heart-disease-prediction-lightgbm.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":[{"cell_type":"code","source":"# Data and Visualize\nfrom IPython.display import clear_output\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# Preprocesing\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, classification_report, plot_confusion_matrix\n\n# Modelling\nfrom sklearn.linear_model import RidgeClassifier\nfrom sklearn.svm import SVC, LinearSVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom lightgbm import LGBMClassifier\nfrom xgboost import XGBClassifier\nimport optuna","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:03.786620Z","iopub.execute_input":"2022-01-01T17:48:03.787424Z","iopub.status.idle":"2022-01-01T17:48:07.400852Z","shell.execute_reply.started":"2022-01-01T17:48:03.787322Z","shell.execute_reply":"2022-01-01T17:48:07.400050Z"},"trusted":true},"execution_count":1,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<style type='text/css'>\n.datatable table.frame { margin-bottom: 0; }\n.datatable table.frame thead { border-bottom: none; }\n.datatable table.frame tr.coltypes td {  color: #FFFFFF;  line-height: 6px;  padding: 0 0.5em;}\n.datatable .bool    { background: #DDDD99; }\n.datatable .object  { background: #565656; }\n.datatable .int     { background: #5D9E5D; }\n.datatable .float   { background: #4040CC; }\n.datatable .str     { background: #CC4040; }\n.datatable .time    { background: #40CC40; }\n.datatable .row_index {  background: var(--jp-border-color3);  border-right: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  font-size: 9px;}\n.datatable .frame tbody td { text-align: left; }\n.datatable .frame tr.coltypes .row_index {  background: var(--jp-border-color0);}\n.datatable th:nth-child(2) { padding-left: 12px; }\n.datatable .hellipsis {  color: var(--jp-cell-editor-border-color);}\n.datatable .vellipsis {  background: var(--jp-layout-color0);  color: var(--jp-cell-editor-border-color);}\n.datatable .na {  color: var(--jp-cell-editor-border-color);  font-size: 80%;}\n.datatable .sp {  opacity: 0.25;}\n.datatable .footer { font-size: 9px; }\n.datatable .frame_dimensions {  background: var(--jp-border-color3);  border-top: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  display: inline-block;  opacity: 0.6;  padding: 1px 10px 1px 5px;}\n</style>\n"},"metadata":{}}]},{"cell_type":"code","source":"data = pd.read_csv(\"../input/heart-failure-prediction/heart.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:07.402827Z","iopub.execute_input":"2022-01-01T17:48:07.403525Z","iopub.status.idle":"2022-01-01T17:48:07.425577Z","shell.execute_reply.started":"2022-01-01T17:48:07.403490Z","shell.execute_reply":"2022-01-01T17:48:07.424928Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"## Attribute Information\n---\n* **Age**: age of the patient [years]\n* **Sex**: sex of the patient \n    * M: Male, \n    * F: Female\n* **ChestPainType**: chest pain type \n    * TA: Typical Angina,\n    * ATA: Atypical Angina,\n    * NAP: Non-Anginal Pain, \n    * ASY: Asymptomatic\n* **RestingBP**: resting blood pressure [mm Hg]\n* **Cholesterol**: serum cholesterol [mm/dl]\n* **FastingBS**: fasting blood sugar \n    * 1: if FastingBS > 120 mg/dl, \n    * 0: otherwise\n* **RestingECG**: resting electrocardiogram results \n    * Normal: Normal, \n    * ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), \n    * LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria\n* **MaxHR**: maximum heart rate achieved [Numeric value between 60 and 202]\n* **ExerciseAngina**: exercise-induced angina \n    * Y: Yes,\n    * N: No\n* **Oldpeak**: oldpeak = ST [Numeric value measured in depression]\n* **ST_Slope**: the slope of the peak exercise ST segment \n    * Up: upsloping, \n    * Flat: flat, \n    * Down: downsloping\n* **HeartDisease**: output class \n    * 1: heart disease,\n    * 0: Normal","metadata":{}},{"cell_type":"code","source":"data","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:07.426733Z","iopub.execute_input":"2022-01-01T17:48:07.427127Z","iopub.status.idle":"2022-01-01T17:48:07.456655Z","shell.execute_reply.started":"2022-01-01T17:48:07.427079Z","shell.execute_reply":"2022-01-01T17:48:07.455843Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n0     40   M           ATA        140          289          0     Normal   \n1     49   F           NAP        160          180          0     Normal   \n2     37   M           ATA        130          283          0         ST   \n3     48   F           ASY        138          214          0     Normal   \n4     54   M           NAP        150          195          0     Normal   \n..   ...  ..           ...        ...          ...        ...        ...   \n913   45   M            TA        110          264          0     Normal   \n914   68   M           ASY        144          193          1     Normal   \n915   57   M           ASY        130          131          0     Normal   \n916   57   F           ATA        130          236          0        LVH   \n917   38   M           NAP        138          175          0     Normal   \n\n     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n0      172              N      0.0       Up             0  \n1      156              N      1.0     Flat             1  \n2       98              N      0.0       Up             0  \n3      108              Y      1.5     Flat             1  \n4      122              N      0.0       Up             0  \n..     ...            ...      ...      ...           ...  \n913    132              N      1.2     Flat             1  \n914    141              N      3.4     Flat             1  \n915    115              Y      1.2     Flat             1  \n916    174              N      0.0     Flat             1  \n917    173              N      0.0       Up             0  \n\n[918 rows x 12 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>Age</th>\n      <th>Sex</th>\n      <th>ChestPainType</th>\n      <th>RestingBP</th>\n      <th>Cholesterol</th>\n      <th>FastingBS</th>\n      <th>RestingECG</th>\n      <th>MaxHR</th>\n      <th>ExerciseAngina</th>\n      <th>Oldpeak</th>\n      <th>ST_Slope</th>\n      <th>HeartDisease</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>40</td>\n      <td>M</td>\n      <td>ATA</td>\n      <td>140</td>\n      <td>289</td>\n      <td>0</td>\n      <td>Normal</td>\n      <td>172</td>\n      <td>N</td>\n      <td>0.0</td>\n      <td>Up</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>49</td>\n      <td>F</td>\n      <td>NAP</td>\n      <td>160</td>\n      <td>180</td>\n      <td>0</td>\n      <td>Normal</td>\n      <td>156</td>\n      <td>N</td>\n      <td>1.0</td>\n      <td>Flat</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>37</td>\n      <td>M</td>\n      <td>ATA</td>\n      <td>130</td>\n      <td>283</td>\n      <td>0</td>\n      <td>ST</td>\n      <td>98</td>\n      <td>N</td>\n      <td>0.0</td>\n      <td>Up</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>48</td>\n      <td>F</td>\n      <td>ASY</td>\n      <td>138</td>\n      <td>214</td>\n      <td>0</td>\n      <td>Normal</td>\n      <td>108</td>\n      <td>Y</td>\n      <td>1.5</td>\n      <td>Flat</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>54</td>\n      <td>M</td>\n      <td>NAP</td>\n      <td>150</td>\n      <td>195</td>\n      <td>0</td>\n      <td>Normal</td>\n      <td>122</td>\n      <td>N</td>\n      <td>0.0</td>\n      <td>Up</td>\n      <td>0</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    </tr>\n    <tr>\n      <th>913</th>\n      <td>45</td>\n      <td>M</td>\n      <td>TA</td>\n      <td>110</td>\n      <td>264</td>\n      <td>0</td>\n      <td>Normal</td>\n      <td>132</td>\n      <td>N</td>\n      <td>1.2</td>\n      <td>Flat</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>914</th>\n      <td>68</td>\n      <td>M</td>\n      <td>ASY</td>\n      <td>144</td>\n      <td>193</td>\n      <td>1</td>\n      <td>Normal</td>\n      <td>141</td>\n      <td>N</td>\n      <td>3.4</td>\n      <td>Flat</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>915</th>\n      <td>57</td>\n      <td>M</td>\n      <td>ASY</td>\n      <td>130</td>\n      <td>131</td>\n      <td>0</td>\n      <td>Normal</td>\n      <td>115</td>\n      <td>Y</td>\n      <td>1.2</td>\n      <td>Flat</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>916</th>\n      <td>57</td>\n      <td>F</td>\n      <td>ATA</td>\n      <td>130</td>\n      <td>236</td>\n      <td>0</td>\n      <td>LVH</td>\n      <td>174</td>\n      <td>N</td>\n      <td>0.0</td>\n      <td>Flat</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>917</th>\n      <td>38</td>\n      <td>M</td>\n      <td>NAP</td>\n      <td>138</td>\n      <td>175</td>\n      <td>0</td>\n      <td>Normal</td>\n      <td>173</td>\n      <td>N</td>\n      <td>0.0</td>\n      <td>Up</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>918 rows × 12 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:07.457897Z","iopub.execute_input":"2022-01-01T17:48:07.458131Z","iopub.status.idle":"2022-01-01T17:48:07.479934Z","shell.execute_reply.started":"2022-01-01T17:48:07.458102Z","shell.execute_reply":"2022-01-01T17:48:07.479338Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 918 entries, 0 to 917\nData columns (total 12 columns):\n #   Column          Non-Null Count  Dtype  \n---  ------          --------------  -----  \n 0   Age             918 non-null    int64  \n 1   Sex             918 non-null    object \n 2   ChestPainType   918 non-null    object \n 3   RestingBP       918 non-null    int64  \n 4   Cholesterol     918 non-null    int64  \n 5   FastingBS       918 non-null    int64  \n 6   RestingECG      918 non-null    object \n 7   MaxHR           918 non-null    int64  \n 8   ExerciseAngina  918 non-null    object \n 9   Oldpeak         918 non-null    float64\n 10  ST_Slope        918 non-null    object \n 11  HeartDisease    918 non-null    int64  \ndtypes: float64(1), int64(6), object(5)\nmemory usage: 86.2+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Attributes","metadata":{}},{"cell_type":"markdown","source":"## Age\n*Age of the patient [years]*","metadata":{}},{"cell_type":"code","source":"data[[\"Age\"]].describe().T","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:07.482032Z","iopub.execute_input":"2022-01-01T17:48:07.482488Z","iopub.status.idle":"2022-01-01T17:48:07.504459Z","shell.execute_reply.started":"2022-01-01T17:48:07.482452Z","shell.execute_reply":"2022-01-01T17:48:07.503906Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"     count       mean       std   min   25%   50%   75%   max\nAge  918.0  53.510893  9.432617  28.0  47.0  54.0  60.0  77.0","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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Age</th>\n      <td>918.0</td>\n      <td>53.510893</td>\n      <td>9.432617</td>\n      <td>28.0</td>\n      <td>47.0</td>\n      <td>54.0</td>\n      <td>60.0</td>\n      <td>77.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"fig = px.histogram(data[\"Age\"], x=\"Age\", width=600, height=400)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:07.505704Z","iopub.execute_input":"2022-01-01T17:48:07.506115Z","iopub.status.idle":"2022-01-01T17:48:08.549030Z","shell.execute_reply.started":"2022-01-01T17:48:07.506085Z","shell.execute_reply":"2022-01-01T17:48:08.548172Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/html":"        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-2.6.3.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "},"metadata":{}},{"output_type":"display_data","data":{"text/html":"<div>                            <div id=\"28bd7d5d-137c-4479-a351-b88c3a6edd32\" class=\"plotly-graph-div\" style=\"height:400px; width:600px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"28bd7d5d-137c-4479-a351-b88c3a6edd32\")) {                    Plotly.newPlot(                        \"28bd7d5d-137c-4479-a351-b88c3a6edd32\",                        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</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Sex\n*Sex of the patient*\n* M: Male\n* F: Female","metadata":{}},{"cell_type":"code","source":"data[\"Sex\"].value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:08.550037Z","iopub.execute_input":"2022-01-01T17:48:08.550250Z","iopub.status.idle":"2022-01-01T17:48:08.557759Z","shell.execute_reply.started":"2022-01-01T17:48:08.550225Z","shell.execute_reply":"2022-01-01T17:48:08.557039Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"M    725\nF    193\nName: Sex, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"fig = px.pie(data[\"Sex\"], names=\"Sex\", width=600, 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right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Cholesterol</th>\n      <td>918.0</td>\n      <td>198.799564</td>\n      <td>109.384145</td>\n      <td>0.0</td>\n      <td>173.25</td>\n      <td>223.0</td>\n      <td>267.0</td>\n      <td>603.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"fig = px.histogram(data[\"Cholesterol\"], x=\"Cholesterol\", width=600, height=400)\nfig.show()\n\n# Looks like there are incorrectly entered value 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</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## MaxHR\n*Maximum heart rate achieved [Numeric value between 60 and 202]*","metadata":{}},{"cell_type":"code","source":"data[[\"MaxHR\"]].describe().T","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:09.063623Z","iopub.execute_input":"2022-01-01T17:48:09.064132Z","iopub.status.idle":"2022-01-01T17:48:09.084278Z","shell.execute_reply.started":"2022-01-01T17:48:09.064086Z","shell.execute_reply":"2022-01-01T17:48:09.083708Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"       count        mean        std   min    25%    50%    75%    max\nMaxHR  918.0  136.809368  25.460334  60.0  120.0  138.0  156.0  202.0","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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>MaxHR</th>\n      <td>918.0</td>\n      <td>136.809368</td>\n      <td>25.460334</td>\n      <td>60.0</td>\n      <td>120.0</td>\n      <td>138.0</td>\n      <td>156.0</td>\n      <td>202.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"fig = px.histogram(data[\"MaxHR\"], x=\"MaxHR\", width=600, 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</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## ExerciseAngina\n*Exercise-induced angina*\n* Y: Yes\n* N: No","metadata":{}},{"cell_type":"code","source":"data[\"ExerciseAngina\"].value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:09.150307Z","iopub.execute_input":"2022-01-01T17:48:09.150621Z","iopub.status.idle":"2022-01-01T17:48:09.156860Z","shell.execute_reply.started":"2022-01-01T17:48:09.150595Z","shell.execute_reply":"2022-01-01T17:48:09.156142Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"N    547\nY    371\nName: ExerciseAngina, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"fig = px.pie(data[\"ExerciseAngina\"], names=\"ExerciseAngina\", width=600, 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</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Oldpeak\n*oldpeak = ST [Numeric value measured in depression]*","metadata":{}},{"cell_type":"code","source":"data[[\"Oldpeak\"]].describe().T","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:09.221716Z","iopub.execute_input":"2022-01-01T17:48:09.221997Z","iopub.status.idle":"2022-01-01T17:48:09.243759Z","shell.execute_reply.started":"2022-01-01T17:48:09.221967Z","shell.execute_reply":"2022-01-01T17:48:09.242905Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"         count      mean      std  min  25%  50%  75%  max\nOldpeak  918.0  0.887364  1.06657 -2.6  0.0  0.6  1.5  6.2","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    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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Preprocessing","metadata":{}},{"cell_type":"markdown","source":"## Outlier Suppression","metadata":{}},{"cell_type":"code","source":"data.loc[data[\"RestingBP\"] < 100, \"RestingBP\"] = 100\ndata.loc[data[\"RestingBP\"] > 170, \"RestingBP\"] = 170\n\ndata.loc[data[\"Cholesterol\"] < 100, \"Cholesterol\"] = 100\ndata.loc[data[\"Cholesterol\"] > 320, \"Cholesterol\"] = 320\n\ndata.loc[data[\"MaxHR\"] < 60, \"MaxHR\"] = 60\ndata.loc[data[\"MaxHR\"] > 202, \"MaxHR\"] = 