heart-failure-complete-eda-visualization.ipynb

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      "/kaggle/input/heart-failure-prediction/heart.csv\n"
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   "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",
    "\n",
    "import numpy as np # linear algebra\n",
    "import 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",
    "\n",
    "import os\n",
    "for 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"
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   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
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   "source": [
    "<br>\n",
    "<h1 style=\"font-size: 42px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #e9f7ef; color: #8f52a4; text-align: center; border-radius: 150px 150px;\"><b>💔 Heart Failure: </b>Complete EDA + Visualization 📊</h1>\n",
    "<br>\n"
   ]
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   "source": [
    "![](https://hips.hearstapps.com/hmg-prod/images/281/lead-image-gettyimages-911641138-1-1518269574.jpg)"
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   "source": [
    "## <font color='#8f52a4'>Table of contents<font><a class='anchor' id='top'></a>"
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    "<div style=\"border-radius:10px; border:#e7c6ff solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "\n",
    "- [Summary Of The Dataset](#summary)\n",
    "- [Reading The Dataset](#reading)\n",
    "- [Preliminary Analysis](#preliminary)\n",
    "- [Exploratory Data Analysis](#EDA)\n",
    "- [Univariate Analysis on Categorical Variables](#EDA_Cat)\n",
    "- [Bivariate Analysis](#BVA)\n",
    "- [End of EDA](#end)"
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   "source": [
    "# <span><h1 style=\"font-size: 36px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #e9f7ef; color: #8f52a4; text-align: center; border-radius: 150px 150px;\">Summary of the Dataset</h1></span><a class='anchor' id='summary'></a>\n",
    "<br>\n",
    "\n",
    "<div style=\"border-radius:10px; border:#e7c6ff solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "\n",
    "#### Context\n",
    "Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease.\n",
    "\n",
    "People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.\n",
    "    \n",
    "#### Attribute Information\n",
    "    \n",
    "- `Age`: age of the patient [years]\n",
    "-`Sex`: sex of the patient [M: Male, F: Female]\n",
    "-`ChestPainType`: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]\n",
    "-`RestingBP`: resting blood pressure [mm Hg]\n",
    "-`Cholesterol`: serum cholesterol [mm/dl]\n",
    "-`FastingBS`: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]\n",
    "-`RestingECG`: resting electrocardiogram results [Normal: Normal, 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 [Y: Yes, N: No]\n",
    "-`Oldpeak`: oldpeak = ST [Numeric value measured in depression]\n",
    "-`ST_Slope`: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]\n",
    "-`HeartDisease`: output class [1: heart disease, 0: Normal]"
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   "source": [
    "# <span><h1 style=\"font-size: 36px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #e9f7ef; color: #8f52a4; text-align: center; border-radius: 150px 150px;\">Reading The Dataset</h1></span><a class='anchor' id='reading'></a>\n",
    "<br>"
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       "      <td>0</td>\n",
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       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  \n",
       "3              Y      1.5     Flat             1  \n",
       "4              N      0.0       Up             0  "
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   "source": [
    "df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')\n",
    "df.head()"
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       "      <td>130</td>\n",
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       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>174</td>\n",
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       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "913   45   M            TA        110          264          0     Normal   \n",
       "914   68   M           ASY        144          193          1     Normal   \n",
       "915   57   M           ASY        130          131          0     Normal   \n",
       "916   57   F           ATA        130          236          0        LVH   \n",
       "917   38   M           NAP        138          175          0     Normal   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "913    132              N      1.2     Flat             1  \n",
       "914    141              N      3.4     Flat             1  \n",
       "915    115              Y      1.2     Flat             1  \n",
       "916    174              N      0.0     Flat             1  \n",
       "917    173              N      0.0       Up             0  "
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    "# <span><h1 style=\"font-size: 36px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #e9f7ef; color: #8f52a4; text-align: center; border-radius: 150px 150px;\">Preliminary Analysis</h1></span><a class='anchor' id='preliminary'></a>\n",
    "<br>"
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 918 entries, 0 to 917\n",
      "Data 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  \n",
      "dtypes: float64(1), int64(6), object(5)\n",
      "memory usage: 86.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62be3933",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "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>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.510893</td>\n",
       "      <td>132.396514</td>\n",
       "      <td>198.799564</td>\n",
       "      <td>0.233115</td>\n",
       "      <td>136.809368</td>\n",
       "      <td>0.887364</td>\n",
       "      <td>0.553377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.432617</td>\n",
       "      <td>18.514154</td>\n",
       "      <td>109.384145</td>\n",
       "      <td>0.423046</td>\n",
       "      <td>25.460334</td>\n",
       "      <td>1.066570</td>\n",
       "      <td>0.497414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-2.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age   RestingBP  Cholesterol   FastingBS       MaxHR  \\\n",
       "count  918.000000  918.000000   918.000000  918.000000  918.000000   \n",
       "mean    53.510893  132.396514   198.799564    0.233115  136.809368   \n",
       "std      9.432617   18.514154   109.384145    0.423046   25.460334   \n",
       "min     28.000000    0.000000     0.000000    0.000000   60.000000   \n",
       "25%     47.000000  120.000000   173.250000    0.000000  120.000000   \n",
       "50%     54.000000  130.000000   223.000000    0.000000  138.000000   \n",
       "75%     60.000000  140.000000   267.000000    0.000000  156.000000   \n",
       "max     77.000000  200.000000   603.000000    1.000000  202.000000   \n",
       "\n",
       "          Oldpeak  HeartDisease  \n",
       "count  918.000000    918.000000  \n",
       "mean     0.887364      0.553377  \n",
       "std      1.066570      0.497414  \n",
       "min     -2.600000      0.000000  \n",
       "25%      0.000000      0.000000  \n",
       "50%      0.600000      1.000000  \n",
       "75%      1.500000      1.000000  \n",
       "max      6.200000      1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8a09986",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:34.334623Z",
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     "shell.execute_reply": "2023-06-07T13:26:34.342827Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age               0\n",
       "Sex               0\n",
       "ChestPainType     0\n",
       "RestingBP         0\n",
       "Cholesterol       0\n",
       "FastingBS         0\n",
       "RestingECG        0\n",
       "MaxHR             0\n",
       "ExerciseAngina    0\n",
       "Oldpeak           0\n",
       "ST_Slope          0\n",
       "HeartDisease      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "17090929",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:34.380666Z",
     "iopub.status.busy": "2023-06-07T13:26:34.380252Z",
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     "shell.execute_reply": "2023-06-07T13:26:34.392382Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa86d28b",
   "metadata": {
    "papermill": {
     "duration": 0.016624,
     "end_time": "2023-06-07T13:26:34.428344",
     "exception": false,
     "start_time": "2023-06-07T13:26:34.411720",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span><h1 style=\"font-size: 36px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #e9f7ef; color: #8f52a4; text-align: center; border-radius: 150px 150px;\">Exploratory Data Analysis </h1></span><a class='anchor' id='EDA'></a>\n",
    "<br>\n",
    "\n",
    "<div style=\"border-radius:10px; border:#e7c6ff solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "\n",
    "- Univariate analysis focuses on analyzing each feature in the dataset independently.\n",
    "- Distribution analysis:\n",
    "    - The distribution of each feature is examined to identify its shape, central tendency, and dispersion.\n",
    "- Identifying potential issues:\n",
    "    - Univariate analysis helps in identifying potential problems with the data such as outliers, skewness, and missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7aed311",
   "metadata": {
    "papermill": {
     "duration": 0.015792,
     "end_time": "2023-06-07T13:26:34.460051",
     "exception": false,
     "start_time": "2023-06-07T13:26:34.444259",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\">Segregating Columns into groups</h2>\n",
    "<br>\n",
    "\n",
    "- **Numerical** -> `Age`, `RestingBP`, `Cholesterol`, `MaxHR`, `OldPeak`\n",
    "- **Categorical** -> `Sex`, `ChestPainType`, `RestingECG`, `ExerciseAngina`, `ST_Slope`, `HeartDisease`,`FastingBS`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baa6ae26",
   "metadata": {
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     "duration": 0.015449,
     "end_time": "2023-06-07T13:26:34.491325",
     "exception": false,
     "start_time": "2023-06-07T13:26:34.475876",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\">Analysis on Age</h2>\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8148fbd8",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    918.000000\n",
       "mean      53.510893\n",
       "std        9.432617\n",
       "min       28.000000\n",
       "25%       47.000000\n",
       "50%       54.000000\n",
       "75%       60.000000\n",
       "max       77.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "455fa31b",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2023-06-07T13:26:34.574728Z",
     "iopub.status.idle": "2023-06-07T13:26:35.222031Z",
     "shell.execute_reply": "2023-06-07T13:26:35.221014Z"
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    },
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(12,5))\n",
    "\n",
    "ax[0] = sns.histplot(data=df, x='Age', linewidth=1, edgecolor='black', ax=ax[0], color='#f7d794')\n",
    "ax[0].set(title='Age Distribution - Histogram')\n",
    "\n",
    "ax[1] = sns.kdeplot(data=df, x='Age', fill=True, edgecolor='black', ax=ax[1], color='#e15f41')\n",
    "ax[1] = sns.rugplot(data=df, x='Age', ax=ax[1], color='#574b90')\n",
    "ax[1].set(title='Age Distribution - KDE Plot')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c80da19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:35.259456Z",
     "iopub.status.busy": "2023-06-07T13:26:35.259064Z",
     "iopub.status.idle": "2023-06-07T13:26:35.266764Z",
     "shell.execute_reply": "2023-06-07T13:26:35.265645Z"
    },
    "papermill": {
     "duration": 0.027579,
     "end_time": "2023-06-07T13:26:35.268812",
     "exception": false,
     "start_time": "2023-06-07T13:26:35.241233",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.19593302867569365"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6d520e",
   "metadata": {
    "papermill": {
     "duration": 0.016729,
     "end_time": "2023-06-07T13:26:35.302267",
     "exception": false,
     "start_time": "2023-06-07T13:26:35.285538",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h3 style=\"font-size: 20px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c3aed6; color: #3d405b; text-align: center; border-radius: 150px 150px;\">Checking For Outliers</h3>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eeeb9edb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:35.337400Z",
     "iopub.status.busy": "2023-06-07T13:26:35.336950Z",
     "iopub.status.idle": "2023-06-07T13:26:35.598937Z",
     "shell.execute_reply": "2023-06-07T13:26:35.597828Z"
    },
    "papermill": {
     "duration": 0.282345,
     "end_time": "2023-06-07T13:26:35.601300",
     "exception": false,
     "start_time": "2023-06-07T13:26:35.318955",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAAD2Srtabk0hNQAAAAAAAkLQDQAAAABAQAi6AQAAAAAICEE3AAAAAAABIegGAAAAACAgBN0AAAAAAASEoBsAAAAAgIAQdAMAAAAAEBCCbgAAAAAAAkLQDQAAAABAQAi6AQAAAAAICEE3AAAAAAABIegGAAAAACAgBN0AAAAAAASEoBsAAAAAgIAQdAMAAAAAEJBIpjsAAOg8XNdVS0tLpruBPaj9Oc/NzZXjOBnuEXYX5wsAsgNBNwBgt7W0tGjSpEmZ7gaA3TBjxgxFo9FMdwMA9nqklwMAAAAAEBBmugEARvqO+5mcSE6mu4GAJVpb9dXTcyRJfc7+mUI5nPNs5sZa9eXCOZnuBgCgHYJuAIARJ5JDALaXCeVwzrNdItMdAACkIL0cAAAAAICAEHQDAAAAABAQgm4AAAAAAAJC0A0AAAAAQEAIugEAAAAACAhBNwAAAAAAASHoBgAAAAAgIATdAAAAAAAEhKAbAAAAAICAEHQDAAAAABAQgm4AAAAAAAJC0A0AAAAAQEAIugEAAAAACAhBNwAAAAAAASHoBgAAAAAgIATdAAAAAAAEhKAbAAAAAICAEHQDAAAAABAQgm4AAAAAAAJC0A0AAAAAQEAIugEAAAAACAhBNwAAAAAAASHoBgAAAAAgIATdAAAAAAAEhKAbAAAAAICAEHQDAAAAABAQgm4AAAAAAAJC0A0AAAAAQEAIugEAAAAACAhBNwAAAAAAASHoBgAAAAAgIATdAAAAAAAEhKAbAAAAAICAEHQDAAAAABAQgm4AAAAAAAISyXQH9gTXddXS0iJJys3NleM4Ge4RAAAAAGC7rhyz7RUz3S0tLZo0aZImTZqUPJEAAAAAgOzQlWO2vSLoBgAAAAAgEwi6AQAAAAAICEE3AAAAAAABIegGAAAAACAgBN0AAAAAAASEoBsAAAAAgIAQdAMAAAAAEBCCbgAAAAAAAkLQDQAAAABAQAi6AQAAAAAICEE3AAAAAAABIegGAAAAACAgBN0AAAAAAASEoBsAAAAAgIAQdAMAAAAAEBCCbgAAAAAAAkLQDQAAAABAQAi6AQAAAAAICEE3AAAAAAABiWS6A0AQ1q5dq3+7Y7rqa+tVVFqka269SlVVVfQHe8zKlSv1wJRpSsRchSKOrr39ag0aNCjT3coamz//VJ8teF6Ku1LYUf9zT1dJvwGZ7laHNdVs0qcLn1e8YavChfkaMO505ZWVZ7pb2Is01daobm2DFHd045U369opV3f6z5ts+wy11Z9sOy5buupxZRNe487HcV3XNfnDuro6lZSUaPPmzSouLrbdL6uam5s1adIkSdKMGTMUjUYz3CMEbeLPJ6lxdYviTkxhN6KCfXP14OMz6A/2mPE//ZlOWnOO9m3aX6vz/qGX91moeYtmZ6w/tj6gv/zyS9145c1S3FFOeYkGnHOGUVD5p7vuU2FzcfI90RCt0xE3X592O9nmL//fEzph1Y+S5/21g5fou7+4KCN9sTUA0Li+Wp88PqfD59wWWwM2tl6fbGtn5UO/VnSD0+HPm2x6qL/m8us0+P2Tku+rj773ih749X0Z6YvN/thqJ5vOlZR95yvbXh8bsu01tqUzxmy7GxMz091JdcUbiE0bqzdq5Mbzkjej5bnzM9qf9dXrVaCS5NfV1euN2sm2855t/ckmiVhC+zbtL0nat2l/JWLxjPbn3+6Y7vmAnnbHDKMP6Gl3ztDINf98b639h16d/1sdfMUlabfjxF0N33hasj9Le85Nuw0p+2bMY3VbPOe9ta4h7TZsHdOnC5/3DgAsXGQ0APDJgmfktkqSq5YNtfq/uU/p0Ct/lXY7to7r7/OfU1FLybagMhbR3+c/p+/dckPa7Xwy/1lF10uO4yjcENPf5z+rQ664LO12/m/2AuXV5CTb+b/ZC3ToVVek3Y6t8xXbXK+TNo5PtrMsx+zzb+qt9/1zsDihzbVbdO+t92UseK+vrfe8r96qXZR2P2yy1R9b7di6v9tSu6lGfyx/SU2hRuUluqllU/r3QZuy7fWxIdveE9g1gu5OqiveQGxyXMdzM3LcDJcvSMgbYFTOM2rm3lvv1/c+HJVs574pD2jGY9PSbsdWsGyrPzZk2wBAQq5W5/0j+dokZJRUZO24Nm+q87wn3tj0vFF/1ldv8DxMNdfUGrXjuCHve9SwxMhnC57XKV+f2xZgLFigw26+1qgtG1paGrVwn2nJWcbWlq1pt/Hp/Oc16pt2xzR/gQ67Jf1jaq1v8D741ten3YYkxesa9S/V57fdv2Q2QGLrXIXisjJg01pT5wlOl0bM7svxukYN39ju9Qkb9sfS+XITrqedRCJh1I6twWsbzyt5hXme91VBYZ5RX2wpKi3y3N+LS80yPm21k20BWEtLq0Zs+pe2TK/8hRntj63XJ5ueM2xdO9hzCLo7qWy7wWab4u7FWtja9gFd0iOzN6OQE/acr5BjFmBsXLfR086KtRuN2rE1aLN+3XrPw91mw/7Y+CC7+6apOvrPbbNEd990rx6Z9aBRf2yI5udoec8FcuXKkaNofo5RO7bOVVPzVs8HdFNz+sGgJLmuqyHtHqZMAx43LE9/3LBRM1Lc9bwnFDcb3Fi/8kOtXrRUjhuS6yS0709HqcegwWm3E3JDnoDwJYPXx43HvUFT3CxLItEa85yrZdEFRu2ELA2Q2DpXjuz0J5QyOOtktD+J1lbve8vwfMmRpx2Ta1CS5Ht9ZPj6rN+w3nMvDG9I//Vx5HjeV3/qs8yoL7aCpvMvO89Ts+O6y68x6o+tdrJtUCKam+e5dqK5mU0RthWgZtOE1zW3XqVpd8zQW7WLVFxarGumXJWRfmD3EXR3UoxwfbuccI6O3Tiqwx/QtnSvrNDqtW3nq0dVd6N24k7c88Ead2JG7dgatEn4A7BKs4c7GzPmm6prPMe0rHqTUV9sqajorsGfe9dbmbB1rmKJmN6s+A/FnFZF3BzFEmaBnD+LRDJ7EA/l5HgCy1CO2aBEQnFvRoFrNqu3etFSjVrXbrZy0TyzoNsXgIVMAjBf0GQ6sOEk5D1XZi+NEkp4B0gMG3JDvoEWw9g94fjOucyu5bhi3vupzO6nKdegYX+chG8QwPB8pWR6Gb5H3VDC8/q4IbNBkpamVm+mhMH1vLVhq/c+WN9o1BdbQdPcR+d7anbM/fX8jLZja1DClryCqOc9kVeQ2UEAWwFqNk14VVVVWQn4s2n2vqsj6O6kGOH6drY+oG254Y7rPOfrhinXGbUTcryzaMt7m6X72Rq0SZkBM3yK3uCbwV++dkPabbiOPzDIbDr3j8eeqsdWz5TjOnIdV5eedbFRf6wNsCWcbcH2P4Nu0wDMdbxp8zJ8nUPd8tS6pUWS1Oq0KNzN7KEsIdczmJAwPLCUdHfDWb2E7zo06Y8/cHcMM2NsBcuSN13Z9L0Vd+Ja1nOBtl0zjuKO4Qy+/5w7hjPmTsezEqQdHJdh0O1/b7mGx5XSjuH58i+LWlZl9nljI1MivzDfc0zdiroZ9cXWWuNsW9O9pa7Ru3xo8xajdmx9/tVsqvG8Rxs31Rn1xxZbAWpXnPDKptn7ro6guxPb/kHqypVhEfouO8Jl6wPaFls3fDfmnblyzSZm7A3ahHwBmOFMSML1z1YaPLSG5XnwVcSsL7Y+gGY9NFuj1rWtF5354GwNHz487XZsnSvXSajVaQtyXdNpNMkbgJnFpgrJ0fCNo5Ovz6s9Fhu1E3EjOntN22sys+9tRu34g2XTgCeumC+wTP9NmlNWrNXr2vqSU1ay6z/aAVdxKwMSKenKlXOMmgnFHZXEypPXTo1jVlAy7IY95/yJvlOM2rE1IxxxIzqlum2tumlmggpztbTnXDkKyVVCjmGKsKuE57ybXsvyzbwbZ0qkDIim35DrG2jp5uYa9cXWWmNbzxi2gjhby4dsff4l4gnPIG8ibv55k01sfR5n07N3Ns3ed3UE3Z2UrRtjNhXCsqk13ur5gM6NZ7iQmiVxx58OaTajYmsQwAk5ntfZCRmmMbq+QM7gGbGivMKzpvvdQ5YY9cVWwbFEzLt+NREze+iwda5CClkp5hcOhzVkXbvU515ms1+xxq2ecx5rNMtGsbU2PFyUp2VuW7AcKjILeMK+AMxk9nT/887UawsXKdbQqEhhN+0/7kyzvsg3ILHfbUbtxOWdyTUaFPsnG2uNbWVbyPFeO4Yxt7U13drSqlHt0rCXhU3X4Id9A1FmgxIp2UOmA3X++7tBE00NTTp7TVvRvWdKHzDqiq21xraeMWyt6Y4l4laWD9kKwFzZWSKTbWx9HmfT7LKtgZ9sGkjIVgTdnZStG6Otwly22HrT1m2o04Vr2h40nsy5w2Y307Zy5UrPB+u1t1+tQYMGpd1OauCU2Q+yaG6ezv607eFuwXfuNWonFAp5Cz4ZpDG2bG3xvie2Nhv1pbF5i2dgo7HZLBgMRRzPB1kokuGBH8fxPPiaBhj+WTTTquy2inzte9pILV00L5nGv+9PRpn1pzmmkJJ5Eko0txq1I3kDDBN5ZeVW9vZ2Q95rUIaDYrZmcv1pxkbr3f/JRrZFIiTvjLDxGvOOLymQJDfhK6BnGDj5ByVM36M2BkMlSSF5smxMTrutQmG21hrbesaY+ciTijYVJLe/m/nwLKNt2UKJkJWMH2tLzywtkbEl2wJCG8/wto7J1ux9Ng0kZCuC7jRk05vW1o3Rv+bPOP3QEltv2tS1cWbHZStYfmDKNE+xlAemTNe8RbPTbic/v5vnRp2fb/ZQb+tabm1t8bzOra1mgUphWYGWJdpm0orKC9Nuw1a6X9xXKToeN8vhnzDxAj02rW1N92WTfm7Ujq1rMOwb2Fixz1NG/XHiISuzp7aKfK197W0VxIqTD+Prfv9f6nHIoek31BLTyPbFngwzAaSOz/A01WzSpwufV7xhq8KF+Row7nTllZWn3U6oKE/L4h2fvbc1k5s6c2q4FjsU9g7Y9DIbsHFceescmGZhh6WlPefJkSNXrhIdeLKyMjvodnzgR5K1gn7hUNhXiyT982WrUNjGTRs9WRvuJtO6AnaenWxty5a61MusGVsBWDgS8vQnHDHdnsKObAsIbTzDT731PjWublHcSWhz7Rbde+t9RgM2tmbvSVPfNYLuNGTTm9bWjTE1PdhyR9Nk601b0bNCb7a0HVf3nmbVwm0Fy/FYwnNc8ZjZB31RSaHvRl1k1I6tazmSE/E83EVyzG4p9ZsbVBBvC5zqatMvumJrzZ9/yyfTB83/9+wSz5ruxU//p4YNG5Z2O7auwdzcqOcazM0xe30cfzq36ZICS6mrsVrfXss5Zg+trvwpsIYF4iwEqJ/Mf1bR9ZLjOAo3xPT3+c/qkCsuS78zTTHPGuq6JrNiRvGQb9eEkGF6eX6Ot+6C4TZ6Id99xzG877hyvXUODM+5E0+oIF6WfH3qnVqjdmzNDjqO0+HMIUlKuAnPVl9x8/L3HR5ga6ir9xZA22xWAE1x+bI2zAbX3JDrDd5DxpUpva+NYdpG98ruerO17fOvR6XZM4+tAOza26/WA1OmKxFLKBQJ6fo7zdLmbcm2gNDGM7y1ARtLumKROdsIutOQTW9aWzfG/Gg3T3rwwu/e3+E2O8LWm3byHddr2h0zVFdbp+LSAuNBCVvrcl3ZqSZrK7C0tg2VL0XYtCiNG3M7XEXY1po/W7N61rZls3QNtrQ0e67BltYWo3aKy4r0ZnPbNRgxzChwLaX22tpmSfIXCzNMoS7M8y4rMEiDjdXW66R2D1OmAwmp+z4bDkj48opNC3e6W71rlk1fY+VFtLlp21Ko5tBWhfLyjZrx7wax1HDGPGXZj+kMddi3HCBsuBygtEhvxjoegIWckEau6/iygri/UKZB2rytAmgpuxSYbqeWkArihcmBlobQZrN2LG3LZuuZx5ZBgwYZDQ4HJdsCQhvP8CmFIE0/RC1hV6VdI+hOQ7a9aW0oKS/2HFNpuVmVXFtsvWmtFZ+ytC43Lz/qGaXPyzdL89xS16BctzD5MNVQV2/Ujq1rOTfXu9dybq75LaXD62AtrflL2QPYcFsjW/1R2PW0o7DhGupEwrte1LCabGtT3DO4MbvQbB1/OJzjSe0Nh81mPW1ts5Sy3tjwAaZ1a6NnMKF1a/pVhFMfpswCAyfuHbBx4mavTdhS9kdK2rNpOnd9k0ZVtw1KmBYcU9jxzOTKMAM2NZAzu3b6n3u6li1YIMVdKeyo/3ljjNrZf9wZ+uTxOVLcUfk+Jbp2ytVG7Vhbl2uhkFpqVpXZybK2xMGVlcKUtrZls/XM01V1xYCworJCq9e2Xcvdqyoy2h+uwV0j6E5DV3zTZtsxZdub1laKVHlFuQZ/3pbO/VH/V4zaaW5u8Yz2r8g3W5dr67yXlpdp8GcdPy6p42sHba3582cTGKecWupPWXmZlarskp2U07yCqGcQwDFMEU7EYr5ZWLPAKWFpWyxbe1o7ce86YZNA1/WtzzSdwLCVYePIztZaKVuPGQbvTsI7mKCE4fZura3etPDWWqN2UgqpGaZblPQboMNuvnbXv7gL9V+vVjyWUMgN6evV3+hvf/ubKisr024n5T3h2tlyzuT+biurSvIP8BoO0jlh7yBdB9bm2Vh/b6v2RzbVLrLNxja72eSGO67zPMfdMOW6THcJu0DQnYZsCwhtyLZjyrYPDlspUtb2WvatjXMNZ64kOx9A1moL+B7qTUpr1/nSuV+rMVu/Gra07/PWhq3e9PJ6syrotqqyO750btNnxJaWFrU/P4mYWfE8W2nhjkLeINcwIEykVGXPXIChSMg7AxsxPCbHd0yGs3oJS9Ww5bpWAh5bVdlTXg7D+6B/b/aE4VaOtnz94gr9y7q2NP7Hps3UiBEj0m/Iwgz1dh2tNp+Tk+u5X+TkmA32+QcfTWeoS7oXa2Fr2+BjaQ+zLEH/zh2mBS7vv/XfdPLXbUsB7r/13zT/hfTvPdlUu0iy9yyXbcdlQ7Y9v2PXCLqRVWwVjcq2G6y1m6N/f1rDh+hsq3qZkiJs8Hi3tWmrp40mg7ReSVLIt3evYXBqK4XfVjt50agiTW3BaV7UbH/a2ppaFaks+XVr/RajdmylhSeUsFIMS5Iaw/Vy5apFTcatpKbkpp8G68bjym9XWLAhbjaA1GvkCK1b9qYchdSkLep1yvFG7dgKluUr8GW89ViBtyCbU2BYNNHSWmxbW6rZkpLubrqnmqXq5Y6FQNfWThnR/BzPgFbUMFMnJ5yjYzeO6nAmU14033Ou8nINl0Wl1P4we49mU+0iyd6zXLYdF/ZOBN1p6IppN9l2TAlfle+EYZXvTRs3eR4SmzaarX3OttfHVlX2bKt6aWWLG0uzMt0rK6xUgbWVBXD+Zed5sj+uu9xsiYOtpQC2qruHivM928SFi82KYfmZBt3hcEQjv2kXOPUye09EfDsMREoK0m7DidtZL1r7/l88hctee2+Jeh8zJP2GfMGyaVq4rTXC8YZmlcW6J9/rNQ0bjNqxlTZvq/ji5s8/1WcLnm9b033u6SrpNyDtdmytW46HW/VSz7kKKaSEEopHzALdnLyIJ9DNyUs/0LVVP6Siort3qdf3zO6DW+oaPef8jc1mg4+26urY2g0ikh/2ZHeV5me2dpGtYLkr1mRC50PQnYZsmz21IduOKSE7aYxNW5v1g01j2x5ac8weWm3NCNsK3m1VKM22qpdWZlQszcrYeo1tZQHMfXS+J/tj7q/nG7VrbymAnQAjrLBObDc7+ErpC2btWBoECIdzPMdlWtgtrrhnFjak9GeubBVSa/Vvs1RnNmPu+lLvTQc2Yk7MUw8g5sSM2pHr+mZOzc65rc8bN+zL2jAsyPbp/N9p1DftqtbPn6/Db0l/nWaPk4bqpRXbgmVXCY37+VlG/YnEczyDNqavsxtzNbLde/3lfdJfjx0tiKp2S1vF+tICs6Dpx2NP1WOrZ8pxHbmOq0vPutionaZmX2ZVs1lmlbX7cti3/WvY7L5ct6lep7QrUrgixyzd3RZbwXK21S+ywdZzZbZNLnVlBN1p6IrpKdl2TLZSv2zNYNiaEbY1uLF+/XqtW7tOiZirrU1bVV1dbVQgJ9uqXoajjpb2nCvnnw+Jobz0z5cr756ypsWMsm2d1OZNdd4ZlU3PG7Vj7bj8+3QbVlOP1W3xHFeszmymyNYgQKzVu6VarNVs7XyibqunsvbSUPr3DH9hLtOibvGWFm9wajj4aKvOQch1fFsDzjFqR3J8WS1m9/dQYVTL1G6ApNBsyUWoqJuWxdtlbRSZZW04cW/NDsONE1T3p7/qX9oFy6+/9Ip+9KMfpd+flDR1s/dWKBL2nC+TXUBsBYOPPzhTBbG2pRuPzXhCw4cPT7udVrfVM7gWd82yAGzdl7tFC6xs/xqPe3e5iBtmG9piM1juaoXUbD1XZtvkW1e2VwTd7d9czc1mD1KSVOhLGywqLepQe9kgr8C7p2x+YX5Gj6m8vEKHfX5ysj8ffu9lo/6EIiHPcYUjIbPj8s04yXWM2vEX+XqzdpFRO/dPmaaTPWvep+mJpx9Lu52rbr5SD9/773qzdpGKS4t01eQrM3renXhYo6rbjuvlfZ5Kuz85kVzPa2PShiStW7dOD019RPWbG1RUUqiJk39lNLBhq52mlibPtdzc0pTR40q4vuJcpns2W6oW7qYMAhg1o4S/GJZrNgvruL7AyeTl8a2hNp1Z9m+DZjpjHg/FPTPU8ZDZg7itrbUcX/GpZYb7a+fk5uuH1aOT7bxavtionVCzqwu+nJL8ena+2TZ6cXkzAeIyuwbjW5qsfN44vi0znYjZ519LU7N+6Bn8mZt2O7F43POeiMViRn1JxNyUzBijZ4xEyLuOv3JeRj9DuxXle85VQXE34/74199n8rjKysp014w7PN8z6c8Dt0/TYX9qe7b8tzum6+4Zd9rqZkbYeq601Y4t7f/trjA40t5uB93Nzc2eF6LOME0tE7ZV293mhhtuMG4nFo9p+T7zpbgjhV11i+Vp0qRJNrqYMbUb6jwP0A3razN6TM0tzVpaOVeOG5LrJJTfHDXqT25ZWEtj7dopM2sn7vr2bHbjRu00Nm3xtNPYtMWonUSrd817vDXRofMVKXXUqAZNnTrVuA0b3LhvLX88/eOydc7r1jZo5Jq2GZUbr7xZxVWFGWsn7n/YjJtdg5u+qVU4tu2Wv6V+i66beIPKe5Wm3Y4SslMt3HF9lbWNmpHrD1ANP6RDvmJYpuuWU7ceM+iPpTXUrU6LZ1Y65rTs/Je/RcgJewMVwyDXv4WZ8XZNIV/wHjIL3hO+nQEShjsDhAu9AU+k0KwuheN4l0q8ZJjOHe6WZ+XzJq6Y5z0aV8yonR0tZzJpx798yKSNlLoCMutL6n2nY5/FHWXrWc5xfOfKcTr9c64k1X3tzaxavmZBpz8uW8+VttoJQktLi/LyzIoLZqPdDrqnTp2q22+/Pci+IANCibAnbXBWv8ye4+aaVo1qt9XJ8sh8RavST/mLRqOK7muWKtie43jTGOWYRQbduudpudoN2HQ3u4nYKpYSi8XUuKHJ059IJP3EF1vtuL6K4SZZjLbOueK+7Ia42Tl3famirmGWXijsDcCW72O2xMGJhzzpmcaViH1r501nGcO+e8/M/czuPa5cK9XL/Q/jIcNZ2IRvmUPcZPsoG4UFJYUV8Z5z0yAuJ+p5bSI5ZtXCJTvFDuOxVu9gqGEla1vB8j6n/lDLaha0FUA79QyjdiIpr7PZ/azypGFausk7AGkiFAtbWVaQul98+p9bKVkbHXh/etfxm24NaKcSvy22nuUcX+aQweYL2clXd0GGy6Kyia3nSlvtYNd2++l48uTJuuqqtjd0XV2d+vTpE0inbMvNbXtAuPfeexU13Crnxitv1rA1Y9pSn6te7vTpKTdeebNnbW/VPpUZPaZfXXCl54O1W16BZsxIv3CZLTdeebMnJenD75mdc1upvW+99ZZmPTQnWQTmoisv1NChQ9Nux9a1bKuda395vd5sbleVvUeF7p9hlqLZUdf98gbvevfKCt03I/1MgAvGXOR90HRdo2t51apVevCuh5PVy6++ZZIOOuigtNu58IyLUx5aTfrzi/Ov8LQTNgzAbFVYDvlmB01nYVNTqM0exsMKeYpGmVQe9w9sGFeIt7Te3ckLe14b5ZmtTHPCIQ2pbndchhXibe1UUPq9g/TShrkKuSElnISqjhxm1M7qxSt0Srs9kl9dvFglV1ySdjvxuHcwIR43G0xYvfTl5Pprxw0pT/maPmNa2u1cOPZiX6Aio3vG+DEXeLNalEi7nZ+NmZASuJv05XxfX1zHrB1b91NbbD3LrVu3zrP0bOrku9WzZ88Aerx7Vq1apRl3PSw35sqJOJp08xU6+OCD024n244LO/fVV1/ptuvukOKObr/+Tl075eouU9httz85o9GocbCaaU672cmOHEfD5gbPTfat2kWd9jXZ7topV3uKVFw75eqMHlOxr1JlSWlxRvtj6/WZdteMZBX0utpGTb9rhlEV9KXPL9eodePbHlp/t1wnnHBC2u1srqn1VjXe1GB0XLbeE1vrmzRuTVuV3oXF92fuvDvyPJR1U65xX/yzlSbtLJz5jKd6+cInnjEqcrKj4lwm/Skp825xEylKf0ssaVt2g+fhN2S41Vck1zsIEDErvpiaQm0WEIZz87z9yU3/NY77qoWbFgVMmdVzDTNj6ht1UruCkktzzQY2Bpx3hpYtaJsRHnDemUbt+HcqME2/X7v8Tf1Lu8yqpcvmqfcx6Q9ittZ410S21pgtv4snfOncCcM13XWNnkJqLzlzjd7r3X1bVPbo2d2oHcdx1BiulytXLWqSQk7a7cTcVk+xzVa1GvWloke5aqvrJblqVpMqelQYtRPyrXcP5YSM2rFVOdrWs0rfvn31b782K8IWhAfvethTq+XBux7RvEWz024n244LO/fotMfaluat7VqF3faKQmq22Nq6IJvK89uqnGnrmGxVqrTVH1uvj60q6Laqzbe0tGpEu4fWl/PT38JFsveeaE4pFpb+2kpb53xLXYNy3cK2tXF1Znu8h8KOWmNtac+hsFmaus0dBjyzg4ZrqCdO/pVuvPJmKe4op7xUA8aZpdI64bDnQdwJm+UxJnyzgwnD2UEn4SuAZpp1WhD1zAqHCtJ/8HUVV0O4Vo5CatIWJUxS1LVtTa6nwrJhYa7U18ZsgKSk3wAddvO1Rn/bXtxp1dKe8+TIkStX8ZDZObdVaM5VPGVAy0TEWpV431IJw6rjtrZPDLlOh7M/Ik6OZ+mZafbHLXff3O6YzJ8xrrvjGj0wZboSsYRCkZBuuNPsur731vv1vQ9HJY/rvikPaMZj6WclZNuuG7Y+j2Ox1AJ66NrqdzCZ01UQdKfBVkDYFcvz2zomWx8cWfca76AKuom8wjzPA31Bodnam0jEuyexyTpsyd57Ijc3x/PBmpubfn9snXNrAxIlRYpVt31dXFJk1E6+b91ptyKzdac5kYiGrGt3XAZ75UpSZWVlsiDcfuN/plCO2cxyIhZXQbxt656GmNnsoBOJeK4dx/BaTi2AZhh1x+IavvH0tte5LP0t3hw3rIJ4SfK1qQvXGHUlx83VRe2qahuvyU2puWB2/1q/8kOtXrQ0udZ435+OUo9Bg9NuJ8fN8x6XYT0AW1uzpRYFNBuUsLXe2NbOANva6vg2S6nb+qV//YT8xdgMl0rYesbo0aOHelb1SAaVFRVm225uXLfRc1wr1m7scN+ywdRb70tm922u3aJ7b73PKLtPSq2mnknZNGnWVRX5dooynczJRgTdabB1s862vbFtyLZjyrb+OCF/cRKzh1ZH3n1u/9RnmVE7TU1bPf1patpq1I5k56GstLxMgz9rC5g/6v9K2m3YOuc29pSVpM21m1WgkuTXtbWbjdppjbd4ZitLY2bB+4SJF+ixaTOT9QAum/Rzo3bWrVunurUNUtzR3x6brQHnnKG8svK02wklUrfuMZFTVKghX7Y9lL3Wa4lRO/IVTXQNiya21jV42mmta0i7jbBCVl4bW1tQhbtFPUFl2GD2XpJWL1rqna1cNM8o6La1/l6yk/3hr57uGA5KxB3ftnWmW4b5lifEDYNuWwOZNgYBHN82oKGI4cmy5M6b7lJt9baBwob6Lbrzprv0/83697TbcR1fkTnTFJssYyu7Lz8/3/O5np+fb7Obacu6CZ0uqH02XdU+lbp2ytWZ7pI1BN0ZYCslN5tk2zFlW3+i0VzPw10012x2sKGu3rsWe3P6D/TSthkVG3st2/oAsjFjHskPe2bySvPNznlTU5NynG0ZBK1Oi5qamozaSSQSvq/NXuPNG+s8FaiXh80eXv7fs0s89QAWP/2fGjYs/cJRD019xLPe6rWFi/TdX1yUdju29mweMO50vbZwkWINjYoUdlP/cacbtRMtLdGQv7UF769+x2zPZv96Y5OAOXVm0LAAmn8LKtNt0BqadfaaycmvZ0ZuM2rH9RdAM93j3Y2rMVzf7mvDtPmQrwK14Tr+hOOqPlyrkELaqi3G299FfNvWGVfEdr71y922aeMmz/lq2mi21Cb1vKffRkl5safYZklFya7/KEAb129SUbwsOfCzcf0mo3YqfOvmu/fsbtROts3A7mibOBNlFaWeZ7nyijKjdmy9Ptk0oZNt59yW9tl0d8+4s9PXzmqPoDsDbKXkZpPzLztPD0yZlqywfN3l12S0P9n2GtuYyZWk5uYWT+rzivynjNoJW1o7WLupJqUgmwkbWSS1mzarW7t05dpNhunKvmwC00Al5J+tNEyLS/j2gzUdINm8qc7zsPDGpvTTnqXU9VaxhkajdvypvabFwvLKyo2Cfr99//VkLatrt+3TaMO16v6lJAYhT8prYzhTmfLgaxh+xXwz5jHDINdWAbSUYN1wQCueiHsHHw0Ll4Vi8hQuM98+quPXjmRvbfiWrY0KO9seeFudFjVuNXyvK6bNkW1p082hrUYz+E0NzTp3zQ3JrxcUZWZ3i+1Crp1sFFvr5rNtBraissK7C0iVWfp9ti3rzKYJnWw759g1gu4MyLaCFzbMfXS+p8Ly3F/Pz+gxZttrbOuDw/UVNHLjZg+b5T3LtLq67YOjomf66cGSvfXPNkZs3biddOWUIkSms4yWijTJda2sadva3OgJnLY2mz1A+9dbme5t7BTkeKoROwXmez/bsGbJq55tn177zyUqMQnmw96qxjJYShJ34r5CYWaF1Pz7I8t0D3PXzoy5f11uyDCoDLsRK3vOh9yQIm5OMug2nY2ztYd03LGzHMD17bVsmoodthRYhpyITll3Xoeun61bGz3HtNVwAMDW7KCtbJRsW7Zo6/W54Y7rPM88N0y5btd/tBM2lrDZen2yaYIpm2bdsXsIumFFtt3wsy3txtYHa8L1VslNuGYP4zfdPdnzgXjTlMm7/qMdiPq2R4oabI8k2VkfZytd2VoRopAv4DHrjpUiRJLk+tZQL6syC95tVS9PbG1VUbtiYfVbzda8b/78U3224Pm2GepzT1dJvwFpt9Na712L3VJvlkqrSEjLey6QK3fbuTKoCRBxcjSqfVBpup+17Oxn7d/v23Qgyj8I4Br3yM5x2Zp5t1WQLeSG7QxuFOZ560CUm83G2QosbQxkplTiN/zsszU7aGvg2hZbM7BdtSiurdcnmyaYbBVZxZ5D0A0rsu2Gb6udbBsEcHzFnhzHMJKTndHj3IIcz8xMtNBstrJmfa1GtU/PdAweNi0FuTHHzjZLpd1L9WZrW+pqWY9So3asBSoJX+qq4cJTW9XLnbisBBifLXjeM0O9bMECo22p3FjME4AtyzPbi9qJu57tkZb1Tr8dN5HwBpUJw/TycEhDqi2sWbY0EBV3fAW+jPdl6/i6ecle+n3ciXkyE2IhwzR1S4MboRbpgnbV3Z8pesCoHdc/mGB4vmI2ZvDdkLrFCztc0d/WBIGtgWtbzwa2ZmCzbfbUVn9sZRtm0+vjyrtrQjc3s9li2DWCbliRbTc0W+1k2yBAj8oenj09/3SYWfVyW/2p21Snkze2BRgromZrzFNTsdN/2Oxe2d0T5PaoNCtIE/YVMzINBiVXW8L1klw1q0k5rtlAVF5+1BOA5eWbbROXOihhPstog60AQ3HXO5hguOQiHMn1tBOOmD3AhEIR73GFDD5mXTtB5YDzztCyBW3r1Aecd6ZRO/6q2jHDgajeI0do3bI3k/uP9z7lB0bt2Fg3L8nKUgBJynGjdrYws7RlmPIiVraWvOTqi707Hlx7iVE7OeGIhq9rG2Bb3jv9wR9bFf1trsm1MXBt67PY1gxsNq1ZttkfWzPv2fT6NDU06ew1bQPMz5SaDa5hzyHohhW2bmi29qG2dWPMtkEAW+ukbPXHjdtZY546o5J+O7YK0tgKBm1VHS+vKNfgzztehE8heUbFTWNcW1uGpZxz0wDDUuAULurmXateVGDUTry12dNOvLUl7TZCsjMDGy0pVW5ZqeINWxUuzFe02Ow+GJZvIKrSLOCpeWelJ6Pl1f9ZrN7HDEm7Hf82S6Zr1SNF3TyVo43PeSjh+dxKGG5hlrJlmGNYsC4W1/CNZyRfn//uaVaJ/8ADD1TvfXslZ2EHDhxo1E5+tJvnes6Ppp8GayvV/cdjT9Vjq9sGEi4962KjdmwFy9n2jJFtRWjpz85l0wAAdg9BN7KKrX2obd0Ybd3Usm20NpLvnQmJdjNLEQ5FvAGP6Z7Wefl5VmZzbcw82DomW1um2EobdBIhT0XjWf3MZuNsbRkWKsrTMrctwAgVm+2/2v/c0z2zuf3PG2PUzj6n/lDLatq1c6rZWnVX/vXG6V+HtqqX/2P+c/rh30a3Bbnzf6uDr0h/ttLWQFSrLzBorTXbYcDNCXnSuRO5Zv1JNLUq4ua3VS9v2mrWH1/6v+lygBzl+mbMbzNqJ16/1fM61240q5dw7633ezKr7pvygGY8Ni3tdkrKiz3Xc2l5+tt9+VPUTbMtZj0027N14swHZ2v48OFpt2MryM22ZwzJzueorbT5bCuKm039yaYBAOyevSLobj8zc+OVN+vaKVd3ib3suqKtDd6HhbfqzSqUSnY+OGzd1LLt5mgrLXzCxAu86YeTfm7UTlFJkRrrt80ItjotKi4pTLuNqbfep8bVLYo7CW2u3aJ7b71PDz4+I+12LrryQu8xXWV2TLa2THn8wZmetMHHZszUI7MeTLudRMJXhC9hVojI1pZhkUieflg9ti0gLDebjSvpN8BoDbdfSvXyJYbVy+UrzmUwK+xEczxZCU6+2aCYrSDX1ppuW/UJ8kvKdMKqH7Wdq4OXGLWTaG31pvFHzbJRwq5jJfXZVtaGrSroG9Zt9Fw/y9duMGrHxudfyL+VY+Uco74kYt7lKImY2bVsK8i1NaiabVtr3X3TVB3957b36N033Wv0uYWdy6YBAOyevSLo9s/MsJdd9sq2gmy2bmrZdnN0fetg3bjZg8f/e3aJZ9Zg8dP/qWHDhqXdjo0Mh43VGzVyY7t07lyzB2hbx2RrKcCm6hrPuVpWvcmonYS8RaxMZ0/zCqKeB3rTgDDR2Ox9+G1sMmrHlrhvwM98//G4L0U4/dfZaXG9+ywbZiW4ri9Ydg1T+OWfvTdcQ90t7NkmTt3M1s33GH60lm6YK8cNyXUS2nf4KKN2HN/yGMdsHMpa6rOtrA3/UIZp9QZbu2Vs60PHBsHDjrdWQtgxe3x1fNupOYbbqdkKcm2txV6/fr3WrV2nRMzV1qatqq6uVmVlZdrt1G6q8e7isKkh7TYke59b2SbbdsaxoSseU7baK4Ju/8xMpqsxYueyrSBbVxVT3JeqZzjrael1tpHhYCud29Yx2Rposbb22f9saRg3+QdIXu1hNkMd8619jrU2m3XIkrBv+xXT/cfDbtiz/tlkX3Vb29YlFLcy0CL5CrsZrumOb2lWcbysbZu4LbVG7XzxHy8p4oYlbSvC+MV/vKQegwan3Y6tNH5bBdDsrcG3U3TM1m4ZNgbBu/syh3pUmRXK7FaSr6WxtqUJRcXpZ1Vtl037Rz8wZZoneH9gynTNWzQ77XaampvlONsGw1qdFjU1m92Xbd3Dsi0gtDWhk02ybbefrmyvCLqLSgopNtBJdMUKk9ko7ISs7NmcTevRbKVz29r7cuXKlZ60wWtvv1qDBg1Ku52Yf39aw1TRih7lqq1uq6Ze0cPs9dm6pck7Q73V7KEsnoh5A8K4YdEoS2yt6baxr3rC8Ra9c40HSEKKuDltaeqmIy2ub6bbcOo0pT6iad2FuOMpUmgaVPqzP4wHN/wF0AwHMT9d+Lw3bd6wXkLKbhCGM++l3UvV8k3bsh/TbQ83b6rz9OeNTc+n3YatzKGGmi0aVd2WybQsZPbZZytQsfV5E48lPK9xPGZ2DboJ+Z4NzDLG/EUB44ZpJLbqCtjSFSd0sm23n65srwi6J07+lW688mYp7qhqn0pdO+XqTHcJAcu2NdTZxlY6ZDatebf1UNYab/U8iOfGzV4bWzMP/srRpgHGLXff3K66eweKC/oGMU1nhCNuxJtC3fc2o3ZssbWmO3X9s0Eqra/onekWVJKl/awttROyNAMbshRUhlz/oIRhO5aqu8frG71LHOq3GLUTKSnwXIMVPdPfXUDafr7+NdnOO73M1s43NW/19KepJf2lJNaWaCV8280lzAaibKVh2/q8sVUvQXI9x2UqJxzVyHb30xW9Fxi1s9FXV2DF2o3GfbIhmyZ0bM0sZ9tuP125DtdeEXRXVlaquGpbCtHdM+5UNBrNcI8QtGxbQ51tnJBvXZthwZ5sWvNuqy91G+p04Zq2CsJP5txh1I6tgj22Agxbr0/7Qcyc8lINGGc2I2yrOJctttZ0Sx2vXu7fEstkCz0pteq4aXqwrXoAtgb7/GnhHVlyYWUwwfceDRkubYnHWjzHFY+lv92cJA04d4yWPz4nOdFw05TJRu3Ubqj1Bjwbao3aSSQSnvdEwrCGiA22luu0tLRqRLtr5+X8hUbt2Pq8ycuPWtkBJBQKed4TK/YxK7KaH833XDt5UbPdKVLvhZn9nMimCR1bM8vZtttPV67DtVcE3QC8otFczwd0NNesGFZXZOtD3tbWY+U9y7S6uuOzVra0H8Tcb/zPFMoxLKRmKf3QFltrukPhsIasa3toXWYwwxMpLdKbrW1Bbk5pkVFfbFXDNk1v90tZQ2343gqXFHi2mwuXmu2vbWtQwlYg50Qi3jXUEbNHtLzSMisTDbbuhY7jeAK55b3MUpZtCIVDngEk0/tyNDfPc+1EczP7GpdXlGvw520B2Ef9XzFqJ88fLOeaBe82tomTpIqeFXqzpe18de9ptpbflmya0Mm2+jPWajJ14TpcBN3AXqi0vEyDP+v4B3RXZOtD/trbr9YDU6YrEUsoFAnp+jvNtoK56e7Jng8y01mrbBMtKVXexkjydW6qyGzQPWDc6Xpt4SLFGhoVKeym/uNON2rHRgXqgeeN1WcLFynW0CIVRnRABvsiSTlF3bSlrq0eQKTYLMj1B8uRErN2Djz/nH++Ph07VzllxVq9ri0wyCkzCwzcsONdG25YETunqFBDvmwLTl8zTOe2xda9MJsCp+vvuDZ5X45FWjT5zuuN2sm2oNJWwGPruGz1Z/Id17dbFlXAUsF2sinVXbJYk6kL1+FyXMOSi3V1dSopKdHmzZtVbFhhc09pbm7WpEmTJEkzZswgvRx7vbVr16as7zXZXqQr4rX5du3vp/uNv9h4prupZlNK4JRXVmazq7CkcX21PvlnunJOeakGnHOG0bnKtnNuqz+bP/9Uny143jO4UbJfv4z1J9Haqi/mPSGpY888tu6FXfGe2lVfm2zrD3auq56rL7/8MqUOV7Yf1+7GxATdAIDdZivoRufRPojjnGc/W0E3AOxpnTFm292Y2GwxCwAAAAAA2CWCbgAAAAAAAkLQDQAAAABAQAi6AQAAAAAICEE3AAAAAAABIegGAAAAACAgBN0AAAAAAASEoBsAAAAAgIAQdAMAAAAAEBCCbgAAAAAAAkLQDQAAAABAQAi6AQAAAAAICEE3AAAAAAABIegGAAAAACAgBN0AAAAAAASEoBsAAAAAgIAQdAMAAAAAEBCCbgAAAAAAAhLJdAf2hNzcXM2YMSP5/wAAAACA7NGVY7a9Iuh2HEfRaDTT3QAAAAAA7EBXjtlILwcAAAAAICAE3QAAAAAABISgGwAAAACAgBB0AwAAAAAQEIJuAAAAAAACQtANAAAAAEBACLoBAAAAAAgIQTcAAAAAAAEh6AYAAAAAICAE3QAAAAAABISgGwAAAACAgBB0AwAAAAAQEIJuAAAAAAACQtANAAAAAEBACLoBAAAAAAgIQTcAAAAAAAEh6AYAAAAAICAE3QAAAAAABISgGwAAAACAgBB0AwAAAAAQEIJuAAAAAAACQtANAAAAAEBACLoBAAAAAAgIQTcAAAAAAAEh6AYAAAAAICAE3QAAAAAABISgGwAAAACAgBB0AwAAAAAQEIJuAAAAAAACQtANAAAAAEBACLoBAAAAAAgIQTcAAAAAAAEh6AYAAAAAICAE3QAAAAAABISgGwAAAACAgEQy3QEAQOfkxlqVyHQnELhEa+sO/x/ZyY1xjgAg2xB0AwCMfLlwTqa7gD3sq6fnZLoLAAB0OqSXAwAAAAAQEGa6AQC7LTc3VzNmzMh0N7AHua6rlpYWSdvOv+M4Ge4Rdldubm6muwAAEEE3ACANjuMoGo1muhvYw/Ly8jLdBQAAOi3SywEAAAAACAhBNwAAAAAAASHoBgAAAAAgIATdAAAAAAAEhKAbAAAAAICAEHQDAAAAABAQgm4AAAAAAAJC0A0AAAAAQEAIugEAAAAACAhBNwAAAAAAASHoBgAAAAAgIATdAAAAAAAEhKAbAAAAAICAEHQDAAAAABAQgm4AAAAAAAJC0A0AAAAAQEAIugEAAAAACEjE9A9d15Uk1dXVWesMAAAAAACdwfZYeHtsvDPGQXd9fb0kqU+fPqZNAAAAAADQqdXX16ukpGSnP3fcXYXlO5FIJPT111+rqKhIjuMYd3BvVVdXpz59+uirr75ScXFxprsDGONaRlfBtYyugmsZXQXXMrKd67qqr69X7969FQrtfOW28Ux3KBTSvvvua/rn+Kfi4mJuIugSuJbRVXAto6vgWkZXwbWMbPZtM9zbUUgNAAAAAICAEHQDAAAAABAQgu4MiUajmjJliqLRaKa7AnQI1zK6Cq5ldBVcy+gquJbRVRgXUgMAAAAAAN+OmW4AAAAAAAJC0A0AAAAAQEAIugEAAAAACAhBd4AeffRRHXroocm9BYcMGaKlS5cmf+66rm677Tb17t1b+fn5Ov7447Vq1aoM9hjYtalTp8pxHF155ZXJ73Eto7O47bbb5DiO57+qqqrkz7mW0ZmsWbNG5557rioqKtStWzcddthhev/995M/53pGZ9CvX7+U+7LjOPrFL34hiesYXQNBd4D23Xdf3XvvvXrvvff03nvv6YQTTtDo0aOTN4r7779f06dP17//+7/r3XffVVVVlU466STV19dnuOfAjr377rt6/PHHdeihh3q+z7WMzuTggw/WN998k/xv5cqVyZ9xLaOzqKmp0dChQ5WTk6OlS5fqL3/5i6ZNm6bS0tLk73A9ozN49913Pffkl19+WZI0ZswYSVzH6