Final Project-Shiming Shen.ipynb

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       "'\\nHeart Disease prediction by Shiming Shen\\n\\ndataset source: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\\n\\nThe dataset contains 14 different attributes from 13 heart disease measurement index and one result attribute, \\nand this dataset is built for presence of heart disease in the patients.\\nI am trying to categorize the disease and non-disease. Also, I would predict the \\nStage of heart disease by high correlated variables.\\n\\nList of Attributes:\\n\\n1. age: continuous\\n2. sex: categorical, 2 values {0: female, 1: male}\\n3. cp (chest pain type): categorical, 4 values\\n    {1: typical angina, 2: atypical angina, 3: non-angina, 4: asymptomatic angina}\\n4. restbp (resting blood pressure on admission to hospital): continuous (mmHg)\\n5. chol (serum cholesterol level): continuous (mg/dl)\\n6. fbs (fasting blood sugar): categorical, 2 values {0: <= 120 mg/dl, 1: > 120 mg/dl}\\n7. restecg (resting electrocardiography): categorical, 3 values\\n    {0: normal, 1: ST-T wave abnormality, 2: left ventricular hypertrophy}\\n8. thalach (maximum heart rate achieved): continuous\\n9. exang (exercise induced angina): categorical, 2 values {0: no, 1: yes}\\n10. oldpeak (ST depression induced by exercise relative to rest): continuous\\n11. slope (slope of peak exercise ST segment): categorical, 3 values \\n    {1: upsloping, 2: flat, 3: downsloping}\\n12. ca (number of major vessels colored by fluoroscopy): discrete (0,1,2,3)\\n13. thal: categorical, 3 values {3: normal, 6: fixed defect, 7: reversible defect}\\n14. num (diagnosis of heart disease): categorical, 5 values \\n    {0: less than 50% narrowing in any major vessel, \\n    1-4: more than 50% narrowing in 1-4 vessels}\\n    \\nIn this assignment, I would like to present the facts of this certain dataset including distribution comparison\\nand descriptive analysis. Then, I would set up the logisitics regression analysis for the dataset in figuring out\\nthe variables would lead to the final correct diagnosis.\\n'"
      ]
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     "execution_count": 284,
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   "source": [
    "'''\n",
    "Heart Disease prediction by Shiming Shen\n",
    "\n",
    "dataset source: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\n",
    "\n",
    "The dataset contains 14 different attributes from 13 heart disease measurement index and one result attribute, \n",
    "and this dataset is built for presence of heart disease in the patients.\n",
    "I am trying to categorize the disease and non-disease. Also, I would predict the \n",
    "Stage of heart disease by high correlated variables.\n",
    "\n",
    "List of Attributes:\n",
    "\n",
    "1. age: continuous\n",
    "2. sex: categorical, 2 values {0: female, 1: male}\n",
    "3. cp (chest pain type): categorical, 4 values\n",
    "    {1: typical angina, 2: atypical angina, 3: non-angina, 4: asymptomatic angina}\n",
    "4. restbp (resting blood pressure on admission to hospital): continuous (mmHg)\n",
    "5. chol (serum cholesterol level): continuous (mg/dl)\n",
    "6. fbs (fasting blood sugar): categorical, 2 values {0: <= 120 mg/dl, 1: > 120 mg/dl}\n",
    "7. restecg (resting electrocardiography): categorical, 3 values\n",
    "    {0: normal, 1: ST-T wave abnormality, 2: left ventricular hypertrophy}\n",
    "8. thalach (maximum heart rate achieved): continuous\n",
    "9. exang (exercise induced angina): categorical, 2 values {0: no, 1: yes}\n",
    "10. oldpeak (ST depression induced by exercise relative to rest): continuous\n",
    "11. slope (slope of peak exercise ST segment): categorical, 3 values \n",
    "    {1: upsloping, 2: flat, 3: downsloping}\n",
    "12. ca (number of major vessels colored by fluoroscopy): discrete (0,1,2,3)\n",
    "13. thal: categorical, 3 values {3: normal, 6: fixed defect, 7: reversible defect}\n",
    "14. num (diagnosis of heart disease): categorical, 5 values \n",
    "    {0: less than 50% narrowing in any major vessel, \n",
