prediction-cancer-data-with-k-nn-95.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/erdemtaha/prediction-cancer-data-with-k-nn-95?scriptVersionId=147575193\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","source":"# Prediction Cancer Data With K-NN \n**We will apply the K-NN algorithm to a data set with 570 cancer cells and 30 features to determine whether the cancer cells in our data are benign or malignant**\n\n**🏆 Test accuracy: 95.32% (3 nn)**\n\n## Content:\n\n   * [**1.** Library and Input File](#1)\n   \n   * [**2.** Load and Check Data](#2)\n   \n   * [**3.** Data manipulation](#3)\n   \n   * [**4.** Data Graph](#4)\n   \n   * [**5.** Diagnosis Change 0 or 1](#5)\n   \n   * [**6.** Data Normalization](#6)\n   \n   * [**7.** Train and Test Split](#7)\n   \n   * [**8.** Find K Values](#8)\n  \n   * [**9.** K-NN Model](#9)\n  \n   * [**10.** Result ](#10)","metadata":{}},{"cell_type":"markdown","source":"<a id = \"1\"></a>\n# 1. Library and Input File","metadata":{}},{"cell_type":"code","source":"#Library\nimport seaborn as sns\nfrom sklearn.metrics import confusion_matrix\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt #data graph drawing\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n#İnput File\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-10-22T13:28:25.807586Z","iopub.execute_input":"2023-10-22T13:28:25.80813Z","iopub.status.idle":"2023-10-22T13:28:25.843906Z","shell.execute_reply.started":"2023-10-22T13:28:25.808077Z","shell.execute_reply":"2023-10-22T13:28:25.841972Z"},"trusted":true},"execution_count":127,"outputs":[{"name":"stdout","text":"cancer-data\n\n/kaggle/input/cancer-data/Cancer_Data.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id = \"2\"></a>\n# 2. Load and Check Data\n**We examined the general features of data**","metadata":{}},{"cell_type":"code","source":"data = pd.read_csv('/kaggle/input/cancer-data/Cancer_Data.csv') \ndata.info() # general information of the data\nprint(data[\"Unnamed: 32\"]) # a column that is completely null\nprint(data[\"id\"]) # an unnecessary value for the algorithm\ndata.tail()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T13:28:25.846882Z","iopub.execute_input":"2023-10-22T13:28:25.847318Z","iopub.status.idle":"2023-10-22T13:28:25.90969Z","shell.execute_reply.started":"2023-10-22T13:28:25.847274Z","shell.execute_reply":"2023-10-22T13:28:25.90874Z"},"trusted":true},"execution_count":128,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 569 entries, 0 to 568\nData 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\ndtypes: float64(31), int64(1), object(1)\nmemory usage: 146.8+ KB\n0     NaN\n1     NaN\n2     NaN\n3     NaN\n4     NaN\n       ..\n564   NaN\n565   NaN\n566   NaN\n567   NaN\n568   NaN\nName: Unnamed: 32, Length: 569, dtype: float64\n0        842302\n1        842517\n2      84300903\n3      84348301\n4      84358402\n         ...   \n564      926424\n565      926682\n566      926954\n567      927241\n568       92751\nName: id, Length: 569, dtype: int64\n","output_type":"stream"},{"execution_count":128,"output_type":"execute_result","data":{"text/plain":"         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n564  926424         M        21.56         22.39          142.00     1479.0   \n565  926682         M        20.13         28.25          131.20     1261.0   \n566  926954         M        16.60         28.08          108.30      858.1   \n567  927241         M        20.60         29.33          140.10     1265.0   \n568   92751         B         7.76         24.54           47.92      181.0   \n\n     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n564          0.11100           0.11590         0.24390              0.13890   \n565          0.09780           0.10340         0.14400              0.09791   \n566          0.08455           0.10230         0.09251              0.05302   \n567          0.11780           0.27700         0.35140              0.15200   \n568          0.05263           0.04362         0.00000              0.00000   \n\n     ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n564  ...          26.40           166.10      2027.0           0.14100   \n565  ...          38.25           155.00      1731.0           0.11660   \n566  ...          34.12           126.70      1124.0           0.11390   \n567  ...          39.42           184.60      1821.0           0.16500   \n568  ...          30.37            59.16       268.6           0.08996   \n\n     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n564            0.21130           0.4107                0.2216          0.2060   \n565            0.19220           0.3215                0.1628          0.2572   \n566            0.30940           0.3403                0.1418          0.2218   \n567            0.86810           0.9387                0.2650          0.4087   \n568            0.06444           0.0000                0.0000          0.2871   \n\n     fractal_dimension_worst  Unnamed: 32  \n564                  0.07115          NaN  \n565                  0.06637          NaN  \n566                  0.07820          NaN  \n567                  0.12400          NaN  \n568                  0.07039          NaN  \n\n[5 rows x 33 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <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      <th>concave points_worst</th>\n      <th>symmetry_worst</th>\n      <th>fractal_dimension_worst</th>\n      <th>Unnamed: 32</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>564</th>\n      <td>926424</td>\n      <td>M</td>\n      <td>21.56</td>\n      <td>22.39</td>\n      <td>142.00</td>\n      <td>1479.0</td>\n      <td>0.11100</td>\n      <td>0.11590</td>\n      <td>0.24390</td>\n      <td>0.13890</td>\n      <td>...</td>\n      <td>26.40</td>\n      <td>166.10</td>\n      <td>2027.0</td>\n      <td>0.14100</td>\n      <td>0.21130</td>\n      <td>0.4107</td>\n      <td>0.2216</td>\n      <td>0.2060</td>\n      <td>0.07115</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>565</th>\n      <td>926682</td>\n      <td>M</td>\n      <td>20.13</td>\n      <td>28.25</td>\n      <td>131.20</td>\n      <td>1261.0</td>\n      <td>0.09780</td>\n      <td>0.10340</td>\n      <td>0.14400</td>\n      <td>0.09791</td>\n      <td>...</td>\n      <td>38.25</td>\n      <td>155.00</td>\n      <td>1731.0</td>\n      <td>0.11660</td>\n      <td>0.19220</td>\n      <td>0.3215</td>\n      <td>0.1628</td>\n      <td>0.2572</td>\n      <td>0.06637</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>566</th>\n      <td>926954</td>\n      <td>M</td>\n      <td>16.60</td>\n      <td>28.08</td>\n      <td>108.30</td>\n      <td>858.1</td>\n      <td>0.08455</td>\n      <td>0.10230</td>\n      <td>0.09251</td>\n      <td>0.05302</td>\n      <td>...</td>\n      <td>34.12</td>\n      <td>126.70</td>\n      <td>1124.0</td>\n      <td>0.11390</td>\n      <td>0.30940</td>\n      <td>0.3403</td>\n      <td>0.1418</td>\n      <td>0.2218</td>\n      <td>0.07820</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>567</th>\n      <td>927241</td>\n      <td>M</td>\n      <td>20.60</td>\n      <td>29.33</td>\n      <td>140.10</td>\n      <td>1265.0</td>\n      <td>0.11780</td>\n      <td>0.27700</td>\n      <td>0.35140</td>\n      <td>0.15200</td>\n      <td>...</td>\n      <td>39.42</td>\n      <td>184.60</td>\n      <td>1821.0</td>\n      <td>0.16500</td>\n      <td>0.86810</td>\n      <td>0.9387</td>\n      <td>0.2650</td>\n      <td>0.4087</td>\n      <td>0.12400</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>568</th>\n      <td>92751</td>\n      <td>B</td>\n      <td>7.76</td>\n      <td>24.54</td>\n      <td>47.92</td>\n      <td>181.0</td>\n      <td>0.05263</td>\n      <td>0.04362</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>...</td>\n      <td>30.37</td>\n      <td>59.16</td>\n      <td>268.6</td>\n      <td>0.08996</td>\n      <td>0.06444</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.2871</td>\n      <td>0.07039</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 33 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id = \"3\"></a>\n# 3. Data manipulation\n**Extracted unnecessary features from the data**","metadata":{}},{"cell_type":"code","source":"data.drop([\"id\",\"Unnamed: 32\"], axis = 1, inplace = True) #\"axis = 1\" indicates to delete the whole column |\"inpalce = True\" means replace master data, does not create a copy\ndata.info()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T13:28:25.911298Z","iopub.execute_input":"2023-10-22T13:28:25.911952Z","iopub.status.idle":"2023-10-22T13:28:25.930797Z","shell.execute_reply.started":"2023-10-22T13:28:25.911912Z","shell.execute_reply":"2023-10-22T13:28:25.929431Z"},"trusted":true},"execution_count":129,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 569 entries, 0 to 568\nData 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\ndtypes: float64(30), object(1)\nmemory usage: 137.9+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id = \"4\"></a>\n# 4. Data Graph\n**We will analyze the distribution of benign (B) and malignant (M) cancers in the data on the graph with two unique features**\n\n**\"M\" = Malignant Cancer**\n\n**\"B\" = Benign Cancer**","metadata":{}},{"cell_type":"code","source":"\nM = data[data.diagnosis == \"M\"] #Diagnosis transfers all values of M to M data\nB = data[data.diagnosis == \"B\"] #Diagnosis transfers all values of B to B data\n\nplt.scatter(M.radius_mean,M.texture_mean, color = \"red\", label = \"Malignant\", alpha = 0.3)\nplt.scatter(B.radius_mean,B.texture_mean, color = \"green\", label = \"Benign\", alpha = 0.3)\n\nplt.xlabel(\"radius_mean\")\nplt.ylabel(\"texture_mean\")\n\nplt.legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T13:28:25.933954Z","iopub.execute_input":"2023-10-22T13:28:25.934415Z","iopub.status.idle":"2023-10-22T13:28:26.238183Z","shell.execute_reply.started":"2023-10-22T13:28:25.934332Z","shell.execute_reply":"2023-10-22T13:28:26.236714Z"},"trusted":true},"execution_count":130,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id = \"5\"></a>\n# 5. Diagnosis Change 0 or 1\n**In order for the data to enter the classification algorithm and receive processing, it must not be object structured, so we convert this data into 1 and 0.