202\n\ndata.loc[data[\"Oldpeak\"] < 0, \"Oldpeak\"] = 0\ndata.loc[data[\"Oldpeak\"] > 2.5, \"Oldpeak\"] = 2.5","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:10.047947Z","iopub.execute_input":"2022-01-01T17:48:10.048195Z","iopub.status.idle":"2022-01-01T17:48:10.060664Z","shell.execute_reply.started":"2022-01-01T17:48:10.048166Z","shell.execute_reply":"2022-01-01T17:48:10.059755Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"# Getting Dummy Variables\ndata = pd.get_dummies(data, columns=[\"Sex\", \"ChestPainType\", \"RestingECG\", \"ExerciseAngina\", \"ST_Slope\"], drop_first=True)","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:10.061960Z","iopub.execute_input":"2022-01-01T17:48:10.062188Z","iopub.status.idle":"2022-01-01T17:48:10.075244Z","shell.execute_reply.started":"2022-01-01T17:48:10.062162Z","shell.execute_reply":"2022-01-01T17:48:10.074445Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"markdown","source":"# Modelling","metadata":{}},{"cell_type":"code","source":"x = data.drop(\"HeartDisease\", axis=1)\ny = data[\"HeartDisease\"]\n\nx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=44, shuffle=True)","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:10.076612Z","iopub.execute_input":"2022-01-01T17:48:10.077030Z","iopub.status.idle":"2022-01-01T17:48:10.086466Z","shell.execute_reply.started":"2022-01-01T17:48:10.077000Z","shell.execute_reply":"2022-01-01T17:48:10.085879Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"code","source":"algorithms = [RidgeClassifier, \n              SVC, \n              LinearSVC, \n              RandomForestClassifier,\n              KNeighborsClassifier, \n              DecisionTreeClassifier,\n              LGBMClassifier,\n              XGBClassifier,\n             ]","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:10.087512Z","iopub.execute_input":"2022-01-01T17:48:10.087871Z","iopub.status.idle":"2022-01-01T17:48:10.094065Z","shell.execute_reply.started":"2022-01-01T17:48:10.087842Z","shell.execute_reply":"2022-01-01T17:48:10.093263Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"df_algorithms = pd.DataFrame(columns=[\"Model\", \"Train Accuracy\", \"Test Accuracy\"])","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:10.095152Z","iopub.execute_input":"2022-01-01T17:48:10.095388Z","iopub.status.idle":"2022-01-01T17:48:10.111010Z","shell.execute_reply.started":"2022-01-01T17:48:10.095362Z","shell.execute_reply":"2022-01-01T17:48:10.110145Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"def autoML(algorithm): \n    \n    model = algorithm().fit(x_train, y_train)\n    train_acc = model.score(x_train, y_train)\n    model_name = algorithm.__name__\n    \n    y_pred = model.predict(x_test)\n    test_acc = accuracy_score(y_test, y_pred)\n\n    return model_name, train_acc, test_acc","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:10.112142Z","iopub.execute_input":"2022-01-01T17:48:10.112631Z","iopub.status.idle":"2022-01-01T17:48:10.125177Z","shell.execute_reply.started":"2022-01-01T17:48:10.112600Z","shell.execute_reply":"2022-01-01T17:48:10.124601Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"for alg in algorithms:\n    \n    model_name, train_acc, test_acc = autoML(alg)\n    \n    df_algorithms = df_algorithms.append({\"Model\" : model_name,\n                                          \"Train Accuracy\": train_acc,\n                                          \"Test Accuracy\": test_acc}, ignore_index=True)","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-01-01T17:48:10.126260Z","iopub.execute_input":"2022-01-01T17:48:10.127075Z","iopub.status.idle":"2022-01-01T17:48:11.174204Z","shell.execute_reply.started":"2022-01-01T17:48:10.127020Z","shell.execute_reply":"2022-01-01T17:48:11.173510Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/sklearn/svm/_base.py:977: ConvergenceWarning:\n\nLiblinear failed to converge, increase the number of iterations.