CJc7FFlZWXuzJkz3UQi4VZVVbn33ntv8mdNTU1uSUmJ+5vf/CaDPQR2rL6+3h04cKD78ssvuyNGjHAnTpzouq7LtYxOZcqUKe7gwYN3+DOuZXQm119/vfv9739/pz/nekZnNXHiRHf//fd3E4kE1zG6DGa695B4PK5nnnlGW7Zs0ZAhQ/TZZ59p7dq1Ovnkk5O/E41GNWLECL399tsZ7CmwY7/4xS906qmn6sQTT/R8n2sZnc0nn3yi3r17q3///jrrrLP06aefSuJaRueyePFiHXnkkRozZox69uypww8/XE888UTy51zP6IxaWlq0YMECXXjhhXIch+sYXQZBd8BWrlypwsJCRaNRXXrppXrhhRd00EEHae3atZKkyspKz+9XVlYmfwZki2eeeUZ/+tOfNHXq1JSfcS2jMznmmGM0b948LV++XE888YTWrl2r4447Ths3buRaRqfy6aef6tFHH9XAgQO1fPlyXXrppbriiis0b948Sdyb0Tm9+OKLqq2t1c9+9jNJXMfoOiKZ7kBXd+CBB+qDDz5QbW2tnn/+eZ1//vl6/fXXkz93HMfz+67rpnwPyKSvvvpKEydO1IoVK5SXl7fT3+NaRmcwatSo5P8PGjRIQ4YM0f7776+5c+fq2GOPlcS1jM4hkUjoyCOP1D333CNJOvzww7Vq1So9+uijGj9+fPL3uJ7RmcyaNUujRo1S7969Pd/nOkZnx0x3wHJzc3XAAQfoyCOP1NSpUzV48GA99NBDyWq5/lG66urqlNE8IJPef/99VVdX63vf+54ikYgikYhef/11Pfzww4pEIsnrlWsZnVFBQYEGDRqkTz75hPsyOpVevXrpoIMO8nzvu9/9rr788ktJ4npGp/PFF1/olVde0UUXXZT8HtcxugqC7j3MdV01Nzerf//+qqqqSlZolLatY3n99dd13HHHZbCHgNcPf/hDrVy5Uh988EHyvyOPPFLnnHOOPvjgAw0YMIBrGZ1Wc3Oz/vrXv6pXr17cl9GpDB06VB9//LHne//3f/+n/fbbT5K4ntHpzJ49Wz179tSpp56a/B7XMboK0ssDdOONN2rUqFHq06eP6uvr9cwzz+gPf/iDli1bltzn+J577tHAgQM1cOBA3XPPPerWrZvGjRuX6a4DSUVFRTrkkEM83ysoKFBFRUXy+1zL6CyuueYa/fjHP1bfvn1VXV2tu+66S3V1dTr//PO5L6NTmTRpko477jjdc889OvPMM/XOO+/o8ccf1+OPPy5JXM/oVBKJhGbPnq3zzz9fkUhbeMJ1jK6CoDtA69at03nnnadvvvlGJSUlOvTQQ7Vs2TKddNJJkqTrrrtOW7du1eWXX66amhodc8wxWrFihYqKijLccyA9XMvoLFavXq2zzz5bGzZsUI8ePXTsscfqv//7v5Ozg1zL6CyOOuoovfDCC5o8ebLuuOMO9e/fXw8++KDOOeec5O9wPaOzeOWVV/Tll1/qwgsvTPkZ1zG6Asd1XTfTnQAAAAAAoCtiTTcAAAAAAAEh6AYAAAAAICAE3QAAAAAABISgGwAAAACAgBB0AwAAAAAQEIJuAAAAAAACQtANAAAAAEBACLoBAAAAAAgIQTcAAAAAAAEh6AYAIMu8/fbbCofDOuWUUzLdFQAA0EGO67pupjsBAADaXHTRRSosLNTMmTP1l7/8RX379s10lwAAgCFmugEAyCJbtmzRc889p8suu0w/+tGPNGfOHM/PFy9erIEDByo/P18/+MEPNHfuXDmOo9ra2uTvvP322xo+fLjy8/PVp08fXXHFFdqyZcuePRAAACCJoBsAgKzy7LPP6sADD9SBBx6oc889V7Nnz9b2pLTPP/9cZ5xxhk477TR98MEHuuSSS3TTTTd5/n7lypUaOXKkfvrTn+qjjz7Ss88+q7feeku//OUvM3E4AADs9UgvBwAgiwwdOlRnnnmmJk6cqFgspl69eunpp5/WiSeeqBtuuEFLlizRypUrk79/88036+6771ZNTY1KS0s1fvx45efn67HHHkv+zltvvaURI0Zoy5YtysvLy8RhAQCw12KmGwCALPHxxx/rnXfe0VlnnSVJikQiGjt2rJ588snkz4866ijP3xx99NGer99//33NmTNHhYWFyf9GjhypRCKhzz77bM8cCAAASIpkugMAAGCbWbNmKRaLaZ999kl+z3Vd5eTkqKamRq7rynEcz9/4E9YSiYQuueQSXXHFFSntU5ANAIA9j6AbAIAsEIvFNG/ePE2bNk0nn3yy52enn366nnrqKX3nO9/RSy+95PnZe++95/n6iCOO0KpVq3TAAQcE3mcAALBrrOkGACALvPjiixo7dqyqq6tVUlLi+dlNN92kl156SYsWLdKBBx6oSZMmacKECfrggw909dVXa/Xq1aqtrVVJSYk++ugjHXvssbrgggt08cUXq6CgQH/961/18ssv65FHHsnQ0QEAsPdiTTcAAFlg1qxZOvHEE1MCbmnbTPcHH3ygmpoa/e53v9OiRYt06KGH6tFHH01WL49Go5KkQw89VK+//ro++eQTDRs2TIcffrhuueUW9erVa48eDwAA2IaZbgAAOrG7775bv/nNb/TVV19luisAAGAHWNMNAEAn8utf/1pHHXWUKioq9F//9V964IEH2IMbAIAsRtANAEAn8sknn+iuu+7Spk2b1LdvX1199dWaPHlyprsFAAB2gvRyAAAAAAACQiE1AAAAAAACQtANAAAAAEBACLoBAAAAAAgIQTcAAAAAAAEh6AYAAAAAICAE3QAAAAAABISgGwAAAACAgBB0AwAAAAAQEIJuAAAAAAAC8v8DxFmVJkD8K68AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 3))\n",
    "sns.boxplot(data=df, x='Age', color='#7ed6df')\n",
    "sns.stripplot(data=df, x='Age', linewidth = 0.6, size=3, color='#be2edd')\n",
    "plt.title('Age Distribution',fontsize= 14)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f848f7c4",
   "metadata": {
    "papermill": {
     "duration": 0.016596,
     "end_time": "2023-06-07T13:26:35.635069",
     "exception": false,
     "start_time": "2023-06-07T13:26:35.618473",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"border-radius:10px; border:#DEB887 solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#DEB887'>🔍 Conclusions:</font></h3>\n",
    "     \n",
    "- No missing values\n",
    "- Data is almost normal \n",
    "- No outliers "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e4e1239",
   "metadata": {
    "papermill": {
     "duration": 0.016762,
     "end_time": "2023-06-07T13:26:35.668686",
     "exception": false,
     "start_time": "2023-06-07T13:26:35.651924",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\">Analysis on RestingBP</h2>\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e6f501bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:35.705194Z",
     "iopub.status.busy": "2023-06-07T13:26:35.704803Z",
     "iopub.status.idle": "2023-06-07T13:26:35.716264Z",
     "shell.execute_reply": "2023-06-07T13:26:35.715307Z"
    },
    "papermill": {
     "duration": 0.032257,
     "end_time": "2023-06-07T13:26:35.718369",
     "exception": false,
     "start_time": "2023-06-07T13:26:35.686112",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    918.000000\n",
       "mean     132.396514\n",
       "std       18.514154\n",
       "min        0.000000\n",
       "25%      120.000000\n",
       "50%      130.000000\n",
       "75%      140.000000\n",
       "max      200.000000\n",
       "Name: RestingBP, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['RestingBP'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a604bda1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:35.755474Z",
     "iopub.status.busy": "2023-06-07T13:26:35.754254Z",
     "iopub.status.idle": "2023-06-07T13:26:36.457670Z",
     "shell.execute_reply": "2023-06-07T13:26:36.456395Z"
    },
    "papermill": {
     "duration": 0.725492,
     "end_time": "2023-06-07T13:26:36.461153",
     "exception": false,
     "start_time": "2023-06-07T13:26:35.735661",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(12,5))\n",
    "\n",
    "ax[0] = sns.histplot(data=df, x='RestingBP', linewidth=1, edgecolor='black', ax=ax[0], color='#D980FA')\n",
    "ax[0].set(title='Resting Blood Pressure Distribution - Histogram')\n",
    "\n",
    "ax[1] = sns.kdeplot(data=df, x='RestingBP', fill=True, edgecolor='black', ax=ax[1], color='#9980FA')\n",
    "ax[1] = sns.rugplot(data=df, x='RestingBP', ax=ax[1], color='#574b90')\n",
    "ax[1].set(title='Resting Blood Pressure Distribution - KDE Plot')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "24733ef2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:36.500279Z",
     "iopub.status.busy": "2023-06-07T13:26:36.499268Z",
     "iopub.status.idle": "2023-06-07T13:26:36.505402Z",
     "shell.execute_reply": "2023-06-07T13:26:36.504733Z"
    },
    "papermill": {
     "duration": 0.027891,
     "end_time": "2023-06-07T13:26:36.507354",
     "exception": false,
     "start_time": "2023-06-07T13:26:36.479463",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1798393100516288"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['RestingBP'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b48e586f",
   "metadata": {
    "papermill": {
     "duration": 0.018108,
     "end_time": "2023-06-07T13:26:36.543946",
     "exception": false,
     "start_time": "2023-06-07T13:26:36.525838",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h3 style=\"font-size: 20px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c3aed6; color: #3d405b; text-align: center; border-radius: 150px 150px;\">Checking For Outliers</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e358a7c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:36.583905Z",
     "iopub.status.busy": "2023-06-07T13:26:36.582631Z",
     "iopub.status.idle": "2023-06-07T13:26:36.878682Z",
     "shell.execute_reply": "2023-06-07T13:26:36.877383Z"
    },
    "papermill": {
     "duration": 0.31884,
     "end_time": "2023-06-07T13:26:36.881171",
     "exception": false,
     "start_time": "2023-06-07T13:26:36.562331",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "sns.boxplot(data=df, x='RestingBP', color='#ED4C67')\n",
    "sns.stripplot(data=df, x='RestingBP', linewidth = 0.6, size=3, color='#833471')\n",
    "plt.title('Resting Blood Pressure Distribution',fontsize= 14)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e46fa29a",
   "metadata": {
    "papermill": {
     "duration": 0.019492,
     "end_time": "2023-06-07T13:26:36.920020",
     "exception": false,
     "start_time": "2023-06-07T13:26:36.900528",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"border-radius:10px; border:#DEB887 solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#DEB887'>🔍 Conclusions:</font></h3>\n",
    "    \n",
    "- No missing values\n",
    "- Almost normal distribution\n",
    "- There are some outliers \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5405d6e4",
   "metadata": {
    "papermill": {
     "duration": 0.018488,
     "end_time": "2023-06-07T13:26:36.957769",
     "exception": false,
     "start_time": "2023-06-07T13:26:36.939281",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\">Analysis on Cholesterol</h2>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "313a0034",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:36.996641Z",
     "iopub.status.busy": "2023-06-07T13:26:36.996234Z",
     "iopub.status.idle": "2023-06-07T13:26:37.007479Z",
     "shell.execute_reply": "2023-06-07T13:26:37.005943Z"
    },
    "papermill": {
     "duration": 0.033812,
     "end_time": "2023-06-07T13:26:37.010159",
     "exception": false,
     "start_time": "2023-06-07T13:26:36.976347",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    918.000000\n",
       "mean     198.799564\n",
       "std      109.384145\n",
       "min        0.000000\n",
       "25%      173.250000\n",
       "50%      223.000000\n",
       "75%      267.000000\n",
       "max      603.000000\n",
       "Name: Cholesterol, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cholesterol'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d133419e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:37.052554Z",
     "iopub.status.busy": "2023-06-07T13:26:37.051802Z",
     "iopub.status.idle": "2023-06-07T13:26:37.728884Z",
     "shell.execute_reply": "2023-06-07T13:26:37.727917Z"
    },
    "papermill": {
     "duration": 0.7012,
     "end_time": "2023-06-07T13:26:37.731011",
     "exception": false,
     "start_time": "2023-06-07T13:26:37.029811",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(12,5))\n",
    "\n",
    "ax[0] = sns.histplot(data=df, x='Cholesterol', linewidth=1, edgecolor='black', ax=ax[0], color='#EA2027')\n",
    "ax[0].set(title='Cholesterol Distribution - Histogram')\n",
    "\n",
    "ax[1] = sns.kdeplot(data=df, x='Cholesterol', fill=True, edgecolor='black', ax=ax[1], color='#F79F1F')\n",
    "ax[1] = sns.rugplot(data=df, x='Cholesterol', ax=ax[1], color='#FFC312')\n",
    "ax[1].set(title='Cholesterol Distribution - KDE Plot')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "546fdcf8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:37.773547Z",
     "iopub.status.busy": "2023-06-07T13:26:37.772769Z",
     "iopub.status.idle": "2023-06-07T13:26:37.779919Z",
     "shell.execute_reply": "2023-06-07T13:26:37.778812Z"
    },
    "papermill": {
     "duration": 0.031507,
     "end_time": "2023-06-07T13:26:37.782438",
     "exception": false,
     "start_time": "2023-06-07T13:26:37.750931",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6100864307268192"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cholesterol'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77e161bb",
