{"cells":[{"metadata":{},"cell_type":"markdown","source":"<p  style=\"text-align: center;\"><font size=\"12\"><b>HEART FAILURE PREDICTION</b></font></p>"},{"metadata":{},"cell_type":"markdown","source":"![](https://inteng-storage.s3.amazonaws.com/img/iea/Xy6xeK3Wwr/sizes/heart-attack-ai-oxford_md.jpg)"},{"metadata":{},"cell_type":"markdown","source":"\nIn this notebook we evaluate several variables to determine how they may relate to whether a patient dies or survives a heart failure event. \n\nThe variables that are included in this data set are:\n* Age                         \n* Anemia  \n* Creatinine Phosphokinase  \n* Diabetes       \n* Ejection Fraction   \n* High Blood Pressure  \n* Platelets   \n* Serum Creatinine \n* Serum Sodium \n* Sex    \n* Smoking \n* Time (Follow-up period (days))\n"},{"metadata":{},"cell_type":"markdown","source":"<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\"  role=\"tab\" aria-controls=\"home\">Table of Contents</h3>\n\n* <a href='#1'>I. Load Libraries & Packages</a>  \n* <a href='#2'>II. Data Overview & Insights</a>  \n* <a href='#3'>III. Outliers</a>  \n* <a href='#4'>IV. Exploratory Data Analysis</a>  \n    * <a href='#4a'>IVa. Univariate Analysis</a>  \n    * <a href='#4b'>IVb. Bivariate Analysis</a>  \n    * <a href='#4c'>IVc. Multivariate Analysis</a>  \n* <a href='#5'>V. Data Normalization</a>  (coming soon) \n* <a href='#6'>VI. Model Development</a>  (coming soon)"},{"metadata":{},"cell_type":"markdown","source":"# <a id=\"1\">I. LIBRARIES & PACKAGES</a>"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"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)\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport plotly.express as px\nimport missingno as msno\n\n# Plotly Packages\nfrom plotly import tools\nimport plotly.figure_factory as ff\nimport plotly.graph_objects as go\nfrom plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\nimport plotly.express as px\nfrom plotly.subplots import make_subplots\ninit_notebook_mode(connected=True)\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","execution_count":2,"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-latest.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "},"metadata":{}},{"output_type":"stream","text":"/kaggle/input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# <a id=\"2\">II. DATA OVERVIEW & INSIGHTS</a>"},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv('../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv')\ndf.head()","execution_count":3,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"text/plain":"    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n0  75.0        0                       582         0                 20   \n1  55.0        0                      7861         0                 38   \n2  65.0        0                       146         0                 20   \n3  50.0        1                       111         0                 20   \n4  65.0        1                       160         1                 20   \n\n   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n0                    1  265000.00               1.9           130    1   \n1                    0  263358.03               1.1           136    1   \n2                    0  162000.00               1.3           129    1   \n3                    0  210000.00               1.9           137    1   \n4                    0  327000.00               2.7           116    0   \n\n   smoking  time  DEATH_EVENT  \n0        0     4            1  \n1        0     6            1  \n2        1     7            1  \n3        0     7            1  \n4        0     8            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>anaemia</th>\n      <th>creatinine_phosphokinase</th>\n      <th>diabetes</th>\n      <th>ejection_fraction</th>\n      <th>high_blood_pressure</th>\n      <th>platelets</th>\n      <th>serum_creatinine</th>\n      <th>serum_sodium</th>\n      <th>sex</th>\n      <th>smoking</th>\n      <th>time</th>\n      <th>DEATH_EVENT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>75.0</td>\n      <td>0</td>\n      <td>582</td>\n      <td>0</td>\n      <td>20</td>\n      <td>1</td>\n      <td>265000.00</td>\n      <td>1.9</td>\n      <td>130</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>55.0</td>\n      <td>0</td>\n      <td>7861</td>\n      <td>0</td>\n      <td>38</td>\n      <td>0</td>\n      <td>263358.03</td>\n      <td>1.1</td>\n      <td>136</td>\n      <td>1</td>\n      <td>0</td>\n      <td>6</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>65.0</td>\n      <td>0</td>\n      <td>146</td>\n      <td>0</td>\n      <td>20</td>\n      <td>0</td>\n      <td>162000.00</td>\n      <td>1.3</td>\n      <td>129</td>\n      <td>1</td>\n      <td>1</td>\n      <td>7</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>50.0</td>\n      <td>1</td>\n      <td>111</td>\n      <td>0</td>\n      <td>20</td>\n      <td>0</td>\n      <td>210000.00</td>\n      <td>1.9</td>\n      <td>137</td>\n      <td>1</td>\n      <td>0</td>\n      <td>7</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>65.0</td>\n      <td>1</td>\n      <td>160</td>\n      <td>1</td>\n      <td>20</td>\n      <td>0</td>\n      <td>327000.00</td>\n      <td>2.7</td>\n      <td>116</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.columns","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n       'ejection_fraction', 'high_blood_pressure', 'platelets',\n       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n       'DEATH_EVENT'],\n      dtype='object')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# CHANGE SPELLING OF ANAEMIA COLUMN TO 'ANEMIA'\ndf.rename(columns={'anaemia':'anemia'}, inplace=True)","execution_count":5,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"df.info()","execution_count":6,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 299 entries, 0 to 298\nData columns (total 13 columns):\n #   Column                    Non-Null Count  Dtype  \n---  ------                    --------------  -----  \n 0   age                       299 non-null    float64\n 1   anemia                    299 non-null    int64  \n 2   creatinine_phosphokinase  299 non-null    int64  \n 3   diabetes                  299 non-null    int64  \n 4   ejection_fraction         299 non-null    int64  \n 5   high_blood_pressure       299 non-null    int64  \n 6   platelets                 299 non-null    float64\n 7   serum_creatinine          299 non-null    float64\n 8   serum_sodium              299 non-null    int64  \n 9   sex                       299 non-null    int64  \n 10  smoking                   299 non-null    int64  \n 11  time                      299 non-null    int64  \n 12  DEATH_EVENT               299 non-null    int64  \ndtypes: float64(3), int64(10)\nmemory usage: 30.5 KB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.describe()","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"              age      anemia  creatinine_phosphokinase    diabetes  \\\ncount  299.000000  299.000000                299.000000  299.000000   \nmean    60.833893    0.431438                581.839465    0.418060   \nstd     11.894809    0.496107                970.287881    0.494067   \nmin     40.000000    0.000000                 23.000000    0.000000   \n25%     51.000000    0.000000                116.500000    0.000000   \n50%     60.000000    0.000000                250.000000    0.000000   \n75%     70.000000    1.000000                582.000000    1.000000   \nmax     95.000000    1.000000               7861.000000    1.000000   \n\n       ejection_fraction  high_blood_pressure      platelets  \\\ncount         299.000000           299.000000     299.000000   \nmean           38.083612             0.351171  263358.029264   \nstd            11.834841             0.478136   97804.236869   \nmin            14.000000             0.000000   25100.000000   \n25%            30.000000             0.000000  212500.000000   \n50%            38.000000             0.000000  262000.000000   \n75%            45.000000             1.000000  303500.000000   \nmax            80.000000             1.000000  850000.000000   \n\n       serum_creatinine  serum_sodium         sex    smoking        time  \\\ncount         299.00000    299.000000  299.000000  299.00000  299.000000   \nmean            1.39388    136.625418    0.648829    0.32107  130.260870   \nstd             1.03451      4.412477    0.478136    0.46767   77.614208   \nmin             0.50000    113.000000    0.000000    0.00000    4.000000   \n25%             0.90000    134.000000    0.000000    0.00000   73.000000   \n50%             1.10000    137.000000    1.000000    0.00000  115.000000   \n75%             1.40000    140.000000    1.000000    1.00000  203.000000   \nmax             9.40000    148.000000    1.000000    1.00000  285.000000   \n\n       DEATH_EVENT  \ncount    299.00000  \nmean       0.32107  \nstd        0.46767  \nmin        0.00000  \n25%        0.00000  \n50%        0.00000  \n75%        1.00000  \nmax        1.00000  ","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>anemia</th>\n      <th>creatinine_phosphokinase</th>\n      <th>diabetes</th>\n      <th>ejection_fraction</th>\n      <th>high_blood_pressure</th>\n      <th>platelets</th>\n      <th>serum_creatinine</th>\n      <th>serum_sodium</th>\n      <th>sex</th>\n      <th>smoking</th>\n      <th>time</th>\n      <th>DEATH_EVENT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>299.000000</td>\n      <td>299.000000</td>\n      <td>299.000000</td>\n      <td>299.000000</td>\n      <td>299.000000</td>\n      <td>299.000000</td>\n      <td>299.000000</td>\n      <td>299.00000</td>\n      <td>299.000000</td>\n      <td>299.000000</td>\n      <td>299.00000</td>\n      <td>299.000000</td>\n      <td>299.00000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>60.833893</td>\n      <td>0.431438</td>\n      <td>581.839465</td>\n      <td>0.418060</td>\n      <td>38.083612</td>\n      <td>0.351171</td>\n      <td>263358.029264</td>\n      <td>1.39388</td>\n      <td>136.625418</td>\n      <td>0.648829</td>\n      <td>0.32107</td>\n      <td>130.260870</td>\n      <td>0.32107</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>11.894809</td>\n      <td>0.496107</td>\n      <td>970.287881</td>\n      <td>0.494067</td>\n      <td>11.834841</td>\n      <td>0.478136</td>\n      <td>97804.236869</td>\n      <td>1.03451</td>\n      <td>4.412477</td>\n      <td>0.478136</td>\n      <td>0.46767</td>\n      <td>77.614208</td>\n      <td>0.46767</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>40.000000</td>\n      <td>0.000000</td>\n      <td>23.000000</td>\n      <td>0.000000</td>\n      <td>14.000000</td>\n      <td>0.000000</td>\n      <td>25100.000000</td>\n      <td>0.50000</td>\n      <td>113.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>4.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>51.000000</td>\n      <td>0.000000</td>\n      <td>116.500000</td>\n      <td>0.000000</td>\n      <td>30.000000</td>\n      <td>0.000000</td>\n      <td>212500.000000</td>\n      <td>0.90000</td>\n      <td>134.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>73.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>60.000000</td>\n      <td>0.000000</td>\n      <td>250.000000</td>\n      <td>0.000000</td>\n      <td>38.000000</td>\n      <td>0.000000</td>\n      <td>262000.000000</td>\n      <td>1.10000</td>\n      <td>137.000000</td>\n      <td>1.000000</td>\n      <td>0.00000</td>\n      <td>115.