter_worst          212 non-null    float64\n",
      " 24  area_worst               212 non-null    float64\n",
      " 25  smoothness_worst         212 non-null    float64\n",
      " 26  compactness_worst        212 non-null    float64\n",
      " 27  concavity_worst          212 non-null    float64\n",
      " 28  concave points_worst     212 non-null    float64\n",
      " 29  symmetry_worst           212 non-null    float64\n",
      " 30  fractal_dimension_worst  212 non-null    float64\n",
      "dtypes: float64(30), object(1)\n",
      "memory usage: 53.0+ KB\n"
     ]
    }
   ],
   "source": [
    "M.info()"
   ]
  },
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     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 357 entries, 19 to 568\n",
      "Data columns (total 31 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   diagnosis                357 non-null    object \n",
      " 1   radius_mean              357 non-null    float64\n",
      " 2   texture_mean             357 non-null    float64\n",
      " 3   perimeter_mean           357 non-null    float64\n",
      " 4   area_mean                357 non-null    float64\n",
      " 5   smoothness_mean          357 non-null    float64\n",
      " 6   compactness_mean         357 non-null    float64\n",
      " 7   concavity_mean           357 non-null    float64\n",
      " 8   concave points_mean      357 non-null    float64\n",
      " 9   symmetry_mean            357 non-null    float64\n",
      " 10  fractal_dimension_mean   357 non-null    float64\n",
      " 11  radius_se                357 non-null    float64\n",
      " 12  texture_se               357 non-null    float64\n",
      " 13  perimeter_se             357 non-null    float64\n",
      " 14  area_se                  357 non-null    float64\n",
      " 15  smoothness_se            357 non-null    float64\n",
      " 16  compactness_se           357 non-null    float64\n",
      " 17  concavity_se             357 non-null    float64\n",
      " 18  concave points_se        357 non-null    float64\n",
      " 19  symmetry_se              357 non-null    float64\n",
      " 20  fractal_dimension_se     357 non-null    float64\n",
      " 21  radius_worst             357 non-null    float64\n",
      " 22  texture_worst            357 non-null    float64\n",
      " 23  perimeter_worst          357 non-null    float64\n",
      " 24  area_worst               357 non-null    float64\n",
      " 25  smoothness_worst         357 non-null    float64\n",
      " 26  compactness_worst        357 non-null    float64\n",
      " 27  concavity_worst          357 non-null    float64\n",
      " 28  concave points_worst     357 non-null    float64\n",
      " 29  symmetry_worst           357 non-null    float64\n",
      " 30  fractal_dimension_worst  357 non-null    float64\n",
      "dtypes: float64(30), object(1)\n",
      "memory usage: 89.2+ KB\n"
     ]
    }
   ],
   "source": [
    "B.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "05a9d158",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:07.263921Z",
     "iopub.status.busy": "2023-03-31T19:20:07.263470Z",
     "iopub.status.idle": "2023-03-31T19:20:07.578154Z",
     "shell.execute_reply": "2023-03-31T19:20:07.576801Z"
    },
    "papermill": {
     "duration": 0.326504,
     "end_time": "2023-03-31T19:20:07.581043",
     "exception": false,
     "start_time": "2023-03-31T19:20:07.254539",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BygBZ67VUXl4eZs2ahalTp+LYsWN45513sGzZMvTq1QtPPvkknnrqKSxbtgz33XcfLl26hN27d6Nv37549NFHW3wtpVKJxMREvPDCC/Dy8oKvry8WLFgAJyenBr1GREREbYmByMIiukZA7alGQWlBo+OIBAhQe6oR0VWeXpr6nnrqKVRUVOBPf/oTFAoFkpOTMWXKFADSgOfXX38dqampKCgogLe3NwYMGGBSGKq1fPly/PWvf8WoUaPg6emJOXPmIC8vD+3bt5frLREREbWYIHIZYaOUlpZCpVJBq9XC09PT4Nj169eRm5uL0NBQk77Ya2eZATAIRQKkXhNzzjKztPLycnTp0gXLli3Ds88+a+nmNKq1ny8REVlOc9/fdXGWmRWI6ROD9Lh0dPHsYlCu9lTbXRj64YcfsHnzZpw5cwbHjh3Dk08+CQCIjo62cMuIiMiR8ZaZlYjpE4Po3tHIOpeFwquFCFAGIKJrBBROitu/2MYsXboUp06dgouLC/r164esrCz4+PhYullEROTAGIisiMJJ0eZT69vafffdh6NHj1q6GURERAYYiIiIiKhldDogKwsoLAQCAoCICEBh23c0GIiIiIjIeBoNMHMmkF9nUWG1Gli5Eoix3TGvHFQtI07Ys0/8XImIbtJogNhYwzAEAAUFUrnGvBuSmxMDkQxqt5m4du2ahVtC5lD7udbfToSIyKHodFLPUGP/SKwtS0mR6tkg3jKTgUKhQMeOHfWbj3bo0IErL9sBURRx7do1FBcXo2PHjg32dCMicihZWQ17huoSRSAvT6oXGdlmzZILA5FM/P39AYA7stuhjh076j9fIiKHVWjknprG1rMyDEQyEQQBAQEB8PX1xY0bNyzdHJKJs7Mze4aIiABpNpmc9awMA5HMFAoFv0CJiMj+RERIs8kKChofRyQI0vEI8+y9aW4cVE1ERES3p1BIU+sBKfzUVfs8Lc1m1yNiICIiIiLjxMQA6elAF8O9N6FWS+U2vA4Rb5kRERGR8WJigOhorlRNREREDk6hsMmp9c3hLTMiIiJyeAxERERE5PAYiIiIiMjhMRARERGRw2MgIiIiIofHQEREREQOj4GIiIiIHB4DERERETk8BiIiIiJyeAxERERE5PAYiIiIiMjhMRARERGRw2MgIiIiIofHQEREREQOj4GIiIiIHB4DERERETk8BiIiIiJyeAxERERE5PAYiIiIiMjhMRARERGRw2MgIiIiIofHQEREREQOr52lG0BEREQtpNMBWVlAYSEQEABERAAKhaVbZdMYiIiIiGyJRgPMnAnk598qU6uBlSuBmBjLtcvGWfSW2eLFi/Hggw9CqVTC19cXY8eOxalTpwzqiKKIhQsXIjAwEG5uboiMjMTJkycN6lRWViI5ORk+Pj5wd3fHmDFjkF/3PxQAJSUlSEhIgEqlgkqlQkJCAv744w9zv0UiIiL5aDRAbKxhGAKAggKpXKOxTLvsgEUD0d69ezFt2jQcPHgQu3btQnV1NaKiolBeXq6vs2TJEixfvhyrVq3CkSNH4O/vj+HDh+Pq1av6OikpKfj000+xZcsW7Nu3D2VlZRg1ahR0Op2+Tnx8PLKzs5GRkYGMjAxkZ2cjISGhTd8vERGRyXQ6qWdIFBseqy1LSZHqUcuJVqS4uFgEIO7du1cURVGsqakR/f39xX/84x/6OtevXxdVKpW4evVqURRF8Y8//hCdnZ3FLVu26OsUFBSITk5OYkZGhiiKovjTTz+JAMSDBw/q6xw4cEAEIP78889GtU2r1YoARK1W2+r3SURE1GJ79oiiFH2af+zZY+mWWhVjv7+tapaZVqsFAHh5eQEAcnNzUVRUhKioKH0dV1dXDBo0CPv37wcAHD16FDdu3DCoExgYiLCwMH2dAwcOQKVSoX///vo64eHhUKlU+jr1VVZWorS01OBBRERkMYWF8tYjA1YTiERRxKxZs/DQQw8hLCwMAFBUVAQA8PPzM6jr5+enP1ZUVAQXFxd06tSp2Tq+vr4Nrunr66uvU9/ixYv1441UKhWCgoJa9waJiIhaIyBA3npkwGoC0fTp0/Hjjz9i8+bNDY4JgmDwXBTFBmX11a/TWP3mzjN37lxotVr9Iy8vz5i3QUREZB4REdJssqa+/wQBCAqS6lGLWUUgSk5Oxo4dO7Bnzx6o1Wp9ub+/PwA06MUpLi7W9xr5+/ujqqoKJSUlzda5cOFCg+tevHixQe9TLVdXV3h6eho8iIiILEahkKbWAw1DUe3ztDSuR2QiiwYiURQxffp0aDQa7N69G6GhoQbHQ0ND4e/vj127dunLqqqqsHfvXgwcOBAA0K9fPzg7OxvUKSwsxIkTJ/R1BgwYAK1Wi8OHD+vrHDp0CFqtVl+HiIjI6sXEAOnpQJcuhuVqtVTOdYhMJohiY/P32kZSUhI2bdqEzz//HL1799aXq1QquLm5AQDefPNNLF68GGvXrkXPnj2xaNEiZGZm4tSpU1AqlQCA5557Dv/5z3+wbt06eHl5Yfbs2bh8+TKOHj0Kxc2k/Mgjj+D8+fN47733AABTpkxBcHAw/v3vfxvV1tLSUqhUKmi1WvYWERGRZXGlaqMZ+/1t0UDU1PidtWvXYtKkSQCkXqRXXnkF7733HkpKStC/f3/885//1A+8BoDr16/jhRdewKZNm1BRUYGhQ4fi3XffNRgIfeXKFcyYMQM7duwAAIwZMwarVq1Cx44djWorAxEREZHtsYlAZEsYiIiIiGyPsd/fVjGomoiIiMiSGIiIiIjI4TEQERERkcNjICIiIiKHx0BEREREDo+BiIiIiBxeO0s3gIiIiG7igosWw0BERERkDTQaYOZMID//VplaLe1fxi05zI63zIiIiCxNowFiYw3DEAAUFEjlGo1l2uVAGIiIiIgsSaeTeoYa2ziitiwlRapHZsNAREREZElZWQ17huoSRSAvT6pHZsMxREREROZg7ADpwkLjzmdsPTIJAxEREZHcWjJAOiDAuHMaW49MwltmREREcmrpAOmICCksCULj5xMEIChIqkdmw0BEREQkF1MGSCsUUs8R0DAU1T5PS+N6RGbGQERERCQXUwdIx8QA6elAly6G5Wq1VM51iMyOY4iIiIjk0poB0jExQHQ0V6q2EAYiIiIiubR2gLRCAURGytYcMh5vmREREcmFA6RtFgMRERGRXDhA2mYxEBEREcmJA6RtEscQERERyY0DpG0OAxEREZEpbrc1BwdI2xQGIiIiopZqydYcZBM4hoiIiByTTgdkZgKbN0s/664e3ZyWbs1BNoGBiIiIHI9GA4SEAIMHA/Hx0s+QkNuHGVO25iCbwEBERESOpTU9PKZuzUFWj4GIiIgcR2t7eFqzNQdZNQYiIiJyHK3t4Wnt1hxktRiIiIjIcbS2h4dbc9gtBiIiInIccmy+yq057BIDEREROQ45eni4NYdd4sKMRETkOGp7eGJjpfBTd3B1S3p4uDWH3WEgIiIi+1dVBbz7LnDmDNC9O7BlC5Ca2nCl6bQ043t4uDWHXWEgIiIi+1N3n7HPPwe2bQNqam4dVyik6fWjRrGHhwAwEBERkb1pbJ+x+nQ6YNkywMkJWLKk7dpGVouDqomIyH40tQp1U5Yvl26nkcNjICIiIvvQ3CrUzb3m3XfN1yayGQxERERkH263CnVTzpyRvy1kcxiIiIjIPpi6f1j37vK2g2wSAxEREdkHU/YPEwQgKUn+tpDNYSAiIiL7cLtVqBsTFwe4uJivTWQzGIiIiMg+NLfPWGOUSmDjRvO2iWwGAxEREdmPpvYZq08QgHXruBAj6TEQERGRfYmJAc6eBfbskVaj9vExPB4UxE1YqQFBFFuyYIPjKi0thUqlglarhaenp6WbQ0RExqq7jQe36HA4xn5/c+sOIiKyb9yElYzAW2ZERETk8BiIiIiIyOExEBEREZHDYyAiIiIih8dARERERA6PgYiIiIgcHqfdExGReXEdILIBJgeiX375BZmZmSguLkZNTY3BsZdffrnVDSMiIjug0QAzZwL5+bfK1GppzzGuFE1WxKSVqt9//30899xz8PHxgb+/P4Q6m+gJgoBjx47J2khrwJWqiYhaSKMBYmOB+l8ztd8Z3D6D2oCx398mBaLg4GAkJSXhb3/7W6saaUsYiIiIWkCnA0JCDHuG6hIEqacoN5e3z8isjP3+NmlQdUlJCSZMmGBy44iIyM5lZTUdhgCp1ygvT6pHZAVMCkQTJkzAzp075W4LERHZi8JC0+vpdEBmJrB5s/RTp5OzZUSNMmlQdY8ePTB//nwcPHgQffv2hbOzs8HxGTNmyNI4IiKyUQEBptXjIGyyEJN6iNasWQMPDw/s3bsXq1atwooVK/SPtLQ0o8/z7bffYvTo0QgMDIQgCPjss88Mjk+aNAmCIBg8wsPDDepUVlYiOTkZPj4+cHd3x5gxY5Bfr5u2pKQECQkJUKlUUKlUSEhIwB9//GHKWyciImNEREhBps6kGwOCAAQFSfVq1Q7Crn+rraBAKtdozNdecngmBaLc3NwmH7/99pvR5ykvL8c999yDVatWNVln5MiRKCws1D++/PJLg+MpKSn49NNPsWXLFuzbtw9lZWUYNWoUdHW6WOPj45GdnY2MjAxkZGQgOzsbCQkJLX/jRERkHIVC6tUBmg5FaWm3BlTrdFLPUGPzfGrLUlJ4+4zMxqILMz7yyCN45JFHmq3j6uoKf3//Ro9ptVp8+OGH2LBhA4YNGwYA+PjjjxEUFISvv/4aI0aMQE5ODjIyMnDw4EH0798fgLRswIABA3Dq1Cn07t1b3jdFROToahdirKwEFi6UgtGVK4Z1vLxujRUqLAQuXDB+EHZkpBkbT47K5ECUn5+PHTt24Ny5c6iqqjI4tnz58lY3rFZmZiZ8fX3RsWNHDBo0CG+88QZ8fX0BAEePHsWNGzcQFRWlrx8YGIiwsDDs378fI0aMwIEDB6BSqfRhCADCw8OhUqmwf//+JgNRZWUlKisr9c9LS0tle09ERHarsTFAjbl8GYiLa/n5jR2sTdRCJgWib775BmPGjEFoaChOnTqFsLAwnD17FqIo4v7775etcY888ggmTJiA4OBg5ObmYv78+RgyZAiOHj0KV1dXFBUVwcXFBZ06dTJ4nZ+fH4qKigAARUVF+gBVl6+vr75OYxYvXoxXXnlFtvdCRGT3mlqIUU7GDtYmaiGTxhDNnTsXqampOHHiBNq3b4/t27cjLy8PgwYNknV9oscffxyPPfYYwsLCMHr0aHz11Vf45Zdf8MUXXzT7OlEUG6yefbs69c2dOxdarVb/yMvLM/2NEBHZu+bGAMmhsUHYRDIyKRDl5OQgMTERANCuXTtUVFTAw8MDr776Kt58801ZG1hXQEAAgoODcfr0aQCAv78/qqqqUFJSYlCvuLgYfn5++joXLlxocK6LFy/q6zTG1dUVnp6eBg8iImrC7RZibI3af7zWHYRNJDOTApG7u7t+fE1gYCDOnDmjP3bp0iV5WtaIy5cvIy8vDwE3u0z79esHZ2dn7Nq1S1+nsLAQJ06cwMCBAwEAAwYMgFarxeHDh/V1Dh06BK1Wq69DREStZM6xPWo19z0jszNpDFF4eDi+++473HnnnXjssceQmpqK48ePQ6PRNFgnqDllZWX49ddf9c9zc3ORnZ0NLy8veHl5YeHChRg/fjwCAgJw9uxZvPjii/Dx8cG4ceMAACqVCs8++yxSU1Ph7e0NLy8vzJ49G3379tXPOuvTpw9GjhyJyZMn47333gMATJkyBaNGjeIMMyIiucg9tmfFCsDPTzpvRAR7hsj8RBOcOXNG/N///ieKoiiWl5eLzz33nNi3b19x3Lhx4tmzZ40+z549e0QADR6JiYnitWvXxKioKLFz586is7Oz2LVrVzExMVE8d+6cwTkqKirE6dOni15eXqKbm5s4atSoBnUuX74sPvnkk6JSqRSVSqX45JNPiiUlJS16z1qtVgQgarXaFr2OiMghVFeLolotioIgitJIItMegiCKQUHS+YhkYOz3t0m73Tsi7nZPRHQbtbPMANMGV9eOFeLtMZKRWXe7B4A//vgDH3zwAebOnYsrNxfcOnbsGAoKCkw9JRERWVprNlaNiZHCTJcuhuVBQcC2bcCePcCmTdLPbduksUF1cawQWZBJPUQ//vgjhg0bBpVKhbNnz+LUqVPo1q0b5s+fj99//x0fffSROdpqUewhIiK7J9fGqrUrVRcWNj8GyNh6RK1g7Pe3SYFo2LBhuP/++7FkyRIolUr873//Q7du3bB//37Ex8fj7NmzrWm7VWIgIiK71tSiio3dxmLgIRti1ltmR44cwdSpUxuUd+nSpdnVn4mIyAq1ZGNVjQYICQEGDwbi46WfISENd6I3th6RlTApELVv377Rvb1OnTqFzp07t7pRRETUhm63qGLtxqpvvCH1ItWvW1AgldeGndreptvVI7IiJgWi6OhovPrqq7hx4wYAaWuMc+fO4e9//zvGjx8vawOJiMjMjF1UceXKpnuRRBGYPBnYudP43iYiK2JSIFq6dCkuXrwIX19fVFRUYNCgQejRoweUSiXeeOMNudtIRETmZOyiijdnFDd7fMQI43qbsrKMbx9RGzBppWpPT0/s27cPu3fvxrFjx1BTU4P7779fvzo0ERHZkIgIaTZZQUHjPTuCALi7A2Vl8l3TnFt9EJnApEBUa8iQIRgyZIhcbSEiIktQKKTbYbGxUvipG4pqn8t9i0vurT6IWsnkQHT48GFkZmaiuLgYNTU1BseWL1/e6oYREVEbql1Usf46RE5OUhiqqJDnOoIg9UZFRMhzPiKZmBSIFi1ahHnz5qF3797w8/ODULtOBWDwZyIisiExMUB0tDS+5/PPgbQ0eXuGar8f0tK4HhFZHZMWZvTz88Obb76JSZMmmaFJ1okLMxKRw9DppDWDmhscbYqgICkMcWsOakPGfn+b1EPk5OSEP//5zyY3joiIrEzdVaUvXGh9GBIEaU+zdeuA4mKuVE1Wz6RA9Pzzz+Of//wn0tLSZG4OERG1KZ1OWnBx5crbT6s3Vu2tsZUrgaFD5TknkZmZFIhmz56Nxx57DN27d8edd94JZ2dng+MarkJKRGT9tm0Dnn0WuHrV9HN4e0s/L1++VaZW89YY2RyTAlFycjL27NmDwYMHw9vbmwOpiYhszZw5wFtvte4cc+cCr70m/ZmbuJKNM2lQtVKpxJYtW/DYY4+Zo01WiYOqichupKcDEya0/jw+PsB777EniKyaWXe79/LyQvfu3U1uHBERtTGdDsjMBDZuBBIT5TnnpUvcrJXshkmBaOHChViwYAGuXbsmd3uIiEhuGo00jX7wYOAvfwHk/n83N2slO2DSGKK3334bZ86cgZ+fH0JCQhoMqj527JgsjSMiolbSaKRenJaPjpDU38qjvrqbtUZGmnYNIitgUiAaO3aszM0gIiLZ6XTSVhymhiEAGDfOuFti3KyVbJxJgWjBggVG1du8eTPGjBkDd3d3Uy5DREStkZXVugUWBQGYOtW4QMTNWsnGmTSGyFhTp07FhQsXzHkJIiJqyueft+71oihNn1erby22WJ8gSFtycLNWsnFmDUQmzOgnIqKWqJ09tnmz9LN2cLNGIy2O2FrFxdKK00DDUMTNWsmOmDUQERGRGdWdPRYfL/0MCZFWoJ4yRZ5rBARI6wylp0t7k9WlVkvlXIeI7IBJY4iIiMjCmpo9VlAAxMXJcw2FAhg4UPpzTAwQHc0VqcluMRAREdma5maPyTlUQacD9u+/NZ1eoeDUerJbvGVGRGRrWjt7rCU4nZ4chFl7iIKDgxss2khERCbQ6W7drvrpp7a7LqfTk4MwayA6ceKEOU9PROQYNBpgxgxpfFBbEQRp0DSn05ODMCkQ6XQ6rFixAlu3bsW5c+dQVVVlcPzKlSuyNI6IyOFpNMD48W17TU6nJwdk0hiiV155BcuXL0dcXBy0Wi1mzZqFmJgYODk5YeHChTI3kYjIQel08k2fb0z79sALL0g9QXVxOj05IEE0YfXE7t274+2338Zjjz0GpVKJ7OxsfdnBgwexadMmc7TVokpLS6FSqaDVauHp6Wnp5hCRI/jmG2DYMPOdf+dOYPhww/FJnE5PdsbY72+TbpkVFRWhb9++AAAPDw9otVoAwKhRozB//nxTTklERPVlZprv3N7ewJAh0p85nZ7ItFtmarUahTenYvbo0QM7d+4EABw5cgSurq7ytY6IiMxjzRr2AhHVYVIgGjduHL755hsAwMyZMzF//nz07NkTTz31FJ555hlZG0hE5LDM0WujVgPbt3N8EFE9Jo0hqu/gwYPYv38/evTogTFjxsjRLqvDMURE1OZ0OsDDA7h+3fRzqNXAunXSJq0cH0QOyKxjiOoLDw9HeHi4HKciInJctYObCwqAixeBr79uXRgCpJ3qhw6Vp31EdszkQLRhwwasXr0aubm5OHDgAIKDg5GWlobQ0FBER0fL2UYiIvun0Uj7k8m5Jccrr/DWGJGRTBpD9K9//QuzZs3Co48+ij/++AM6nQ4A0LFjR6SlpcnZPiIi+1e7+KKcYUitBl56Sb7zEdk5kwLRO++8g/fffx8vvfQSFHXuRT/wwAM4fvy4bI0jIrJrOp201lBionznFATpsXIlxwoRtYBJgSg3Nxf33Xdfg3JXV1eUl5e3ulFERHYvPV0a5DxsGFBWJt95uco0kUlMGkMUGhqK7OxsBAcHG5R/9dVXuPPOO2VpGBGR3ZozB3jrLfnO17kzsGIF0KULZ5ERmcikQPTCCy9g2rRpuH79OkRRxOHDh7F582YsXrwYH3zwgdxtJCKyH9u2yRuGBAFYvZo9QkStZFIgevrpp1FdXY05c+bg2rVriI+PR5cuXbBy5Uo88cQTcreRiMg+VFUB//d/8p0vKEjakZ5hiKjVWhyIqqursXHjRowePRqTJ0/GpUuXUFNTA19fX3O0j4jIPmg0wNSpQGmpPOdbuhRISeHtMSKZmLRSdYcOHZCTk9NgDJE940rVRNQidXeQP30aWLBAvnN7ewMXLjAMERnBrCtV9+/fHz/88INDBSIiIqOZY5HFurgxK5HsTApESUlJSE1NRX5+Pvr16wd3d3eD43fffbcsjSMisjkaDRAbC7R+m8iG1GppfSGOGSKSnUm3zJycml6+SBAE/crV9oS3zIjotioqAH9/+cYJAZxST9RKZr1llpuba3LDiIjs0pw5wLJlQE2NvOfllHqiNmFSIKodO/TTTz/h3LlzqKqq0h8TBIFji4jIsci90GItbs5K1GZMumX222+/Ydy4cTh+/DgEQUDtKQRBAADeMiMix1FRAXh4yN8z1KUL8PvvvEVG1ErGfn+btJfZzJkzERoaigsXLqBDhw44ceIEvv32WzzwwAPIzMw0tc1ERLZFo5H2I5M7DAHAlCkMQ0RtyKRbZgcOHMDu3bvRuXNnODk5QaFQ4KGHHsLixYsxY8YM/PDDD3K3k4jIuphzNhkA9OxpnvMSUaNM6iHS6XTw8PAAAPj4+OD8+fMApLFFp06dkq91RETWSKeTenDMFYYAqeeJiNqMST1EYWFh+PHHH9GtWzf0798fS5YsgYuLC9asWYNu3brJ3UYiIutQu/r03LnA5cvmuYYgSOsNRUSY5/xE1CiTAtG8efNQXl4OAHj99dcxatQoREREwNvbG5988omsDSQisgrmWH1aEAx7mW5OTEFaGscPEbUxk2aZNebKlSvo1KmTfqaZveEsMyIHJud4IW9vaesNoGHA4u71RLIz68KMjfHy8pLrVERE1kOnk4KLqWGoc2dg0CDgjjuAyEjpUdv7Ex19awPYgACuRE1kQSYNqpbLt99+i9GjRyMwMBCCIOCzzz4zOC6KIhYuXIjAwEC4ubkhMjISJ0+eNKhTWVmJ5ORk+Pj4wN3dHWPGjEF+vS7tkpISJCQkQKVSQaVSISEhAX/88YeZ3x0R2YXMTNNvk61YIYWdbduA114Dhg41DDwKhRSQJk40DEpE1OYsGojKy8txzz33YNWqVY0eX7JkCZYvX45Vq1bhyJEj8Pf3x/Dhw3H16lV9nZSUFHz66afYsmUL9u3bh7KyMowaNcpgccj4+HhkZ2cjIyMDGRkZyM7ORkJCgtnfHxHZLl2NDpkfvYrNL4xEZgiga+loALUaSE5myCGyEbKNIWotQRDw6aefYuzYsQCk3qHAwECkpKTgb3/7GwCpN8jPzw9vvvkmpk6dCq1Wi86dO2PDhg14/PHHAQDnz59HUFAQvvzyS4wYMQI5OTm48847cfDgQfTv3x8AcPDgQQwYMAA///wzevfubVT7OIaIyM7UzhgrLAR8faWy4mLA1xeaS1mY+dMy5DuV6aurtcDKDCAmx8jzb9/OsUBEVsCsK1W3hdzcXBQVFSEqKkpf5urqikGDBmH//v0AgKNHj+LGjRsGdQIDAxEWFqavc+DAAahUKn0YAoDw8HCoVCp9HSJyMBoNEBICDB4MxMcDw4ZJj/h4aJKHITbnFeQLZQYvKfAEYuMATZ/bnFsQgK1bGYaIbIxsg6rlVlRUBADw8/MzKPfz88Pvv/+ur+Pi4oJOnTo1qFP7+qKiIvjW/uuvDl9fX32dxlRWVqKyslL/vLS01LQ3QkTWpZkZYzoBmDkSEAGg3i0yUQAEEUgZCUT/DCia6lvfulU6PxHZFKvtIapVfxq/KIq3ndpfv05j9W93nsWLF+sHYatUKgQFBbWw5URkdW4zYywrGMhXoUEYqiUKQJ5KqtdA587SbTKGISKbZLWByN/fHwAa9OIUFxfre438/f1RVVWFkpKSZutcuHChwfkvXrzYoPeprrlz50Kr1eofeXl5rXo/RGQFsrKanTFW6GHcaRrU8/CQzsvbZEQ2y2oDUWhoKPz9/bFr1y59WVVVFfbu3YuBAwcCAPr16wdnZ2eDOoWFhThx4oS+zoABA6DVanH48GF9nUOHDkGr1errNMbV1RWenp4GDyKyYTod8M03zVYJKGv2cNP1XngBcHExrV1EZBUsOoaorKwMv/76q/55bm4usrOz4eXlha5duyIlJQWLFi1Cz5490bNnTyxatAgdOnRAfHw8AEClUuHZZ59FamoqvL294eXlhdmzZ6Nv374YNmwYAKBPnz4YOXIkJk+ejPfeew8AMGXKFIwaNcroGWZEZOOM3HYj4ndpNlmBp3R7rD5BBNSlUj09b2/gpZfkbS8RtTmLBqLvv/8egwcP1j+fNWsWACAxMRHr1q3DnDlzUFFRgaSkJJSUlKB///7YuXMnlEql/jUrVqxAu3btEBcXh4qKCgwdOhTr1q2Dos7aHxs3bsSMGTP0s9HGjBnT5NpHRGRnWrDthkKUptbHxknhp24oEm6+PC2j3oDqNWu41hCRHbCadYisHdchIrIxOp20yvSECUC9cYa3o+kjzTbLV90qC9JKYUi/DpFSCaxbx3FDRFauzfcyIyKyGunpQFIScPGiSS+PyZGm1mcFSwOoA8qk22QKEVIQmjULmD+fPUNEdoSBiIjsy5w5wFtvtfo0ChGIPHvzSefOwIYVQJcu3ICVyE4xEBGR7am77UbdXeK3bZMlDBkQBGD1at4aI7JzDEREZFsamzGmVgPLlwP/93/yXkutBlauZBgicgAMRERkO5qaMVZQAMTFyXutV16RptPz9hiRQ2AgIiLb0Ny2G3JPlk1JAV5+Wd5zEpFVYyAiItvQzLYbOqGJGWGmio5uxYuJyBYxEBGRbSgsbLS4sTWD1FppgUX9mkHGEgRp3FBEhOntJCKbZLV7mRERGQgIaFCk6SOtKp1fb621Ak+pXNOnBecXbi5LnZbGcUNEDoiBiIhsQ0SE1Htzk06QeoZEAKi371jtlhspI6V6DYSHS3uQ1aVWSws6ckYZkUNiICIi26BQABMn6p9mBd+8TdZY4IEUivJUUj0DCxYABw4AFy4Ae/YAmzZJP3NzGYaIHBjHEBGRdaq/+OKlS8DSpfrDhR7Gncagnre3tOUGIAWsyEjZmktEto2BiIisT73FF3UCkBXqhMK7RP0ssoAy405lUI870xNRExiIiMi6pKdLO9TfdGsWWY2+TK0FVmRIPws8b40ZqksQAXWpFJ644jQR3Q7HEBGR9di2DXjiCegEIDMEeH4EML6JWWRxccDE49Jzod6aQ4IIQADS7kiBYvce4OxZhiEiahZ7iIjIsnQ6IDNT2kA1Pb3RdYXqEwUp9GzpC3yyDZg1ot46RIIn0uLWIqYPQxARGYeBiIgsR6MBpkwBLl+Wnt5cV8iYRaZrZ5F1vgacTauzUrWTJyK+vwiFs4tZm05E9oWBiIjkVX92WERE4wOZNRpg/Hj90yon4K+jGl9XqDmFHtI2HZFnIS2umL4WYBgiohbiGCIiko9GA4SEAIMHA/Hx0s+QEKm8Lp0OmDHj1sv6AF1mARfd0aIwBNSZRRYUxIUVichk7CEiInloNEBsbMOd5wsKpPK6YSUrSypHy26T1aWfRRb/d2D4iKZ7ooiIjMBAREStp9NJ6wbVD0OAVCYI0vGzZ4EzZ4AvvpBe1sz2G82pnVWWdsQbiszXGYSIqNUYiIio9bKy9IsoNkoUpeOpqYYvC25+NllT1KVAWgYQ8zoXWiQieTAQEVHrFRaa9LICpZEVb/YIpRwAon8BImrUUKzgQotEJB8GIiJqvYCAZg/rhDrT4m9uvaEQbw6iNkLH68CHO4CYqJnA3LEcL0REsmMgIqLWi4iQtscoKGgwjqixhRbVWmBlBtDZyP3I3v4CiBn1ArBkiYyNJiK6hdPuiaj1FApg4sRGw1BsI1tv5HtK5We8jDt9UOoChiEiMisGIiJqPY0GWLrUoKjZGWSCVP5+P6m3qLk590GeakQ8NV/e9hIR1cNAREStU1UFTJ3aoHdIP4Osqen0gnR88tGbVRoJRQIEpI1cCYUTxwsRkXkxEBGRyXTbtyHzvk7Y7H8JmSFSr1CtAg/jztH9CpC+FVDrOhiUB3kGIT0unRu0ElGb4KBqIjKeTgd88w2wYQM02gOY2eMM8uNuHa4dLB2TA1w0MhBd9GmPlOSNiB4bjaxzWSi8WogAZQAiukawZ4iI2gwDEREZR6MBEhOBsjJo+gDj4xpWKbg5WDp9K9C53LjTdn7rXeCBGCgARIZEytliIiKj8ZYZEd1e7c70ZWXQCcCUUTfL640PEm8+TxkJ+F817tRdfEJlayYRkanYQ0REzdPpoJsxHVkh0sKKO7sBl5tZUFEUgLybg6nV2ptT7hsbWC0CQaogRHSNME+7iYhagIGIiJr1yetPYMpfClHq1rLXFbtL44li4wCIt3qPAGn2GAQgbWQaxwkRkVVgICKiW3Q6ICsLuvMFyOpwEak/rcQx8SzQwjAESFt0RJ6VxhM1WKnaU420kWmcQUZEVoOBiIgkGg0wcyY0ynzDANPUOkLN8Lom7VcGSDPOol/ZgqwH/TiDjIisFgMREUG39RNk/v0JrB4ApN/Z+vPNPCht3goASEmBYsLjiGz9aYmIzIaBiMiB6Wp0eO3dJ/BmYTquJ8pwQhHwvga8lFWnLDpahhMTEZkXAxGRA9LV6PDat69hUeZruCHUAC4ynFSU7q6t+U+d3qGgICCCs8iIyPoxEBE5gpuDpVFYCE2700j85S2UVZeZND6oSQKwcLc0Zkh6LgBpaYCCY4WIyPoxEBHZu5uDpZGfj/Q+wITaFablDEM39bxy8w9BQVIYiuEsMiKyDQxERPZMowFiYwFRxMa7gL+Mh1mCUK2AlHlA96HSbTL2DBGRDWEgIrJXOp3UMySKePD/gO+7wGxhSIAAtacaEU8vBDidnohsEPcyI7JTum8zkdkuH6HJ5g9DAFedJiLbxkBEZIc+OfEJvLJGY/Ak4Kw3ZAlDTjrgsVOAzzXDk6k91UiPS+eq00Rk03jLjMhO6Gp0yMrNxOzPknC07Bf5TiwC7asA7T87wCX1b9DN/TuyCvZz1WkisisMRES2rqoKmpV/xYySTShwrZT33DfXE9roEgeXK5sAhQIKAJEhkfJeh4jIwnjLjMiG6V5IxatRrhhfvhYFLjKHIQDelU7YHvwCYhZ+wlljRGTX2ENEZGt0Oui+zcQbaxKwvGshtINlPPfNHqHxN7rjuQeeQ+SoZCic5VjGmojIujEQEdkSjQbpKybj6YeuoOwO+U/vJbTH+7EbEHNXrPwnJyKyYgxERLZCo8Hs1eOxbCjkmUIvAn6lwHAhBF3vHIAhD09CZPehHCBNRA6JgYjI2lVVoSJtGcJzX8SPAyFbGEo5AKz4SQ2c/ZXjg4jI4TEQEVmxqtkpuP/aSpz0BeAvzzlVFcAHO4DYHADbVzIMERGBgYjIKlVUVWDgPH9ke5QCSplOKgJuN4DitwCXTt7A9jXcfJWI6CYGIiIrUVVdhZWHVmLxvsUouV4CuMt48puzxz5WxMJl51+ByEj2DBER1cFARGQF5uyag7f2v2W283u3U2LN+HXcXoOIqAkMREQWoqvRIfNsJl765iUcKjgkFcq8AavrDeDFIS/jpciXOXuMiKgZDEREFqDJ0WDKv6fgcsVlqUDmICTUAHEngY0Tt0IxZIK8JyciskMMRERtbP0P6zFpxyT5T3xznNDgXCBjbxe4LH+bg6aJiIzEQETURioqyuC1xAvXccMs53cSBKR2fAxLIlOB/xfBQdNERC1g1Zu7Lly4EIIgGDz8/W8txiKKIhYuXIjAwEC4ubkhMjISJ0+eNDhHZWUlkpOT4ePjA3d3d4wZMwb5+flt/VbIQelqdMg88w36vRKIDm8qzRKGlM5KLB2+FBUvXceSmf/mDDIiIhNYdSACgLvuuguFhYX6x/Hjx/XHlixZguXLl2PVqlU4cuQI/P39MXz4cFy9elVfJyUlBZ9++im2bNmCffv2oaysDKNGjYJOp7PE2yEHoavR4dW9r6LzGyoM/ngYjqFQ9nFCAPBAwAMofbEUqQNT4dKOm7ASEZnK6m+ZtWvXzqBXqJYoikhLS8NLL72EmJvjJNavXw8/Pz9s2rQJU6dOhVarxYcffogNGzZg2LBhAICPP/4YQUFB+PrrrzFixIg2fS/kGBoMmDYDAQKeD38ey0YsM9s1iIgcidX3EJ0+fRqBgYEIDQ3FE088gd9++w0AkJubi6KiIkRFRenrurq6YtCgQdi/fz8A4OjRo7hx44ZBncDAQISFhenrEMlCp4Nuzzf4+7LRGP/JeFy+Zr4wBAAZT2YwDBERyciqe4j69++Pjz76CL169cKFCxfw+uuvY+DAgTh58iSKiooAAH5+fgav8fPzw++//w4AKCoqgouLCzp16tSgTu3rm1JZWYnKykr989LSUjneEtkh3fZteGX903jt3nJAAbPcGqslQIDaU42h3Yaa7yJERA7IqgPRI488ov9z3759MWDAAHTv3h3r169HeHg4AEAQDL99RFFsUFafMXUWL16MV155xcSWk72rXVRxlWYuPis9AvST+QIiGgQr4WZB2sg0LrJIRCQzq79lVpe7uzv69u2L06dP68cV1e/pKS4u1vca+fv7o6qqCiUlJU3WacrcuXOh1Wr1j7y8PBnfCdkyTY4GnZd0xrANw/BZ+RGpV0huAuDt5m1QpPZUIz0undtvEBGZgU0FosrKSuTk5CAgIAChoaHw9/fHrl279Merqqqwd+9eDBw4EADQr18/ODs7G9QpLCzEiRMn9HWa4urqCk9PT4MHObaKqgqM/Hgkxm8dj5LKktu/wETebt7YHrcdF2ZfwJ7EPdgUswl7Evcgd2YuwxARkZlY9S2z2bNnY/To0ejatSuKi4vx+uuvo7S0FImJiRAEASkpKVi0aBF69uyJnj17YtGiRejQoQPi4+MBACqVCs8++yxSU1Ph7e0NLy8vzJ49G3379tXPOiNqkk4HZGWhqiAP9+XPw0/Xz5n1cm7t3PDZ459haLeh+ltikSGRZr0mERFJrDoQ5efnY+LEibh06RI6d+6M8PBwHDx4EMHBwQCAOXPmoKKiAklJSSgpKUH//v2xc+dOKJVK/TlWrFiBdu3aIS4uDhUVFRg6dCjWrVsHBReuo+ZoNMDMmUi9Ix/L/wyzDpQGpPFBH8d8jKgeUbevTEREshNEURQt3QhbUFpaCpVKBa1Wy9tndq7qk81YmRaPhZHANVeYPQwFeQYhbWQab4cREZmBsd/fVt1DRNSWdDU6xC3/MzRlh4CR5r3W/Ij56NO5DwKUAYjoGsFZY0REFsZARARgy/EtmKiZKD0xY4+QWqnGykdWsjeIiMjKMBCRQ6uoqkDXFV1x6fols13jLp+7MDdiLrp4dmFvEBGRlWIgIoejq9Hh6zNf40nNk7h83bxbbMweMBtvRb1l1msQEVHrMRCRw6iqrsLTnz2NTSc3mfU63u29MefPc5ASnsId6ImIbAQDEdm12i02XvzmRRw+f9is13Jr54aPxn6E2LtizXodIiKSHwMR2SVdjQ5vZL2BN/e9iWvV18x6LRcnF7z40IuYN2gexwcREdkoBiKyK1WVFZi6dhw2XfgaVdCZ9VoRXSMwL2KewcrSRERkmxiIyG6krhqD5Zf+bfaFFNu3a4+NMRs5dZ6IyI4wEJHNq6iqQNd/+OFSzVWzhqHBwYMx589zMLz7cPYIERHZGQYiskm6Gh2yzmUh9b+pOFZ0TCo0YxjaHredPUJERHaMgYhszraT25D0RRIuVZhvMcVaak81Vo7kytJERPaOgYhsQlV1Fd79/l2sPbYWP1780azXaufUDs898Bxi+sRwZWkiIgfBQERWb/bO2Vh2YJlZryHUAAPPAS8Pmo+hTy1gCCIicjAMRGRVdDU67Px1J5YdWIY8bR6KyotQWlVqnouJgOc1QJMOROrUUKxYCcTw1hgRkSNiICKLqx0g/fnPn+PdI++iqqbKvBcUgZ7t1TjSbxVUl64BjwYAERGAgr1CRESOioGILKotB0hDBGJPAFuOqKH47SwDEBER6TEQUZuo7QUqvFqIAGUABqoH4qnPnsInJz9pk+v7lAP/+gKIzRGA9JUMQ0REZICBiMxOk6PBzIyZyC/Nb9Prut0AJn8PjDsFRPwOKNRBQHoaxwkREVEDDERkVpocDcZvHd+m13ww8EEsHroYkUERUHy3HygsBAI4ToiIiJrGQERmU1FVgYnpE9vsep6unvhw9IeIvSv2VmFkZJtdn4iIbBcDEclKV6ND5tlMzNszDwfzD7bJNQM8ArBh3AZEhkRy/SAiIjIJAxHJZvPxzXj6s6dRWVPZZtd8Pvx5LB+xvM2uR0RE9omBiExSd9aYr7svkr5Iwi9XfjHrNds5tUOARwC6enbF2D5jMeNPM+DSzsWs1yQiIsfAQEQtZolZYxPunIDN4zfzlhgREZkFAxE1q3ZM0O7c3cgtycWJiydwvPh4m13frZ0b1o9djwl3TWizaxIRkeNhIKImaXI0mLxjMq5cv9Im1+vRsQeGdhuKovIiKF2USLg7AUO7DWWvEBERmR0DETVQVV2Fqf+ZinX/W9dm10wdkIqlUUvb7HpERER1MRARgDrT5XfPw6GCQxAhtsl1BwUNws6ndnJwNBERWRQDEWHbyW145vNnUHajrE2v+8n4TxAXFtem1yQiImqMk6UbQJY1Z9ccxKXHtWkYUivV2B63nWGIiIisBnuIHEDt7bDMs5kAgMiQSESGROLTnE/x1v63zHpthaDAxpiN8PPw0+90H9E1ggOliYjIqjAQ2TlNjgZT/j0Flysu68tez3odXu29cKPmhlmvPffPc/HakNcYfoiIyOoxENmx5naaN/dU+q2xW7l2EBER2QwGIhtXdwuNurejdDU6zPhqhlmvLUCAu4s7yqpujT8K8gxC2sg0xPSJMeu1iYiI5MRAZMPST6Yj6cskXLx2UV+m9lRj5ciV8HLzQsHVAtmv6apwxd1+d+Pxux5H8p+SoXBSNBrIiIiIbAkDkY2avXM2lh1Y1qA8vzQfsVtjMbP/TNmuJUDAY70eQ+qA1EYDT2RIpGzXIiIisgQGIhvUVBiqJULExuMbZblWWOcwHJ1ylAsnEhGRXeM6RDYm/WR6s2Go1sVrF+Hj5nPbet5u3vBw8WhQLkBA6oBUHE86zjBERER2jz1ENkRXo0PSl0lG1//LPX9B2sG0ZuusGb0G0b2jsTt3Nzb8uAFXK68iIjgC0x+cziBEREQOg4HIgpqaIdaUrHNZBgOobye6dzQiukY0WIcIkHqG1oxeo58NNrz7cAzvPty0N0JERGTjGIgsRJOjwcyMmcgvzdeX1c4Qa2rKeuHVQqPP37lDZ33Aiu4d3ehK1ZwNRkREJGEgsgBNjgaxW2Mb7ChfUFqA2K2xSI9LbzQUBSgDjL7Gu4++qw88CicFhnYbiqHdhrau4URERHaKg6rbmK5Gh5kZMxuEIQD6spSMFOhqdA2OR3SNgNpTfdtrpA5IRexdsa1vLBERkYNgIGpjWeeyDG6T1SdCRF5pHrLOZTU4pnBSYOXIlRAgNPn61AGpWBq1VJa2EhEROQoGojZm7DigpurF9IlBelx6g56izh06Y1vsNoYhIiIiE3AMURszdhxQc/Vi+sQgunc0t8