   "metadata": {
    "papermill": {
     "duration": 0.019802,
     "end_time": "2023-06-07T13:26:37.822777",
     "exception": false,
     "start_time": "2023-06-07T13:26:37.802975",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h3 style=\"font-size: 20px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c3aed6; color: #3d405b; text-align: center; border-radius: 150px 150px;\">Checking For Outliers</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "df613891",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:37.864663Z",
     "iopub.status.busy": "2023-06-07T13:26:37.864220Z",
     "iopub.status.idle": "2023-06-07T13:26:38.152684Z",
     "shell.execute_reply": "2023-06-07T13:26:38.151871Z"
    },
    "papermill": {
     "duration": 0.312101,
     "end_time": "2023-06-07T13:26:38.154796",
     "exception": false,
     "start_time": "2023-06-07T13:26:37.842695",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "sns.boxplot(data=df, x='Cholesterol', color='#C4E538')\n",
    "sns.stripplot(data=df, x='Cholesterol', linewidth = 0.6, size=3, color='#009432')\n",
    "plt.title('Cholestrol Distribution',fontsize= 14)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac84fe71",
   "metadata": {
    "papermill": {
     "duration": 0.020148,
     "end_time": "2023-06-07T13:26:38.195736",
     "exception": false,
     "start_time": "2023-06-07T13:26:38.175588",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h3 style=\"font-size: 20px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c3aed6; color: #3d405b; text-align: center; border-radius: 150px 150px;\">Exploring the outliers</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0aaa0323",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:38.238740Z",
     "iopub.status.busy": "2023-06-07T13:26:38.238033Z",
     "iopub.status.idle": "2023-06-07T13:26:38.258797Z",
     "shell.execute_reply": "2023-06-07T13:26:38.258072Z"
    },
    "papermill": {
     "duration": 0.044637,
     "end_time": "2023-06-07T13:26:38.260745",
     "exception": false,
     "start_time": "2023-06-07T13:26:38.216108",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "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>293</th>\n",
       "      <td>65</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>93</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>TA</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>127</td>\n",
       "      <td>N</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>61</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>110</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>50</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>139</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>57</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ST</td>\n",
       "      <td>131</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>67</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>150</td>\n",
       "      <td>N</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>43</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>120</td>\n",
       "      <td>N</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>48</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ST</td>\n",
       "      <td>110</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>56</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>122</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>62</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>133</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ST</td>\n",
       "      <td>119</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "293   65   M           ASY        115            0          0     Normal   \n",
       "294   32   M            TA         95            0          1     Normal   \n",
       "295   61   M           ASY        105            0          1     Normal   \n",
       "296   50   M           ASY        145            0          1     Normal   \n",
       "297   57   M           ASY        110            0          1         ST   \n",
       "..   ...  ..           ...        ...          ...        ...        ...   \n",
       "508   67   M           ASY        120            0          1     Normal   \n",
       "514   43   M           ASY        122            0          0     Normal   \n",
       "518   48   M           NAP        102            0          1         ST   \n",
       "535   56   M           ASY        130            0          0        LVH   \n",
       "536   62   M           NAP        133            0          1         ST   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "293     93              Y      0.0     Flat             1  \n",
       "294    127              N      0.7       Up             1  \n",
       "295    110              Y      1.5       Up             1  \n",
       "296    139              Y      0.7     Flat             1  \n",
       "297    131              Y      1.4       Up             1  \n",
       "..     ...            ...      ...      ...           ...  \n",
       "508    150              N      1.5     Down             1  \n",
       "514    120              N      0.5       Up             1  \n",
       "518    110              Y      1.0     Down             1  \n",
       "535    122              Y      1.0     Flat             1  \n",
       "536    119              Y      1.2     Flat             1  \n",
       "\n",
       "[152 rows x 12 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Cholesterol'] == 0) & df['HeartDisease'] == 1]"
   ]
  },
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    {
     "data": {
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       "  <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",
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       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>ATA</td>\n",
       "      <td>113</td>\n",
       "      <td>468</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>127</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
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       "      <td>NAP</td>\n",
       "      <td>145</td>\n",
       "      <td>518</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>130</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Flat</td>\n",
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       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>118</td>\n",
       "      <td>529</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>130</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>120</td>\n",
       "      <td>466</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>152</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
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       "      <th>149</th>\n",
       "      <td>54</td>\n",
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       "      <td>ASY</td>\n",
       "      <td>130</td>\n",
       "      <td>603</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>125</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
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       "      <td>44</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>135</td>\n",
       "      <td>491</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>135</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>58</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>132</td>\n",
       "      <td>458</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>69</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>115</td>\n",
       "      <td>564</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>160</td>\n",
       "      <td>N</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Flat</td>\n",
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      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "28    53   F           ATA        113          468          0     Normal   \n",
       "30    53   M           NAP        145          518          0     Normal   \n",
       "76    32   M           ASY        118          529          0     Normal   \n",
       "103   40   M           ASY        120          466          1     Normal   \n",
       "149   54   M           ASY        130          603          1     Normal   \n",
       "250   44   M           ASY        135          491          0     Normal   \n",
       "496   58   M           ASY        132          458          1     Normal   \n",
       "616   67   F           NAP        115          564          0        LVH   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "28     127              N      0.0       Up             0  \n",
       "30     130              N      0.0     Flat             1  \n",
       "76     130              N      0.0     Flat             1  \n",
       "103    152              Y      1.0     Flat             1  \n",
       "149    125              Y      1.0     Flat             1  \n",
       "250    135              N      0.0     Flat             1  \n",
       "496     69              N      1.0     Down             0  \n",
       "616    160              N      1.6     Flat             0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Cholesterol'] > 450)]"
   ]
  },
  {
   "cell_type": "markdown",
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    "<div style=\"border-radius:10px; border:#DEB887 solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#DEB887'>🔍 Conclusions:</font></h3>\n",
    "    \n",
    "- No missing values\n",
    "- This follows a Bimodal distribution \n",
    "- According to the articles I read online, Serum cholesterol levels of zero are highly unlikely in a physiological context. And often high cholesterol is related to risk of heart disease. But in our data, we can see that even people having 0 cholesterol (with other normal parameters) are prone to heart disease. So this seems like an error in value entering to me\n",
    "- On the other side of the spectrum, values above 400, are high but they are not outliers cause the range goes as high as 800. And these people also have a risk of heart disease with high cholesterol. So, this seems kind of legit"
   ]
  },
  {
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   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\">Analysis on Max Heart Rate</h2>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9e326070",
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "count    918.000000\n",
       "mean     136.809368\n",
       "std       25.460334\n",
       "min       60.000000\n",
       "25%      120.000000\n",
       "50%      138.000000\n",
       "75%      156.000000\n",
       "max      202.000000\n",
       "Name: MaxHR, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MaxHR'].describe()"
   ]
  },
  {
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   "execution_count": 24,
