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{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","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":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-06-24T08:34:31.424816Z","iopub.execute_input":"2021-06-24T08:34:31.425220Z","iopub.status.idle":"2021-06-24T08:34:31.447439Z","shell.execute_reply.started":"2021-06-24T08:34:31.425138Z","shell.execute_reply":"2021-06-24T08:34:31.446129Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/heart-disease-uci/heart.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/heart-disease-uci/heart.csv')","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:31.449451Z","iopub.execute_input":"2021-06-24T08:34:31.450209Z","iopub.status.idle":"2021-06-24T08:34:31.473088Z","shell.execute_reply.started":"2021-06-24T08:34:31.450131Z","shell.execute_reply":"2021-06-24T08:34:31.472038Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:31.475059Z","iopub.execute_input":"2021-06-24T08:34:31.475734Z","iopub.status.idle":"2021-06-24T08:34:31.507336Z","shell.execute_reply.started":"2021-06-24T08:34:31.475687Z","shell.execute_reply":"2021-06-24T08:34:31.506337Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":" age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n0 63 1 3 145 233 1 0 150 0 2.3 0 \n1 37 1 2 130 250 0 1 187 0 3.5 0 \n2 41 0 1 130 204 0 0 172 0 1.4 2 \n3 56 1 1 120 236 0 1 178 0 0.8 2 \n4 57 0 0 120 354 0 1 163 1 0.6 2 \n\n ca thal target \n0 0 1 1 \n1 0 2 1 \n2 0 2 1 \n3 0 2 1 \n4 0 2 1 ","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>age</th>\n <th>sex</th>\n <th>cp</th>\n <th>trestbps</th>\n <th>chol</th>\n <th>fbs</th>\n <th>restecg</th>\n <th>thalach</th>\n <th>exang</th>\n <th>oldpeak</th>\n <th>slope</th>\n <th>ca</th>\n <th>thal</th>\n <th>target</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>63</td>\n <td>1</td>\n <td>3</td>\n <td>145</td>\n <td>233</td>\n <td>1</td>\n <td>0</td>\n <td>150</td>\n <td>0</td>\n <td>2.3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>37</td>\n <td>1</td>\n <td>2</td>\n <td>130</td>\n <td>250</td>\n <td>0</td>\n <td>1</td>\n <td>187</td>\n <td>0</td>\n <td>3.5</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>41</td>\n <td>0</td>\n <td>1</td>\n <td>130</td>\n <td>204</td>\n <td>0</td>\n <td>0</td>\n <td>172</td>\n <td>0</td>\n <td>1.4</td>\n <td>2</td>\n <td>0</td>\n <td>2</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>56</td>\n <td>1</td>\n <td>1</td>\n <td>120</td>\n <td>236</td>\n <td>0</td>\n <td>1</td>\n <td>178</td>\n <td>0</td>\n <td>0.8</td>\n <td>2</td>\n <td>0</td>\n <td>2</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>57</td>\n <td>0</td>\n <td>0</td>\n <td>120</td>\n <td>354</td>\n <td>0</td>\n <td>1</td>\n <td>163</td>\n <td>1</td>\n <td>0.6</td>\n <td>2</td>\n <td>0</td>\n <td>2</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test = train_test_split(df.iloc[:,0:-1],df.iloc[:,-1],test_size=0.2,random_state=2)","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:31.508710Z","iopub.execute_input":"2021-06-24T08:34:31.509138Z","iopub.status.idle":"2021-06-24T08:34:32.482663Z","shell.execute_reply.started":"2021-06-24T08:34:31.509107Z","shell.execute_reply":"2021-06-24T08:34:32.481627Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:32.485205Z","iopub.execute_input":"2021-06-24T08:34:32.485530Z","iopub.status.idle":"2021-06-24T08:34:32.692442Z","shell.execute_reply.started":"2021-06-24T08:34:32.485496Z","shell.execute_reply":"2021-06-24T08:34:32.691540Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"clf1 = LogisticRegression()\nclf2 = DecisionTreeClassifier()","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:32.693861Z","iopub.execute_input":"2021-06-24T08:34:32.694277Z","iopub.status.idle":"2021-06-24T08:34:32.699267Z","shell.execute_reply.started":"2021-06-24T08:34:32.694237Z","shell.execute_reply":"2021-06-24T08:34:32.698311Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"clf1.fit(X_train,y_train)\nclf2.fit(X_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:32.700654Z","iopub.execute_input":"2021-06-24T08:34:32.700942Z","iopub.status.idle":"2021-06-24T08:34:32.757612Z","shell.execute_reply.started":"2021-06-24T08:34:32.700913Z","shell.execute_reply":"2021-06-24T08:34:32.756552Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n","output_type":"stream"},{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"DecisionTreeClassifier()"},"metadata":{}}]},{"cell_type":"code","source":"y_pred1 = clf1.predict(X_test)\ny_pred2 = clf2.