**\n\n**1 = M (Malignant Cancer Cell)**\n\n**0 = B (Benign Cancer Cell)**","metadata":{}},{"cell_type":"code","source":"data.diagnosis = [1 if each == \"M\" else 0 for each in data.diagnosis]\ny = data.diagnosis\nx_data = data.drop([\"diagnosis\"],axis = 1)\nprint(\"X Data \\n\",x_data)\nprint(\"Y Data \\n\",y)","metadata":{"execution":{"iopub.status.busy":"2023-10-22T13:28:26.239727Z","iopub.execute_input":"2023-10-22T13:28:26.240109Z","iopub.status.idle":"2023-10-22T13:28:26.270427Z","shell.execute_reply.started":"2023-10-22T13:28:26.24007Z","shell.execute_reply":"2023-10-22T13:28:26.269161Z"},"trusted":true},"execution_count":131,"outputs":[{"name":"stdout","text":"X Data \n      radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n0          17.99         10.38          122.80     1001.0          0.11840   \n1          20.57         17.77          132.90     1326.0          0.08474   \n2          19.69         21.25          130.00     1203.0          0.10960   \n3          11.42         20.38           77.58      386.1          0.14250   \n4          20.29         14.34          135.10     1297.0          0.10030   \n..           ...           ...             ...        ...              ...   \n564        21.56         22.39          142.00     1479.0          0.11100   \n565        20.13         28.25          131.20     1261.0          0.09780   \n566        16.60         28.08          108.30      858.1          0.08455   \n567        20.60         29.33          140.10     1265.0          0.11780   \n568         7.76         24.54           47.92      181.0          0.05263   \n\n     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n0             0.27760         0.30010              0.14710         0.2419   \n1             0.07864         0.08690              0.07017         0.1812   \n2             0.15990         0.19740              0.12790         0.2069   \n3             0.28390         0.24140              0.10520         0.2597   \n4             0.13280         0.19800              0.10430         0.1809   \n..                ...             ...                  ...            ...   \n564           0.11590         0.24390              0.13890         0.1726   \n565           0.10340         0.14400              0.09791         0.1752   \n566           0.10230         0.09251              0.05302         0.1590   \n567           0.27700         0.35140              0.15200         0.2397   \n568           0.04362         0.00000              0.00000         0.1587   \n\n     fractal_dimension_mean  ...  radius_worst  texture_worst  \\\n0                   0.07871  ...        25.380          17.33   \n1                   0.05667  ...        24.990          23.41   \n2                   0.05999  ...        23.570          25.53   \n3                   0.09744  ...        14.910          26.50   \n4                   0.05883  ...        22.540          16.67   \n..                      ...  ...           ...            ...   \n564                 0.05623  ...        25.450          26.40   \n565                 0.05533  ...        23.690          38.25   \n566                 0.05648  ...        18.980          34.12   \n567                 0.07016  ...        25.740          39.42   \n568                 0.05884  ...         9.456          30.37   \n\n     perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n0             184.60      2019.0           0.16220            0.66560   \n1             158.80      1956.0           0.12380            0.18660   \n2             152.50      1709.0           0.14440            0.42450   \n3              98.87       567.7           0.20980            0.86630   \n4             152.20      1575.0           0.13740            0.20500   \n..               ...         ...               ...                ...   \n564           166.10      2027.0           0.14100            0.21130   \n565           155.00      1731.0           0.11660            0.19220   \n566           126.70      1124.0           0.11390            0.30940   \n567           184.60      1821.0           0.16500            0.86810   \n568            59.16       268.6           0.08996            0.06444   \n\n     concavity_worst  concave points_worst  symmetry_worst  \\\n0             0.7119                0.2654          0.4601   \n1             0.2416                0.1860          0.2750   \n2             0.4504                0.2430          0.3613   \n3             0.6869                0.2575          0.6638   \n4             0.4000                0.1625          0.2364   \n..               ...                   ...             ...   \n564           0.4107                0.2216          0.2060   \n565           0.3215                0.1628          0.2572   \n566           0.3403                0.1418          0.2218   \n567           0.9387                0.2650          0.4087   \n568           0.0000                0.0000          0.2871   \n\n     fractal_dimension_worst  \n0                    0.11890  \n1                    0.08902  \n2                    0.08758  \n3                    0.17300  \n4                    0.07678  \n..                       ...  \n564                  0.07115  \n565                  0.06637  \n566                  0.07820  \n567                  0.12400  \n568                  0.07039  \n\n[569 rows x 30 columns]\nY Data \n 0      1\n1      1\n2      1\n3      1\n4      1\n      ..\n564    1\n565    1\n566    1\n567    1\n568    0\nName: diagnosis, Length: 569, dtype: int64\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id = \"6\"></a>\n# 6. Data Normalization\n**we reduce all data to values between 0 and 1 so that operations can be performed and so that very large or very small values in the data cannot manipulate the data**","metadata":{}},{"cell_type":"code","source":"x = (x_data - np.min(x_data))/(np.max(x_data) - np.min(x_data))","metadata":{"execution":{"iopub.status.busy":"2023-10-22T13:28:26.271902Z","iopub.execute_input":"2023-10-22T13:28:26.272432Z","iopub.status.idle":"2023-10-22T13:28:26.283442Z","shell.execute_reply.started":"2023-10-22T13:28:26.272357Z","shell.execute_reply":"2023-10-22T13:28:26.282025Z"},"trusted":true},"execution_count":132,"outputs":[]},{"cell_type":"code","source":"# X data info\nx","metadata":{"execution":{"iopub.status.busy":"2023-10-22T13:28:26.285133Z","iopub.execute_input":"2023-10-22T13:28:26.28555Z","iopub.status.idle":"2023-10-22T13:28:26.325717Z","shell.execute_reply.started":"2023-10-22T13:28:26.285495Z","shell.execute_reply":"2023-10-22T13:28:26.324714Z"},"trusted":true},"execution_count":133,"outputs":[{"execution_count":133,"output_type":"execute_result","data":{"text/plain":"     radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n0       0.521037      0.022658        0.545989   0.363733         0.593753   \n1       0.643144      0.272574        0.615783   0.501591         0.289880   \n2       0.601496      0.390260        0.595743   0.449417         0.514309   \n3       0.210090      0.360839        0.233501   0.102906         0.811321   \n4       0.629893      0.156578        0.630986   0.489290         0.430351   \n..           ...           ...             ...        ...              ...   \n564     0.690000      0.428813        0.678668   0.566490         0.526948   \n565     0.622320      0.626987        0.604036   0.474019         0.407782   \n566     0.455251      0.621238        0.445788   0.303118         0.288165   \n567     0.644564      0.663510        0.665538   0.475716         0.588336   \n568     0.036869      0.501522        0.028540   0.015907         0.000000   \n\n     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n0            0.792037        0.703140             0.731113       0.686364   \n1            0.181768        0.203608             0.348757       0.379798   \n2            0.431017        0.462512             0.635686       0.509596   \n3            0.811361        0.565604             0.522863       0.776263   \n4            0.347893        0.463918             0.518390       0.378283   \n..                ...             ...                  ...            ...   \n564          0.296055        0.571462             0.690358       0.336364   \n565          0.257714        0.337395             0.486630       0.349495   \n566          0.254340        0.216753             0.263519       0.267677   \n567          0.790197        0.823336             0.755467       0.675253   \n568          0.074351        0.000000             0.000000       0.266162   \n\n     fractal_dimension_mean  ...  radius_worst  texture_worst  \\\n0                  0.605518  ...      0.620776       0.141525   \n1                  0.141323  ...      0.606901       0.303571   \n2                  0.211247  ...      0.556386       0.360075   \n3                  1.000000  ...      0.248310       0.385928   \n4                  0.186816  ...      0.519744       0.123934   \n..                      ...  ...           ...            ...   \n564                0.132056  ...      0.623266       0.383262   \n565                0.113100  ...      0.560655       0.699094   \n566                0.137321  ...      0.393099       0.589019   \n567                0.425442  ...      0.633582       0.730277   \n568                0.187026  ...      0.054287       0.489072   \n\n     perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n0           0.668310    0.450698          0.601136           0.619292   \n1           0.539818    0.435214          0.347553           0.154563   \n2           0.508442    0.374508          0.483590           0.385375   \n3           0.241347    0.094008          0.915472           0.814012   \n4           0.506948    0.341575          0.437364           0.172415   \n..               ...         ...               ...                ...   \n564         0.576174    0.452664          0.461137           0.178527   \n565         0.520892    0.379915          0.300007           0.159997   \n566         0.379949    0.230731          0.282177           0.273705   \n567         0.668310    0.402035          0.619626           0.815758   \n568         0.043578    0.020497          0.124084           0.036043   \n\n     concavity_worst  concave points_worst  symmetry_worst  \\\n0           0.568610              0.912027        0.598462   \n1           0.192971              0.639175        0.233590   \n2           0.359744              0.835052        0.403706   \n3           0.548642              0.884880        1.000000   \n4           0.319489              0.558419        0.157500   \n..               ...                   ...             ...   \n564         0.328035              0.761512        0.097575   \n565         0.256789              0.559450        0.198502   \n566         0.271805              0.487285        0.128721   \n567         0.749760              0.910653        0.497142   \n568         0.000000              0.000000        0.257441   \n\n     fractal_dimension_worst  \n0                   0.418864  \n1                   0.222878  \n2                   0.213433  \n3                   0.773711  \n4                   0.142595  \n..                       ...  \n564                 0.105667  \n565                 0.074315  \n566                 0.151909  \n567                 0.452315  \n568                 0.100682  \n\n[569 rows x 30 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.521037</td>\n      <td>0.022658</td>\n      <td>0.545989</td>\n      <td>0.363733</td>\n      <td>0.593753</td>\n      <td>0.792037</td>\n      <td>0.703140</td>\n      <td>0.731113</td>\n      <td>0.686364</td>\n      <td>0.605518</td>\n      <td>...</td>\n      <td>0.620776</td>\n      <td>0.141525</td>\n      <td>0.668310</td>\n      <td>0.450698</td>\n      <td>0.601136</td>\n      <td>0.619292</td>\n      <td>0.568610</td>\n      <td>0.912027</td>\n      <td>0.598462</td>\n      <td>0.418864</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.643144</td>\n      <td>0.272574</td>\n      <td>0.615783</td>\n      <td>0.501591</td>\n      <td>0.289880</td>\n      <td>0.181768</td>\n      <td>0.203608</td>\n      <td>0.348757</td>\n      <td>0.379798</td>\n      <td>0.141323</td>\n      <td>...</td>\n      <td>0.606901</td>\n      <td>0.303571</td>\n      <td>0.539818</td>\n      <td>0.435214</td>\n      <td>0.347553</td>\n      <td>0.154563</td>\n      <td>0.192971</td>\n      <td>0.639175</td>\n      <td>0.233590</td>\n      <td>0.222878</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.601496</td>\n      <td>0.390260</td>\n      <td>0.595743</td>\n      <td>0.449417</td>\n      <td>0.514309</td>\n      <td>0.431017</td>\n      <td>0.462512</td>\n      <td>0.635686</td>\n      <td>0.509596</td>\n      <td>0.211247</td>\n      <td>...</td>\n      <td>0.556386</td>\n      <td>0.360075</td>\n      <td>0.508442</td>\n      <td>0.374508</td>\n      <td>0.483590</td>\n      <td>0.385375</td>\n      <td>0.359744</td>\n      <td>0.835052</td>\n      <td>0.403706</td>\n      <td>0.213433</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.210090</td>\n      <td>0.360839</td>\n      <td>0.233501</td>\n      <td>0.102906</td>\n      <td>0.811321</td>\n      <td>0.811361</td>\n      <td>0.565604</td>\n      <td>0.522863</td>\n      <td>0.776263</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>0.248310</td>\n      <td>0.385928</td>\n      <td>0.241347</td>\n      <td>0.094008</td>\n      <td>0.915472</td>\n      <td>0.814012</td>\n      <td>0.548642</td>\n      <td>0.884880</td>\n      <td>1.000000</td>\n      <td>0.773711</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.629893</td>\n      <td>0.156578</td>\n      <td>0.630986</td>\n      <td>0.489290</td>\n      <td>0.430351</td>\n      <td>0.347893</td>\n      <td>0.463918</td>\n      <td>0.518390</td>\n      <td>0.378283</td>\n      <td>0.186816</td>\n      <td>...</td>\n      <td>0.519744</td>\n      <td>0.123934</td>\n      <td>0.506948</td>\n      <td>0.341575</td>\n      <td>0.437364</td>\n      <td>0.172415</td>\n      <td>0.319489</td>\n      <td>0.558419</td>\n      <td>0.157500</td>\n      <td>0.142595</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>564</th>\n      <td>0.690000</td>\n      <td>0.428813</td>\n      <td>0.678668</td>\n      <td>0.566490</td>\n      <td>0.526948</td>\n      <td>0.296055</td>\n      <td>0.571462</td>\n      <td>0.690358</td>\n      <td>0.336364</td>\n      <td>0.132056</td>\n      <td>...</td>\n      <td>0.623266</td>\n      <td>0.383262</td>\n      <td>0.576174</td>\n      <td>0.452664</td>\n      <td>0.461137</td>\n      <td>0.178527</td>\n      <td>0.328035</td>\n      <td>0.761512</td>\n      <td>0.097575</td>\n      <td>0.105667</td>\n    </tr>\n    <tr>\n      <th>565</th>\n      <td>0.622320</td>\n      <td>0.626987</td>\n      <td>0.604036</td>\n      <td>0.474019</td>\n      <td>0.407782</td>\n      <td>0.257714</td>\n      <td>0.337395</td>\n      <td>0.486630</td>\n      <td>0.349495</td>\n      <td>0.113100</td>\n      <td>...</td>\n      <td>0.560655</td>\n      <td>0.699094</td>\n      <td>0.520892</td>\n      <td>0.379915</td>\n      <td>0.300007</td>\n      <td>0.159997</td>\n      <td>0.256789</td>\n      <td>0.559450</td>\n      <td>0.198502</td>\n      <td>0.074315</td>\n    </tr>\n    <tr>\n      <th>566</th>\n      <td>0.455251</td>\n      <td>0.621238</td>\n      <td>0.445788</td>\n      <td>0.303118</td>\n      <td>0.288165</td>\n      <td>0.254340</td>\n      <td>0.216753</td>\n      <td>0.263519</td>\n      <td>0.267677</td>\n      <td>0.137321</td>\n      <td>...</td>\n      <td>0.393099</td>\n      <td>0.589019</td>\n      <td>0.379949</td>\n      <td>0.230731</td>\n      <td>0.282177</td>\n      <td>0.273705</td>\n      <td>0.271805</td>\n      <td>0.487285</td>\n      <td>0.128721</td>\n      <td>0.151909</td>\n    </tr>\n    <tr>\n      <th>567</th>\n      <td>0.644564</td>\n      <td>0.663510</td>\n      <td>0.665538</td>\n      <td>0.475716</td>\n      <td>0.588336</td>\n      <td>0.790197</td>\n      <td>0.823336</td>\n      <td>0.755467</td>\n      <td>0.675253</td>\n      <td>0.425442</td>\n      <td>...</td>\n      <td>0.633582</td>\n      <td>0.730277</td>\n      <td>0.668310</td>\n      <td>0.402035</td>\n      <td>0.619626</td>\n      <td>0.815758</td>\n      <td>0.749760</td>\n      <td>0.910653</td>\n      <td>0.497142</td>\n      <td>0.452315</td>\n    </tr>\n    <tr>\n      <th>568</th>\n      <td>0.036869</td>\n      <td>0.501522</td>\n      <td>0.028540</td>\n      <td>0.015907</td>\n      <td>0.000000</td>\n      <td>0.074351</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.266162</td>\n      <td>0.187026</td>\n      <td>...</td>\n      <td>0.054287</td>\n      <td>0.489072</td>\n      <td>0.043578</td>\n      <td>0.020497</td>\n      <td>0.124084</td>\n      <td>0.036043</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.257441</td>\n      <td>0.100682</td>\n    </tr>\n  </tbody>\n</table>\n<p>569 rows × 30 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id = \"7\"></a>\n# 7. Train and Test  Split\n**Main data was divided into 70% training data and 30% test data**","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nx_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.3,random_state=1)","metadata":{"execution":{"iopub.status.busy":"2023-10-22T13:28:26.326957Z","iopub.execute_input":"2023-10-22T13:28:26.327592Z","iopub.status.idle":"2023-10-22T13:28:26.336623Z","shell.execute_reply.started":"2023-10-22T13:28:26.327551Z","shell.execute_reply":"2023-10-22T13:28:26.335108Z"},"trusted":true},"execution_count":134,"outputs":[]},{"cell_type":"code","source":"# X_train data info\nprint(\"X Train\")\nx_train","metadata":{"execution":{"iopub.status.busy":"2023-10-22T13:28:26.338078Z","iopub.execute_input":"2023-10-22T13:28:26.338435Z","iopub.status.idle":"2023-10-22T13:28:26.381475Z","shell.execute_reply.started":"2023-10-22T13:28:26.338398Z","shell.execute_reply":"2023-10-22T13:28:26.38024Z"},"trusted":true},"execution_count":135,"outputs":[{"name":"stdout","text":"X Train\n","output_type":"stream"},{"execution_count":135,"output_type":"execute_result","data":{"text/plain":"     radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n249     0.214823      0.176530        0.207864   0.111474         0.439379   \n58      0.287236      0.324653        0.268261   0.162757         0.252505   \n476     0.341663      0.365911        0.335982   0.201442         0.331137   \n529     0.240854      0.126141        0.235229   0.127975         0.517920   \n422     0.219083      0.213392        0.218851   0.112280         0.507087   \n..           ...           ...             ...        ...              ...   \n129     0.606228      0.521136        0.598507   0.444751         0.441184   \n144     0.178380      0.177883        0.169097   0.089841         0.228401   \n72      0.483648      0.500845        0.486559   0.333362         0.491740   \n235     0.333617      0.390260        0.317877   0.195080         0.343685   \n37      0.286289      0.294555        0.268261   0.161315         0.335831   \n\n     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n249          0.180050        0.101406             0.145577       0.415657   \n58           0.056776        0.001621             0.020711       0.383333   \n476          0.280412        0.118627             0.151988       0.225253   \n529          0.216889        0.088590             0.139066       0.301515   \n422          0.298816        0.166284             0.223509       0.417172   \n..                ...             ...                  ...            ...   \n129          0.427949        0.596298             0.571074       0.576768   \n144          0.098184        0.052741             0.039140       0.171212   \n72           0.501871        0.396439             0.394831       0.437879   \n235          0.153580        0.034255             0.094235       0.230808   \n37           0.056070        0.060028             0.145278       0.205556   \n\n     fractal_dimension_mean  ...  radius_worst  texture_worst  \\\n249                0.246841  ...      0.167912       0.244403   \n58                 0.106361  ...      0.224120       0.272655   \n476                0.213353  ...      0.303095       0.406183   \n529                0.339511  ...      0.196371       0.099947   \n422                0.278854  ...      0.167556       0.203891   \n..                      ...  ...           ...            ...   \n129                0.235257  ...      0.522946       0.574627   \n144                0.145746  ...      0.143010       0.231876   \n72                 0.314027  ...      0.547492       0.581023   \n235                0.176706  ...      0.263252       0.486674   \n37                 0.182603  ...      0.191035       0.287580   \n\n     perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n249         0.151751    0.075354          0.447269           0.127010   \n58          0.198366    0.107870          0.204253           0.033588   \n476         0.307236    0.158106          0.291422           0.306206   \n529         0.181832    0.089633          0.534438           0.131861   \n422         0.156980    0.071397          0.464439           0.184058   \n..               ...         ...               ...                ...   \n129         0.489516    0.345016          0.371987           0.348119   \n144         0.136361    0.062918          0.240573           0.092179   \n72          0.503959    0.367627          0.576702           0.690893   \n235         0.238358    0.130333          0.379912           0.120315   \n37          0.169580    0.088650          0.170640           0.018337   \n\n     concavity_worst  concave points_worst  symmetry_worst  \\\n249         0.144089              0.330172        0.216637   \n58          0.001474              0.038179        0.172285   \n476         0.200639              0.460137        0.191011   \n529         0.129553              0.254055        0.239700   \n422         0.183866              0.379725        0.240883   \n..               ...                   ...             ...   \n129         0.453115              0.595189        0.342992   \n144         0.077915              0.117285        0.144885   \n72          0.524441              0.652577        0.344569   \n235         0.049768              0.273643        0.130298   \n37          0.038602              0.172268        0.083185   \n\n     fractal_dimension_worst  \n249                 0.151187  \n58                  0.051489  \n476                 0.154401  \n529                 0.167126  \n422                 0.126131  \n..                       ...  \n129                 0.194215  \n144                 0.082973  \n72                  0.517250  \n235                 0.138594  \n37                  0.043618  \n\n[398 rows x 30 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>249</th>\n      <td>0.214823</td>\n      <td>0.176530</td>\n      <td>0.207864</td>\n      <td>0.111474</td>\n      <td>0.439379</td>\n      <td>0.180050</td>\n      <td>0.101406</td>\n      <td>0.145577</td>\n      <td>0.415657</td>\n      <td>0.246841</td>\n      <td>...</td>\n      <td>0.167912</td>\n      <td>0.244403</td>\n      <td>0.151751</td>\n      <td>0.075354</td>\n      <td>0.447269</td>\n      <td>0.127010</td>\n      <td>0.144089</td>\n      <td>0.330172</td>\n      <td>0.216637</td>\n      <td>0.151187</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>0.287236</td>\n      <td>0.324653</td>\n      <td>0.268261</td>\n      <td>0.162757</td>\n      <td>0.252505</td>\n      <td>0.056776</td>\n      <td>0.001621</td>\n      <td>0.020711</td>\n      <td>0.383333</td>\n      <td>0.106361</td>\n      <td>...</td>\n      <td>0.224120</td>\n      <td>0.272655</td>\n      <td>0.198366</td>\n      <td>0.107870</td>\n      <td>0.204253</td>\n      <td>0.033588</td>\n      <td>0.001474</td>\n      <td>0.038179</td>\n      <td>0.172285</td>\n      <td>0.051489</td>\n    </tr>\n    <tr>\n      <th>476</th>\n      <td>0.341663</td>\n      <td>0.365911</td>\n      <td>0.335982</td>\n      <td>0.201442</td>\n      <td>0.331137</td>\n      <td>0.280412</td>\n      <td>0.118627</td>\n      <td>0.151988</td>\n      <td>0.225253</td>\n      <td>0.213353</td>\n      <td>...</td>\n      <td>0.303095</td>\n      <td>0.406183</td>\n      <td>0.307236</td>\n      <td>0.158106</td>\n      <td>0.291422</td>\n      <td>0.306206</td>\n      <td>0.200639</td>\n      <td>0.460137</td>\n      <td>0.191011</td>\n      <td>0.154401</td>\n    </tr>\n    <tr>\n      <th>529</th>\n      <td>0.240854</td>\n      <td>0.126141</td>\n      <td>0.235229</td>\n      <td>0.127975</td>\n      <td>0.517920</td>\n      <td>0.216889</td>\n      <td>0.088590</td>\n      <td>0.139066</td>\n      <td>0.301515</td>\n      <td>0.339511</td>\n      <td>...</td>\n      <td>0.196371</td>\n      <td>0.099947</td>\n      <td>0.181832</td>\n      <td>0.089633</td>\n      <td>0.534438</td>\n      <td>0.131861</td>\n      <td>0.129553</td>\n      <td>0.254055</td>\n      <td>0.239700</td>\n      <td>0.167126</td>\n    </tr>\n    <tr>\n      <th>422</th>\n      <td>0.219083</td>\n      <td>0.213392</td>\n      <td>0.218851</td>\n      <td>0.112280</td>\n      <td>0.507087</td>\n      <td>0.298816</td>\n      <td>0.166284</td>\n      <td>0.223509</td>\n      <td>0.417172</td>\n      <td>0.278854</td>\n      <td>...</td>\n      <td>0.167556</td>\n      <td>0.203891</td>\n      <td>0.156980</td>\n      <td>0.071397</td>\n      <td>0.464439</td>\n      <td>0.184058</td>\n      <td>0.183866</td>\n      <td>0.379725</td>\n      <td>0.240883</td>\n      <td>0.126131</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>129</th>\n      <td>0.606228</td>\n      <td>0.521136</td>\n      <td>0.598507</td>\n      <td>0.444751</td>\n      <td>0.441184</td>\n      <td>0.427949</td>\n      <td>0.596298</td>\n      <td>0.571074</td>\n      <td>0.576768</td>\n      <td>0.235257</td>\n      <td>...</td>\n      <td>0.522946</td>\n      <td>0.574627</td>\n      <td>0.489516</td>\n      <td>0.345016</td>\n      <td>0.371987</td>\n      <td>0.348119</td>\n      <td>0.453115</td>\n      <td>0.595189</td>\n      <td>0.342992</td>\n      <td>0.194215</td>\n    </tr>\n    <tr>\n      <th>144</th>\n      <td>0.178380</td>\n      <td>0.177883</td>\n      <td>0.169097</td>\n      <td>0.089841</td>\n      <td>0.228401</td>\n      <td>0.098184</td>\n      <td>0.052741</td>\n      <td>0.039140</td>\n      <td>0.171212</td>\n      <td>0.145746</td>\n      <td>...</td>\n      <td>0.143010</td>\n      <td>0.231876</td>\n      <td>0.136361</td>\n      <td>0.062918</td>\n      <td>0.240573</td>\n      <td>0.092179</td>\n      <td>0.077915</td>\n      <td>0.117285</td>\n      <td>0.144885</td>\n      <td>0.082973</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>0.483648</td>\n      <td>0.500845</td>\n      <td>0.486559</td>\n      <td>0.333362</td>\n      <td>0.491740</td>\n      <td>0.501871</td>\n      <td>0.396439</td>\n      <td>0.394831</td>\n      <td>0.437879</td>\n      <td>0.314027</td>\n      <td>...</td>\n      <td>0.547492</td>\n      <td>0.581023</td>\n      <td>0.503959</td>\n      <td>0.367627</td>\n      <td>0.576702</td>\n      <td>0.690893</td>\n      <td>0.524441</td>\n      <td>0.652577</td>\n      <td>0.344569</td>\n      <td>0.517250</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>0.333617</td>\n      <td>0.390260</td>\n      <td>0.317877</td>\n      <td>0.195080</td>\n      <td>0.343685</td>\n      <td>0.153580</td>\n      <td>0.034255</td>\n      <td>0.094235</td>\n      <td>0.230808</td>\n      <td>0.176706</td>\n      <td>...</td>\n      <td>0.263252</td>\n      <td>0.486674</td>\n      <td>0.238358</td>\n      <td>0.130333</td>\n      <td>0.379912</td>\n      <td>0.120315</td>\n      <td>0.049768</td>\n      <td>0.273643</td>\n      <td>0.130298</td>\n      <td>0.138594</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>0.286289</td>\n      <td>0.294555</td>\n      <td>0.268261</td>\n      <td>0.161315</td>\n      <td>0.335831</td>\n      <td>0.056070</td>\n      <td>0.060028</td>\n      <td>0.145278</td>\n      <td>0.205556</td>\n      <td>0.182603</td>\n      <td>...</td>\n      <td>0.191035</td>\n      <td>0.287580</td>\n      <td>0.169580</td>\n      <td>0.088650</td>\n      <td>0.170640</td>\n      <td>0.018337</td>\n      <td>0.038602</td>\n      <td>0.172268</td>\n      <td>0.083185</td>\n      <td>0.043618</td>\n    </tr>\n  </tbody>\n</table>\n<p>398 rows × 30 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# y_train data info\nprint(\"Y Train\")\ny_train","metadata":{"execution":{"iopub.status.busy":"2023-10-22T13:28:26.386166Z","iopub.execute_input":"2023-10-22T13:28:26.3869Z","iopub.status.idle":"2023-10-22T13:28:26.39928Z","shell.execute_reply.started":"2023-10-22T13:28:26.386858Z","shell.execute_reply":"2023-10-22T13:28:26.397841Z"},"trusted":true},"execution_count":136,"outputs":[{"name":"stdout","text":"Y Train\n","output_type":"stream"},{"execution_count":136,"output_type":"execute_result","data":{"text/plain":"249    0\n58     0\n476    0\n529    0\n422    0\n      ..\n129    1\n144    0\n72     1\n235    0\n37     0\nName: diagnosis, Length: 398, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"print(\"X Test\")\nx_test","metadata":{"execution":{"iopub.status.busy":"2023-10-22T13:28:26.400747Z","iopub.execute_input":"2023-10-22T13:28:26.401134Z","iopub.status.idle":"2023-10-22T13:28:26.442397Z","shell.execute_reply.started":"2023-10-22T13:28:26.401058Z","shell.execute_reply":"2023-10-22T13:28:26.441103Z"},"trusted":true},"execution_count":137,"outputs":[{"name":"stdout","text":"X Test\n","output_type":"stream"},{"execution_count":137,"output_type":"execute_result","data":{"text/plain":"     radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n421     0.364854      0.144403        0.376132   0.217434         0.455629   \n47      0.292915      0.302672        0.291549   0.165896         0.570281   \n292     0.282503      0.213392        0.271923   0.157031         0.432157   \n186     0.536182      0.299966        0.516965   0.380700         0.300172   \n414     0.385678      0.679743        0.365697   0.244327         0.275977   \n..           ...           ...             ...        ...              ...   \n6       0.533343      0.347311        0.523875   0.380276         0.379164   \n487     0.589663      0.308083        0.582614   0.434146         0.507990   \n11      0.416442      0.276632        0.413309   0.270414         0.401462   \n268     0.278716      0.219817        0.266671   0.156394         0.375734   \n419     0.197785      0.395671        0.187686   0.100445         0.443893   \n\n     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n421          0.503711        0.339738             0.313121       0.518182   \n47           0.318140        0.287254             0.364811       0.539394   \n292          0.184191        0.144213             0.167495       0.338384   \n186          0.200294        0.191401             0.288966       0.283333   \n414          0.081805        0.109794             0.136133       0.400000   \n..                ...             ...                  ...            ...   \n6            0.274891        0.264058             0.367793       0.370707   \n487          0.384700        0.528585             0.593439       0.385354   \n11           0.336850        0.233224             0.328330       0.394949   \n268          0.131311        0.091378             0.080268       0.479798   \n419          0.123919        0.020982             0.053479       0.280303   \n\n     fractal_dimension_mean  ...  radius_worst  texture_worst  \\\n421                0.507582  ...      