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>70.000000</td>\n      <td>1.000000</td>\n      <td>582.000000</td>\n      <td>1.000000</td>\n      <td>45.000000</td>\n      <td>1.000000</td>\n      <td>303500.000000</td>\n      <td>1.40000</td>\n      <td>140.000000</td>\n      <td>1.000000</td>\n      <td>1.00000</td>\n      <td>203.000000</td>\n      <td>1.00000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>95.000000</td>\n      <td>1.000000</td>\n      <td>7861.000000</td>\n      <td>1.000000</td>\n      <td>80.000000</td>\n      <td>1.000000</td>\n      <td>850000.000000</td>\n      <td>9.40000</td>\n      <td>148.000000</td>\n      <td>1.000000</td>\n      <td>1.00000</td>\n      <td>285.000000</td>\n      <td>1.00000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## **MISSING VALUES**\n\nThere are no missing values"},{"metadata":{"trusted":true},"cell_type":"code","source":"missing_percentage=df.isna().sum()*100/df.shape[0]\nmissing_percentage","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"age                         0.0\nanemia                      0.0\ncreatinine_phosphokinase    0.0\ndiabetes                    0.0\nejection_fraction           0.0\nhigh_blood_pressure         0.0\nplatelets                   0.0\nserum_creatinine            0.0\nserum_sodium                0.0\nsex                         0.0\nsmoking                     0.0\ntime                        0.0\nDEATH_EVENT                 0.0\ndtype: float64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_survived = df.loc[df['DEATH_EVENT'] == 0]\ndf_died = df.loc[df['DEATH_EVENT'] == 1]\n\ndf_cat = df[['anemia', 'diabetes', 'high_blood_pressure', 'sex', 'smoking']]\ndf_cont = df[['age', 'creatinine_phosphokinase', 'ejection_fraction', 'platelets', 'serum_creatinine', 'serum_sodium', 'time']]","execution_count":9,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#PRINT VALUE COUNTS FOR VARIABLES WITH BINARY VALUES:\n\nprint(\"ANEMIA:\")\nprint(df['anemia'].value_counts())\nprint(\"\")\nprint(\"DIABETES:\")\nprint(df['diabetes'].value_counts())\nprint(\"\")\nprint(\"HIGH BLOOD PRESSURE:\")\nprint(df['high_blood_pressure'].value_counts())\nprint(\"\")\nprint(\"SEX:\")\nprint(df['sex'].value_counts())\nprint(\"\")\nprint(\"SMOKING:\")\nprint(df['smoking'].value_counts())\nprint(\"\")\nprint(\"DEATH EVENT:\")\nprint(df['DEATH_EVENT'].value_counts())\nprint(\"\")","execution_count":10,"outputs":[{"output_type":"stream","text":"ANEMIA:\n0    170\n1    129\nName: anemia, dtype: int64\n\nDIABETES:\n0    174\n1    125\nName: diabetes, dtype: int64\n\nHIGH BLOOD PRESSURE:\n0    194\n1    105\nName: high_blood_pressure, dtype: int64\n\nSEX:\n1    194\n0    105\nName: sex, dtype: int64\n\nSMOKING:\n0    203\n1     96\nName: smoking, dtype: int64\n\nDEATH EVENT:\n0    203\n1     96\nName: DEATH_EVENT, dtype: int64\n\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### GET RANGES OF CONTINUOUS VARIABLES"},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Range of Age Column: \", df['age'].min(), \"to\", df['age'].max())\nprint(\"\")\nprint(\"Range of Creatinine Phosphokinase Column: \", df['creatinine_phosphokinase'].min(), \"to\", df['creatinine_phosphokinase'].max())\nprint(\"\")\nprint(\"Range of Platelets Column: \", df['platelets'].min(), \"to\", df['platelets'].max())\nprint(\"\")\nprint(\"Range of Serum Creatinine Column: \", df['serum_creatinine'].min(), \"to\", df['serum_creatinine'].max())\nprint(\"\")\nprint(\"Range of Serum Sodium Column: \", df['serum_sodium'].min(), \"to\", df['serum_sodium'].max())\nprint(\"\")\nprint(\"Range of Time Column: \", df['time'].min(), \"to\", df['time'].max())\n\n","execution_count":11,"outputs":[{"output_type":"stream","text":"Range of Age Column:  40.0 to 95.0\n\nRange of Creatinine Phosphokinase Column:  23 to 7861\n\nRange of Platelets Column:  25100.0 to 850000.0\n\nRange of Serum Creatinine Column:  0.5 to 9.4\n\nRange of Serum Sodium Column:  113 to 148\n\nRange of Time Column:  4 to 285\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# <a id='3'>III. OUTLIERS</a>\n\nWe'll visualize some of the major outliers and delete the rows that contain