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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(12,5))\n",
    "\n",
    "ax[0] = sns.histplot(data=df, x='MaxHR', linewidth=1, edgecolor='black', ax=ax[0], color='#00a8ff')\n",
    "ax[0].set(title='max Heart Rate Distribution - Histogram')\n",
    "\n",
    "ax[1] = sns.kdeplot(data=df, x='MaxHR', fill=True, edgecolor='black', ax=ax[1], color='#9c88ff')\n",
    "# ax[1] = sns.rugplot(data=df, x='MaxHR', ax=ax[1], color='#487eb0')\n",
    "ax[1].set(title='max Heart Rate Distribution - KDE Plot')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "12ec6b6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:39.168622Z",
     "iopub.status.busy": "2023-06-07T13:26:39.168137Z",
     "iopub.status.idle": "2023-06-07T13:26:39.175443Z",
     "shell.execute_reply": "2023-06-07T13:26:39.174402Z"
    },
    "papermill": {
     "duration": 0.035152,
     "end_time": "2023-06-07T13:26:39.178143",
     "exception": false,
     "start_time": "2023-06-07T13:26:39.142991",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.14435941846180994"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MaxHR'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebdd9b58",
   "metadata": {
    "papermill": {
     "duration": 0.022587,
     "end_time": "2023-06-07T13:26:39.224170",
     "exception": false,
     "start_time": "2023-06-07T13:26:39.201583",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h3 style=\"font-size: 20px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c3aed6; color: #3d405b; text-align: center; border-radius: 150px 150px;\">Checking For Outliers</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0fa9fad8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:39.271038Z",
     "iopub.status.busy": "2023-06-07T13:26:39.270609Z",
     "iopub.status.idle": "2023-06-07T13:26:39.554743Z",
     "shell.execute_reply": "2023-06-07T13:26:39.553480Z"
    },
    "papermill": {
     "duration": 0.311195,
     "end_time": "2023-06-07T13:26:39.557478",
     "exception": false,
     "start_time": "2023-06-07T13:26:39.246283",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "sns.boxplot(data=df, x='MaxHR', color='#52b69a')\n",
    "sns.stripplot(data=df, x='MaxHR', linewidth = 0.6, size=3, color='#d9ed92')\n",
    "plt.title('Max Heart Rate Distribution',fontsize= 14)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3aaabbc",
   "metadata": {
    "papermill": {
     "duration": 0.022187,
     "end_time": "2023-06-07T13:26:39.602838",
     "exception": false,
     "start_time": "2023-06-07T13:26:39.580651",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h3 style=\"font-size: 20px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c3aed6; color: #3d405b; text-align: center; border-radius: 150px 150px;\">Exploring the outliers</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e4f109e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:39.649871Z",
     "iopub.status.busy": "2023-06-07T13:26:39.649451Z",
     "iopub.status.idle": "2023-06-07T13:26:39.669999Z",
     "shell.execute_reply": "2023-06-07T13:26:39.669107Z"
    },
    "papermill": {
     "duration": 0.046867,
     "end_time": "2023-06-07T13:26:39.672030",
     "exception": false,
     "start_time": "2023-06-07T13:26:39.625163",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "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>370</th>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>63</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>51</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>65</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ST</td>\n",
       "      <td>67</td>\n",
       "      <td>N</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>58</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>132</td>\n",
       "      <td>458</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>69</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Down</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "370   60   M           ASY        135            0          0     Normal   \n",
       "390   51   M           ASY        140            0          0     Normal   \n",
       "402   65   M           ASY        145            0          1         ST   \n",
       "496   58   M           ASY        132          458          1     Normal   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "370     63              Y      0.5       Up             1  \n",
       "390     60              N      0.0     Flat             1  \n",
       "402     67              N      0.7     Flat             1  \n",
       "496     69              N      1.0     Down             0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"MaxHR < 70\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02fc74d8",
   "metadata": {
    "papermill": {
     "duration": 0.022867,
     "end_time": "2023-06-07T13:26:39.718097",
     "exception": false,
     "start_time": "2023-06-07T13:26:39.695230",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"border-radius:10px; border:#DEB887 solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#DEB887'>🔍 Conclusions:</font></h3>\n",
    "    \n",
    "- No null values\n",
    "- Almost normally distributed\n",
    "- There are handful of outliers \n",
    "- Most people have a max heart rate of 150, which seems okay as most of the people in this dataset are aged between 50 - 65"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f36463c",
   "metadata": {
    "papermill": {
     "duration": 0.022218,
     "end_time": "2023-06-07T13:26:39.762938",
     "exception": false,
     "start_time": "2023-06-07T13:26:39.740720",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span><h1 style=\"font-size: 36px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #e9f7ef; color: #8f52a4; text-align: center; border-radius: 150px 150px;\">Univariate Analysis on Categorical Variables</h1></span><a class='anchor' id='EDA_Cat'></a>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c07769b4",
   "metadata": {
    "papermill": {
     "duration": 0.024547,
     "end_time": "2023-06-07T13:26:39.810282",
     "exception": false,
     "start_time": "2023-06-07T13:26:39.785735",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\">Analysis on Heart Disease</h2>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eb4e66bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:39.858275Z",
     "iopub.status.busy": "2023-06-07T13:26:39.857273Z",
     "iopub.status.idle": "2023-06-07T13:26:40.317553Z",
     "shell.execute_reply": "2023-06-07T13:26:40.316387Z"
    },
    "papermill": {
     "duration": 0.487222,
     "end_time": "2023-06-07T13:26:40.320114",
     "exception": false,
     "start_time": "2023-06-07T13:26:39.832892",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4.5))\n",
    "ax = sns.barplot(data=df['HeartDisease'].value_counts().reset_index(), x='index', y='HeartDisease', \n",
    "           linewidth=1, edgecolor='black', palette='Set3', ax=ax)\n",
    "ax.set(title='Count of Heart Disease vs No Heart Disease', ylabel='Count', xlabel='Heart Disease')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42941ebf",
   "metadata": {
    "papermill": {
     "duration": 0.022738,
     "end_time": "2023-06-07T13:26:40.365982",
     "exception": false,
     "start_time": "2023-06-07T13:26:40.343244",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"border-radius:10px; border:#DEB887 solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#DEB887'>🔍 Conclusions:</font></h3>\n",
    "    \n",
    "- We have more patients with risk of heart disease in this dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a658453a",
   "metadata": {
    "papermill": {
     "duration": 0.022844,
     "end_time": "2023-06-07T13:26:40.411949",
     "exception": false,
     "start_time": "2023-06-07T13:26:40.389105",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\">Analysis on Sex</h2>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "017aeede",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:40.462652Z",
     "iopub.status.busy": "2023-06-07T13:26:40.462219Z",
     "iopub.status.idle": "2023-06-07T13:26:40.740919Z",
     "shell.execute_reply": "2023-06-07T13:26:40.739934Z"
    },
    "papermill": {
     "duration": 0.306248,
     "end_time": "2023-06-07T13:26:40.743085",
     "exception": false,
     "start_time": "2023-06-07T13:26:40.436837",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4.5))\n",
    "ax = sns.barplot(data=df['Sex'].value_counts().reset_index(), x='index', y='Sex', \n",
    "           linewidth=1, edgecolor='black', palette='Set2', ax=ax)\n",
    "ax.set(title='Count of Male vs Female', ylabel='Count', xlabel='Sex')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "149cde80",
   "metadata": {
    "papermill": {
     "duration": 0.024349,
     "end_time": "2023-06-07T13:26:40.791801",
     "exception": false,
     "start_time": "2023-06-07T13:26:40.767452",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"border-radius:10px; border:#DEB887 solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#DEB887'>🔍 Conclusions:</font></h3>\n",
    "\n",
    "\n",
    "- The dataset predominantly consists of records from males, outnumbering records from females.\n",
    "- The male records significantly outnumber the female records in this dataset, with a ratio of more than three to one.\n",
    "- It is important to be mindful of this gender imbalance in our dataset to avoid introducing bias when making assumptions or drawing conclusions.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cf36f8c",
   "metadata": {
    "papermill": {
     "duration": 0.023223,
     "end_time": "2023-06-07T13:26:40.838690",
     "exception": false,
     "start_time": "2023-06-07T13:26:40.815467",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\">Analysis on Chest Pain Type</h2>\n",
    "<br>\n",
    "\n",
    "\n",
    "<div style=\"border-radius:10px; border:#e7c6ff solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#e7c6ff'>Abbreviations for Chest Pain Explanation</font></h3>\n",
    "\n",
    "\n",
    "- **ASY** : ASY stands for \"Asymptomatic\" chest pain. It indicates that the individuals in the dataset did not experience any noticeable chest pain symptoms. They may have been included in the dataset for other reasons, such as a medical examination or routine check-up.\n",
    "    \n",
    "- **NAP** : NAP stands for \"Non-Anginal Pain\" chest pain. Non-anginal pain refers to chest discomfort or pain that is not related to a reduced blood supply to the heart. It is typically not caused by underlying heart disease but may still be a cause of concern and require further evaluation.\n",
    "    \n",
    "- **ATA** : ATA stands for \"Atypical Angina\" chest pain. Atypical angina refers to chest pain that does not fit the typical pattern of symptoms associated with angina. It may have different characteristics or be triggered by factors other than physical exertion or emotional stress.\n",