0.303451       0.168443   \n47                 0.375105  ...      0.275347       0.424574   \n292                0.310447  ...      0.206688       0.210821   \n186                0.090354  ...      0.475987       0.382196   \n414                0.062763  ...      0.331910       0.663380   \n..                      ...  ...           ...            ...   \n6                  0.157119  ...      0.531839       0.416844   \n487                0.235678  ...      0.570260       0.489606   \n11                 0.228728  ...      0.444326       0.406716   \n268                0.162805  ...      0.212380       0.309701   \n419                0.241786  ...      0.157595       0.450426   \n\n     perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n421         0.317197    0.153362          0.396421           0.326193   \n47          0.260919    0.141123          0.709437           0.377711   \n292         0.191245    0.098358          0.509344           0.174162   \n186         0.442203    0.301022          0.344912           0.210738   \n414         0.297276    0.183396          0.288120           0.069244   \n..               ...         ...               ...                ...   \n6           0.511928    0.349194          0.482269           0.223448   \n487         0.515414    0.382127          0.529816           0.334924   \n11          0.428756    0.273742          0.451892           0.517711   \n268         0.193536    0.101332          0.359440           0.148936   \n419         0.143682    0.067047          0.376610           0.081022   \n\n     concavity_worst  concave points_worst  symmetry_worst  \\\n421         0.257109              0.380756        0.248768   \n47          0.399840              0.717526        0.460280   \n292         0.178994              0.362887        0.357776   \n186         0.282588              0.539863        0.323477   \n414         0.123562              0.225945        0.328800   \n..               ...                   ...             ...   \n6           0.302236              0.663918        0.295289   \n487         0.474121              0.707904        0.335305   \n11          0.316693              0.621993        0.438991   \n268         0.159105              0.198625        0.401932   \n419         0.028610              0.147973        0.278139   \n\n     fractal_dimension_worst  \n421                 0.242949  \n47                  0.412305  \n292                 0.267611  \n186                 0.094057  \n414                 0.043356  \n..                       ...  \n6                   0.187853  \n487                 0.229896  \n11                  0.326381  \n268                 0.102191  \n419                 0.106192  \n\n[171 rows x 30 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>421</th>\n      <td>0.364854</td>\n      <td>0.144403</td>\n      <td>0.376132</td>\n      <td>0.217434</td>\n      <td>0.455629</td>\n      <td>0.503711</td>\n      <td>0.339738</td>\n      <td>0.313121</td>\n      <td>0.518182</td>\n      <td>0.507582</td>\n      <td>...</td>\n      <td>0.303451</td>\n      <td>0.168443</td>\n      <td>0.317197</td>\n      <td>0.153362</td>\n      <td>0.396421</td>\n      <td>0.326193</td>\n      <td>0.257109</td>\n      <td>0.380756</td>\n      <td>0.248768</td>\n      <td>0.242949</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>0.292915</td>\n      <td>0.302672</td>\n      <td>0.291549</td>\n      <td>0.165896</td>\n      <td>0.570281</td>\n      <td>0.318140</td>\n      <td>0.287254</td>\n      <td>0.364811</td>\n      <td>0.539394</td>\n      <td>0.375105</td>\n      <td>...</td>\n      <td>0.275347</td>\n      <td>0.424574</td>\n      <td>0.260919</td>\n      <td>0.141123</td>\n      <td>0.709437</td>\n      <td>0.377711</td>\n      <td>0.399840</td>\n      <td>0.717526</td>\n      <td>0.460280</td>\n      <td>0.412305</td>\n    </tr>\n    <tr>\n      <th>292</th>\n      <td>0.282503</td>\n      <td>0.213392</td>\n      <td>0.271923</td>\n      <td>0.157031</td>\n      <td>0.432157</td>\n      <td>0.184191</td>\n      <td>0.144213</td>\n      <td>0.167495</td>\n      <td>0.338384</td>\n      <td>0.310447</td>\n      <td>...</td>\n      <td>0.206688</td>\n      <td>0.210821</td>\n      <td>0.191245</td>\n      <td>0.098358</td>\n      <td>0.509344</td>\n      <td>0.174162</td>\n      <td>0.178994</td>\n      <td>0.362887</td>\n      <td>0.357776</td>\n      <td>0.267611</td>\n    </tr>\n    <tr>\n      <th>186</th>\n      <td>0.536182</td>\n      <td>0.299966</td>\n      <td>0.516965</td>\n      <td>0.380700</td>\n      <td>0.300172</td>\n      <td>0.200294</td>\n      <td>0.191401</td>\n      <td>0.288966</td>\n      <td>0.283333</td>\n      <td>0.090354</td>\n      <td>...</td>\n      <td>0.475987</td>\n      <td>0.382196</td>\n      <td>0.442203</td>\n      <td>0.301022</td>\n      <td>0.344912</td>\n      <td>0.210738</td>\n      <td>0.282588</td>\n      <td>0.539863</td>\n      <td>0.323477</td>\n      <td>0.094057</td>\n    </tr>\n    <tr>\n      <th>414</th>\n      <td>0.385678</td>\n      <td>0.679743</td>\n      <td>0.365697</td>\n      <td>0.244327</td>\n      <td>0.275977</td>\n      <td>0.081805</td>\n      <td>0.109794</td>\n      <td>0.136133</td>\n      <td>0.400000</td>\n      <td>0.062763</td>\n      <td>...</td>\n      <td>0.331910</td>\n      <td>0.663380</td>\n      <td>0.297276</td>\n      <td>0.183396</td>\n      <td>0.288120</td>\n      <td>0.069244</td>\n      <td>0.123562</td>\n      <td>0.225945</td>\n      <td>0.328800</td>\n      <td>0.043356</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.533343</td>\n      <td>0.347311</td>\n      <td>0.523875</td>\n      <td>0.380276</td>\n      <td>0.379164</td>\n      <td>0.274891</td>\n      <td>0.264058</td>\n      <td>0.367793</td>\n      <td>0.370707</td>\n      <td>0.157119</td>\n      <td>...</td>\n      <td>0.531839</td>\n      <td>0.416844</td>\n      <td>0.511928</td>\n      <td>0.349194</td>\n      <td>0.482269</td>\n      <td>0.223448</td>\n      <td>0.302236</td>\n      <td>0.663918</td>\n      <td>0.295289</td>\n      <td>0.187853</td>\n    </tr>\n    <tr>\n      <th>487</th>\n      <td>0.589663</td>\n      <td>0.308083</td>\n      <td>0.582614</td>\n      <td>0.434146</td>\n      <td>0.507990</td>\n      <td>0.384700</td>\n      <td>0.528585</td>\n      <td>0.593439</td>\n      <td>0.385354</td>\n      <td>0.235678</td>\n      <td>...</td>\n      <td>0.570260</td>\n      <td>0.489606</td>\n      <td>0.515414</td>\n      <td>0.382127</td>\n      <td>0.529816</td>\n      <td>0.334924</td>\n      <td>0.474121</td>\n      <td>0.707904</td>\n      <td>0.335305</td>\n      <td>0.229896</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>0.416442</td>\n      <td>0.276632</td>\n      <td>0.413309</td>\n      <td>0.270414</td>\n      <td>0.401462</td>\n      <td>0.336850</td>\n      <td>0.233224</td>\n      <td>0.328330</td>\n      <td>0.394949</td>\n      <td>0.228728</td>\n      <td>...</td>\n      <td>0.444326</td>\n      <td>0.406716</td>\n      <td>0.428756</td>\n      <td>0.273742</td>\n      <td>0.451892</td>\n      <td>0.517711</td>\n      <td>0.316693</td>\n      <td>0.621993</td>\n      <td>0.438991</td>\n      <td>0.326381</td>\n    </tr>\n    <tr>\n      <th>268</th>\n      <td>0.278716</td>\n      <td>0.219817</td>\n      <td>0.266671</td>\n      <td>0.156394</td>\n      <td>0.375734</td>\n      <td>0.131311</td>\n      <td>0.091378</td>\n      <td>0.080268</td>\n      <td>0.479798</td>\n      <td>0.162805</td>\n      <td>...</td>\n      <td>0.212380</td>\n      <td>0.309701</td>\n      <td>0.193536</td>\n      <td>0.101332</td>\n      <td>0.359440</td>\n      <td>0.148936</td>\n      <td>0.159105</td>\n      <td>0.198625</td>\n      <td>0.401932</td>\n      <td>0.102191</td>\n    </tr>\n    <tr>\n      <th>419</th>\n      <td>0.197785</td>\n      <td>0.395671</td>\n      <td>0.187686</td>\n      <td>0.100445</td>\n      <td>0.443893</td>\n      <td>0.123919</td>\n      <td>0.020982</td>\n      <td>0.053479</td>\n      <td>0.280303</td>\n      <td>0.241786</td>\n      <td>...</td>\n      <td>0.157595</td>\n      <td>0.450426</td>\n      <td>0.143682</td>\n      <td>0.067047</td>\n      <td>0.376610</td>\n      <td>0.081022</td>\n      <td>0.028610</td>\n      <td>0.147973</td>\n      <td>0.278139</td>\n      <td>0.106192</td>\n    </tr>\n  </tbody>\n</table>\n<p>171 rows × 30 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"print(\"Y Test\")\ny_test","metadata":{"execution":{"iopub.status.busy":"2023-10-22T13:28:26.444268Z","iopub.execute_input":"2023-10-22T13:28:26.444626Z","iopub.status.idle":"2023-10-22T13:28:26.454863Z","shell.execute_reply.started":"2023-10-22T13:28:26.444591Z","shell.execute_reply":"2023-10-22T13:28:26.453564Z"},"trusted":true},"execution_count":138,"outputs":[{"name":"stdout","text":"Y Test\n","output_type":"stream"},{"execution_count":138,"output_type":"execute_result","data":{"text/plain":"421    0\n47     1\n292    0\n186    1\n414    1\n      ..