RF-Classification_CKD.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"X49Q_-6qIfuQ"},"outputs":[],"source":["#importing the Libraies\n","import seaborn as sns\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import pandas as pd"],"id":"X49Q_-6qIfuQ"},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZOLbFg0nI5CK","executionInfo":{"status":"ok","timestamp":1690614901246,"user_tz":-330,"elapsed":20931,"user":{"displayName":"LALITHA MANGALAM M","userId":"16044975285095314425"}},"outputId":"6dab34db-5573-4375-f61a-e079940e6111"},"id":"ZOLbFg0nI5CK","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bKhA2i3gIfuU"},"outputs":[],"source":["dataset=pd.read_csv(\"/content/drive/My Drive/dataset/CKD.csv\")"],"id":"bKhA2i3gIfuU"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"id":"t1nv4sJ0IfuU","executionInfo":{"status":"ok","timestamp":1690614902119,"user_tz":-330,"elapsed":23,"user":{"displayName":"LALITHA MANGALAM M","userId":"16044975285095314425"}},"outputId":"01ad6b19-7982-4c85-e696-bb87f3ddb978"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["           age         bp sg   al   su     rbc        pc         pcc  \\\n","0     2.000000  76.459948  c  3.0  0.0  normal  abnormal  notpresent   \n","1     3.000000  76.459948  c  2.0  0.0  normal    normal  notpresent   \n","2     4.000000  76.459948  a  1.0  0.0  normal    normal  notpresent   \n","3     5.000000  76.459948  d  1.0  0.0  normal    normal  notpresent   \n","4     5.000000  50.000000  c  0.0  0.0  normal    normal  notpresent   \n","..         ...        ... ..  ...  ...     ...       ...         ...   \n","394  51.492308  70.000000  a  0.0  0.0  normal    normal  notpresent   \n","395  51.492308  70.000000  c  0.0  2.0  normal    normal  notpresent   \n","396  51.492308  70.000000  c  3.0  0.0  normal    normal  notpresent   \n","397  51.492308  90.000000  a  0.0  0.0  normal    normal  notpresent   \n","398  51.492308  80.000000  a  0.0  0.0  normal    normal  notpresent   \n","\n","             ba         bgr  ...        pcv            wc        rc  htn   dm  \\\n","0    notpresent  148.112676  ...  38.868902   8408.191126  4.705597   no   no   \n","1    notpresent  148.112676  ...  34.000000  12300.000000  4.705597   no   no   \n","2    notpresent   99.000000  ...  34.000000   8408.191126  4.705597   no   no   \n","3    notpresent  148.112676  ...  38.868902   8408.191126  4.705597   no   no   \n","4    notpresent  148.112676  ...  36.000000  12400.000000  4.705597   no   no   \n","..          ...         ...  ...        ...           ...       ...  ...  ...   \n","394  notpresent  219.000000  ...  37.000000   9800.000000  4.400000   no   no   \n","395  notpresent  220.000000  ...  27.000000   8408.191126  4.705597  yes  yes   \n","396  notpresent  110.000000  ...  26.000000   9200.000000  3.400000  yes  yes   \n","397  notpresent  207.000000  ...  38.868902   8408.191126  4.705597  yes  yes   \n","398  notpresent  100.000000  ...  53.000000   8500.000000  4.900000   no   no   \n","\n","     cad  appet    pe  ane classification  \n","0     no    yes   yes   no            yes  \n","1     no    yes  poor   no            yes  \n","2     no    yes  poor   no            yes  \n","3     no    yes  poor  yes            yes  \n","4     no    yes  poor   no            yes  \n","..   ...    ...   ...  ...            ...  \n","394   no    yes  poor   no            yes  \n","395   no    yes  poor  yes            yes  \n","396   no   poor  poor   no            yes  \n","397   no    yes  poor  yes            yes  \n","398   no    yes  poor   no             no  \n","\n","[399 rows x 25 columns]"],"text/html":["\n","\n","  <div id=\"df-e5027849-df6f-494d-91e1-ccf3242a3d22\">\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>bp</th>\n","      <th>sg</th>\n","      <th>al</th>\n","      <th>su</th>\n","      <th>rbc</th>\n","      <th>pc</th>\n","      <th>pcc</th>\n","      <th>ba</th>\n","      <th>bgr</th>\n","      <th>...</th>\n","      <th>pcv</th>\n","      <th>wc</th>\n","      <th>rc</th>\n","      <th>htn</th>\n","      <th>dm</th>\n","      <th>cad</th>\n","      <th>appet</th>\n","      <th>pe</th>\n","      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<td>yes</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>394</th>\n","      <td>51.492308</td>\n","      <td>70.000000</td>\n","      <td>a</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>normal</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>219.000000</td>\n","      <td>...