    "    \n",
    "- **TA** : TA stands for \"Typical Angina\" chest pain. Typical angina refers to chest pain that follows a predictable pattern and is commonly associated with coronary artery disease. It is typically described as a squeezing or pressure-like sensation in the chest that is brought on by physical exertion or emotional stress and is relieved with rest or nitroglycerin medication."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "510265bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:40.888773Z",
     "iopub.status.busy": "2023-06-07T13:26:40.888036Z",
     "iopub.status.idle": "2023-06-07T13:26:41.136281Z",
     "shell.execute_reply": "2023-06-07T13:26:41.135383Z"
    },
    "papermill": {
     "duration": 0.275753,
     "end_time": "2023-06-07T13:26:41.138403",
     "exception": false,
     "start_time": "2023-06-07T13:26:40.862650",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4.5))\n",
    "ax = sns.barplot(data=df['ChestPainType'].value_counts().reset_index(), x='index', y='ChestPainType', \n",
    "           linewidth=1, edgecolor='black', palette='Set1', ax=ax)\n",
    "ax.set(title='Count of Chest Pain Types', ylabel='Count', xlabel='Chest Pain Type')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84f6d158",
   "metadata": {
    "papermill": {
     "duration": 0.024003,
     "end_time": "2023-06-07T13:26:41.186812",
     "exception": false,
     "start_time": "2023-06-07T13:26:41.162809",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"border-radius:10px; border:#DEB887 solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#DEB887'>🔍 Conclusions:</font></h3>\n",
    "    \n",
    "- The majority of individuals in the dataset did not report any noticeable symptoms of chest pain.\n",
    "- This suggests that noticeable chest pain may not be the primary symptom associated with heart disease.\n",
    "- We can be sure of this when we do the bivarite analysis of Chest Pain Type and the Heart Disease Column "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cadd754a",
   "metadata": {
    "papermill": {
     "duration": 0.023729,
     "end_time": "2023-06-07T13:26:41.235068",
     "exception": false,
     "start_time": "2023-06-07T13:26:41.211339",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <span><h1 style=\"font-size: 36px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #e9f7ef; color: #8f52a4; text-align: center; border-radius: 150px 150px;\">Bivariate Analysis </h1></span><a class='anchor' id='BVA'></a>\n",
    "<br>\n",
    "\n",
    "<div style=\"border-radius:10px; border:#e7c6ff solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "    \n",
    "Bivariate analysis is the examination of the relationship between two variables. Here are three key points about bivariate analysis:\n",
    "- Relationship Exploration: Bivariate analysis helps uncover patterns, associations, or connections between two variables. It allows us to understand how changes in one variable correspond to changes in another variable.\n",
    "\n",
    "- Correlation Assessment: Bivariate analysis helps us assess the strength and direction of the relationship between two variables. It enables us to determine if the variables are positively, negatively, or not correlated at all.\n",
    "\n",
    "- Visual Representation: Bivariate analysis often involves the use of visualizations such as scatter plots, line graphs, or heatmaps. These visual representations provide a clear and concise way to understand the relationship between the two variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9e14afb",
   "metadata": {
    "papermill": {
     "duration": 0.023804,
     "end_time": "2023-06-07T13:26:41.282917",
     "exception": false,
     "start_time": "2023-06-07T13:26:41.259113",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\"><b>Analysis between Heart Disease and Age: </b><br> Which age group is most prone to heart disease?</h2>\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "49d5c926",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:41.334525Z",
     "iopub.status.busy": "2023-06-07T13:26:41.333253Z",
     "iopub.status.idle": "2023-06-07T13:26:41.778885Z",
     "shell.execute_reply": "2023-06-07T13:26:41.777654Z"
    },
    "papermill": {
     "duration": 0.474809,
     "end_time": "2023-06-07T13:26:41.782014",
     "exception": false,
     "start_time": "2023-06-07T13:26:41.307205",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "sns.kdeplot(data = df[df['HeartDisease'] == 1], x='Age', fill=True,   color='#ff99ac', label='No Heart Disease')\n",
    "sns.kdeplot(data = df[df['HeartDisease'] == 0], x='Age', fill=True,  color='#ff5c8a', label='Has Heart Disease')\n",
    "plt.legend()\n",
    "plt.title('Age and Heart Disease')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5362468b",
   "metadata": {
    "papermill": {
     "duration": 0.025421,
     "end_time": "2023-06-07T13:26:41.833233",
     "exception": false,
     "start_time": "2023-06-07T13:26:41.807812",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"border-radius:10px; border:#DEB887 solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#DEB887'>🔍 Conclusions:</font></h3>\n",
    "    \n",
    "- Individuals under the age of 50 have a lower likelihood of experiencing heart disease.\n",
    "- Once individuals reach the age of 50, the risk of developing heart disease significantly increases.\n",
    "- The age group between 55 and 60 shows the highest susceptibility to heart disease.\n",
    "- Individuals above the age of 60 consistently have a higher chance of developing heart disease compared to not having it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "362fbd8f",
   "metadata": {
    "papermill": {
     "duration": 0.02441,
     "end_time": "2023-06-07T13:26:41.882223",
     "exception": false,
     "start_time": "2023-06-07T13:26:41.857813",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\"><b>Analysis between Heart Disease and Gender: </b><br> Which gender is most prone to heart disease?</h2>\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "14f25d20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:41.933287Z",
     "iopub.status.busy": "2023-06-07T13:26:41.932631Z",
     "iopub.status.idle": "2023-06-07T13:26:42.679756Z",
     "shell.execute_reply": "2023-06-07T13:26:42.678621Z"
    },
    "papermill": {
     "duration": 0.775755,
     "end_time": "2023-06-07T13:26:42.682286",
     "exception": false,
     "start_time": "2023-06-07T13:26:41.906531",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_df = pd.crosstab(df['HeartDisease'], df['Sex'])\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(20,7))\n",
    "\n",
    "ax[0] = sns.heatmap(data=temp_df, annot=True, ax=ax[0],fmt='0g', linewidths=0.01, cmap='summer_r', linecolor='#95d5b2')\n",
    "ax[0].set(title='Heatmap of Gender vs Heart Disease', ylabel='Heart Disease')\n",
    "\n",
    "ax[1] = sns.countplot(data=df, x=\"Sex\", hue=\"HeartDisease\", palette = 'Set2', ax=ax[1], edgecolor='black')\n",
    "ax[1].set(title='Countplot of Gender vs Heart Disease', ylabel='Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64f99903",
   "metadata": {
    "papermill": {
     "duration": 0.025252,
     "end_time": "2023-06-07T13:26:42.733654",
     "exception": false,
     "start_time": "2023-06-07T13:26:42.708402",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"border-radius:10px; border:#DEB887 solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#DEB887'>🔍 Conclusions:</font></h3>\n",
    "    \n",
    "- The occurrence of heart disease is more prevalent among males compared to females.\n",
    "- It's important to note that our dataset contains significantly more data for males than females, almost three times as much.\n",
    "- However, when considering the percentages, approximately 25% of females in the dataset have heart disease, while a higher percentage of around 63% of males have heart disease.\n",
    "- These observations highlight the gender disparity in the prevalence of heart disease, with a higher proportion of males being affected."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae493d7",
   "metadata": {
    "papermill": {
     "duration": 0.029113,
     "end_time": "2023-06-07T13:26:42.788257",
     "exception": false,
     "start_time": "2023-06-07T13:26:42.759144",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\"><b>Analysis between Heart Disease and Cholesterol: </b><br>Does Cholesterol play a role in heart disease?</h2>\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "16b8f304",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:42.845074Z",
     "iopub.status.busy": "2023-06-07T13:26:42.844643Z",
     "iopub.status.idle": "2023-06-07T13:26:43.292329Z",
     "shell.execute_reply": "2023-06-07T13:26:43.291166Z"
    },
    "papermill": {
     "duration": 0.477645,
     "end_time": "2023-06-07T13:26:43.295056",
     "exception": false,
     "start_time": "2023-06-07T13:26:42.817411",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "sns.kdeplot(data = df[df['HeartDisease'] == 1], x='Cholesterol', fill=True,   color='#b5e48c', label='No Heart Disease')\n",
    "sns.kdeplot(data = df[df['HeartDisease'] == 0], x='Cholesterol', fill=True,  color='#52b69a', label='Has Heart Disease')\n",
    "plt.legend()\n",
    "plt.title('Age and Heart Disease')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6df44b3",
   "metadata": {
    "papermill": {
     "duration": 0.028853,
     "end_time": "2023-06-07T13:26:43.351323",
     "exception": false,
     "start_time": "2023-06-07T13:26:43.322470",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"border-radius:10px; border:#DEB887 solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#DEB887'>🔍 Conclusions:</font></h3>\n",
    "    \n",
    "#### Cholesterol Ranges\n",
    "- Normal: less than 150 mg/dL. \n",
    "- Mild hypertriglyceridemia: 150 to 499 mg/dL. \n",
    "- Moderate hypertriglyceridemia: 500 to 886 mg/dL. \n",
    "- Very high or severe hypertriglyceridemia: greater than 886 mg/dL.\n",
    "   \n",
    "<br>\n",
    "    \n",
    "- The analysis reveals that individuals with cholesterol levels below 150 mg/dL are generally considered healthy.\n",
    "- While there are still a significant number of healthy individuals above this range, the number of individuals prone to heart disease is substantially higher.\n",
    "- These findings suggest that higher cholesterol levels do indeed pose an increased risk of heart disease.\n",
    "-These observations support the notion that elevated cholesterol levels contribute to a higher likelihood of developing heart disease."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c751c4e",
   "metadata": {
    "papermill": {
     "duration": 0.026749,