\n6      1\n487    1\n11     1\n268    0\n419    0\nName: diagnosis, Length: 171, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id = \"8\"></a>\n# 8. Find K Values\n**We draw a graph to find the perfect k values. The value we choose is 3**","metadata":{}},{"cell_type":"code","source":"from sklearn.neighbors import KNeighborsClassifier\nScore_list = []\n\nfor each in range(1,15):\n    knn2 = KNeighborsClassifier(n_neighbors = each)\n    knn2.fit(x_train,y_train)\n    Score_list.append(knn2.score(x_test,y_test))\n    \nplt.plot(range(1,15),Score_list)\nplt.xlabel(\"k values\")\nplt.ylabel(\"accuracy\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T13:28:26.456798Z","iopub.execute_input":"2023-10-22T13:28:26.457196Z","iopub.status.idle":"2023-10-22T13:28:27.065938Z","shell.execute_reply.started":"2023-10-22T13:28:26.457132Z","shell.execute_reply":"2023-10-22T13:28:27.064448Z"},"trusted":true},"execution_count":139,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAkkAAAGwCAYAAAC99fF4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABXsElEQVR4nO3deXiTVd4+8Dt70p3SfW+RpdCyFWjZ3AUZRRkXFgF1BmfU6TuCjqMyDjPqODDoBS+vC8ygP0bZUcYFFBdGEReEQoFCKbLTfaEtTbrQNk2e3x9pArWhQJrmZLk/15XrkvRJnm9ilzvn+Z5zZJIkSSAiIiKiDuSiCyAiIiJyRwxJRERERHYwJBERERHZwZBEREREZAdDEhEREZEdDElEREREdjAkEREREdmhFF2ApzKbzSgrK0NgYCBkMpnocoiIiOgqSJKE+vp6xMTEQC7veqyIIclBZWVliI+PF10GEREROaC4uBhxcXFdHsOQ5KDAwEAAljc5KChIcDVERER0NQwGA+Lj421/x7vCkOQg6yW2oKAghiQiIiIPczWtMmzcJiIiIrKDIYmIiIjIDoYkIiIiIjsYkoiIiIjsYEgiIiIisoMhiYiIiMgOhiQiIiIiOxiSiIiIiOxgSCIiIiKyQ3hIWr58OZKTk6HVapGRkYHvvvuuy+PffPNNpKamQqfToX///li9enWnY+rq6pCdnY3o6GhotVqkpqZi27Zttq+/8MILkMlkHW5RUVFOf21ERETkuYRuS7Jp0ybMmzcPy5cvx9ixY/Gvf/0LkyZNQkFBARISEjodv2LFCsyfPx9vvfUWRo4ciZycHPzmN79Br169MHnyZABAa2srbrvtNkRERGDz5s2Ii4tDcXFxpz1aBg0ahP/+97+2fysUip59sURERORRhIakpUuXYs6cOXjkkUcAAMuWLcMXX3yBFStWYNGiRZ2OX7NmDR599FFMmzYNAJCSkoLdu3dj8eLFtpC0atUq1NbWYteuXVCpVACAxMTETs+lVCo5ekRERESXJSwktba2Ijc3F88991yH+ydMmIBdu3bZfUxLSwu0Wm2H+3Q6HXJycmA0GqFSqbBlyxaMHj0a2dnZ+PjjjxEeHo4HHngAzz77bIfRohMnTiAmJgYajQaZmZlYuHAhUlJSLltvS0sLWlpabP82GAyOvGwir9RmMqPC0Cy6DPIQ0cE6KORX3lzUV5jNEoxmMzRKXtFwN8JCUnV1NUwmEyIjIzvcHxkZiYqKCruPmThxIt5++21MmTIFw4cPR25uLlatWgWj0Yjq6mpER0fj9OnT+PrrrzFz5kxs27YNJ06cQHZ2Ntra2vCXv/wFAJCZmYnVq1ejX79+qKysxMsvv4wxY8bgyJEj6N27t91zL1q0CC+++KJz3wQiLyBJEqat3I3cwvOiSyEPMTKpF957dPRV7cLuC5567yC2F1Tio+yx6BsZeOUHkMsIvdwGoNMPiSRJl/3BWbBgASoqKpCVlQVJkhAZGYmHH34Yr7zyim2UyGw2IyIiAitXroRCoUBGRgbKysrw6quv2kLSpEmTbM+Znp6O0aNHo0+fPnj33Xfx1FNP2T33/PnzO3zNYDAgPj6+W6+dyBvsOVNrC0gapfC5IOTmWtrM2Hv2PHILz2NEUqjocoQ7W92Ijw6WAQBW/XAGi+4ZLLgiupSwkBQWFgaFQtFp1KiqqqrT6JKVTqfDqlWr8K9//QuVlZWIjo7GypUrERgYiLCwMABAdHQ0VCpVh0trqampqKioQGtrK9Rqdafn9ff3R3p6Ok6cOHHZejUaDTQajSMvlcirrdldCACYMSoBi+5JF1wNubtnNufhvX0lWLO7kCEJwLo9hbb//uhAGeb/IhVBWpXAiuhSwj72qdVqZGRkYPv27R3u3759O8aMGdPlY1UqFeLi4qBQKLBx40bceeedkMstL2Xs2LE4efIkzGaz7fjjx48jOjrabkACLP1GR48eRXR0dDdfFZFvqapvxhf5lg86s7I6z0gl+rnZWUkAgM8OV6C6oaXrg71cs9GE93NLAAB+agUuGE34oP3f5B6Ejo0/9dRTePvtt7Fq1SocPXoUTz75JIqKivDYY48BsFzievDBB23HHz9+HGvXrsWJEyeQk5OD6dOnIz8/HwsXLrQd8/jjj6OmpgZz587F8ePH8emnn2LhwoXIzs62HfP0009j586dOHPmDPbs2YP77rsPBoMBDz30kOtePJEX2JRTjDazhOEJIRgUEyy6HPIA6XHBGBIXjFaTGe/tKxZdjlCfHCpHXZMRsSE6PDOxPwDLyKwkSYIrIyuhIWnatGlYtmwZXnrpJQwdOhTffvsttm3bZpuyX15ejqKiItvxJpMJS5YswZAhQ3DbbbehubkZu3btQlJSku2Y+Ph4fPnll9i7dy8GDx6MJ554AnPnzu0wi66kpAQzZsxA//79cc8990CtVmP37t12lwogIvvaTGasz7H8fM4ezZ8dunqzsizfL+t2F8Fk9t1AYL1U/UBmAu7NiIO/WoFT5xrx4+kawZWRlUxiZHWIwWBAcHAw9Ho9goKCRJdD5HJfHqnAb9fkItRfjV3P3QytitOX6eo0G03IXPgV9BeMWPXwCNw8wH4fqjc7XKLH5De+h0ohw4/zb0FYgAbPf3gY6/YU4Y70aLw5c7joEr3Wtfz95lQUInKI9VPw/SPiGJDommhVCkwdEQcAWPNj4RWO9k5r239+JqVFIyzAMinIOsL2xZEKVHLdMbfAkERE1+xMdSO+O1ENmQyYOYqX2ujaPZBp+b755vg5FNU0Ca7GtfRNRnycVwqg46Xq1OggjEzqhTazhI05vt2v5S4Ykojomq1r/xR8Y79wJPT2E1wNeaLkMH+M7xsGSQLW5fjWaNLm/SVoNpoxICoQIxJ7dfiadTRpQ04R2kxmew8nF2JIIqJrcum0ZTZsU3fMbg8E7+8rQbPRJLga15AkyfYhY2ZWYqfFk29Pi0JvfzUqDM3479EqESXSJRiSiOiabM0rg/6CZdryDf0iRJdDHuzmARGIDtaitrEVn+WXiy7HJXadqsHp6kb4qxX45bDYTl/XKBWYNtKym4O1b4nEYUgiomuy1vYpOIGblFK3KBVyPDDKsgiprzRwW1/nPcPjEKCxv+nFA5kJkMmA709W4/S5BleWRz/DkEREV+1QSR3ySvRQK+SYOoJ7F1L3TRsVD6Vchv1FdThSphddTo+q0Ddj+9FKABd7j+yJ6+WHm/tbRmnX7Sm67HHU8xiSiOiq2aYtp0fZpi0TdUdEoBa3p0UBANbu9u5AsCHHsnjmqKRQ9I8K7PLYWaOt/VrFuNDqG/1a7oghiYiuir7JiC15lt3KZ3fxKZjoWllHVT46UApDs1FwNT3DaDJjQ/sK9bOuYsLDDX3DER+qg6G5DVvbf+7I9RiSiOiqXDptOeNn05aJuiMzORR9IwK8eoPX7QWVqKpvQViAGrcPirri8XK5DDPb15JawwZuYRiSiOiKzGbJdqltlp1py0TdIZPJbMtJrN1T5JUbvFp/fqaNjIdaeXV/eqeOsBx7uFSPvOK6HqyOLochiYiuaNepGpypbkSARokpdqYtE3XXL4fFwk+twMmqBuw+XSu6HKc6WVWPXadqIJcBM9pn812NUH817kyPBsDRJFEYkojoitbsPgsAuGd47GWnLRN1R6BWZQvg3rY+kLUh/eYBkYjrdW0r1Fv7l7bmlaGuqdXptVHXGJKIqEvl+gu2lX+7mrZM1F2zMi9u8FrlJRu8NrW24T/7LX1Ws7KufhTJalh8CAZGB6GlzYzNXtqv5c4YkoioSxtyii3TlpND0S+y62nLRN0xMCYIIxLbN3jd6x0bvG45WIb65jYkhPrh+r7h1/z4Dv1auwthNntfv5Y7Y0giossymszY2D5tmdP+yRWso5Xr93j+Bq+SJNl6iWZlJUDu4Ar1dw+NQaBGibM1Tfj+ZLUzS6QrYEgiosu6OG1Zg4lXMW2ZqLsmpUchtH2D169+8uwNXg8W1+FImQFqpRz3Zzi+Qr2fWol7M+IAsIHb1RiSiOiyrPtMTb+GactE3eFNG7xaA82dg6PRy1/dreey9jN9dbQSZXUXul0bXR3+1iMiu05W1ePH0+3TljOvveGUyFEPjLJs8PrdCc/d4LW2sRWfHCoH4JxL1ddFBGJ0Sm+YJdhW7qaex5BERHZZpy3fkhqJ2BCd4GrIl8SH+uEmD9/g9f19xWhtMyMtNghD40Oc8pzWBu4NOZbnpp7HkEREnTS1tuE/7dON2bBNIli/7zbnlnjcBq9ms2QLd7MynbdC/W0DIxERqEF1Qwu+LKhwynNS1xiSiKiTjw+Wob6lDYm9/TDuujDR5ZAPur6fZYNX/QUjth7yrA1evz1xDkW1TQjUKnHX0BinPa9KIcf09hW7rf2C1LMYkoioA0mSbL+AZ2UmOjxtmag7FHIZHhh1cX0gT2Kt976MOPipnbtC/YxR8VDIZdhzphbHK+ud+tzUGUMSEXVwoLgOBeUGaJRy3Nc+7ZhIhKkj4qBWyHGoxHM2eC0534Sv25cumJnp/EvV0cE63Jpq6dfytPDoiRiSiKiDtT9apy3HdHvaMlF39A7Q4I7Blg1ePSUQbMgpglkCxvTpjesiAnrkHLOzkgAAH+wvRWNLW4+cgywYkojIpsO05dFs2CbxrOsDbfGADV5b2kzY1L6dSk9OeBjTpzdSwvzR0NKGjw6W9th5iCGJiC7x/r5itJrMSI8NxpC4YNHlEGF4Qi+kesgGr5/nV6C6oRWRQRrcOjCyx84jl8swsz2ErfmxEJLE/dx6CkMSEQGwTFteu+fiPlPOmrZM1B0ymcw2KrNuT5Fbb/C6rn1tsekjE6BS9Oyf1/uGx0GrkuOninrsLzrfo+fyZQxJRAQA2HniHIprL1imLQ+JFV0OkY11g9cz1Y344ZR7bvD6U4UBOWdroZDLMGNUz69QH+ynwl1DLMsLcDmAnsOQREQALjZs358RD51aIbgaoov8NUrcM9wS3N01EFgbyycMjERUsNYl57Q2cG87XIHqhhaXnNPXMCQREYprm/D1sfZpy1ncp43cz6z2S27/PVqJcr17bfDa0NKGD/dbGqhnuXCF+vQ4S+9gq8mM9/YVu+y8voQhiYiwIacIkgSMva43+oT3zLRlou7oGxmIrJRQywavbraf24cHStHYakJKuD/G9Ont0nNbQ9n6PUUwuXG/lqdiSCLyca6atkzUXdZAsGGv+2zwKkmS7VK1M/dpu1qTh8QgWKdCyfkL2Hm8yqXn9gUMSUQ+7vP8CtQ0tk9bTu25actE3TVhYBTCAzU4V+8+G7zuPXsexyrroVXJca+AFeq1KgXubz+vu/ZreTKGJCIfZ204nTEqAcoenrZM1B1qpRwzRsYDcJ8VuK113D0kFsE6lZAarGsmfXP8HIprm4TU4K34G5HIh/1UYcDes+ddNm2ZqLtmZCZAIZdh9+lanBC8weu5+hZ8li9+hfrkMH+M7xsGSbKsJUXOw5BE5MOsn4InDopEZJBrpi0TdUd0sA63DHCPDV7f21cMo0nC0PgQpMWKXaHe2k/43r5iNBtNQmvxJgxJRD6qvtl4cdpyD+xWTtRTrKM2Ijd4NZklrG8ftXHltP/LuXlABKKDtahtbLWNblH3MSQR+aiPLpm2PNrF05aJumNsnzAkh/mjvqUNHx8sE1LDjp+qUFp3ASF+Ktw5OFpIDZdSKuR4oP2S+drdvOTmLAxJRD5IkiSsab9UMTvL9dOWibpDLpdhZqYlEKzZLWaDV+vPz9QR8dCq3GOF+mmj4qGUy5BbeB5HyvSiy/EKDElEPmjv2fM4XtkAnUqBe4a7ftoyUXfdlxEHjVKOo+UG7C+qc+m5C2sa8e2JcwBgG71xBxGBWkxMiwLA0SRnYUgi8kHWT8F3D40RNm2ZqDtC/NS2DV5d3cC9fo9lhfrr+4UjKczfpee+EmsD98cHS2FoNgquxvMxJBH5mHP1Lfi8vbHTHRpOiRxlbeD+9FA5aly0wWuz0YRN+9x3hfrM5FD0jQhAU6vJNjGDHMeQRORjrNOWhyWIn7ZM1B2D40Iw2LbBa4lLzvnpoXLUNRkRG6LDze1LEbgTmUxmC4+i+rW8CUMSkQ8xmSWs231xnykiT2fb4DWn0CUbvK7dY12hPh4KuXtOePjlsFj4qRU4WdWA3adrRZfj0RiSiHzI1z9VoUzfjBA/Fe5wg2nLRN01ebClr6649gK+PX6uR8+VX6rHgaI6qBQyTG3fHsUdBWpVmDIsFsDFUEeOYUgi8iHWBtdpbjRtmag7dGoF7rNu8NrDDdzWn5/b06IREejeK9RbR4q/yK9AlaFZcDWeiyGJyEcU1jRi5/FzkMmABzLdZ9oyUXdZ10zacayqxzZ41V8w2haunOUBPz8DY4KQkdgLbWYJG/cWiy7HYzEkEfkI68aX1/cNR2Jv95q2TNQdKeEBtg1e1+f0zPpAH+wvwQWjCf0iAzAqObRHzuFs1tl3G3KK0GYyC67GMzEkEfmAZqMJ77nxtGWi7rI2cG/aW4yWNudu8OqpK9RPSo9CqL8a5fpmfPVTlehyPBJDEpEPuHTa8k1uOG2ZqLtuuXSD18MVTn3uH0/V4PS5RvirFbaGaE+gUSowrb3B3NULbnoLhiQiH2D9FPxAZoLbTlsm6g6lQo4Ztg1enRsIrDPEpgyLRaDWs1aof2BUAmQy4LsT1ThT3Si6HI/DkETk5fJL9ThY3D5teYT7Tlsm6q7pIy0bvO4rPI+CMoNTnrPS0IwvjlQC8MwV6uND/XBTf8vo8TqOJl0zhiQiL2f9VD0pLRrhgRrB1RD1nIggLSYOat/g1UnrA23IKYLJLGFkUi+kRgc55TldzdqH+H5uCS60Ordfy9sxJBF5Mf0FIz46aNm/yRM/BRNdK+v3+UcHSlHfzQ1ejSYzNrTPlvPkn5/r+4UjrpcO+gtGbD1UJrocj8KQROTF/pNbgmajGf0jAzEyqZfocoh6XFZKKK6zbvB6oHsbvH51tBKVhhb09lfj9rQoJ1Xoegq5DDPbF5fkJbdrw5BE5KUkSbJdcpg12nOmLRN1h0wms11eWvNj9zZ4tU54mDYyHhqlZ69QP3VEHNQKOfJK9MgrrhNdjsdgSCLyUpdOW/6lB01bJuquXw6PhU6lwImqBuw549gGr6fONeCHkzWQyWCbNefJegdo8Iv09n4tjiZdNYYkIi9l/RT8y+GxCNAoBVdD5DpBl27w6mAgWLfb0ot0c/8IxIf6Oa02kWaPtoywbckrQ11Tq+BqPANDEpEXqtA348sCz522TNRds7Isoz+f51egqv7aNnhtam3D+7mWFepnjfaen5/hCZYZei1tZmzOLRFdjkdgSCLyQhv3WqYtj0oKxYAoz5y2TNQdg2KCMTwhBG1mCZtyrm2D1615ZahvbkN8qA439A3voQpd79J+rXV7imA2O96v5SuEh6Tly5cjOTkZWq0WGRkZ+O6777o8/s0330Rqaip0Oh369++P1atXdzqmrq4O2dnZiI6OhlarRWpqKrZt29at8xJ5ikunLc/M8vxeCiJHWS8vXcsGr5fu0zYzMxFyL1uh/u6hMQjQKHGmuhE/nKoWXY7bExqSNm3ahHnz5uH555/HgQMHMH78eEyaNAlFRfZ3cV6xYgXmz5+PF154AUeOHMGLL76I7OxsbN261XZMa2srbrvtNpw9exabN2/GsWPH8NZbbyE29mLj6rWel8iT/LfAMm05LMCzpy0TddektGiE+qtRpm/G11e5wWteiR75pQaolXKvXKHeX6PEvcO716/lS4SGpKVLl2LOnDl45JFHkJqaimXLliE+Ph4rVqywe/yaNWvw6KOPYtq0aUhJScH06dMxZ84cLF682HbMqlWrUFtbi48++ghjx45FYmIixo0bhyFDhjh8XgBoaWmBwWDocCNyR9Zp/94wbZmoO7QqhS3orLnKQLDmR8txd6ZbApY3svYpbi+oRLn+guBq3JuwkNTa2orc3FxMmDChw/0TJkzArl277D6mpaUFWq22w306nQ45OTkwGi0rq27ZsgWjR49GdnY2IiMjkZaWhoULF8JkMjl8XgBYtGgRgoODbbf4eO/7hEGe72SVd01bJuqumZkXN3g9e4UNXs83tuKT9hWpZ3rxhIe+kYHITA6FWQI27OEVlK4IC0nV1dUwmUyIjIzscH9kZCQqKirsPmbixIl4++23kZubC0mSsG/fPqxatQpGoxHV1ZZrq6dPn8bmzZthMpmwbds2/PnPf8aSJUvw97//3eHzAsD8+fOh1+ttt+Lia2sEJHKFde2jSLcMiEBcL++YtkzUHfGhfrixn6X5et0V9nPbnFuCljYzBkYHYXhCiAuqE8fWr7W3GMar7NfyRcIbt3++CrAkSZddGXjBggWYNGkSsrKyoFKpcPfdd+Phhx8GACgUlssKZrMZERERWLlyJTIyMjB9+nQ8//zznS6lXct5AUCj0SAoKKjDjcidNLW22ab1cto/0UXWQPDevhI0G+1v8Go2X1yhfrYPrFA/YWAUwgM1OFffgi+PVIoux20JC0lhYWFQKBSdRm+qqqo6jfJY6XQ6rFq1Ck1NTTh79iyKioqQlJSEwMBAhIWFAQCio6PRr18/W2gCgNTUVFRUVKC1tdWh8xJ5Auu05YRQP1zvRdOWibrrhn4RiA1p3+A1z/4Gr9+drEZhTRMCNUrcPTTGxRW6nlopx4yR1n6ts2KLcWPCQpJarUZGRga2b9/e4f7t27djzJgxXT5WpVIhLi4OCoUCGzduxJ133gm53PJSxo4di5MnT8Jsvjh8ePz4cURHR0OtVnfrvETuSpIkrP7ROm05weumLRN1h0Iusy2HsfYyPTjWmV73ZsTBT+0bK9RPH5UAuQzYfboWJyrrRZfjloRebnvqqafw9ttvY9WqVTh69CiefPJJFBUV4bHHHgNg6QN68MEHbccfP34ca9euxYkTJ5CTk4Pp06cjPz8fCxcutB3z+OOPo6amBnPnzsXx48fx6aefYuHChcjOzr7q8xJ5moPFdThSZpm2fL8XTlsm6q6pI+ItG7wW1+FQSV2Hr5XWXcBXR60r1PvOhIeYEB1uTbVcQVnHBm67hMbladOmoaamBi+99BLKy8uRlpaGbdu2ITHRcv24vLy8w9pFJpMJS5YswbFjx6BSqXDTTTdh165dSEpKsh0THx+PL7/8Ek8++SQGDx6M2NhYzJ07F88+++xVn5fI06xt32fqzsHeO22ZqDvC2jd4/ehgGdbuLsQr94XYvrZhTxHMEjA6pTeuiwgUV6QAs0cn4suCSvwntwR/nNgf/tznsQOZJElcl9wBBoMBwcHB0Ov1bOImoc43tiJz0VdobTPjg9+NwfCEXqJLInJL+87W4r5//gitSo49829FsJ8KrW1mjPnH16huaMGbDwzHHYOjRZfpUmazhJuXfIOzNU1Y+Mt0PJDp/SNp1/L3W/jsNiLqnvdzi9HaZsagmCAMiw8RXQ6R28pI7IUBUYFoNpqxeb9lJugXRypQ3dCC8EANJgzyvck7crnMNht2ze5CcNykI4YkIg9mNku2XoLZWd4/bZmoO2QymW05gLW7C2E2X9ynbcaoBKgUvvkn8b6MOGiUchwtN2B/UZ3octyKb35HEHkJ27RlrRJ3+cC0ZaLumjI01rbB6zu7ziLnTC0UchlmjPLdCQ8hfmrcNcTy+4P7uXXEkETkwaz7TN073HemLRN1h79GiXvaN3h9+dMCAMCtqRGIDtaJLEs46yW3Tw+Vo6ahRXA17oO/VYmuUX2zEfoLRtFloLaxFV//ZJ22zJmZRFdrVlYiVv9YCHN7+83srCSh9biDIfEhGBwXjEMleryfW4LHbugjuiS3wJBEdA1+qjDgrjd+QGub++x1NKZPb1wXESC6DCKP0a99g9c9Z2qREuaPMX16iy7JLczKSsQzmw9h7e5C/GZ8ChRclJaX24iuxarvz6C1zQylXAaNUi78Fuqvxu9v7iv6bSHyOH+Y0B9xvXR45vb+XKG+3eTBMQjSKlFy/gK+PX5OdDlugSNJRFdJ32TElvZ9nzY9moWMxFDBFRGRo0Ylh+L7Z28WXYZb0akVuH9EPP7f92ewZnchbhoQIbok4TiSRHSVNu8vQbPRjNToIC7YSEReaWb7YpI7jlWhuLZJcDXiMSQRXQWzWbJNjZ2VlcD1iIjIK6WEB2DcdWGQJGB9DvdzY0giugq7TtXgTHUjAjRKTBkaK7ocIqIeY50tu2lvMVraTIKrEYshiegqWEeR7h0eyw0gicir3ZoagaggLWobW/F5foXocoRiSCK6gnL9BWw/almPaCbXIyIiL6dUyG0b3VoXrPVVDElEV7Ahpxgms4TM5FD0iwwUXQ4RUY+bPjIeSrkM+wrP42i5QXQ5wjAkEXXBaDJjY3vzonVjTCIibxcRpMXEQVEAfHs/N4Ykoi5sL6hEVX0LwgI0mDAwSnQ5REQuY23g/vBAKeqbxW/FJAJDElEXrNfjZ4yKh1rJHxci8h1ZKaG4LiIATa0mfHigVHQ5QvC3PtFlnKyqx4+nayCXATNGJYguh4jIpWQyGWZd0sAtSZLgilyPIYnoMtbutvQi3ZoaiZgQneBqiIhc756MOOhUCpyoakDOmVrR5bgcQxKRHU2tbfhPbgmAi9fliYh8TZBWhSnDLAvorvHBBm6GJCI7Pj5YhvqWNiT19sO468JEl0NEJMysLMslt8/zK1BV3yy4GtdiSCL6GUmSbA3bs7ISIZdznzYi8l2DYoIxPCEEbWYJ7+0tFl2OSzEkEf3MgeI6FJQboFHKcV9GnOhyiIiEs64Tt35PEdpMZsHVuA5DEtHPrG0fRZo8JAYhfmrB1RARiTcpLRq9/FQo0zfj65+qRJfjMgxJRJeobWzFJ4fKAQCz2bBNRAQA0KoUmDoyHoBvNXAzJBFd4v19xWg1mZEeG4wh8SGiyyEichszRyVCJgO+O1GNs9WNostxCYYkonZms4S1eyyfkDiKRETUUUJvP9zQLxwAsG6Pb4wmMSQRtdt54hyKay8gSKvE5CExosshInI71g+Q7+0rQbPRJLianseQRNTO2rB9/4h46NQKwdUQEbmfG/tHIDZEB/0Fo61/05sxJBEBKK5twtfHLDM2ZmZynzYiInsUchlmti8u6QsN3AxJRAA25BRBkoBx14UhJTxAdDlERG5r6oh4qBQy5BXX4XCJXnQ5PYohiXxeS5sJm9pXkeU+bUREXQsL0OAX6dEAgLVePprEkEQ+7/P8CtQ0tiIqSItbUyNEl0NE5PasDdwf55VC32QUXE3PYUgin2f9JDRjVAKUCv5IEBFdSUZiLwyICkSz0YzN+0tEl9Nj+BeBfNrRcgP2nj0PpVyG6aPiRZdDROQRZDKZrT1h3e5CSJIkuKKewZBEPs06ijRhUCQig7SCqyEi8hxThsUiQKPE6epG7DpVI7qcHsGQRD6rvtmIjw6UAmDDNhHRtQrQKHHP8FgAwJofvbOBmyGJfNZHB0rR2GpCn3B/jE7pLbocIiKPY/2Auf1oJSr0zYKrcT6GJPJJkiTZFkKbnZUImUwmuCIiIs/TLzIQo5JDYTJL2JBTJLocp2NIIp+09+x5HK9sgE6lwD0ZcaLLISLyWNblADbkFMFoMguuxrkYksgnWUeRpgyLQZBWJbgaIiLPNXFQFMICNKiqb8H2gkrR5TgVQxL5nKr6Znyeb9mYkQ3bRETdo1bKMX2kZQkVb1uBmyGJfM57e4thNEkYlhCCQTHBosshIvJ4MzITIJcBu07V4GRVvehynIYhiXyKySxh/R5Lc+FsjiIRETlFbIgOt6RGAgDW7vaeBm6GJPIpX/9UhTJ9M3r5qWwbNBIRUfdZ2xf+k1uCptY2wdU4B0MS+RRrw/bUEfHQqhSCqyEi8h7jrwtDYm8/1Le0YcvBMtHlOAVDEvmMwppGfHv8HGQy4IHMBNHlEBF5FblchlmZltGk1T96x35uDEnkM9a19yLd0C8cib39BVdDROR97suIg1opR0G5AQeK60SX020MSeQTmo0mvLevGAAbtomIekovfzUmD44B4B3LATAkkU/45FA56pqMiA3R4cb+EaLLISLyWrNHWz6IfnKoHLWNrYKr6R6GJPIJ1k80D2QmQCHnPm1ERD1lSFww0mKD0NpmxvvtI/ieiiGJvN7hEj0OFtdBpZBhWvuqsERE1DNkMpmtrWHdniKYzZ7bwM2QRF7POoo0KS0aYQEawdUQEXm/u4bEIlCrRFFtE749cU50OQ5jSCKvpr9gxMd5pQAuXicnIqKepVMrcH+G5+/nxpBEXu0/uSVoNpoxICoQIxJ7iS6HiMhnzMyyrEf31U9VKDnfJLgaxzAkkdeSJMn2CWZmViJkMjZsExG5Sp/wAIy9rjckCdiQ45n7uTEkkdfadaoGp6sb4a9W4JfDYkWXQ0Tkc6wN3Jv2FqOlzSS4mmvHkEReyzqKdM/wOARolIKrISLyPbemRiIySIPqhlZ8nl8hupxrxpBEXqlC34wvCyoBXNyZmoiIXEupkGPGKEtv0rrdnnfJjSGJvNKGnCKYzBJGJYWif1Sg6HKIiHzWjFGWRXxzztbipwqD6HKuCUMSeR2jyYyNey2fWGZx2j8RkVCRQVpMHBQJwPOWA3AoJH3zzTdOK2D58uVITk6GVqtFRkYGvvvuuy6Pf/PNN5GamgqdTof+/ftj9erVHb7+zjvvQCaTdbo1NzfbjnnhhRc6fT0qKsppr4nE+m9BJSoNLQgLUOP2Qfz/SkQk2qxMywfWD/eXoqGlTXA1V8+hkHT77bejT58+ePnll1Fc7Pi+LJs2bcK8efPw/PPP48CBAxg/fjwmTZqEoiL71y1XrFiB+fPn44UXXsCRI0fw4osvIjs7G1u3bu1wXFBQEMrLyzvctFpth2MGDRrU4euHDx92+HWQe1nT/kll2sh4qJUcLCUiEm10n95ICfdHY6sJHx4oFV3OVXPoL0hZWRnmzp2LDz74AMnJyZg4cSLee+89tLZe226/S5cuxZw5c/DII48gNTUVy5YtQ3x8PFasWGH3+DVr1uDRRx/FtGnTkJKSgunTp2POnDlYvHhxh+OsI0OX3n5OqVR2+Hp4eHiXtba0tMBgMHS4kfs5WdWAXadqIJfB1ixIRERiXbqf29ofCyFJnrGfm0MhKTQ0FE888QT279+Pffv2oX///sjOzkZ0dDSeeOIJ5OXlXfE5WltbkZubiwkTJnS4f8KECdi1a5fdx7S0tHQaEdLpdMjJyYHRaLTd19DQgMTERMTFxeHOO+/EgQMHOj3XiRMnEBMTg+TkZEyfPh2nT5/ust5FixYhODjYdouP50ap7mjdHsso0s0DIhHXy09wNUREZHXP8DjoVAocq6zH3rPnRZdzVbp9LWLo0KF47rnnkJ2djcbGRqxatQoZGRkYP348jhw5ctnHVVdXw2QyITIyssP9kZGRqKiwv5bCxIkT8fbbbyM3NxeSJGHfvn1YtWoVjEYjqqurAQADBgzAO++8gy1btmDDhg3QarUYO3YsTpw4YXuezMxMrF69Gl988QXeeustVFRUYMyYMaipqblsvfPnz4der7fdunOZkXpGU2sbNueWAABmZXEUiYjInQTrVLh7aAwAz2ngdjgkGY1GbN68Gb/4xS+QmJiIL774Am+88QYqKytx5swZxMfH4/7777/i8/x8qwhJki67fcSCBQswadIkZGVlQaVS4e6778bDDz8MAFAoFACArKwszJo1C0OGDMH48ePx3nvvoV+/fnj99ddtzzNp0iTce++9SE9Px6233opPP/0UAPDuu+9etk6NRoOgoKAON3IvWw6Wob65DQmhfri+b9eXT4mIyPWs69Z9ll+Oc/Utgqu5ModC0u9//3tER0fjscceQ79+/XDgwAH8+OOPeOSRR+Dv74/4+Hj84x//wE8//XTZ5wgLC4NCoeg0alRVVdVpdMlKp9Nh1apVaGpqwtmzZ1FUVISkpCQEBgYiLCzM/guUyzFy5MgOI0k/5+/vj/T09C6PIfcmSZKtYXtWVgLkcu7TRkTkbtJigzE0PgRGk4T39rn/FRmHQlJBQQFef/11lJWVYdmyZUhLS+t0TExMDHbs2HHZ51Cr1cjIyMD27ds73L99+3aMGTOmy/OrVCrExcVBoVBg48aNuPPOOyGX238pkiTh4MGDiI6OvuzztbS04OjRo10eQ+7tYHEdjpQZoFbKcX8G+8WIiNyVtYF7/R7Lor/uzKENrb766qsrP7FSiRtuuKHLY5566inMnj0bI0aMwOjRo7Fy5UoUFRXhscceA2DpAyotLbWthXT8+HHk5OQgMzMT58+fx9KlS5Gfn9/hMtmLL76IrKws9O3bFwaDAa+99hoOHjyIN99803bM008/jcmTJyMhIQFVVVV4+eWXYTAY8NBDDznydpAbsI4i3Tk4Gr381YKrISKiy7ljcDT+9mkBSusuYMdPVbh1oP2rR+7AoZC0aNEiREZG4te//nWH+1etWoVz587h2WefvarnmTZtGmpqavDSSy+hvLwcaWlp2LZtGxITLSmzvLy8w5pJJpMJS5YswbFjx6BSqXDTTTdh165dSEpKsh1TV1eH3/72t6ioqEBwcDCGDRuGb7/9FqNGjbIdU1JSghkzZqC6uhrh4eHIysrC7t27beclz3K+sRWfHCoHcPETChERuSetSoFpI+Lxr29PY83uQrcOSTLJgcUKkpKSsH79+k6Xxfbs2YPp06fjzJkzTivQXRkMBgQHB0Ov17OJW7CV357Cwm0/IS02CFv/Z9xlG/+JiMg9FNY04oZXvwEA7PzjjUjs7e+yc1/L32+HepIqKirs9u+Eh4ejvLzckackcojZLGFt+87SszITGZCIiDxAYm9/3NDPMgt5/R77u2y4A4dCUnx8PH744YdO9//www+IiYnpdlFEV+vbE+dQVNuEQK0Sdw3l9x4Rkaewtkds2leMZqNJcDX2OdST9Mgjj2DevHkwGo24+eabAViauZ955hn84Q9/cGqBRF2xjiLdlxEHP7VD385ERCTATQMiEBuiQ2ndBXx6qBz3ZsSJLqkTh/6qPPPMM6itrcXvfvc7235tWq0Wzz77LObPn+/UAokup+R8E77+qRIAMDOTDdtERJ5EIZfhgcwEvPrFMazdU+iWIcmhy20ymQyLFy/GuXPnsHv3buTl5aG2thZ/+ctfnF0f0WVtyCmCWQLG9OmN6yICRJdDRETXaOqIeKgUMhwoqkN+qV50OZ10a++2gIAAjBw5EmlpadBoNM6qieiKWtvM2LTXslorp/0TEXmm8EANJqVZJoK5435uDjdx7N27F++//z6Kiopsl9ysPvjgg24XRtSVz49UoLqhFRGBGrdeY4OIiLo2KysRW/LK8PHBMsz/RSqCdSrRJdk4NJK0ceNGjB07FgUFBfjwww9hNBpRUFCAr7/+GsHBwc6ukaiTtT9aPnHMGJUAlaJbA6JERCTQyKRe6B8ZiAtGEz7YXyK6nA4c+uuycOFC/O///i8++eQTqNVq/N///R+OHj2KqVOnIiEhwdk1EnXwU4UBOWdroZDLMGMUv9+IiDyZTCbDrNGWtok1uwvhwBrXPcahkHTq1CnccccdAACNRoPGxkbIZDI8+eSTWLlypVMLJPq5de3T/icMjERUsFZwNURE1F2/HBYLf7UCp8814sdTNaLLsXEoJIWGhqK+vh4AEBsbi/z8fACWfdOampqcVx3RzzS0tNmGY2exYZuIyCsEaJT45fBYAMDaPe7TwO1QSBo/fjy2b98OAJg6dSrmzp2L3/zmN5gxYwZuueUWpxZIdKkPD5SisdWElHB/jOnTW3Q5RETkJNYPvl8cqUSloVlwNRYOzW5744030NxseQHz58+HSqXC999/j3vuuQcLFixwaoFEVpIk2Rq2uU8bEZF3GRAVhFFJocg5W4sNOUWYd2s/0SVd+0hSW1sbtm7dCrnc8lC5XI5nnnkGW7ZswdKlS9GrVy+nF0kEAPsKz+NYZT20KrlbrsxKRETdMzPLMhlnQ04RjCaz4GocCElKpRKPP/44WlpaeqIeosta0z6KdPeQWLdaR4OIiJzj9rQohAWoUWlowVdHK0WX41hPUmZmJg4cOODsWogu61x9Cz7LLwcAzB7Nhm0iIm+kUSowbWQ8AMtyAKI51JP0u9/9Dn/4wx9QUlKCjIwM+Pv7d/j64MGDnVKcr6rQN6PZaEJSmP+VD/YR7+0rhtEkYUh8CNJiuWApEZG3mjEqAcu/OYUfTtbg1LkG9AkXtzenQyFp2rRpAIAnnnjCdp9MJoMkSZDJZDCZTM6pzge9u+ss/rrlCG4fFIV/zs4QXY5bkCQJG3IsayNxnzYiIu8W18sPtwyIwH+PVmHd7iL8ZfJAYbU4FJLOnDnj7DqoXb/IQADAoZI6sYW4kdK6Cyg5fwEKuQx3pEeLLoeIiHrYrKxE7Dh2DvXNRqF1OBSSEhP5ab6npMcFQyYDyvTNOFffgvBAjeiShDtcogcA9I8MhE6tEFwNERH1tOv7huOHZ28WvquCQyFp9erVXX79wQcfdKgYsqw62ic8ACerGnC4tA43D+AO93ntIWlIPHuRiIh8gVwuEx6QAAdD0ty5czv822g0oqmpCWq1Gn5+fgxJ3TQ4LhgnqxqQV6xnSMLFS4+D40KE1kFERL7FoSUAzp8/3+HW0NCAY8eOYdy4cdiwYYOza/Q5Q9rDAPuSALNZsl1uGxzHkSQiInIdh0KSPX379sU//vGPTqNMdO2sYeBQiR6SJAmuRqwzNY2ob2mDRim3NbUTERG5gtNCEgAoFAqUlZU58yl9Ump0EJRyGWoaW1Fad0F0OUJZR9MGxgRBpXDqtysREVGXHOpJ2rJlS4d/S5KE8vJyvPHGGxg7dqxTCvNlWpUC/aMCcaTMgMMlesT18hNdkjB5xe1N2+xHIiIiF3MoJE2ZMqXDv2UyGcLDw3HzzTdjyZIlzqjL5w2OC8GRMgPySvSY5MNrAx0uZT8SERGJ4VBIMpvF78zr7YbEBWNDjm83b7eZzDhSZg1JIWKLISIin8MmDzdlDQWHS/Qwm32zeft4ZQOajWYEapRI4T52RETkYg6FpPvuuw//+Mc/Ot3/6quv4v777+92UQT0iwyAViVHfUsbztQ0ii5HCOsoWlpsMORymdhiiIjI5zgUknbu3Ik77rij0/233347vv32224XRYBSIcegGOtSAHViixHEutL2YK60TUREAjgUkhoaGqBWqzvdr1KpYDAYul0UWaTHWsKBdYaXr7GttB0bIrQOIiLyTQ6FpLS0NGzatKnT/Rs3bsTAgQO7XRRZWPcqs87w8iXNRhOOVdQD4Mw2IiISw6HZbQsWLMC9996LU6dO4eabbwYAfPXVV9iwYQPef/99pxboy6zN20fK9GgzmaH0ocUUj5Yb0GaWEOqvRlwvnehyiIjIBzkUku666y589NFHWLhwITZv3gydTofBgwfjv//9L2644QZn1+izknv7I1CjRH1LG45XNmBgTJDoklzm0CX7tclkbNomIiLXcygkAcAdd9xht3mbnEculyE9Lhi7TtXgUEmdT4WkPGs/EtdHIiIiQRy6frN3717s2bOn0/179uzBvn37ul0UXWQNCdaZXr7COpI0hP1IREQkiEMhKTs7G8XFxZ3uLy0tRXZ2dreLoousIcGXlgFoaGnDqXMNAIB0hiQiIhLEoZBUUFCA4cOHd7p/2LBhKCgo6HZRdJE1JByrqEez0SS4Gtc4XKKHJAHRwVpEBGpFl0NERD7KoZCk0WhQWVnZ6f7y8nIolQ63OZEdsSE69PZXo80s4Wi5b6xBZVsfiaNIREQkkEMh6bbbbsP8+fOh11/sk6mrq8Of/vQn3HbbbU4rjgCZTGYLC4d8pC/pUCk3tSUiIvEcGvZZsmQJrr/+eiQmJmLYsGEAgIMHDyIyMhJr1qxxaoFkCQs7jp2zzfjydtaRpCEMSUREJJBDISk2NhaHDh3CunXrkJeXB51Oh1/96leYMWMGVCqVs2v0edaVt31hJKm2sRXFtRcAsGmbiIjEcriByN/fH+PGjUNCQgJaW1sBAJ999hkAy2KT5DzWy06nzjWgoaUNARrv7fuyjiIlh/kjWMfATURE4jj01/b06dP45S9/icOHD0Mmk0GSpA6rIptMvjELy1XCAjSIDdGhtO4CDpfoMbpPb9El9ZhLV9omIiISyaHG7blz5yI5ORmVlZXw8/NDfn4+du7ciREjRuCbb75xcokEAOmxvrFekvX1WV8vERGRKA6FpB9//BEvvfQSwsPDIZfLoVAoMG7cOCxatAhPPPGEs2skAIOtfUml3t2XZFtpOz5EbCFEROTzHApJJpMJAQEBAICwsDCUlZUBABITE3Hs2DHnVUc21ple3jySVKFvRlV9C+QyYJAP7VNHRETuyaGepLS0NBw6dAgpKSnIzMzEK6+8ArVajZUrVyIlJcXZNRKAtPbLT8W1F1Db2IpQf7XgipzPusRBv8hA+Km9tzmdiIg8g0MjSX/+859hNpsBAC+//DIKCwsxfvx4bNu2Da+99ppTCySLYJ0KKWH+ALx3NIkrbRMRkTtx6OP6xIkTbf+dkpKCgoIC1NbWolevXh1muZFzDY4LxunqRhwq0ePG/hGiy3G6izPbQsQWQkREBAdHkuwJDQ1lQOph6V7clyRJ0sWmbYYkIiJyA04LSdTzhrRfhsor0UOSJMHVOFdhTRP0F4xQK+ToHxUouhwiIiKGJE8yKCYYCrkM5+pbUGloEV2OU1mXNkiNDoRayW9LIiISj3+NPIhOrUDfCMvSC9622e2h4joA7EciIiL3wZDkYbx1vSRuR0JERO6GIcnD2FbeLvGelbdNZgn5ZVxpm4iI3AtDkoe5OJLkPc3bJ6sa0NRqgp9agT7hAaLLISIiAsCQ5HH6RQZCrZBDf8GIwpom0eU4hbW/Ki3W0phORETkDhiSPIxaKUdq+75m3rLZrbW/agj7kYiIyI0wJHkga5iwzgjzdIfb+6vSObONiIjciPCQtHz5ciQnJ0Or1SIjIwPfffddl8e/+eabSE1NhU6nQ//+/bF69eoOX3/nnXcgk8k63Zqbm7t1Xncy+JK+JE/X2mbG0fJ6ABxJIiIi9yI0JG3atAnz5s3D888/jwMHDmD8+PGYNGkSioqK7B6/YsUKzJ8/Hy+88AKOHDmCF198EdnZ2di6dWuH44KCglBeXt7hptVqHT6vu7GGifwyPUxmz27e/qnCgFaTGSF+KiSE+okuh4iIyEZoSFq6dCnmzJmDRx55BKmpqVi2bBni4+OxYsUKu8evWbMGjz76KKZNm4aUlBRMnz4dc+bMweLFizscJ5PJEBUV1eHWnfMCQEtLCwwGQ4ebKCnhAfBXK9DUasLJqgZhdThDnvVSW2ww9/4jIiK3Iiwktba2Ijc3FxMmTOhw/4QJE7Br1y67j2lpaekwIgQAOp0OOTk5MBqNtvsaGhqQmJiIuLg43HnnnThw4EC3zgsAixYtQnBwsO0WHx9/1a/V2RRyGdJirfu41QmrwxmsfVXc1JaIiNyNsJBUXV0Nk8mEyMjIDvdHRkaioqLC7mMmTpyIt99+G7m5uZAkCfv27cOqVatgNBpRXV0NABgwYADeeecdbNmyBRs2bIBWq8XYsWNx4sQJh88LAPPnz4der7fdiouLu/Pyu826MrWnr7zNlbaJiMhdKUUX8PNLLJIkXfayy4IFC1BRUYGsrCxIkoTIyEg8/PDDeOWVV6BQKAAAWVlZyMrKsj1m7NixGD58OF5//XW89tprDp0XADQaDTQazTW/vp5ibd4+7MHN202tbThR1d60zZW2iYjIzQgbSQoLC4NCoeg0elNVVdVplMdKp9Nh1apVaGpqwtmzZ1FUVISkpCQEBgYiLCzM7mPkcjlGjhxpG0ly5LzuyHp56mh5PVrbzGKLcdCRMgPMEhARqEFkkPbKDyAiInIhYSFJrVYjIyMD27dv73D/9u3bMWbMmC4fq1KpEBcXB4VCgY0bN+LOO++EXG7/pUiShIMHDyI6Orrb53Un8aE69PJTodVkxk8V4prIuyOvvR9pMPuRiIjIDQm93PbUU09h9uzZGDFiBEaPHo2VK1eiqKgIjz32GABLH1BpaaltLaTjx48jJycHmZmZOH/+PJYuXYr8/Hy8++67tud88cUXkZWVhb59+8JgMOC1117DwYMH8eabb171eT2BTCZDelwIvj1+Dnkleo8MGtZ+JK6PRERE7khoSJo2bRpqamrw0ksvoby8HGlpadi2bRsSExMBAOXl5R3WLjKZTFiyZAmOHTsGlUqFm266Cbt27UJSUpLtmLq6Ovz2t79FRUUFgoODMWzYMHz77bcYNWrUVZ/XUwyJC8a3x89ZZohleVbtwMWm88HsRyIiIjckk7xlK3kXMxgMCA4Ohl6vR1BQkJAathdU4jer96F/ZCC+ePJ6ITU4St9kxJCXvgQAHFhwG3r5qwVXREREvuBa/n4L35aEHGedNn+iqh5NrW2Cq7k2h0rrAAAJoX4MSERE5JYYkjxYZJAWkUEamCXLTDFPwvWRiIjI3TEkeThrw7Z1ppinsPUjMSQREZGbYkjycENsK2971qKSF0eSQsQWQkREdBkMSR7OGjI8aXuSqvpmlOubIZPBtgcdERGRu2FI8nDWy1Vna5qgbzJe4Wj3cKjYMop0XXgAAjTCd8YhIiKyiyHJw4X4qZEQ6gfg4owxd3exHylEaB1ERERdYUjyAoM9rC8pz7rSdjwvtRERkftiSPICQzyoL0mSJBwuZdM2ERG5P4YkL+BJI0kl5y+gtrEVSrkMA6ICRZdDRER0WQxJXiAtNhhyGVCub0ZVfbPocrpkDXIDogOhVSkEV0NERHR5DElewF+jxHURAQAuzhxzV2zaJiIiT8GQ5CU8Zb2kvPb6hnClbSIicnMMSV7C2peU58Z9SWazhPxSyx5zHEkiIiJ3x5DkJS4dSZIkSWwxl3G6ugENLW3QquTo2355kIiIyF0xJHmJ1OhAqBQynG8youT8BdHl2GVt2k6LCYZSwW89IiJyb/xL5SU0SgUGRAUBcN+lAKx1pbMfiYiIPABDkhe5uF5SndhCLuNi03aI0DqIiIiuBkOSF7GGjzw3DElGkxkFZdambY4kERGR+2NI8iKD2/dCyy81wGx2r+btYxX1aGkzI1CrRFJvf9HlEBERXRFDkhe5LjwAWpUcDS1tOF3dILqcDqz9SIPjgiGXywRXQ0REdGUMSV5EqZAjLcY993HjSttERORpGJK8zMX1ktwtJFnq4UrbRETkKRiSvMyQeOvK23ViC7lEs9GEY5X1AIB0jiQREZGHYEjyMtaRpIIyA4wms9hi2h0pM8BklhAWoEZMsFZ0OURERFeFIcnLJPX2Q5BWiZY2M45V1IsuB0DHfiSZjE3bRETkGRiSvIxMJnO7vqRLZ7YRERF5CoYkL5TuZitvc6VtIiLyRAxJXmhInPssA2BoNuL0uUYAHEkiIiLPwpDkhayX245V1qPZaBJaS36pJajFhujQO0AjtBYiIqJrwZDkhaKDtQgL0MBklnCkfb80UdiPREREnoohyQvJZLJLLrnVCa2FK20TEZGnYkjyUu4ywy2vmCttExGRZ2JI8lLWy1siV96uaWhBad0FAEAaQxIREXkYhiQvZQ1Jp881wtBsFFKDdRQrJdwfQVqVkBqIiIgcxZDkpXoHaBAbogNwcYaZq13c1DZEyPmJiIi6gyHJi1k3uxXVl3SxaZuX2