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:32.760647Z","iopub.execute_input":"2021-06-24T08:34:32.761074Z","iopub.status.idle":"2021-06-24T08:34:32.770846Z","shell.execute_reply.started":"2021-06-24T08:34:32.761028Z","shell.execute_reply":"2021-06-24T08:34:32.769178Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score,confusion_matrix\nprint(\"Accuracy of Logistic Regression\",accuracy_score(y_test,y_pred1))\nprint(\"Accuracy of Decision Trees\",accuracy_score(y_test,y_pred2))","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:32.773310Z","iopub.execute_input":"2021-06-24T08:34:32.773824Z","iopub.status.idle":"2021-06-24T08:34:32.784866Z","shell.execute_reply.started":"2021-06-24T08:34:32.773753Z","shell.execute_reply":"2021-06-24T08:34:32.784045Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"Accuracy of Logistic Regression 0.9016393442622951\nAccuracy of Decision Trees 0.8360655737704918\n","output_type":"stream"}]},{"cell_type":"code","source":"confusion_matrix(y_test,y_pred1)","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:32.785852Z","iopub.execute_input":"2021-06-24T08:34:32.786162Z","iopub.status.idle":"2021-06-24T08:34:32.801255Z","shell.execute_reply.started":"2021-06-24T08:34:32.786135Z","shell.execute_reply":"2021-06-24T08:34:32.800520Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"array([[26, 6],\n [ 0, 29]])"},"metadata":{}}]},{"cell_type":"code","source":"print(\"Logistic Regression Confusion Matrix\\n\")\npd.DataFrame(confusion_matrix(y_test,y_pred1),columns=list(range(0,2)))","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:32.802166Z","iopub.execute_input":"2021-06-24T08:34:32.802424Z","iopub.status.idle":"2021-06-24T08:34:32.817498Z","shell.execute_reply.started":"2021-06-24T08:34:32.802397Z","shell.execute_reply":"2021-06-24T08:34:32.816619Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Logistic Regression Confusion Matrix\n\n","output_type":"stream"},{"execution_count":11,"output_type":"execute_result","data":{"text/plain":" 0 1\n0 26 6\n1 0 29","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>0</th>\n <th>1</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>26</td>\n <td>6</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0</td>\n <td>29</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"print(\"Decision Tree Confusion Matrix\\n\")\npd.DataFrame(confusion_matrix(y_test,y_pred2),columns=list(range(0,2)))","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:32.818555Z","iopub.execute_input":"2021-06-24T08:34:32.818821Z","iopub.status.idle":"2021-06-24T08:34:32.833086Z","shell.execute_reply.started":"2021-06-24T08:34:32.818795Z","shell.execute_reply":"2021-06-24T08:34:32.832078Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Decision Tree Confusion Matrix\n\n","output_type":"stream"},{"execution_count":12,"output_type":"execute_result","data":{"text/plain":" 0 1\n0 24 8\n1 2 27","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>0</th>\n <th>1</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>24</td>\n <td>8</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>27</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"result = pd.DataFrame()\nresult['Actual Label'] = y_test\nresult['Logistic Regression Prediction'] = y_pred1\nresult['Decision Tree Prediction'] = y_pred2","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:32.834236Z","iopub.execute_input":"2021-06-24T08:34:32.834601Z","iopub.status.idle":"2021-06-24T08:34:32.843105Z","shell.execute_reply.started":"2021-06-24T08:34:32.834570Z","shell.execute_reply":"2021-06-24T08:34:32.842084Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"result.sample(10)","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:32.844369Z","iopub.execute_input":"2021-06-24T08:34:32.844710Z","iopub.status.idle":"2021-06-24T08:34:32.865011Z","shell.execute_reply.started":"2021-06-24T08:34:32.844679Z","shell.execute_reply":"2021-06-24T08:34:32.864018Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":" Actual Label Logistic Regression Prediction Decision Tree Prediction\n257 0 0 0\n65 1 1 1\n164 1 1 1\n29 1 1 1\n296 0 1 0\n184 0 0 0\n161 1 1 1\n292 0 0 0\n53 1 1 1\n147 1 1 1","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>Actual