{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyN1XagztLOzsfPrdgtTm2ZH"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# **UTS Data Mining**"],"metadata":{"id":"hLKqVf4RRQBz"}},{"cell_type":"markdown","source":["### Klasifikasi Data Pada Dataset Breats Cancer"],"metadata":{"id":"dpr8fvexVU_U"}},{"cell_type":"markdown","source":["Lakukan analisa terhadap data pada\n","https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Coimbra dengan menggunakan klasifikasi\n","\n"," - Metode KNN\n","\n"," - Metode pohon keputusan (Desision tree)"],"metadata":{"id":"Sod8cg80Vacv"}},{"cell_type":"markdown","source":["## Data"],"metadata":{"id":"qRrRZ65QV7WZ"}},{"cell_type":"code","execution_count":60,"metadata":{"id":"6hUZnrOCQ1af","executionInfo":{"status":"ok","timestamp":1665968941292,"user_tz":-420,"elapsed":371,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}}},"outputs":[],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import numba\n","import cv2 as cv\n","import pandas as pd\n","from sklearn.decomposition import PCA\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.metrics import confusion_matrix\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import classification_report\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.metrics import accuracy_score\n","from sklearn import tree\n","from matplotlib import pyplot as plt \n"]},{"cell_type":"code","source":["data = pd.read_csv(\"https://raw.githubusercontent.com/LALA09-erha/Python-StrukturData/master/dataR2.csv\")\n","data_1 = pd.read_csv(\"https://raw.githubusercontent.com/LALA09-erha/Python-StrukturData/master/dataR2.csv\")\n","data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"EBWEN1F6Vv-Q","executionInfo":{"status":"ok","timestamp":1665968942371,"user_tz":-420,"elapsed":706,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"333f0037-efdd-4bd6-f92f-8fc93be0ba09"},"execution_count":61,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Age        BMI  Glucose  Insulin      HOMA   Leptin  Adiponectin  \\\n","0     48  23.500000       70    2.707  0.467409   8.8071     9.702400   \n","1     83  20.690495       92    3.115  0.706897   8.8438     5.429285   \n","2     82  23.124670       91    4.498  1.009651  17.9393    22.432040   \n","3     68  21.367521       77    3.226  0.612725   9.8827     7.169560   \n","4     86  21.111111       92    3.549  0.805386   6.6994     4.819240   \n","..   ...        ...      ...      ...       ...      ...          ...   \n","111   45  26.850000       92    3.330  0.755688  54.6800    12.100000   \n","112   62  26.840000      100    4.530  1.117400  12.4500    21.420000   \n","113   65  32.050000       97    5.730  1.370998  61.4800    22.540000   \n","114   72  25.590000       82    2.820  0.570392  24.9600    33.750000   \n","115   86  27.180000      138   19.910  6.777364  90.2800    14.110000   \n","\n","     Resistin    MCP.1  Classification  \n","0     7.99585  417.114               1  \n","1     4.06405  468.786               1  \n","2     9.27715  554.697               1  \n","3    12.76600  928.220               1  \n","4    10.57635  773.920               1  \n","..        ...      ...             ...  \n","111  10.96000  268.230               2  \n","112   7.32000  330.160               2  \n","113  10.33000  314.050               2  \n","114   3.27000  392.460               2  \n","115   4.35000   90.090               2  \n","\n","[116 rows x 10 columns]"],"text/html":["\n","  <div id=\"df-b6bb1a51-5dc2-4591-9fdb-1301fdcdf119\">\n","    <div class=\"colab-df-container\">\n","      <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>Age</th>\n","      <th>BMI</th>\n","      <th>Glucose</th>\n","      <th>Insulin</th>\n","      <th>HOMA</th>\n","      <th>Leptin</th>\n","      <th>Adiponectin</th>\n","      <th>Resistin</th>\n","      <th>MCP.1</th>\n","      <th>Classification</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>48</td>\n","      <td>23.500000</td>\n","      <td>70</td>\n","      <td>2.707</td>\n","      <td>0.467409</td>\n","      <td>8.8071</td>\n","      <td>9.702400</td>\n","      <td>7.99585</td>\n","      <td>417.114</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>83</td>\n","      <td>20.690495</td>\n","      <td>92</td>\n","      <td>3.115</td>\n","      <td>0.706897</td>\n","      <td>8.8438</td>\n","      <td>5.429285</td>\n","      <td>4.06405</td>\n","      <td>468.786</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>82</td>\n","      <td>23.124670</td>\n","      <td>91</td>\n","      <td>4.498</td>\n","      <td>1.009651</td>\n","      <td>17.9393</td>\n","      <td>22.432040</td>\n","      <td>9.27715</td>\n","      <td>554.697</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>68</td>\n","      <td>21.367521</td>\n","      <td>77</td>\n","      <td>3.226</td>\n","      <td>0.612725</td>\n","      <td>9.8827</td>\n","      <td>7.169560</td>\n","      <td>12.76600</td>\n","      <td>928.220</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>86</td>\n","      <td>21.111111</td>\n","      <td>92</td>\n","      <td>3.549</td>\n","      <td>0.805386</td>\n","      <td>6.6994</td>\n","      <td>4.819240</td>\n","      <td>10.57635</td>\n","      <td>773.920</td>\n","      <td>1</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","    </tr>\n","    <tr>\n","      <th>111</th>\n","      <td>45</td>\n","      <td>26.850000</td>\n","      <td>92</td>\n","      <td>3.330</td>\n","      <td>0.755688</td>\n","      <td>54.6800</td>\n","      <td>12.100000</td>\n","      <td>10.96000</td>\n","      <td>268.230</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>112</th>\n","      <td>62</td>\n","      <td>26.840000</td>\n","      <td>100</td>\n","      <td>4.530</td>\n","      <td>1.117400</td>\n","      <td>12.4500</td>\n","      <td>21.420000</td>\n","      <td>7.32000</td>\n","      <td>330.160</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>113</th>\n","      <td>65</td>\n","      <td>32.050000</td>\n","      <td>97</td>\n","      <td>5.730</td>\n","      <td>1.370998</td>\n","      <td>61.4800</td>\n","      <td>22.540000</td>\n","      <td>10.33000</td>\n","      <td>314.050</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>114</th>\n","      <td>72</td>\n","      <td>25.590000</td>\n","      <td>82</td>\n","      <td>2.820</td>\n","      <td>0.570392</td>\n","      <td>24.9600</td>\n","      <td>33.750000</td>\n","      <td>3.27000</td>\n","      <td>392.460</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>115</th>\n","      <td>86</td>\n","      <td>27.180000</td>\n","      <td>138</td>\n","      <td>19.910</td>\n","      <td>6.777364</td>\n","      <td>90.2800</td>\n","      <td>14.110000</td>\n","      <td>4.35000</td>\n","      <td>90.090</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>116 rows × 10 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6bb1a51-5dc2-4591-9fdb-1301fdcdf119')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":61}]},{"cell_type":"markdown","source":["### Ubah Label  1=Healthy controls dan 2=Patients"],"metadata":{"id":"aThXgWNsbHzw"}},{"cell_type":"code","source":["data_1['Classification'] = data_1['Classification'].apply({1:'Healthy controls', 2:'Patients'}.get)\n","data_1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"gegaDyhFbMGu","executionInfo":{"status":"ok","timestamp":1665968942371,"user_tz":-420,"elapsed":19,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"b71a3d33-f5ea-458f-8de5-f3b6a6013bc8"},"execution_count":62,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Age        BMI  Glucose  Insulin      HOMA   Leptin  Adiponectin  \\\n","0     48  23.500000       70    2.707  0.467409   8.8071     9.702400   \n","1     83  20.690495       92    3.115  0.706897   8.8438     5.429285   \n","2     82  23.124670       91    4.498  1.009651  17.9393    22.432040   \n","3     68  21.367521       77    3.226  0.612725   9.8827     7.169560   \n","4     86  21.111111       92    3.549  0.805386   6.6994     4.819240   \n","..   ...        ...      ...      ...       ...      ...          ...   \n","111   45  26.850000       92    3.330  0.755688  54.6800    12.100000   \n","112   62  26.840000      100    4.530  1.117400  12.4500    21.420000   \n","113   65  32.050000       97    5.730  1.370998  61.4800    22.540000   \n","114   72  25.590000       82    2.820  0.570392  24.9600    33.750000   \n","115   86  27.180000      138   19.910  6.777364  90.2800    14.110000   \n","\n","     Resistin    MCP.1    Classification  \n","0     7.99585  417.114  Healthy controls  \n","1     4.06405  468.786  Healthy controls  \n","2     9.27715  554.697  Healthy controls  \n","3    12.76600  928.220  Healthy controls  \n","4    10.57635  773.920  Healthy controls  \n","..        ...      ...               ...  \n","111  10.96000  268.230          Patients  \n","112   7.32000  330.160          Patients  \n","113  10.33000  314.050          Patients  \n","114   3.27000  392.460          Patients  \n","115   4.35000   90.090          Patients  \n","\n","[116 rows x 10 columns]"],"text/html":["\n","  <div 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<td>8.8071</td>\n","      <td>9.702400</td>\n","      <td>7.99585</td>\n","      <td>417.114</td>\n","      <td>Healthy controls</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>83</td>\n","      <td>20.690495</td>\n","      <td>92</td>\n","      <td>3.115</td>\n","      <td>0.706897</td>\n","      <td>8.8438</td>\n","      <td>5.429285</td>\n","      <td>4.06405</td>\n","      <td>468.786</td>\n","      <td>Healthy controls</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>82</td>\n","      <td>23.124670</td>\n","      <td>91</td>\n","      <td>4.498</td>\n","      <td>1.009651</td>\n","      <td>17.9393</td>\n","      <td>22.432040</td>\n","      <td>9.27715</td>\n","      <td>554.697</td>\n","      <td>Healthy controls</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>68</td>\n","      <td>21.367521</td>\n","      <td>77</td>\n","      <td>3.226</td>\n","      <td>0.612725</td>\n","      <td>9.8827</td>\n","      <td>7.169560</td>\n","      <td>12.76600</td>\n","      <td>928.220</td>\n","      <td>Healthy controls</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>86</td>\n","      <td>21.111111</td>\n","      <td>92</td>\n","      <td>3.549</td>\n","      <td>0.805386</td>\n","      <td>6.6994</td>\n","      <td>4.819240</td>\n","      <td>10.57635</td>\n","      <td>773.920</td>\n","      <td>Healthy controls</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","    </tr>\n","    <tr>\n","      <th>111</th>\n","      <td>45</td>\n","      <td>26.850000</td>\n","      <td>92</td>\n","      <td>3.330</td>\n","      <td>0.755688</td>\n","      <td>54.6800</td>\n","      <td>12.100000</td>\n","      <td>10.96000</td>\n","      <td>268.230</td>\n","      <td>Patients</td>\n","    </tr>\n","    <tr>\n","      <th>112</th>\n","      <td>62</td>\n","      <td>26.840000</td>\n","      <td>100</td>\n","      <td>4.530</td>\n","      <td>1.117400</td>\n","      <td>12.4500</td>\n","      <td>21.420000</td>\n","      <td>7.32000</td>\n","      <td>330.160</td>\n","      <td>Patients</td>\n","    </tr>\n","    <tr>\n","      <th>113</th>\n","      <td>65</td>\n","      <td>32.050000</td>\n","      <td>97</td>\n","      <td>5.730</td>\n","      <td>1.370998</td>\n","      <td>61.4800</td>\n","      <td>22.540000</td>\n","      <td>10.33000</td>\n","      <td>314.050</td>\n","      <td>Patients</td>\n","    </tr>\n","    <tr>\n","      <th>114</th>\n","      <td>72</td>\n","      <td>25.590000</td>\n","      <td>82</td>\n","      <td>2.820</td>\n","      <td>0.570392</td>\n","      <td>24.9600</td>\n","      <td>33.750000</td>\n","      <td>3.27000</td>\n","      <td>392.460</td>\n","      <td>Patients</td>\n","    </tr>\n","    <tr>\n","      <th>115</th>\n","      <td>86</td>\n","      <td>27.180000</td>\n","      <td>138</td>\n","      <td>19.910</td>\n","      <td>6.777364</td>\n","      <td>90.2800</td>\n","      <td>14.110000</td>\n","      <td>4.35000</td>\n","      <td>90.090</td>\n","      <td>Patients</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>116 rows × 10 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26355953-1b12-467f-975f-f1f09a67681f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 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Erha","userId":"13359221303846732984"}},"outputId":"54096114-b865-411f-aa0c-4aa67a76afd1"},"execution_count":63,"outputs":[{"output_type":"execute_result","data":{"text/plain":["pandas.core.frame.DataFrame"]},"metadata":{},"execution_count":63}]},{"cell_type":"markdown","source":["## **Metode KNN Clafification**"],"metadata":{"id":"D7uw5jXPV0KT"}},{"cell_type":"markdown","source":["split data"],"metadata":{"id":"zw2xoSNZWEZj"}},{"cell_type":"code","source":["data.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"p5c-HQgcVzz7","executionInfo":{"status":"ok","timestamp":1665968942372,"user_tz":-420,"elapsed":17,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"3fdee74b-86fa-4455-d3c3-54fec150d83b"},"execution_count":64,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(116, 10)"]},"metadata":{},"execution_count":64}]},{"cell_type":"markdown","source":["memisahkan data dengan kelas ke dalam dua variabel, X dan y\n"],"metadata":{"id":"GZv2HM0RWOJL"}},{"cell_type":"code","source":["X = data.drop(columns=[\"Classification\"])\n","X.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"grTmEtjrWSWX","executionInfo":{"status":"ok","timestamp":1665968942373,"user_tz":-420,"elapsed":17,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"03d8ce55-5353-43a4-b74e-b32526dd01f9"},"execution_count":65,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Age        BMI  Glucose  Insulin      HOMA   Leptin  Adiponectin  Resistin  \\\n","0   48  23.500000       70    2.707  0.467409   8.8071     9.702400   7.99585   \n","1   83  20.690495       92    3.115  0.706897   8.8438     5.429285   4.06405   \n","2   82  23.124670       91    4.498  1.009651  17.9393    22.432040   9.27715   \n","3   68  21.367521       77    3.226  0.612725   9.8827     7.169560  12.76600   \n","4   86  21.111111       92    3.549  0.805386   6.6994     4.819240  10.57635   \n","\n","     MCP.1  \n","0  417.114  \n","1  468.786  \n","2  554.697  \n","3  928.220  \n","4  773.920  "],"text/html":["\n","  <div id=\"df-8049c1f8-29d0-4530-9c21-bdd93ae27385\">\n","    <div class=\"colab-df-container\">\n","      <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>Age</th>\n","      <th>BMI</th>\n","      <th>Glucose</th>\n","      <th>Insulin</th>\n","      <th>HOMA</th>\n","      <th>Leptin</th>\n","      <th>Adiponectin</th>\n","      <th>Resistin</th>\n","      <th>MCP.1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>48</td>\n","      <td>23.500000</td>\n","      <td>70</td>\n","      <td>2.707</td>\n","      <td>0.467409</td>\n","      <td>8.8071</td>\n","      <td>9.702400</td>\n","      <td>7.99585</td>\n","      <td>417.114</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>83</td>\n","      <td>20.690495</td>\n","      <td>92</td>\n","      <td>3.115</td>\n","      <td>0.706897</td>\n","      <td>8.8438</td>\n","      <td>5.429285</td>\n","      <td>4.06405</td>\n","      <td>468.786</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>82</td>\n","      <td>23.124670</td>\n","      <td>91</td>\n","      <td>4.498</td>\n","      <td>1.009651</td>\n","      <td>17.9393</td>\n","      <td>22.432040</td>\n","      <td>9.27715</td>\n","      <td>554.697</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>68</td>\n","      <td>21.367521</td>\n","      <td>77</td>\n","      <td>3.226</td>\n","      <td>0.612725</td>\n","      <td>9.8827</td>\n","      <td>7.169560</td>\n","      <td>12.76600</td>\n","      <td>928.220</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>86</td>\n","      <td>21.111111</td>\n","      <td>92</td>\n","      <td>3.549</td>\n","      <td>0.805386</td>\n","      <td>6.6994</td>\n","      <td>4.819240</td>\n","      <td>10.57635</td>\n","      <td>773.920</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8049c1f8-29d0-4530-9c21-bdd93ae27385')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-8049c1f8-29d0-4530-9c21-bdd93ae27385');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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Erha","userId":"13359221303846732984"}},"outputId":"d17573a9-2a43-4f44-ab65-0e071c6bfa32"},"execution_count":66,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","       2, 2, 2, 2, 2, 2])"]},"metadata":{},"execution_count":66}]},{"cell_type":"markdown","source":["### Memisahkan Data Training dan Test"],"metadata":{"id":"y6ysGd44W5Di"}},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","#split dataset into train and test data\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y)"],"metadata":{"id":"V_hTrkuvW-6a","executionInfo":{"status":"ok","timestamp":1665968942373,"user_tz":-420,"elapsed":15,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}}},"execution_count":67,"outputs":[]},{"cell_type":"markdown","source":["### membuat Model "],"metadata":{"id":"gWlT0p-wXFaX"}},{"cell_type":"markdown","source":["Dengan menetapkan N = 3 atau 3 tetangga"],"metadata":{"id":"lg5yuM-WXL02"}},{"cell_type":"code","source":["from sklearn.neighbors import KNeighborsClassifier\n","# Create KNN classifier\n","knn = KNeighborsClassifier(n_neighbors = 3)\n","# Fit the classifier to the data\n","knn.fit(X_train,y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4assHks4XIud","executionInfo":{"status":"ok","timestamp":1665968942374,"user_tz":-420,"elapsed":15,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"f765524c-0060-4f26-86d4-7e67e9da8171"},"execution_count":68,"outputs":[{"output_type":"execute_result","data":{"text/plain":["KNeighborsClassifier(n_neighbors=3)"]},"metadata":{},"execution_count":68}]},{"cell_type":"markdown","source":["### Melakukan testing data pada model"],"metadata":{"id":"hsWOaL61XS3M"}},{"cell_type":"code","source":["#show first 5 model predictions on the test data\n","knn.predict(X_test)[1:25]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jIK0-RseXZC9","executionInfo":{"status":"ok","timestamp":1665968942374,"user_tz":-420,"elapsed":14,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"34032908-6739-41b4-a159-52ae775362a2"},"execution_count":69,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2,\n","       2])"]},"metadata":{},"execution_count":69}]},{"cell_type":"markdown","source":["### Menghitung akurasi yang didapat"],"metadata":{"id":"nTkQ4ErPXbmY"}},{"cell_type":"code","source":["#check accuracy of our model on the test data\n","knn.score(X_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rXyEo81tXios","executionInfo":{"status":"ok","timestamp":1665968942375,"user_tz":-420,"elapsed":14,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"a0356960-9466-4a6a-99a7-70eabb3ed024"},"execution_count":70,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.3333333333333333"]},"metadata":{},"execution_count":70}]},{"cell_type":"markdown","source":["### Melakukan K-Fold Cross Validation"],"metadata":{"id":"N-eRXq2lXr1q"}},{"cell_type":"code","source":["from sklearn.model_selection