oiIyPMwJHmxi83bdS4/tyRJyOP0fyIi8mAMSV7M1rxd7PqRpHJ9M6obWqCQyzAwmiGJiIg8D0OSF0uPtYST0roLqGlocem5raNX/SIDoVMrXHpuIiIiZ2BI8mKBWhVSwv0BuL4vKa+E6yMREZFnY0jyctaZZa5eL4krbRMRkadjSPJy1r6kwy4cSZIkiXu2ERGRx2NI8nKDbSNJekiS5JJznq1pQn1zG9RKOfpHBbrknERERM7GkOTlBsUEQSmXobqhBeX6Zpec03qpbWB0EFQKfosREZFn4l8wL6dVKdAv0jKa46r1kripLREReQOGJB9gXXk7z0V9SWzaJiIib8CQ5APSY0MAuGYkqc1kRn5Z+0hSPEeSiIjIczEk+QDrDLNDJXqYzT3bvH2iqgHNRjMCNEqkhAX06LmIiIh6EkOSD+gfFQiNUo765jYU1jb16LmsSw2kxQZBLpf16LmIiIh6EkOSD1Ap5BgYEwSg5y+5WRetHMJ+JCIi8nAMST7CtvJ2D292a11EMp0z24iIyMMxJPmIi31JdT12jpY2E36qMADgSBIREXk+hiQfYZ2On1+mR5vJ3CPnOFpeD6NJQi8/FeJ66XrkHERERK7CkOQjUsL8EaBRotloxomqhh45x6XrI8lkbNomIiLPxpDkI+RyGdJiLc3bPbXZLVfaJiIib8KQ5ENszds91Jd0uNTyvFxpm4iIvAFDkg+xhpdDPTCS1NjShpPtl/EGcySJiIi8AEOSD7GGl58qDGhpMzn1ufNL9TBLQFSQFhFBWqc+NxERkQgMST4krpcOof5qGE0SjpbXO/W5raNTHEUiIiJvITwkLV++HMnJydBqtcjIyMB3333X5fFvvvkmUlNTodPp0L9/f6xevfqyx27cuBEymQxTpkzpcP8LL7wAmUzW4RYVFeWMl+PWZDIZ0mN7Zr0k20rb8SFOfV4iIiJRhIakTZs2Yd68eXj++edx4MABjB8/HpMmTUJRUZHd41esWIH58+fjhRdewJEjR/Diiy8iOzsbW7du7XRsYWEhnn76aYwfP97ucw0aNAjl5eW22+HDh5362tyVdeaZs1fe5kgSERF5G6EhaenSpZgzZw4eeeQRpKamYtmyZYiPj8eKFSvsHr9mzRo8+uijmDZtGlJSUjB9+nTMmTMHixcv7nCcyWTCzJkz8eKLLyIlJcXucymVSkRFRdlu4eHhTn997sjavG2dieYMdU2tKGrfOHdwbIjTnpeIiEgkYSGptbUVubm5mDBhQof7J0yYgF27dtl9TEtLC7Tajk3BOp0OOTk5MBqNtvteeuklhIeHY86cOZc9/4kTJxATE4Pk5GRMnz4dp0+f7rLelpYWGAyGDjdPNDjeMtJzsqoBjS1tTnlO6yhSUm8/BPupnPKcREREogkLSdXV1TCZTIiMjOxwf2RkJCoqKuw+ZuLEiXj77beRm5sLSZKwb98+rFq1CkajEdXV1QCAH374Af/v//0/vPXWW5c9d2ZmJlavXo0vvvgCb731FioqKjBmzBjU1NRc9jGLFi1CcHCw7RYfH+/AqxYvIlCL6GAtzJJlRpozXLrSNhERkbcQ3rj98+0rJEm67JYWCxYswKRJk5CVlQWVSoW7774bDz/8MABAoVCgvr4es2bNwltvvYWwsLDLnnPSpEm49957kZ6ejltvvRWffvopAODdd9+97GPmz58PvV5vuxUXF1/jK3UfFze7dU5IymM/EhEReSFhISksLAwKhaLTqFFVVVWn0SUrnU6HVatWoampCWfPnkVRURGSkpIQGBiIsLAwnDp1CmfPnsXkyZOhVCqhVCqxevVqbNmyBUqlEqdOnbL7vP7+/khPT8eJEycuW69Go0FQUFCHm6ca7OSVtzmSRERE3khYSFKr1cjIyMD27ds73L99+3aMGTOmy8eqVCrExcVBoVBg48aNuPPOOyGXyzFgwAAcPnwYBw8etN3uuusu3HTTTTh48OBlL5G1tLTg6NGjiI6Odtrrc2fOHEmqNDSj0tACuQy2veGIiIi8gVLkyZ966inMnj0bI0aMwOjRo7Fy5UoUFRXhscceA2C5xFVaWmpbC+n48ePIyclBZmYmzp8/j6VLlyI/P992mUyr1SItLa3DOUJCQgCgw/1PP/00Jk+ejISEBFRVVeHll1+GwWDAQw895IJXLZ51BlpRbRPON7ail7/a4efKK64DAPSNCISfWui3ExERkVMJ/as2bdo01NTU4KWXXkJ5eTnS0tKwbds2JCYmAgDKy8s7rJlkMpmwZMkSHDt2DCqVCjfddBN27dqFpKSkazpvSUkJZsyYgerqaoSHhyMrKwu7d++2ndfbBfupkNTbD2drmnC4VI/r+zm+/MHhUvYjERGRd5JJkiSJLsITGQwGBAcHQ6/Xe2R/0hMbDmBLXhmentAP/3NzX4ef58FVOfj2+Dn8bUoaZmf5RsgkIiLPdS1/v4XPbiMxrCM/ed3oS5Ikyda0PYQjSURE5GUYknyUdY+17uzhVlx7AXVNRqgUMvSPCnROYURERG6CIclHDYoJglwGVBpaUGlodug5rEsIpEYHQaNUOLE6IiIi8RiSfJSfWom+EZbRH+sMtWt1cX0kXmojIiLvw5Dkw7q7XtLFlbZDnFUSERGR22BI8mGDrX1JDuzhZjJLONL+uCEMSURE5IUYknzYENtIUh2udSWI0+ca0Nhqgp9agesiAnqiPCIiIqEYknzYgKggqBVy1DUZUVx74Zoea73UlhYTDIXc/obEREREnowhyYeplXKkRrc3b1/jUgDWpu10Nm0TEZGXYkjycdam62tdL+li0zZDEhEReSeGJB+X7sDK261tZhwtMwBg0zYREXkvhiQfZw05+aV6mMxX17x9rKIerSYzgnUqJPb268HqiIiIxGFI8nHXRQTAT61AU6sJp881XNVjDpXWAbBcapPJ2LRNRETeiSHJxynkMqTFXNslt0PF7EciIiLvx5BEl6y8XXdVx+fZtiMJ6ZmCiIiI3ABDEtlW3r6akaQLrSacqLJcluNIEhEReTOGJMLgWEvYOVpmQGubuctjj5RZGrzDAzWICtK6ojwiIiIhGJIIib39EKxTodVkxrGK+i6PtY42DWHTNhEReTmGJIJMJrvYl9Q+c+1yDrMfiYiIfARDEgG4pHm7uOu+pENcaZuIiHwEQxIBuDgy1NUebvoLRpyubuxwPBERkbdiSCIAF1fePlHVgAutJrvH5JdaRpHiQ3UI9Ve7qjQiIiIhGJIIABAVrEVEoAYms4QjZfYvudnWR4oNcV1hREREgjAkkc3gK2x2y5W2iYjIlzAkkY21z+hyK28f4sw2IiLyIQxJZGMdITpsZyTpXH0LyvTNkMmAdI4kERGRD2BIIhvrCNHp6kboLxg7fO1w+/pJfcIDEKBRurgyIiIi12NIIptQfzXiQ3UALs5ks8pjPxIREfkYhiTq4HLrJVn7kYawH4mIiHwEQxJ1MMTOytuSJNlW2mY/EhER+QqGJOogvX0NpEtnuJXWXUBNYyuUchkGRgeJKYyIiMjFGJKog/S4YMhkQJm+GefqWwBc3K+tf1QgtCqFyPKIiIhchiGJOgjQKNEnPADAxRltFze1DRFUFRERkesxJFEntpW32/uSLjZtsx+JiIh8B0MSdTLkkpW3zWbJtrgkR5KIiMiXMCRRJ9aRpEMlepypaUR9Sxs0Sjn6RQYIroyIiMh1GJKok9ToICjlMtQ0tuLz/AoAwKCYICgV/HYhIiLfwb961IlWpUD/qEAAwPo9RQB4qY2IiHwPQxLZZQ1FpXUXAABD4tm0TUREvoUhiez6+Uw2jiQREZGvYUgiuy4NRYEaJZJ7+4srhoiISACGJLKrX2QAtCrLt0d6XDDkcpngioiIiFyLIYnsUirkGBRjueTGS21EROSLlKILIPf14OhEnG9qxX0ZsaJLISIicjmGJLqsu4fG4u6hDEhEROSbeLmNiIiIyA6GJCIiIiI7GJKIiIiI7GBIIiIiIrKDIYmIiIjIDoYkIiIiIjsYkoiIiIjsYEgiIiIisoMhiYiIiMgOhiQiIiIiOxiSiIiIiOxgSCIiIiKygyGJiIiIyA6GJCIiIiI7lKIL8FSSJAEADAaD4EqIiIjoaln/blv/jneFIclB9fX1AID4+HjBlRAREdG1qq+vR3BwcJfHyKSriVLUidlsRllZGQIDAyGTyUSX0yMMBgPi4+NRXFyMoKAg0eW4Db4vnfE96YzviX18Xzrje2JfT70vkiShvr4eMTExkMu77jriSJKD5HI54uLiRJfhEkFBQfzBtYPvS2d8Tzrje2If35fO+J7Y1xPvy5VGkKzYuE1ERERkB0MSERERkR0MSXRZGo0Gf/3rX6HRaESX4lb4vnTG96Qzvif28X3pjO+Jfe7wvrBxm4iIiMgOjiQRERER2cGQRERERGQHQxIRERGRHQxJRERERHYwJFEnixYtwsiRIxEYGIiIiAhMmTIFx44dE12WW1m0aBFkMhnmzZsnuhThSktLMWvWLPTu3Rt+fn4YOnQocnNzRZclTFtbG/785z8jOTkZOp0OKSkpeOmll2A2m0WX5lLffvstJk+ejJiYGMhkMnz00Ucdvi5JEl544QXExMRAp9PhxhtvxJEjR8QU6yJdvSdGoxHPPvss0tPT4e/vj5iYGDz44IMoKysTV7ALXOn75FKPPvooZDIZli1b5rL6GJKok507dyI7Oxu7d+/G9u3b0dbWhgkTJqCxsVF0aW5h7969WLlyJQYPHiy6FOHOnz+PsWPHQqVS4bPPPkNBQQGWLFmCkJAQ0aUJs3jxYvzzn//EG2+8gaNHj+KVV17Bq6++itdff110aS7V2NiIIUOG4I033rD79VdeeQVLly7FG2+8gb179yIqKgq33XabbV9Mb9TVe9LU1IT9+/djwYIF2L9/Pz744AMcP34cd911l4BKXedK3ydWH330Efbs2YOYmBgXVdZOIrqCqqoqCYC0c+dO0aUIV19fL/Xt21favn27dMMNN0hz584VXZJQzz77rDRu3DjRZbiVO+64Q/r1r3/d4b577rlHmjVrlqCKxAMgffjhh7Z/m81mKSoqSvrHP/5hu6+5uVkKDg6W/vnPfwqo0PV+/p7Yk5OTIwGQCgsLXVOUYJd7T0pKSqTY2FgpPz9fSkxMlP73f//XZTVxJImuSK/XAwBCQ0MFVyJednY27rjjDtx6662iS3ELW7ZswYgRI3D//fcjIiICw4YNw1tvvSW6LKHGjRuHr776CsePHwcA5OXl4fvvv8cvfvELwZW5jzNnzqCiogITJkyw3afRaHDDDTdg165dAitzL3q9HjKZzKdHZs1mM2bPno0//vGPGDRokMvPzw1uqUuSJOGpp57CuHHjkJaWJrocoTZu3Ij9+/dj7969oktxG6dPn8aKFSvw1FNP4U9/+hNycnLwxBNPQKPR4MEHHxRdnhDPPvss9Ho9BgwYAIVCAZPJhL///e+YMWOG6NLcRkVFBQAgMjKyw/2RkZEoLCwUUZLbaW5uxnPPPYcHHnjApze9Xbx4MZRKJZ544gkh52dIoi79z//8Dw4dOoTvv/9edClCFRcXY+7cufjyyy+h1WpFl+M2zGYzRowYgYULFwIAhg0bhiNHjmDFihU+G5I2bdqEtWvXYv369Rg0aBAOHjyIefPmISYmBg899JDo8tyKTCbr8G9Jkjrd54uMRiOmT58Os9mM5cuXiy5HmNzcXPzf//0f9u/fL+z7gpfb6LJ+//vfY8uWLdixYwfi4uJElyNUbm4uqqqqkJGRAaVSCaVSiZ07d+K1116DUqmEyWQSXaIQ0dHRGDhwYIf7UlNTUVRUJKgi8f74xz/iueeew/Tp05Geno7Zs2fjySefxKJFi0SX5jaioqIAXBxRsqqqquo0uuRrjEYjpk6dijNnzmD79u0+PYr03XffoaqqCgkJCbbfu4WFhfjDH/6ApKQkl9TAkSTqRJIk/P73v8eHH36Ib775BsnJyaJLEu6WW27B4cOHO9z3q1/9CgMGDMCzzz4LhUIhqDKxxo4d22l5iOPHjyMxMVFQReI1NTVBLu/4+VOhUPjcEgBdSU5ORlRUFLZv345hw4YBAFpbW7Fz504sXrxYcHXiWAPSiRMnsGPHDvTu3Vt0SULNnj27U//nxIkTMXv2bPzqV79ySQ0MSdRJdnY21q9fj48//hiBgYG2T3vBwcHQ6XSCqxMjMDCwU0+Wv78/evfu7dO9Wk8++STGjBmDhQsXYurUqcjJycHKlSuxcuVK0aUJM3nyZPz9739HQkICBg0ahAMHDmDp0qX49a9/Lbo0l2poaMDJkydt/z5z5gwOHjyI0NBQJCQkYN68eVi4cCH69u2Lvn37YuHChfDz88MDDzwgsOqe1dV7EhMTg/vuuw/79+/HJ598ApPJZPvdGxoaCrVaLarsHnWl75OfB0WVSoWoqCj079/fNQW6bB4deQwAdm///ve/RZfmVrgEgMXWrVultLQ0SaPRSAMGDJBWrlwpuiShDAaDNHfuXCkhIUHSarVSSkqK9Pzzz0stLS2iS3OpHTt22P098tBDD0mSZFkG4K9//asUFRUlaTQa6frrr5cOHz4stuge1tV7cubMmcv+7t2xY4fo0nvMlb5Pfs7VSwDIJEmSXBPHiIiIiDwHG7eJiIiI7GBIIiIiIrKDIYmIiIjIDoYkIiIiIjsYkoiIiIjsYEgiIiIisoMhiYiIiMgOhiQiIiIiOxiSiMgj3HjjjZg3b55Lz/nOO+8gJCTEpeckIvfBkERERERkB0MSERERkR0MSUTkkT7//HMEBwdj9erVnb5mNpsRFxeHf/7znx3u379/P2QyGU6fPg0AWLp0KdLT0+Hv74/4+Hj87ne/Q0NDw2XP+fDDD2PKlCkd7ps3bx5uvPFG278lScIrr7yClJQU6HQ6DBkyBJs3b7Z9/fz585g5cybCw8Oh0+nQt29f/Pvf/3bgHSCinsaQREQeZ+PGjZg6dSpWr16NBx98sNPX5XI5pk+fjnXr1nW4f/369Rg9ejRSUlJsx7322mvIz8/Hu+++i6+//hrPPPNMt2r785//jH//+99YsWIFjhw5gieffBKzZs3Czp07AQALFixAQUEBPvvsMxw9ehQrVqxAWFhYt85JRD1DKboAIqJrsXz5cvzpT3/Cxx9/jJtuuumyx82cORNLly5FYWEhEhMTYTabsXHjRvzpT3+yHXNpI3hycjL+9re/4fHHH8fy5csdqq2xsRFLly7F119/jdGjRwMAUlJS8P333+Nf//oXbrjhBhQVFWHYsGEYMWIEACApKcmhcxFRz2NIIiKP8Z///AeVlZX4/vvvMWrUqC6PHTZsGAYMGIANGzbgueeew86dO1FVVYWpU6fajtmxYwcWLlyIgoICGAwGtLW1obm5GY2NjfD397/m+goKCtDc3Izbbrutw/2tra0YNmwYAODxxx/Hvffei/3792PChAmYMmUKxowZc83nIqKex8ttROQxhg4divDwcPz73/+GJElXPH7mzJlYv349AMultokTJ9oubRUWFuIXv/gF0tLS8J///Ae5ubl48803AQBGo9Hu88nl8k7nvfRYs9kMAPj0009x8OBB262goMDWlzRp0iQUFhZi3rx5KCsrwy233IKnn376Gt8JInIFhiQi8hh9+vTBjh078PHHH+P3v//9FY9/4IEHcPjwYeTm5mLz5s2YOXOm7Wv79u1DW1sblixZgqysLPTr1w9lZWVdPl94eDjKy8s73Hfw4EHbfw8cOBAajQZFRUW47rrrOtzi4+M7PM/DDz+MtWvXYtmyZVi5cuVVvgNE5Eq83EZEHqVfv37YsWMHbrzxRiiVSixbtuyyxyYnJ2PMmDGYM2cO2tracPfdd9u+1qdPH7S1teH111/H5MmT8cMPP3SaDfdzN998M1599VWsXr0ao0ePxtq1a5Gfn2+7lBYYGIinn34aTz75JMxmM8aNGweDwYBdu3YhICAADz30EP7yl78gIyMDgwYNQktLCz755BOkpqY65b0hIufiSBIReZz+/fvj66+/xoYNG/CHP/yhy2NnzpyJvLw83HPPPdDpdLb7hw4diqVLl2Lx4sVIS0vDunXrsGjRoi6fa+LEiViwYAGeeeYZjBw5EvX19Z1m1/3tb3/DX/7yFyxatAipqamYOHEitm7diuTkZACAWq3G/PnzMXjwYFx//fVQKBTYuHGjg+8EEfUkmXQ1F/aJiIiIfAxHkoiIiIjsYEgiIiIisoMhiYiIiMgOhiQiIiIiOxiSiIiIiOxgSCIiIiKygyGJiIiIyA6GJCIiIiI7GJKIiIiI7GBIIiIiIrKDIYmIiIjIjv8PIRuoZElI/m4AAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id = \"9\"></a>\n# 9. K-NN Model\n**Here we examine the accuracy score of the K-nn Model and we get a score of 0.953216373742690059 which indicates that we have trained a good model in general, of course, better results can be obtained by playing with the values.**","metadata":{}},{"cell_type":"code","source":"from sklearn.neighbors import KNeighborsClassifier\nknn = KNeighborsClassifier(n_neighbors=3)\nknn.fit(x_train,y_train)\nprediction = knn.predict(x_test)\nprint(\"Accuracy Score\")\nprint(\"------------------------\")\nprint(\"{} nn Acc Score {} \".format(3,knn.score(x_test,y_test)))\nprint(\"------------------------\\n\\n\")\n#seaborntable\ncm = confusion_matrix(y_test, prediction.reshape(-1))\nplt.figure(figsize=(8, 6))\nsns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=np.unique(y_test), yticklabels=np.unique(y_test))\nplt.xlabel('Prediction')\nplt.ylabel('True Values')\nplt.title('Classifcation Result')\ncorrect_predictions = np.trace(cm)\ntotal_predictions = np.sum(cm)\nincorrect_predictions = total_predictions - correct_predictions\nplt.show()\n#Result\nprint(\"\\n\")\nprint(\"Result Evaluation\")\nprint(\"------------------------\")\nprint(f'Sum True Prediction: {correct_predictions}\\n')\nprint(f'Sum False Prediction: {incorrect_predictions}\\n')","metadata":{"execution":{"iopub.status.busy":"2023-10-22T13:28:27.067646Z","iopub.execute_input":"2023-10-22T13:28:27.068677Z","iopub.status.idle":"2023-10-22T13:28:27.391517Z","shell.execute_reply.started":"2023-10-22T13:28:27.068632Z","shell.execute_reply":"2023-10-22T13:28:27.390282Z"},"trusted":true},"execution_count":140,"outputs":[{"name":"stdout","text":"Accuracy Score\n------------------------\n3 nn Acc Score 0.9532163742690059 \n------------------------\n\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n\nResult Evaluation\n------------------------\nSum True Prediction: 163\n\nSum False Prediction: 8\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id = \"10\"></a>\n# 10. Result\n## 🏆 Test accuracy: 95.32% (3 nn)\n\n## 📊 Result Evaluation\n* **Sum True Prediction: 163**\n\n* **Sum False Prediction: 8**","metadata":{}}]}