them. This will improve the accuracy of our predictive models. "},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.box(df, x=\"creatinine_phosphokinase\")\nfig.update_layout(title_text='CREATININE 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EXPLORATORY DATA ANALYSIS</a>\n\nIn this section we'll explore our data and create some visualizion to give us further insight. "},{"metadata":{},"cell_type":"markdown","source":"## <a id='4a'>IVa. UNIVARIATE ANALYSIS</a>\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"values = df['DEATH_EVENT'].value_counts()\n\nfig = make_subplots(rows=1, cols=2, \n                    specs=[[{\"type\": \"xy\"}, {\"type\": \"domain\"}]],\n                    subplot_titles=('Death Event Count', 'Death Event Percentage'))\n\nfig.add_trace(go.Bar(y=values, \n                     name='Death Event Count', \n                     marker=dict(color=['#2ad4cb','#e6c822'])), row=1, col=1)\n\nfig.add_trace(go.Pie(labels=['Survived','Died'], \n                     values=values, \n                     name='Death Event Percentage',\n                     hole = 0.5,\n                     marker=dict(colors=['#2ad4cb','#e6c822'])), row=1, col=2)\n\n\nfig.update_layout(height=500, \n                  title_text='DEATH EVENT STATS',\n                  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BIVARIATE ANALYSIS</a>\n\n\nIn this section we'll visualize how each categorical and continuous variable correlates with our target variable, \"DEATH EVENT\". "},{"metadata":{},"cell_type":"markdown","source":"<a id=\"cat2\"></a>\n\n### CATEGORICAL VARIABLES"},{"metadata":{},"cell_type":"markdown","source":"### ANEMIA x DEATH EVENT\n\n**OBSERVATIONS**\n\n* The dataset is closely split between patients both with and without anemia. A small majority of patients (103 or 52.3%) have no anemia. While 97 or 47.7% of patients do have anemia. \n* Patients with anemia are more likely to die than those without. 27.7% of patients with anemia died while 18.4% of patients without anemia died. 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Those with diabetes died at a rate approximately 1.8% higher than those without. 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</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### SEX x DEATH EVENT\n\n**OBSERVATIONS**\n\n* In this dataset males outnumber females by 11.4% \n* Female patients died at a 1.4% higher rate than males. 23.7% of females died while 22.3% of males died.  "},{"metadata":{"trusted":true},"cell_type":"code","source":"df_sex = df.groupby(['sex', 'DEATH_EVENT'])[['DEATH_EVENT']].count()\ndf_sex.columns = ['count']\ndf_sex.reset_index(inplace=True)\n\nsex_count = df_sex.groupby(['sex'])[['count']].sum()\nsex_count.reset_index(inplace=True)\n\nfemale_death = df_sex.loc[df_sex['sex'] == 0]\nmale_death = df_sex.loc[df_sex['sex'] == 1]\n\nsubplot_titles=['SEX COUNT', 'SEX x DEATH EVENT COUNT', 'SEX PERCENTAGES', \n                'OVERALL SEX & DEATH EVENT', 'FEMALE x DEATH', 'MALE x DEATH']\n\nfig = make_subplots(rows=3, cols=2, specs=[[{\"type\": \"xy\"}, {\"type\": \"xy\"}],\n                                           [{\"type\": \"domain\"}, {\"type\": \"domain\"}],\n                                           [{\"type\": \"domain\"}, {\"type\": \"domain\"}]],\n                   subplot_titles=subplot_titles,\n                   vertical_spacing = 0.13)\n\nlabel1 = ['Female', 'Male']\nlabel2 = ['Female: Survived', 'Female: Died', 'Male: Survived', 'Male: Died']\nlabel3 = ['Female: Survived', 'Female: Died']\nlabel4 = ['Male: Survived', 'Male: Died']\n\nfig.add_trace(go.Bar(x=label1, y=sex_count['count'], name='Sex Count', marker_color='rgb(26, 118, 255)'), row=1, col=1)\nfig.add_trace(go.Bar(x=label2, y=df_sex['count'], name='Sex vs Death Event', marker_color='rgb(235, 186, 40)'), row=1, col=2)\nfig.add_trace(go.Pie(labels=label1, values=sex_count['count']), row=2, col=1)\nfig.add_trace(go.Pie(labels=label2, values=df_sex['count']), row=2, col=2)\nfig.add_trace(go.Pie(labels=label3, values=female_death['count']), row=3, col=1)\nfig.add_trace(go.Pie(labels=label4, values=male_death['count']), row=3, col=2)\n\n# fig.update_traces(hoverinfo=\"label+name+value\")\nfig.update_layout(height=1000, showlegend=True, title_text='SEX x DEATH 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</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### SMOKING x DEATH EVENT\n\n**OBSERVATIONS:** \n\n* Most people in the dataset are non smokers. 