Label</th>\n <th>Logistic Regression Prediction</th>\n <th>Decision Tree Prediction</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>257</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>65</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>164</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>29</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>296</th>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>184</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>161</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>292</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>53</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>147</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import recall_score,precision_score,f1_score","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:32.866546Z","iopub.execute_input":"2021-06-24T08:34:32.866863Z","iopub.status.idle":"2021-06-24T08:34:32.876388Z","shell.execute_reply.started":"2021-06-24T08:34:32.866832Z","shell.execute_reply":"2021-06-24T08:34:32.875419Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"print(\"For Logistic regression Model\")\nprint(\"-\"*50)\ncdf = pd.DataFrame(confusion_matrix(y_test,y_pred1),columns=list(range(0,2)))\nprint(cdf)\nprint(\"-\"*50)\nprint(\"Precision - \",precision_score(y_test,y_pred1))\nprint(\"Recall - \",recall_score(y_test,y_pred1))\nprint(\"F1 score - \",f1_score(y_test,y_pred1))","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:32.877690Z","iopub.execute_input":"2021-06-24T08:34:32.878201Z","iopub.status.idle":"2021-06-24T08:34:32.899989Z","shell.execute_reply.started":"2021-06-24T08:34:32.878110Z","shell.execute_reply":"2021-06-24T08:34:32.898973Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"For Logistic regression Model\n--------------------------------------------------\n 0 1\n0 26 6\n1 0 29\n--------------------------------------------------\nPrecision - 0.8285714285714286\nRecall - 1.0\nF1 score - 0.90625\n","output_type":"stream"}]},{"cell_type":"code","source":"print(\"For DT Model\")\nprint(\"-\"*50)\ncdf = pd.DataFrame(confusion_matrix(y_test,y_pred2),columns=list(range(0,2)))\nprint(cdf)\nprint(\"-\"*50)\nprint(\"Precision - \",precision_score(y_test,y_pred2))\nprint(\"Recall - \",recall_score(y_test,y_pred2))\nprint(\"F1 score - \",f1_score(y_test,y_pred2))","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:32.901183Z","iopub.execute_input":"2021-06-24T08:34:32.901682Z","iopub.status.idle":"2021-06-24T08:34:32.917403Z","shell.execute_reply.started":"2021-06-24T08:34:32.901650Z","shell.execute_reply":"2021-06-24T08:34:32.916694Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"For DT Model\n--------------------------------------------------\n 0 1\n0 24 8\n1 2 27\n--------------------------------------------------\nPrecision - 0.7714285714285715\nRecall - 0.9310344827586207\nF1 score - 0.8437500000000001\n","output_type":"stream"}]},{"cell_type":"code","source":"precision_score(y_test,y_pred1,average=None)","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:34:53.255977Z","iopub.execute_input":"2021-06-24T08:34:53.256368Z","iopub.status.idle":"2021-06-24T08:34:53.265970Z","shell.execute_reply.started":"2021-06-24T08:34:53.256327Z","shell.execute_reply":"2021-06-24T08:34:53.264821Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"array([1. , 0.82857143])"},"metadata":{}}]},{"cell_type":"code","source":"precision_score(y_test,y_pred2,average=None)","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:35:24.059999Z","iopub.execute_input":"2021-06-24T08:35:24.060362Z","iopub.status.idle":"2021-06-24T08:35:24.068799Z","shell.execute_reply.started":"2021-06-24T08:35:24.060331Z","shell.execute_reply":"2021-06-24T08:35:24.067812Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"array([0.92307692, 0.77142857])"},"metadata":{}}]},{"cell_type":"code","source":"recall_score(y_test,y_pred2,average=None)","metadata":{"execution":{"iopub.status.busy":"2021-06-24T08:35:52.675520Z","iopub.execute_input":"2021-06-24T08:35:52.675877Z","iopub.status.idle":"2021-06-24T08:35:52.684318Z","shell.execute_reply.started":"2021-06-24T08:35:52.675848Z","shell.execute_reply":"2021-06-24T08:35:52.683253Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"array([0.75 , 0.93103448])"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
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