    "    1-4: more than 50% narrowing in 1-4 vessels}\n",
    "    \n",
    "In this assignment, I would like to present the facts of this certain dataset including distribution comparison\n",
    "and descriptive analysis. Then, I would set up the logisitics regression analysis for the dataset in figuring out\n",
    "the variables would lead to the final correct diagnosis.\n",
    "'''"
   ]
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   "execution_count": 285,
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   "source": [
    "#Import necessary libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "\n",
    "import seaborn as sns\n",
    "from pandas import DataFrame,Series\n",
    "from sklearn import linear_model\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: convert_objects is deprecated.  To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n",
      "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
      "  import sys\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>53.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
       "1  67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
       "2  67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
       "3  37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
       "4  41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
       "5  56.0  1.0  2.0     120.0  236.0  0.0      0.0    178.0    0.0      0.8   \n",
       "6  62.0  0.0  4.0     140.0  268.0  0.0      2.0    160.0    0.0      3.6   \n",
       "7  57.0  0.0  4.0     120.0  354.0  0.0      0.0    163.0    1.0      0.6   \n",
       "8  63.0  1.0  4.0     130.0  254.0  0.0      2.0    147.0    0.0      1.4   \n",
       "9  53.0  1.0  4.0     140.0  203.0  1.0      2.0    155.0    1.0      3.1   \n",
       "\n",
       "   slope   ca  thal  num  \n",
       "0    3.0  0.0   6.0    0  \n",
       "1    2.0  3.0   3.0    2  \n",
       "2    2.0  2.0   7.0    1  \n",
       "3    3.0  0.0   3.0    0  \n",
       "4    1.0  0.0   3.0    0  \n",
       "5    1.0  0.0   3.0    0  \n",
       "6    3.0  2.0   3.0    3  \n",
       "7    1.0  0.0   3.0    0  \n",
       "8    2.0  1.0   7.0    2  \n",
       "9    3.0  0.0   7.0    1  "
      ]
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     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "#Load Dataset\n",
    "columns = [\"age\", \"sex\", \"cp\", \"trestbps\", \"chol\",\"fbs\", \"restecg\", \n",
    "           \"thalach\",\"exang\", \"oldpeak\",\"slope\", \"ca\", \"thal\", \"num\"]\n",
    "df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\", \n",
    "                  header = None, \n",
    "                  names = columns)\n",
    "dfa = df.convert_objects(convert_numeric=True)\n",
    "dfa.head(10)"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      "age         303 non-null float64\n",
      "sex         303 non-null float64\n",
      "cp          303 non-null float64\n",
      "trestbps    303 non-null float64\n",
      "chol        303 non-null float64\n",
      "fbs         303 non-null float64\n",
      "restecg     303 non-null float64\n",
      "thalach     303 non-null float64\n",
      "exang       303 non-null float64\n",
      "oldpeak     303 non-null float64\n",
      "slope       303 non-null float64\n",
      "ca          299 non-null float64\n",
      "thal        301 non-null float64\n",
      "num         303 non-null int64\n",
      "dtypes: float64(13), int64(1)\n",
      "memory usage: 33.2 KB\n"
     ]
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      "text/plain": [
       "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
       "1  67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
       "2  67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
       "3  37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
       "4  41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
       "\n",
       "   slope   ca  thal  num  \n",
       "0    3.0  0.0   6.0    0  \n",
       "1    2.0  3.0   3.0    2  \n",
       "2    2.0  2.0   7.0    1  \n",
       "3    3.0  0.0   3.0    0  \n",