131 or 66.5% non smoker, 66 or 33.5% smoker. \n* 24.2% of smokers died. 22.1% of non smokers died. "},{"metadata":{"trusted":true},"cell_type":"code","source":"df_smoking = df.groupby(['smoking', 'DEATH_EVENT'])[['DEATH_EVENT']].count()\ndf_smoking.columns = ['count']\ndf_smoking.reset_index(inplace=True)\n\nsmoking_count = df_smoking.groupby(['smoking'])[['count']].sum()\nsmoking_count.reset_index(inplace=True)\n\nnonsmoking_death = df_smoking.loc[df_smoking['smoking'] == 0]\nsmoking_death = df_smoking.loc[df_smoking['smoking'] == 1]\n\nsubplot_titles=['SMOKING COUNT', 'SMOKING x DEATH EVENT COUNT', 'SMOKING PERCENTAGES', \n                'OVERALL SMOKING & DEATH EVENT', 'NO SMOKING x DEATH', 'SMOKING x DEATH']\n\nfig = make_subplots(rows=3, cols=2, specs=[[{\"type\": \"xy\"}, {\"type\": \"xy\"}],\n                                           [{\"type\": \"domain\"}, {\"type\": \"domain\"}],\n                                           [{\"type\": \"domain\"}, {\"type\": \"domain\"}]],\n                   subplot_titles=subplot_titles,\n                   vertical_spacing = 0.13)\n\nlabel1 = ['Non Smoker', 'Smoker']\nlabel2 = ['Non Smoker: Survived', 'Non Smoker: Died', 'Smoker: Survived', 'Smoker: Died']\nlabel3 = ['Non Smoker: Survived', 'Non Smoker: Died']\nlabel4 = ['Smoker: Survived', 'Smoker: Died']\n\nfig.add_trace(go.Bar(x=label1, y=smoking_count['count'], name='Smoker Count', marker_color='rgb(26, 118, 255)'), row=1, col=1)\nfig.add_trace(go.Bar(x=label2, y=df_smoking['count'], name='Smoker vs Death Event', marker_color='rgb(235, 186, 40)'), row=1, col=2)\nfig.add_trace(go.Pie(labels=label1, values=smoking_count['count']), row=2, col=1)\nfig.add_trace(go.Pie(labels=label2, values=df_smoking['count']), row=2, col=2)\nfig.add_trace(go.Pie(labels=label3, values=nonsmoking_death['count']), row=3, col=1)\nfig.add_trace(go.Pie(labels=label4, values=smoking_death['count']), row=3, col=2)\n\n# fig.update_traces(hoverinfo=\"label+name+value\")\nfig.update_layout(height=1000, showlegend=True, title_text='SMOKING x DEATH EVENT')\n\nfig.show()","execution_count":28,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"marker":{"color":"rgb(26, 118, 255)"},"name":"Smoker Count","type":"bar","x":["Non Smoker","Smoker"],"xaxis":"x","y":[131,66],"yaxis":"y"},{"marker":{"color":"rgb(235, 186, 40)"},"name":"Smoker vs Death Event","type":"bar","x":["Non Smoker: Survived","Non Smoker: Died","Smoker: Survived","Smoker: Died"],"xaxis":"x2","y":[102,29,50,16],"yaxis":"y2"},{"domain":{"x":[0,0.45],"y":[0.3766666666666667,0.6233333333333334]},"labels":["Non Smoker","Smoker"],"type":"pie","values":[131,66]},{"domain":{"x":[0.55,1],"y":[0.3766666666666667,0.6233333333333334]},"labels":["Non Smoker: Survived","Non Smoker: Died","Smoker: Survived","Smoker: Died"],"type":"pie","values":[102,29,50,16]},{"domain":{"x":[0,0.45],"y":[0,0.24666666666666667]},"labels":["Non Smoker: Survived","Non Smoker: 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     </script>        </div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### CONTINUOUS VARIABLES\n\nThe charts below will display the count of the various continuous variables ('age', 'creatinine_phosphokinase', 'ejection_fraction', 'platelets', 'serum_creatinine', 'serum_sodium', 'time') according to whether or not a patient died or survived. "},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.histogram(df, x=\"age\", \n                   