wgIiKSCQNRG6sdB1RQWtDoOCIBAtSeakR0jWj2PAonBbfMICIikglvmbWx2nFAABqMBap9njYyjb09REREbYiByAJqxwF18exiUK72VDc55Z6IiIjMRxBFseF9G2qgtLQUKpUKWq0Wnp6espyzpStVExERUcsY+/3NMUQWxHFARERE1oG3zIiIiMjhMRARERGRw2MgIiIiIofHQEREREQOj4GIiIiIHB4DERERETk8BiIiIiJyeAxERERE5PAYiIiIiMjhcaVqI9XucFJaWmrhlhAREZGxar+3b7dTGQORka5evQoACAoKsnBLiIiIqKWuXr0KlUrV5HFu7mqkmpoanD9/HkqlEoIgWLo5dqW0tBRBQUHIy8uTbeNckh8/J9vBz8p28LMyP1EUcfXqVQQGBsLJqemRQuwhMpKTkxPUarWlm2HXPD09+T8EG8DPyXbws7Id/KzMq7meoVocVE1EREQOj4GIiIiIHB4DEVmcq6srFixYAFdXV0s3hZrBz8l28LOyHfysrAcHVRMREZHDYw8REREROTwGIiIiInJ4DERERETk8BiIqE18++23GD16NAIDAyEIAj777DOD46IoYuHChQgMDISbmxsiIyNx8uRJyzTWwd3us5o0aRIEQTB4hIeHW6axDmzx4sV48MEHoVQq4evri7Fjx+LUqVMGdfh7ZR2M+az4e2V5DETUJsrLy3HPPfdg1apVjR5fsmQJli9fjlWrVuHIkSPw9/fH8OHD9VumUNu53WcFACNHjkRhYaH+8eWXX7ZhCwkA9u7di2nTpuHgwYPYtWsXqqurERUVhfLycn0d/l5ZB2M+K4C/VxYnErUxAOKnn36qf15TUyP6+/uL//jHP/Rl169fF1Uqlbh69WoLtJBq1f+sRFEUExMTxejoaIu0h5pWXFwsAhD37t0riiJ/r6xZ/c9KFPl7ZQ3YQ0QWl5ubi6KiIkRFRenLXF1dMWjQIOzfv9+CLaOmZGZmwtfXF7169cLkyZNRXFxs6SY5PK1WCwDw8vICwN8ra1b/s6rF3yvLYiAiiysqKgIA+Pn5GZT7+fnpj5H1eOSRR7Bx40bs3r0by5Ytw5EjRzBkyBBUVlZaumkOSxRFzJo1Cw899BDCwsIA8PfKWjX2WQH8vbIG3NyVrIYgCAbPRVFsUEaW9/jjj+v/HBYWhgceeADBwcH44osvEBMTY8GWOa7p06fjxx9/xL59+xoc4++VdWnqs+LvleWxh4gszt/fHwAa/Ku1uLi4wb9uyfoEBAQgODgYp0+ftnRTHFJycjJ27NiBPXv2QK1W68v5e2V9mvqsGsPfq7bHQEQWFxoaCn9/f+zatUtfVlVVhb1792LgwIEWbBkZ4/Lly8jLy0NAQIClm+JQRFHE9OnTodFosHv3boSGhhoc5++V9bjdZ9UY/l61Pd4yozZRVlaGX3/9Vf88NzcX2dnZ8PLyQteuXZGSkoJFixahZ8+e6NmzJxYtWoQOHTogPj7egq12TM19Vl5eXli4cCHGjx+PgIAAnD17Fi+++CJ8fHwwbtw4C7ba8UybNg2bNm3C559/DqVSqe8JUqlUcHNzgyAI/L2yErf7rMrKyvh7ZQ0sOcWNHMeePXtEAA0eiYmJoihKU4QXLFgg+vv7i66uruLDDz8sHj9+3LKNdlDNfVbXrl0To6KixM6dO4vOzs5i165dxcTERPHcuXOWbrbDaewzAiCuXbtWX4e/V9bhdp8Vf6+sA3e7JyIiIofHMURERETk8BiIiIiIyOExEBEREZHDYyAiIiIih8dARERERA6PgYiIiIgcHgMREREROTwGIiIiInJ4DEREZNXWrVuHjh076p8vXLgQ9957r8XaQ0T2iYGIiGzK7Nmz8c0331i6GURkZ7i5KxG1iaqqKri4uLT6PB4eHvDw8JChRUREt7CHiIjMIjIyEtOnT8esWbPg4+OD4cOHY/ny5ejbty/c3d0RFBSEpKQklJWVGbxu3bp16Nq1Kzp06IBx48bh8uXLBsfr3zKLjIxESkqKQZ2xY8di0qRJ+ufvvvsuevbsifbt28PPzw+xsbFGv4fk5GSkpKSgU6dO8PPzw5o1a1BeXo6nn34aSqUS3bt3x1dffWXwup9++gmPPvooPDw84Ofnh4SEBFy6dEl/PCMjAw899BA6duwIb29vjBo1CmfOnNEfP3v2LARBgEajweDBg9GhQwfcc889OHDggFHtJqKWYyAiIrNZv3492rVrh++++w7vvfcenJyc8Pbbb+PEiRNYv349du/ejTlz5ujrHzp0CM888wySkpKQnZ2NwYMH4/XXX29VG77//nvMmDEDr776Kk6dOoWMjAw8/PDDLXoPPj4+OHz4MJKTk/Hcc89hwoQJGDhwII4dO4YRI0YgISEB165dAwAUFhZi0KBBuPfee/H9998jIyMDFy5cQFxcnP6c5eXlmDVrFo4cOYJvvvkGTk5OGDduHGpqagyu/dJLL2H27NnIzs5Gr169MHHiRFRXV7fq74OImiASEZnBoEGDxHvvvbfZOlu3bhW9vb31zydOnCiOHDnSoM7jjz8uqlQq/fMFCxaI99xzj8F1Zs6cafCa6OhoMTExURRFUdy+fbvo6ekplpaWmvQeHnroIf3z6upq0d3dXUxISNCXFRYWigDEAwcOiKIoivPnzxejoqIMzpOXlycCEE+dOtXodYqLi0UA4vHjx0VRFMXc3FwRgPjBBx/o65w8eVIEIObk5LT4fRDR7bGHiIjM5oEHHjB4vmfPHgwfPhxdunSBUqnEU089hcuXL6O8vBwAkJOTgwEDBhi8pv7zlho+fDiCg4PRrVs3JCQkYOPGjfreHGPcfffd+j8rFAp4e3ujb9+++jI/Pz8AQHFxMQDg6NGj2LNnj36sk4eHB+644w4A0N8WO3PmDOLj49GtWzd4enoiNDQUAHDu3Lkmrx0QEGBwHSKSFwMREZmNu7u7/s+///47Hn30UYSFhWH79u04evQo/vnPfwIAbty4AQAQRbHF13BycmrwutrzAYBSqcSxY8ewefNmBAQE4OWXX8Y999yDP/74w6jzOzs7GzwXBMGgTBAEANDf7qqpqcHo0aORnZ1t8Dh9+rT+Vt3o0aNx+fJlvP/++zh06BAOHToEQBp43tS161+HiOTFWWZE1Ca+//57VFdXY9myZXBykv4ttnXrVoM6d955Jw4ePGhQVv95fZ07d0ZhYaH+uU6nw4kTJzB48GB9Wbt27TBs2DAMGzYMCxYsQMeOHbF7927ExMS09m01cP/992P79u0ICQlBu3YN/xd7+fJl5OTk4L333kNERAQAYN++fbK3g4hahj1ERNQmunfvjurqarzzzjv47bffsGHDBqxevdqgzowZM5CRkYElS5bgl19+wapVq5CRkdHseYcMGYIvvvgCX3zxBX7++WckJSUZ9P785z//wdtvv43s7Gz8/vvv+Oijj1BTU4PevXub421i2rRpuHLlCiZOnIjDhw/jt99+w86dO/HMM89Ap9OhU6dO8Pb2xpo1a/Drr79i9+7dmDVrllnaQkTGYyAiojZx7733Yvny5XjzzTcRFhaGjRs3YvHixQZ1wsPD8cEHH+Cdd97Bvffei507d2LevHnNnveZZ55BYmIinnrqKQwaNAihoaEGvUMdO3aERqPBkCFD0KdPH6xevRqbN2/GXXfdZZb3GRgYiO+++w46nQ4jRoxAWFgYZs6cCZVKBScnJzg5OWHLli04evQowsLC8Pzzz+Ott94yS1uIyHiCaMpNeyIiIiI7wh4iIiIicngMRETkkM6dO2cwNb7+o/4UeCKyb7xlRkQOqbq6GmfPnm3yeFOzxIjIPjEQERERkcPjLTMiIiJyeAxERERE5PAYiIiIiMjhMRARERGRw2MgIiIiIofHQEREREQOj4GIiIiIHB4DERERETm8/w8zcj+NnKN9YwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(M.radius_mean,M.area_mean,color=\"red\",label=\"malignant\")\n",
    "plt.scatter(B.radius_mean,B.area_mean,color=\"green\",label=\"benign\")\n",
    "plt.legend() \n",
    "plt.xlabel(\"radius_mean\")\n",
    "plt.ylabel(\"area_mean\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "716b7d24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:07.598355Z",
     "iopub.status.busy": "2023-03-31T19:20:07.597871Z",