</td>\n","      <td>37.000000</td>\n","      <td>9800.000000</td>\n","      <td>4.400000</td>\n","      <td>no</td>\n"," 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</div>\n"]},"metadata":{},"execution_count":6}],"source":["dataset"],"id":"t1nv4sJ0IfuU"},{"cell_type":"code","execution_count":null,"metadata":{"id":"BPxTbz6dIfuV"},"outputs":[],"source":["dataset=pd.get_dummies(dataset,drop_first=True)"],"id":"BPxTbz6dIfuV"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y96fq7kYIfuV","executionInfo":{"status":"ok","timestamp":1690614902120,"user_tz":-330,"elapsed":19,"user":{"displayName":"LALITHA MANGALAM M","userId":"16044975285095314425"}},"outputId":"5495d6d8-b160-40ac-9b17-c246aa9d8e73"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['age', 'bp', 'al', 'su', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hrmo', 'pcv',\n","       'wc', 'rc', 'sg_b', 'sg_c', 'sg_d', 'sg_e', 'rbc_normal', 'pc_normal',\n","       'pcc_present', 'ba_present', 'htn_yes', 'dm_yes', 'cad_yes',\n","       'appet_yes', 'pe_yes', 'ane_yes', 'classification_yes'],\n","      dtype='object')"]},"metadata":{},"execution_count":8}],"source":["dataset.columns"],"id":"Y96fq7kYIfuV"},{"cell_type":"code","execution_count":null,"metadata":{"id":"4KvtDp2tIfuV"},"outputs":[],"source":["indep=dataset[['age', 'bp', 'al', 'su', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hrmo', 'pcv',\n","       'wc', 'rc', 'sg_b', 'sg_c', 'sg_d', 'sg_e', 'rbc_normal', 'pc_normal',\n","       'pcc_present', 'ba_present', 'htn_yes', 'dm_yes', 'cad_yes',\n","       'appet_yes', 'pe_yes', 'ane_yes']]\n","dep=dataset['classification_yes']"],"id":"4KvtDp2tIfuV"},{"cell_type":"code","execution_count":null,"metadata":{"id":"UqDe8YByIfuW"},"outputs":[],"source":["#split into training set and test\n","from sklearn.model_selection import train_test_split\n","x_train, x_test, y_train, y_test = train_test_split(indep, dep, test_size = 1/3, random_state = 0)\n"],"id":"UqDe8YByIfuW"},{"cell_type":"code","execution_count":null,"metadata":{"id":"gRGqaKi8IfuW"},"outputs":[],"source":["# from sklearn.preprocessing import StandardScaler\n","# sc = StandardScaler()\n","# X_train_= sc.fit_transform(X_train)\n","# X_test_= sc.transform(X_test)"],"id":"gRGqaKi8IfuW"},{"cell_type":"code","execution_count":null,"metadata":{"id":"TbXjDlUCIfuW"},"outputs":[],"source":["from sklearn.ensemble import RandomForestClassifier"],"id":"TbXjDlUCIfuW"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":134},"id":"rlLHno3IIfuX","executionInfo":{"status":"ok","timestamp":1690614994831,"user_tz":-330,"elapsed":7188,"user":{"displayName":"LALITHA MANGALAM M","userId":"16044975285095314425"}},"outputId":"30734ca8-3a99-4700-e104-95805c609235"},"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 12 candidates, totalling 60 fits\n"]},{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(estimator=RandomForestClassifier(), n_jobs=-1,\n","             param_grid={'criterion': ['gini', 'entropy'],\n","                         'max_features': ['auto', 'sqrt', 'log2'],\n","                         'n_estimators': [10, 100]},\n","             scoring='f1', verbose=3)"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=RandomForestClassifier(), n_jobs=-1,\n","             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n","                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n","                         &#x27;n_estimators&#x27;: [10, 100]},\n","             scoring=&#x27;f1&#x27;, verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=RandomForestClassifier(), n_jobs=-1,\n","             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n","                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n","                         &#x27;n_estimators&#x27;: [10, 100]},\n","             scoring=&#x27;f1&#x27;, verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":20}],"source":["from sklearn.model_selection import GridSearchCV\n","\n","param_grid = {'criterion':['gini','entropy'],\n","              'max_features': ['auto','sqrt','log2'],\n","              'n_estimators':[10,100]}\n","\n","\n","\n","grid = GridSearchCV(RandomForestClassifier(), param_grid, refit = True, verbose = 3,n_jobs=-1,scoring='f1')\n","\n","# fitting the model for grid search\n","grid.fit(x_train, y_train)\n","\n","\n"],"id":"rlLHno3IIfuX"},{"cell_type":"code","execution_count":null,"metadata":{"id":"0sM2rUCeIfuX"},"outputs":[],"source":["# print best parameter after tuning\n","#print(grid.best_params_)\n","re=grid.cv_results_\n","#print(re)\n","grid_predictions = grid.predict(x_test)\n","\n","\n","from sklearn.metrics import confusion_matrix\n","cm = confusion_matrix(y_test, grid_predictions)\n","\n","\n","\n","# print classification report\n","from sklearn.metrics import classification_report\n","clf_report = classification_report(y_test, grid_predictions)\n","\n","\n"],"id":"0sM2rUCeIfuX"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RsCBk-6qIfuX","executionInfo":{"status":"ok","timestamp":1690615021538,"user_tz":-330,"elapsed":383,"user":{"displayName":"LALITHA MANGALAM M","userId":"16044975285095314425"}},"outputId":"893e5278-482e-49b6-f44c-d9bfddf6ed46"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'criterion': 'entropy', 'max_features': 'log2', 'n_estimators': 100}\n"]}],"source":["print(grid.best_params_)"],"id":"RsCBk-6qIfuX"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xMoBvVukIfuY","executionInfo":{"status":"ok","timestamp":1690615034240,"user_tz":-330,"elapsed":11,"user":{"displayName":"LALITHA MANGALAM M","userId":"16044975285095314425"}},"outputId":"819f8e8b-2fcb-4bea-b55a-37ee14b3afcb"},"outputs":[{"output_type":"stream","name":"stdout","text":["The f1_macro value for best parameter {'criterion': 'entropy', 'max_features': 'log2', 'n_estimators': 100}: 0.9849624060150376\n"]}],"source":["from sklearn.metrics import f1_score\n","f1_macro=f1_score(y_test,grid_predictions,average='weighted')\n","print(\"The f1_macro value for best parameter {}:\".format(grid.best_params_),f1_macro)\n"],"id":"xMoBvVukIfuY"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"R2zaeMv5IfuY","executionInfo":{"status":"ok","timestamp":1690615035837,"user_tz":-330,"elapsed":4,"user":{"displayName":"LALITHA MANGALAM M","userId":"16044975285095314425"}},"outputId":"79b10525-e0a3-407c-df5c-b21c7f2e2b3e"},"outputs":[{"output_type":"stream","name":"stdout","text":["The confusion Matrix:\n"," [[50  1]\n"," [ 1 81]]\n"]}],"source":["print(\"The confusion Matrix:\\n\",cm)"],"id":"R2zaeMv5IfuY"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ctc41CRjIfuZ","executionInfo":{"status":"ok","timestamp":1690615037032,"user_tz":-330,"elapsed":3,"user":{"displayName":"LALITHA MANGALAM M","userId":"16044975285095314425"}},"outputId":"4baeb1db-5504-4b07-8fd3-ad1ae572b6f0"},"outputs":[{"output_type":"stream","name":"stdout","text":["The report:\n","               precision    recall  f1-score   support\n","\n","           0       0.98      0.98      0.98        51\n","           1       0.99      0.99      0.99        82\n","\n","    accuracy                           0.98       133\n","   macro avg       0.98      0.98      0.98       133\n","weighted avg       0.98      0.98      0.98       133\n","\n"]}],"source":["print(\"The report:\\n\",clf_report)"],"id":"Ctc41CRjIfuZ"},{"cell_type":"code","execution_count":null,"metadata":{"id":"XUIV08BBIfud","colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"status":"ok","timestamp":1690615096340,"user_tz":-330,"elapsed":1332,"user":{"displayName":"LALITHA MANGALAM M","userId":"16044975285095314425"}},"outputId":"f29f8d7b-5ba0-4658-fe80-0e2f184ddbf2"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["tp, fp, tn, fn = cm.ravel()\n","results = np.array([[tp, fp], [fn, tn]])\n","labels = ['Positive', 'Negative']\n","colors = ['tab:green', 'tab:red']\n","\n","plt.bar(labels, results[:, 0], color=colors[0], label='True Positive')\n","plt.bar(labels, results[:, 1], bottom=results[:, 0], color=colors[1], label='False Positive / Negative')\n","\n","plt.xlabel('Actual Class')\n","plt.ylabel('Count')\n","plt.title('Classifier Results')\n","plt.legend()\n","plt.show()"],"id":"XUIV08BBIfud"},{"cell_type":"code","source":[],"metadata":{"id":"doSn9ZJXgxr_"},"id":"doSn9ZJXgxr_","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.4"},"colab":{"provenance":[{"file_id":"16BUpYVjZWsUF-GhtXNQ4gxm06VT47pIO","timestamp":1690563277819}]}},"nbformat":4,"nbformat_minor":5}