import cross_val_score\n","import numpy as np\n","#create a new KNN model\n","knn_cv = KNeighborsClassifier(n_neighbors=6)\n","#train model with cv of 5 \n","cv_scores = cross_val_score(knn_cv, X, y, cv=5)\n","#print each cv score (accuracy) and average them\n","print(cv_scores)\n","print(\"cv_scores mean:{}\".format(np.mean(cv_scores)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ckTLa_-JXw6O","executionInfo":{"status":"ok","timestamp":1665968942375,"user_tz":-420,"elapsed":13,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"990f8df7-701f-4854-df30-0fc3dd934260"},"execution_count":71,"outputs":[{"output_type":"stream","name":"stdout","text":["[0.45833333 0.60869565 0.47826087 0.43478261 0.43478261]\n","cv_scores mean:0.4829710144927536\n"]}]},{"cell_type":"markdown","source":["### Hypertuning model parameters menggunakan GridSearchCV"],"metadata":{"id":"qlafZt9RX3w-"}},{"cell_type":"code","source":["from sklearn.model_selection import GridSearchCV\n","#create new a knn model\n","knn2 = KNeighborsClassifier()\n","#create a dictionary of all values we want to test for n_neighbors\n","param_grid = {'n_neighbors': np.arange(1, 25)}\n","#use gridsearch to test all values for n_neighbors\n","knn_gscv = GridSearchCV(knn2, param_grid, cv=5)\n","#fit model to data\n","knn_gscv.fit(X, y)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gPvp3uHWX3bv","executionInfo":{"status":"ok","timestamp":1665968943112,"user_tz":-420,"elapsed":749,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"b7546251-3a7c-43d0-f6cb-63571cb2f9e6"},"execution_count":72,"outputs":[{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n","             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n","       18, 19, 20, 21, 22, 23, 24])})"]},"metadata":{},"execution_count":72}]},{"cell_type":"markdown","source":["### Mengecek `n` yang terbaik"],"metadata":{"id":"l35W9dfAYBq_"}},{"cell_type":"code","source":["#check top performing n_neighbors value\n","knn_gscv.best_params_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L43f0nfhYAVt","executionInfo":{"status":"ok","timestamp":1665968943112,"user_tz":-420,"elapsed":13,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"61b7b176-4994-42da-e21f-b9c305a75255"},"execution_count":73,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'n_neighbors': 23}"]},"metadata":{},"execution_count":73}]},{"cell_type":"markdown","source":["### Mengecek Hasil dari `n` yang terbaik"],"metadata":{"id":"NOq5StitYNNs"}},{"cell_type":"code","source":["#check mean score for the top performing value of n_neighbors\n","knn_gscv.best_score_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-EZ81H3nYWGR","executionInfo":{"status":"ok","timestamp":1665968943113,"user_tz":-420,"elapsed":11,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"b167e16d-6c31-4392-c4a8-935cea196344"},"execution_count":74,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.5688405797101449"]},"metadata":{},"execution_count":74}]},{"cell_type":"markdown","source":["### Kesimpulan"],"metadata":{"id":"41GgEPpdYbao"}},{"cell_type":"markdown","source":["Dari Hasil diatas bahwa n yang terbaik yaitu 23 dengan hasil 0.5688405797101449\n"],"metadata":{"id":"pC7zWViEYfBI"}},{"cell_type":"markdown","source":["## **Metode Decision Tree**"],"metadata":{"id":"s6vmcHsZYss8"}},{"cell_type":"code","source":["data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"p5OFFwbxYwYK","executionInfo":{"status":"ok","timestamp":1665968943113,"user_tz":-420,"elapsed":9,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"6aa64caa-7694-4fe9-94c4-113e7822829f"},"execution_count":75,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Age        BMI  Glucose  Insulin      HOMA   Leptin  Adiponectin  \\\n","0     48  23.500000       70    2.707  0.467409   8.8071     9.702400   \n","1     83  20.690495       92    3.115  0.706897   8.8438     5.429285   \n","2     82  23.124670       91    4.498  1.009651  17.9393    22.432040   \n","3     68  21.367521       77    3.226  0.612725   9.8827     7.169560   \n","4     86  21.111111       92    3.549  0.805386   6.6994     4.819240   \n","..   ...        ...      ...      ...       ...      ...          ...   \n","111   45  26.850000       92    3.330  0.755688  54.6800    12.100000   \n","112   62  26.840000      100    4.530  1.117400  12.4500    21.420000   \n","113   65  32.050000       97    5.730  1.370998  61.4800    22.540000   \n","114   72  25.590000       82    2.820  0.570392  24.9600    33.750000   \n","115   86  27.180000      138   19.910  6.777364  90.2800    14.110000   \n","\n","     Resistin    MCP.1  Classification  \n","0     7.99585  417.114               1  \n","1     4.06405  468.786               1  \n","2     9.27715  554.697               1  \n","3    12.76600  928.220               1  \n","4    10.57635  773.920               1  \n","..        ...      ...             ...  \n","111  10.96000  268.230               2  \n","112   7.32000  330.160               2  \n","113  10.33000  314.050               2  \n","114   3.27000  392.460               2  \n","115   4.35000   90.090               2  \n","\n","[116 rows x 10 columns]"],"text/html":["\n","  <div id=\"df-6870b01a-8f70-4e06-99af-422582a068b0\">\n","    <div class=\"colab-df-container\">\n","      <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>Age</th>\n","      <th>BMI</th>\n","      <th>Glucose</th>\n","      <th>Insulin</th>\n","      <th>HOMA</th>\n","      <th>Leptin</th>\n","      <th>Adiponectin</th>\n","      <th>Resistin</th>\n","      <th>MCP.1</th>\n","      <th>Classification</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>48</td>\n","      <td>23.500000</td>\n","      <td>70</td>\n","      <td>2.707</td>\n","      <td>0.467409</td>\n","      <td>8.8071</td>\n","      <td>9.702400</td>\n","      <td>7.99585</td>\n","      <td>417.114</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>83</td>\n","      <td>20.690495</td>\n","      <td>92</td>\n","      <td>3.115</td>\n","      <td>0.706897</td>\n","      <td>8.8438</td>\n","      <td>5.429285</td>\n","      <td>4.06405</td>\n","      <td>468.786</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>82</td>\n","      <td>23.124670</td>\n","      <td>91</td>\n","      <td>4.498</td>\n","      <td>1.009651</td>\n","      <td>17.9393</td>\n","      <td>22.432040</td>\n","      <td>9.27715</td>\n","      <td>554.697</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>68</td>\n","      <td>21.367521</td>\n","      <td>77</td>\n","      <td>3.226</td>\n","      <td>0.612725</td>\n","      <td>9.8827</td>\n","      <td>7.169560</td>\n","      <td>12.76600</td>\n","      <td>928.220</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>86</td>\n","      <td>21.111111</td>\n","      <td>92</td>\n","      <td>3.549</td>\n","      <td>0.805386</td>\n","      <td>6.6994</td>\n","      <td>4.819240</td>\n","      <td>10.57635</td>\n","      <td>773.920</td>\n","      <td>1</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","    </tr>\n","    <tr>\n","      <th>111</th>\n","      <td>45</td>\n","      <td>26.850000</td>\n","      <td>92</td>\n","      <td>3.330</td>\n","      <td>0.755688</td>\n","      <td>54.6800</td>\n","      <td>12.100000</td>\n","      <td>10.96000</td>\n","      <td>268.230</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>112</th>\n","      <td>62</td>\n","      <td>26.840000</td>\n","      <td>100</td>\n","      <td>4.530</td>\n","      <td>1.117400</td>\n","      <td>12.4500</td>\n","      <td>21.420000</td>\n","      <td>7.32000</td>\n","      <td>330.160</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>113</th>\n","      <td>65</td>\n","      <td>32.050000</td>\n","      <td>97</td>\n","      <td>5.730</td>\n","      <td>1.370998</td>\n","      <td>61.4800</td>\n","      <td>22.540000</td>\n","      <td>10.33000</td>\n","      <td>314.050</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>114</th>\n","      <td>72</td>\n","      <td>25.590000</td>\n","      <td>82</td>\n","      <td>2.820</td>\n","      <td>0.570392</td>\n","      <td>24.9600</td>\n","      <td>33.750000</td>\n","      <td>3.27000</td>\n","      <td>392.460</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>115</th>\n","      <td>86</td>\n","      <td>27.180000</td>\n","      <td>138</td>\n","      <td>19.910</td>\n","      <td>6.777364</td>\n","      <td>90.2800</td>\n","      <td>14.110000</td>\n","      <td>4.35000</td>\n","      <td>90.090</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>116 rows × 10 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6870b01a-8f70-4e06-99af-422582a068b0')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","    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y)"],"metadata":{"id":"L7D5GIAZY5PK","executionInfo":{"status":"ok","timestamp":1665968943114,"user_tz":-420,"elapsed":10,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}}},"execution_count":76,"outputs":[]},{"cell_type":"code","source":["data.value_counts"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nFqcLKf_cnec","executionInfo":{"status":"ok","timestamp":1665971752738,"user_tz":-420,"elapsed":497,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"fab06595-f15e-423b-e286-a2b6db347b2e"},"execution_count":86,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<bound method DataFrame.value_counts of      Age        BMI  Glucose  Insulin      HOMA   Leptin  Adiponectin  \\\n","0     48  23.500000       70    2.707  0.467409   8.8071     9.702400   \n","1     83  20.690495       92    3.115  0.706897   8.8438     5.429285   \n","2     82  23.124670       91    4.498  1.009651  17.9393    22.432040   \n","3     68  21.367521       77    3.226  0.612725   9.8827     7.169560   \n","4     86  21.111111       92    3.549  0.805386   6.6994     4.819240   \n","..   ...        ...      ...      ...       ...      ...          ...   \n","111   45  26.850000       92    3.330  0.755688  54.6800    12.100000   \n","112   62  26.840000      100    4.530  1.117400  12.4500    21.420000   \n","113   65  32.050000       97    5.730  1.370998  61.4800    22.540000   \n","114   72  25.590000       82    2.820  0.570392  24.9600    33.750000   \n","115   86  27.180000      138   19.910  6.777364  90.2800    14.110000   \n","\n","     Resistin    MCP.1  Classification  \n","0     7.99585  417.114               1  \n","1     4.06405  468.786               1  \n","2     9.27715  554.697               1  \n","3    12.76600  928.220               1  \n","4    10.57635  773.920               1  \n","..        ...      ...             ...  \n","111  10.96000  268.230               2  \n","112   7.32000  330.160               2  \n","113  10.33000  314.050               2  \n","114   3.27000  392.460               2  \n","115   4.35000   90.090               2  \n","\n","[116 rows x 10 columns]>"]},"metadata":{},"execution_count":86}]},{"cell_type":"markdown","source":["### Membuat Grafik Decision Tree dari hasil Klasifikasi"],"metadata":{"id":"n_dgydTfY73I"}},{"cell_type":"code","source":["fig = plt.figure(figsize=(15,10))\n","_ = tree.plot_tree(clf, feature_names=list(data.columns.values)[:9], class_names=list(data.columns.values)[4] ,filled=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":575},"id":"rwpsaFHOZFms","executionInfo":{"status":"ok","timestamp":1665968944258,"user_tz":-420,"elapsed":1152,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"6161520e-98b3-4956-d850-71d82a15d878"},"execution_count":78,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]}]}