     "iopub.status.idle": "2023-03-31T19:20:07.883066Z",
     "shell.execute_reply": "2023-03-31T19:20:07.881716Z"
    },
    "papermill": {
     "duration": 0.297285,
     "end_time": "2023-03-31T19:20:07.885831",
     "exception": false,
     "start_time": "2023-03-31T19:20:07.588546",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(M.radius_mean,M.texture_mean,color=\"red\",label=\"malignant\") \n",
    "plt.scatter(B.radius_mean,B.texture_mean,color=\"green\",label=\"benign\")\n",
    "plt.xlabel(\"radius_mean\")\n",
    "plt.ylabel(\"texture_mean\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "daf221ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:07.905703Z",
     "iopub.status.busy": "2023-03-31T19:20:07.905260Z",
     "iopub.status.idle": "2023-03-31T19:20:07.912824Z",
     "shell.execute_reply": "2023-03-31T19:20:07.911317Z"
    },
    "papermill": {
     "duration": 0.021469,
     "end_time": "2023-03-31T19:20:07.916124",
     "exception": false,
     "start_time": "2023-03-31T19:20:07.894655",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data.diagnosis = [1 if each==\"M\" else 0 for each in data.diagnosis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b2245d34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:07.934949Z",
     "iopub.status.busy": "2023-03-31T19:20:07.934054Z",
     "iopub.status.idle": "2023-03-31T19:20:07.940232Z",
     "shell.execute_reply": "2023-03-31T19:20:07.938680Z"
    },
    "papermill": {
     "duration": 0.019082,
     "end_time": "2023-03-31T19:20:07.943094",
     "exception": false,
     "start_time": "2023-03-31T19:20:07.924012",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = data.diagnosis.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "54184492",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:07.961578Z",
     "iopub.status.busy": "2023-03-31T19:20:07.960443Z",
     "iopub.status.idle": "2023-03-31T19:20:07.966962Z",
     "shell.execute_reply": "2023-03-31T19:20:07.965397Z"
    },
    "papermill": {
     "duration": 0.018543,
     "end_time": "2023-03-31T19:20:07.969696",
     "exception": false,
     "start_time": "2023-03-31T19:20:07.951153",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_data= data.iloc[:,1:3].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4957f3f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:07.987793Z",
     "iopub.status.busy": "2023-03-31T19:20:07.987310Z",
     "iopub.status.idle": "2023-03-31T19:20:07.993918Z",
     "shell.execute_reply": "2023-03-31T19:20:07.992498Z"
    },
    "papermill": {
     "duration": 0.018884,
     "end_time": "2023-03-31T19:20:07.996685",
     "exception": false,
     "start_time": "2023-03-31T19:20:07.977801",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x = (x_data - np.min(x_data))/(np.max(x_data) - np.min(x_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1b8af4e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:08.014428Z",
     "iopub.status.busy": "2023-03-31T19:20:08.013967Z",
     "iopub.status.idle": "2023-03-31T19:20:08.290782Z",
     "shell.execute_reply": "2023-03-31T19:20:08.289260Z"
    },
    "papermill": {
     "duration": 0.289182,
     "end_time": "2023-03-31T19:20:08.293893",
     "exception": false,
     "start_time": "2023-03-31T19:20:08.004711",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "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=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b27c2f39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:08.312241Z",
     "iopub.status.busy": "2023-03-31T19:20:08.311814Z",
     "iopub.status.idle": "2023-03-31T19:20:08.549892Z",
     "shell.execute_reply": "2023-03-31T19:20:08.548337Z"
    },
    "papermill": {
     "duration": 0.251197,
     "end_time": "2023-03-31T19:20:08.552946",
     "exception": false,
     "start_time": "2023-03-31T19:20:08.301749",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors = 3) \n",
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "01359eff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:08.571137Z",
     "iopub.status.busy": "2023-03-31T19:20:08.570741Z",
     "iopub.status.idle": "2023-03-31T19:20:08.590458Z",
     "shell.execute_reply": "2023-03-31T19:20:08.588909Z"
    },
    "papermill": {
     "duration": 0.032392,
     "end_time": "2023-03-31T19:20:08.593445",
     "exception": false,