\n\n/opt/conda/lib/python3.7/site-packages/xgboost/sklearn.py:1224: UserWarning:\n\nThe use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n\n","output_type":"stream"},{"name":"stdout","text":"[17:48:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n","output_type":"stream"}]},{"cell_type":"code","source":"df_algorithms.sort_values(by=[\"Test Accuracy\", \"Train Accuracy\"],ascending=False)","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:11.175476Z","iopub.execute_input":"2022-01-01T17:48:11.175893Z","iopub.status.idle":"2022-01-01T17:48:11.194024Z","shell.execute_reply.started":"2022-01-01T17:48:11.175859Z","shell.execute_reply":"2022-01-01T17:48:11.193278Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"                    Model  Train Accuracy  Test Accuracy\n6          LGBMClassifier        1.000000       0.902174\n3  RandomForestClassifier        1.000000       0.869565\n7           XGBClassifier        1.000000       0.869565\n0         RidgeClassifier        0.872881       0.847826\n2               LinearSVC        0.854722       0.804348\n1                     SVC        0.714286       0.804348\n4    KNeighborsClassifier        0.771186       0.739130\n5  DecisionTreeClassifier        1.000000       0.717391","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>Model</th>\n      <th>Train Accuracy</th>\n      <th>Test Accuracy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>6</th>\n      <td>LGBMClassifier</td>\n      <td>1.000000</td>\n      <td>0.902174</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>RandomForestClassifier</td>\n      <td>1.000000</td>\n      <td>0.869565</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>XGBClassifier</td>\n      <td>1.000000</td>\n      <td>0.869565</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>RidgeClassifier</td>\n      <td>0.872881</td>\n      <td>0.847826</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LinearSVC</td>\n      <td>0.854722</td>\n      <td>0.804348</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>SVC</td>\n      <td>0.714286</td>\n      <td>0.804348</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>KNeighborsClassifier</td>\n      <td>0.771186</td>\n      <td>0.739130</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>DecisionTreeClassifier</td>\n      <td>1.000000</td>\n      <td>0.717391</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"model = LGBMClassifier().fit(x_train, y_train)\ny_pred = model.predict(x_test)\naccuracy_score(y_test, y_pred)","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:11.195153Z","iopub.execute_input":"2022-01-01T17:48:11.195809Z","iopub.status.idle":"2022-01-01T17:48:11.283875Z","shell.execute_reply.started":"2022-01-01T17:48:11.195778Z","shell.execute_reply":"2022-01-01T17:48:11.283272Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"0.9021739130434783"},"metadata":{}}]},{"cell_type":"code","source":"print(classification_report(y_pred, y_test))","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:11.285163Z","iopub.execute_input":"2022-01-01T17:48:11.285606Z","iopub.status.idle":"2022-01-01T17:48:11.296290Z","shell.execute_reply.started":"2022-01-01T17:48:11.285572Z","shell.execute_reply":"2022-01-01T17:48:11.295387Z"},"trusted":true},"execution_count":38,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.89      0.91      0.90        43\n           1       0.92      0.90      0.91        49\n\n    accuracy                           0.90        92\n   macro avg       0.90      0.90      0.90        92\nweighted avg       0.90      0.90      0.90        92\n\n","output_type":"stream"}]},{"cell_type":"code","source":"plot_confusion_matrix(model,\n                      x_test,\n                      y_test,\n                      display_labels=[\"Normal\", \"Heart Disease\"],\n                      cmap=plt.cm.Blues,\n                     )","metadata":{"execution":{"iopub.status.busy":"2022-01-01T17:48:11.297467Z","iopub.execute_input":"2022-01-01T17:48:11.298577Z","iopub.status.idle":"2022-01-01T17:48:11.562601Z","shell.execute_reply.started":"2022-01-01T17:48:11.298518Z","shell.execute_reply":"2022-01-01T17:48:11.561912Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f51a3bbc850>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}