color=\"DEATH_EVENT\",\n                   color_discrete_sequence=['#e6c822','#2ad4cb'],\n                   marginal=\"box\", \n                   nbins=10, hover_data=df.columns) \n\nfig.update_layout(height=500, title_text='AGE x DEATH EVENT', 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\"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", 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MULTIVARIATE ANALYSIS</a>\n"},{"metadata":{},"cell_type":"markdown","source":"### DEATH & SURVIVAL PERCENTAGES BY SEX & HIGH BLOOD PRESSURE"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_de = df.groupby(['DEATH_EVENT', 'sex', 'high_blood_pressure'])[['age']].count()\ndf_de.reset_index(inplace=True)\ndf_de.rename(columns={'age':'count'}, inplace=True)\n\nsurvived_female = df_de.loc[(df_de['DEATH_EVENT'] == 0) & (df_de['sex'] == 0)]\nsurvived_male = df_de.loc[(df_de['DEATH_EVENT'] == 0) & (df_de['sex'] == 1)]\ndied_female = df_de.loc[(df_de['DEATH_EVENT'] == 1) & (df_de['sex'] == 0)]\ndied_male = df_de.loc[(df_de['DEATH_EVENT'] == 1) & (df_de['sex'] == 1)]\n\nsubplot_titles = ['FEMALE x SURVIVED' , 'MALE x SURVIVED', 'FEMALE x DIED', 'MALE x DIED']\n\nfig = make_subplots(rows=2, cols=2, specs=[[{\"type\": \"domain\"}, {\"type\": \"domain\"}],\n                                           [{\"type\": \"domain\"}, {\"type\": \"domain\"}]],\n                   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'smoking'])[['age']].count()\ndf_de2.reset_index(inplace=True)\ndf_de2.rename(columns={'age':'count'}, inplace=True)\n\nsurvived_female = df_de2.loc[(df_de2['DEATH_EVENT'] == 0) & (df_de2['sex'] == 0)]\nsurvived_male = df_de2.loc[(df_de2['DEATH_EVENT'] == 0) & (df_de2['sex'] == 1)]\ndied_female = df_de2.loc[(df_de2['DEATH_EVENT'] == 1) & (df_de2['sex'] == 0)]\ndied_male = df_de2.loc[(df_de2['DEATH_EVENT'] == 1) & (df_de2['sex'] == 1)]\n\nsubplot_titles = ['FEMALE x SURVIVED' , 'MALE x SURVIVED', 'FEMALE x DIED', 'MALE x DIED']\n\nfig = make_subplots(rows=2, cols=2, specs=[[{\"type\": \"domain\"}, {\"type\": \"domain\"}],\n                                           [{\"type\": \"domain\"}, {\"type\": \"domain\"}]],\n                   subplot_titles=subplot_titles,\n                   vertical_spacing = 0.10)\n\nlabel = ['Non-Smoking', 'Smoking']\n\nfig.add_trace(go.Pie(labels=label, values=survived_female['count'], hole=0.5, marker=dict(colors=['#2ad4cb','#e6c822']), rotation=-45), 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{\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"SEX & AGE x DEATH EVENT\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"age\"}}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.0, 1.0], \"matches\": \"x\", \"showgrid\": true, \"showticklabels\": false}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 0.7326], \"title\": {\"text\": \"count\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.7426, 1.0], \"matches\": \"y2\", \"showgrid\": false, \"showline\": false, \"showticklabels\": false, \"ticks\": \"\"}},                        {\"responsive\": true}                    ).then(function(){\n                        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Survivors"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_de0_age = df[['DEATH_EVENT', 'sex', 'age']].loc[df['DEATH_EVENT'] == 0]\ndf_de0_age\n\nfig = px.histogram(df_de0_age, \n                   x=\"age\", \n                   color=\"sex\", \n                   marginal=\"box\",\n                   color_discrete_sequence=['#e6c822','#2ad4cb'],\n                   hover_data=df_de0_age.columns)\n\nfig.update_layout(height=500, title_text='SEX & AGE of the Survivors', showlegend=True)\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## DATA NORMALIZATION"},{"metadata":{},"cell_type":"markdown","source":"## MODEL DEVELOPMENT"}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}