     "end_time": "2023-06-07T13:26:43.406373",
     "exception": false,
     "start_time": "2023-06-07T13:26:43.379624",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\"><b>Analysis between Heart Disease and Chest Pain Type: </b><br>What does Chest Pain Type tell us about Heart Disease?</h2>\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20f9996d",
   "metadata": {
    "papermill": {
     "duration": 0.026841,
     "end_time": "2023-06-07T13:26:43.460524",
     "exception": false,
     "start_time": "2023-06-07T13:26:43.433683",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"border-radius:10px; border:#e7c6ff solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#e7c6ff'>💡Reminder: Abbreviations for Chest Pain Explanation</font></h3>\n",
    "\n",
    "\n",
    "- **ASY** : ASY stands for \"Asymptomatic\" chest pain. It indicates that the individuals in the dataset did not experience any noticeable chest pain symptoms. They may have been included in the dataset for other reasons, such as a medical examination or routine check-up.\n",
    "    \n",
    "- **NAP** : NAP stands for \"Non-Anginal Pain\" chest pain. Non-anginal pain refers to chest discomfort or pain that is not related to a reduced blood supply to the heart. It is typically not caused by underlying heart disease but may still be a cause of concern and require further evaluation.\n",
    "    \n",
    "- **ATA** : ATA stands for \"Atypical Angina\" chest pain. Atypical angina refers to chest pain that does not fit the typical pattern of symptoms associated with angina. It may have different characteristics or be triggered by factors other than physical exertion or emotional stress.\n",
    "    \n",
    "- **TA** : TA stands for \"Typical Angina\" chest pain. Typical angina refers to chest pain that follows a predictable pattern and is commonly associated with coronary artery disease. It is typically described as a squeezing or pressure-like sensation in the chest that is brought on by physical exertion or emotional stress and is relieved with rest or nitroglycerin medication."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ed9aae09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:43.522860Z",
     "iopub.status.busy": "2023-06-07T13:26:43.522054Z",
     "iopub.status.idle": "2023-06-07T13:26:44.415774Z",
     "shell.execute_reply": "2023-06-07T13:26:44.414070Z"
    },
    "papermill": {
     "duration": 0.92979,
     "end_time": "2023-06-07T13:26:44.418650",
     "exception": false,
     "start_time": "2023-06-07T13:26:43.488860",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_df = pd.crosstab(df['HeartDisease'], df['ChestPainType'])\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(20,7))\n",
    "\n",
    "ax[0] = sns.heatmap(data=temp_df,annot=True,fmt='0g', ax = ax[0],linewidths=0.01, cmap='mako_r', linecolor='#cae9ff')\n",
    "ax[0].set(title='Heatmap of Chest Pain Type vs Heart Disease', ylabel='Heart Disease')\n",
    "\n",
    "ax[1] = sns.countplot(data=df, x=\"ChestPainType\", hue=\"HeartDisease\", palette = 'Set2', ax=ax[1], edgecolor='black')\n",
    "ax[1].set(title='Countplot of Chest Pain Type vs Heart Disease', ylabel='Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ad16195",
   "metadata": {
    "papermill": {
     "duration": 0.028648,
     "end_time": "2023-06-07T13:26:44.475744",
     "exception": false,
     "start_time": "2023-06-07T13:26:44.447096",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"border-radius:10px; border:#DEB887 solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#DEB887'>🔍 Conclusions:</font></h3>\n",
    "    \n",
    "- A significant proportion of people who have heart disease do not experience any chest pain symptoms. This type of chest pain is called Asymptomatic Chest Pain.\n",
    "- The next largest group of people with heart disease experience Atypical Angina, which means they may have chest pain or discomfort that differs from the typical chest pain associated with heart disease.\n",
    "- These findings suggest that heart disease can manifest without the presence of chest pain. It implies that not all cases of heart disease are accompanied by chest pains, and individuals may have heart disease without experiencing typical chest pain symptoms\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9908e90b",
   "metadata": {
    "papermill": {
     "duration": 0.028705,
     "end_time": "2023-06-07T13:26:44.532476",
     "exception": false,
     "start_time": "2023-06-07T13:26:44.503771",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\"><b>Analysis between Heart Disease and Exercise Angina: </b><br>Can exercise cause heart disease?</h2>\n",
    "<br>\n",
    "\n",
    "<div style=\"border-radius:10px; border:#e7c6ff solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#e7c6ff'>🤔 What is Exercise Angina?</font></h3>\n",
    "    \n",
    "Exercise angina, also known as exertional angina or angina pectoris, refers to chest pain or discomfort that occurs during physical activity or exercise. It is a symptom typically associated with coronary artery disease, which is a condition where the arteries that supply blood to the heart become narrowed or blocked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f68a75d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:44.591145Z",
     "iopub.status.busy": "2023-06-07T13:26:44.590290Z",
     "iopub.status.idle": "2023-06-07T13:26:45.321076Z",
     "shell.execute_reply": "2023-06-07T13:26:45.320026Z"
    },
    "papermill": {
     "duration": 0.763538,
     "end_time": "2023-06-07T13:26:45.323625",
     "exception": false,
     "start_time": "2023-06-07T13:26:44.560087",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_df = pd.crosstab(df['HeartDisease'], df['ExerciseAngina'])\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(20,7))\n",
    "\n",
    "ax[0] = sns.heatmap(data=temp_df,annot=True,fmt='0g', ax = ax[0],linewidths=0.01, cmap='mako_r', linecolor='#cae9ff')\n",
    "ax[0].set(title='Heatmap of Exercise Angina vs Heart Disease', ylabel='Heart Disease')\n",
    "\n",
    "ax[1] = sns.countplot(data=df, x=\"ExerciseAngina\", hue=\"HeartDisease\", palette = 'Set2', ax=ax[1], edgecolor='black')\n",
    "ax[1].set(title='Countplot of Exercise Angina vs Heart Disease', ylabel='Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be999314",
   "metadata": {
    "papermill": {
     "duration": 0.029042,
     "end_time": "2023-06-07T13:26:45.381666",
     "exception": false,
     "start_time": "2023-06-07T13:26:45.352624",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "<h2 style=\"font-size: 28px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #c7f9cc; color: #ef476f; text-align: center; border-radius: 150px 150px;\"><b>Analysis between Heart Disease and Fasting Blood Sugar: </b><br>How is Fasting Blood Sugar related to Heart Disease?</h2>\n",
    "<br>\n",
    "\n",
    "<div style=\"border-radius:10px; border:#e7c6ff solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#e7c6ff'>🤔 What is Fasting Blood Sugar?</font></h3>\n",
    "    \n",
    "- Fasting blood sugar, also known as fasting blood glucose, refers to the level of glucose (sugar) in the bloodstream after an overnight fast. It is typically measured in milligrams per deciliter (mg/dL) or millimoles per liter (mmol/L).\n",
    "- Fasting blood sugar is an important parameter used in diagnosing and monitoring diabetes. \n",
    "- It provides information about the body's ability to regulate blood glucose levels in the absence of food intake. \n",
    "- Normally, when a person has fasted for at least 8 hours, the fasting blood sugar level should fall within a specific range.\n",
    "\n",
    "<br>\n",
    "    \n",
    "#### Note\n",
    "- **In this dataset 0 means normal Fasting Blood Sugar level and 1 means Abnormal Blood Sugar level** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f663b585",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-07T13:26:45.440690Z",
     "iopub.status.busy": "2023-06-07T13:26:45.440223Z",
     "iopub.status.idle": "2023-06-07T13:26:45.816370Z",
     "shell.execute_reply": "2023-06-07T13:26:45.815333Z"
    },
    "papermill": {
     "duration": 0.408845,
     "end_time": "2023-06-07T13:26:45.819051",
     "exception": false,
     "start_time": "2023-06-07T13:26:45.410206",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_df = pd.crosstab(df['HeartDisease'], df['FastingBS'])\n",
    "\n",
    "fig, ax = plt.subplots(figsize= (6,4))\n",
    "\n",
    "ax = sns.heatmap(data=temp_df,annot=True,fmt='0g', ax = ax,linewidths=0.01, cmap='summer_r', linecolor='#cae9ff')\n",
    "ax.set(title='Heatmap of Fasting Blood Sugar vs Heart Disease', ylabel='Heart Disease', xlabel='Fasting Blood Sugar')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f0bfb2",
   "metadata": {
    "papermill": {
     "duration": 0.031052,
     "end_time": "2023-06-07T13:26:45.879320",
     "exception": false,
     "start_time": "2023-06-07T13:26:45.848268",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div style=\"border-radius:10px; border:#DEB887 solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "<h3 align=\"left\"><font color='#DEB887'>🔍 Conclusions:</font></h3>\n",
    "    \n",
    "\n",
    "- Based on the heatmap analysis, it is evident that individuals with normal fasting blood sugar levels are also susceptible to heart disease. In fact, they exhibit a higher likelihood of developing heart disease compared to those with abnormal fasting blood sugar levels. \n",
    "- This suggests that while fasting blood sugar levels can be a factor in the development or presence of heart disease, it is not the sole determining factor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c9fa8a7",
   "metadata": {
    "papermill": {
     "duration": 0.03109,
     "end_time": "2023-06-07T13:26:45.942215",
     "exception": false,
     "start_time": "2023-06-07T13:26:45.911125",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<span><h1 style=\"font-size: 36px; font-family: 'Roboto Mono', monospace; font-weight: normal; background-color: #e9f7ef; color: #8f52a4; text-align: center; border-radius: 150px 150px;\">End of EDA</h1></span><a class='anchor' id='end'></a>\n",
    "<br>\n",
    "\n",
    "<div style=\"border-radius:10px; border:#e7c6ff solid; padding: 15px; background-color: #f6f5f5; font-size:100%; text-align:left\">\n",
    "    \n",
    "- I'll continue with multi variate analysis later\n",
    "- Thank you so much for going through my notebook! If you have any suggestions, please feel free to put them in the comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f9785d5",
   "metadata": {
    "papermill": {
     "duration": 0.029799,
     "end_time": "2023-06-07T13:26:46.001432",
     "exception": false,
     "start_time": "2023-06-07T13:26:45.971633",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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