     "start_time": "2023-03-31T19:20:08.561053",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1,\n",
       "       1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_head = knn.predict(x_test)\n",
    "y_head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2c149ae5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:08.613172Z",
     "iopub.status.busy": "2023-03-31T19:20:08.612763Z",
     "iopub.status.idle": "2023-03-31T19:20:08.627261Z",
     "shell.execute_reply": "2023-03-31T19:20:08.625335Z"
    },
    "papermill": {
     "duration": 0.028341,
     "end_time": "2023-03-31T19:20:08.631179",
     "exception": false,
     "start_time": "2023-03-31T19:20:08.602838",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "when k is 3, accuracy of knn classification 0.8421052631578947 \n"
     ]
    }
   ],
   "source": [
    "print(\"when k is {}, accuracy of knn classification {} \".format(3,knn.score(x_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "625c7c5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:08.650464Z",
     "iopub.status.busy": "2023-03-31T19:20:08.649990Z",
     "iopub.status.idle": "2023-03-31T19:20:08.979882Z",
     "shell.execute_reply": "2023-03-31T19:20:08.978535Z"
    },
    "papermill": {
     "duration": 0.342847,
     "end_time": "2023-03-31T19:20:08.982766",
     "exception": false,
     "start_time": "2023-03-31T19:20:08.639919",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy is 0.8596491228070176 with K = 5\n"
     ]
    }
   ],
   "source": [
    "test_accuracy = []\n",
    "for each in range(1,15):\n",
    "    knn2 = KNeighborsClassifier(n_neighbors = each)\n",
    "    knn2.fit(x_train,y_train)\n",
    "    test_accuracy.append(knn2.score(x_test,y_test))\n",
    "    \n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(range(1,15),test_accuracy)\n",
    "plt.title('K Values VS Accuracy')\n",
    "plt.xlabel(\"k values\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.grid()\n",
    "plt.show()\n",
    "print(\"Best accuracy is {} with K = {}\".format(np.max(test_accuracy),1+test_accuracy.index(np.max(test_accuracy))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "67bad89a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:09.004050Z",
     "iopub.status.busy": "2023-03-31T19:20:09.002916Z",
     "iopub.status.idle": "2023-03-31T19:20:09.549367Z",
     "shell.execute_reply": "2023-03-31T19:20:09.547734Z"
    },
    "papermill": {
     "duration": 0.560231,
     "end_time": "2023-03-31T19:20:09.552352",
     "exception": false,
     "start_time": "2023-03-31T19:20:08.992121",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = []\n",
    "for i in range (1,40):\n",
    "  knn= KNeighborsClassifier(n_neighbors=i)\n",
    "  knn.fit(x_train,y_train)\n",
    "  pred_i = knn.predict(x_test)\n",
    "  error.append(np.mean(pred_i != y_test))\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(range(1,40), error, color='red',linestyle='dashed',marker='o',markerfacecolor='blue',markersize=10)\n",
    "plt.title('Error Rate K Value')\n",
    "plt.xlabel('K Value')\n",
    "plt.ylabel('Mean Error')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "35d534e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:09.575521Z",
     "iopub.status.busy": "2023-03-31T19:20:09.574087Z",
     "iopub.status.idle": "2023-03-31T19:20:09.583938Z",
     "shell.execute_reply": "2023-03-31T19:20:09.582483Z"
    },
    "papermill": {
     "duration": 0.024837,
     "end_time": "2023-03-31T19:20:09.587311",
     "exception": false,
     "start_time": "2023-03-31T19:20:09.562474",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b2008093",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-31T19:20:09.609386Z",
     "iopub.status.busy": "2023-03-31T19:20:09.608900Z",
     "iopub.status.idle": "2023-03-31T19:20:09.883407Z",
     "shell.execute_reply": "2023-03-31T19:20:09.881789Z"
    },
    "papermill": {
     "duration": 0.289516,
     "end_time": "2023-03-31T19:20:09.886816",
     "exception": false,
     "start_time": "2023-03-31T19:20:09.597300",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=(5, 5))\n",
    "sns.heatmap(cm,annot=True,fmt= '.0f',linewidths=0.5,linecolor=\"red\",ax=ax)\n",
    "plt.xlabel(\"y_pred\")\n",
    "plt.ylabel(\"y_test\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 17.155238,
   "end_time": "2023-03-31T19:20:10.823257",
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