       "4    1.0  0.0   3.0    0  "
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data cleaning Process\n",
    "dfa.info()\n",
    "\n",
    "df1 = dfa.dropna()\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distributions compared for disease and non-disease\n",
    "fig, axes = plt.subplots( nrows=5, ncols=3, figsize=(20,20) )\n",
    "plt.subplots_adjust( wspace=0.20, hspace=0.20, top=0.97 )\n",
    "\n",
    "bins = np.linspace(20, 80, 15)\n",
    "axes[0,0].hist(df1[df1.num>0].age.tolist(),bins,color=[\"red\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[0,0].hist(df1[df1.num==0].age,bins,color=[\"blue\"],histtype=\"step\",label=\"non-disease\",normed=True)\n",
    "axes[0,0].set_xlabel(\"Age (years)\")\n",
    "axes[0,0].set_ylim(0.0,0.070)\n",
    "axes[0,0].legend(prop={'size': 10},loc=\"upper left\")\n",
    "\n",
    "axes[0,1].hist(df1[df1.num>0].sex.tolist(),color=[\"red\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[0,1].hist(df1[df1.num==0].sex,color=[\"blue\"],histtype=\"step\",label=\"non-disease\",normed=True)\n",
    "axes[0,1].set_xlabel(\"Sex (0=female,1=male)\")\n",
    "axes[0,1].legend(prop={'size': 10},loc=\"upper left\")\n",
    "\n",
    "axes[0,2].hist(df1[df1.num>0].cp.tolist(),color=[\"red\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[0,2].hist(df1[df1.num==0].cp,color=[\"blue\"],histtype=\"step\",label=\"non-disease\",normed=True)\n",
    "axes[0,2].set_xlabel(\"Type of Chest Pain [cp]\")\n",
    "axes[0,2].legend(prop={'size': 10},loc=\"upper left\")\n",
    "\n",
    "bins = np.linspace(80, 200, 15)\n",
    "axes[1,0].hist(df1[df1.num>0].trestbps.tolist(),bins,color=[\"red\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[1,0].hist(df1[df1.num==0].trestbps,bins,color=[\"blue\"],histtype=\"step\",label=\"non-disease\",normed=True)\n",
    "axes[1,0].set_xlabel(\"Resting Blood Pressure [trestbps]\")\n",
    "axes[1,0].legend(prop={'size': 10},loc=\"upper right\")\n",
    "\n",
    "axes[1,1].hist(df1[df1.num>0].chol.tolist(),color=[\"red\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[1,1].hist(df1[df1.num==0].chol,color=[\"blue\"],histtype=\"step\",label=\"non-disease\",normed=True)\n",
    "axes[1,1].set_xlabel(\"Serum Cholesterol [chol]\")\n",
    "axes[1,1].legend(prop={'size': 10},loc=\"upper right\")\n",
    "\n",
    "axes[1,2].hist(df1[df1.num>0].fbs.tolist(),color=[\"red\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[1,2].hist(df1[df1.num==0].fbs,color=[\"blue\"],histtype=\"step\",label=\"non-disease\",normed=True)\n",
    "axes[1,2].set_xlabel(\"Fasting blood sugar [fbs]\")\n",
    "axes[1,2].legend(prop={'size': 10},loc=\"upper right\")\n",
    "\n",
    "axes[2,0].hist(df1[df1.num>0].restecg.tolist(),color=[\"red\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[2,0].hist(df1[df1.num==0].restecg,color=[\"blue\"],histtype=\"step\",label=\"non-disease\",normed=True)\n",
    "axes[2,0].set_xlabel(\"Rest ECG\")\n",
    "axes[2,0].set_ylim(0.0,4.0)\n",
    "axes[2,0].legend(prop={'size': 10},loc=\"upper right\")\n",
    "\n",
    "axes[2,1].hist(df1[df1.num>0].thalach.tolist(),color=[\"red\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[2,1].hist(df1[df1.num==0].thalach,color=[\"blue\"],histtype=\"step\",label=\"non-disease\",normed=True)\n",
    "axes[2,1].set_xlabel(\"thalach\")\n",
    "axes[2,1].legend(prop={'size': 10},loc=\"upper left\")\n",
    "\n",
    "axes[2,2].hist(df1[df1.num>0].exang.tolist(),color=[\"red\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[2,2].hist(df1[df1.num==0].exang,color=[\"blue\"],histtype=\"step\",label=\"non-disease\",normed=True)\n",
    "axes[2,2].set_xlabel(\"exang\")\n",
    "axes[2,2].legend(prop={'size': 10},loc=\"upper right\")\n",
    "\n",
    "axes[3,0].hist(df1[df1.num>0].oldpeak.tolist(),color=[\"red\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[3,0].hist(df1[df1.num==0].oldpeak,color=[\"blue\"],histtype=\"step\",label=\"non-disease\",normed=True)\n",
    "axes[3,0].set_xlabel(\"oldpeak\")\n",
    "axes[3,0].legend(prop={'size': 10},loc=\"upper right\")\n",
    "\n",
    "axes[3,1].hist(df1[df1.num>0].slope.tolist(),color=[\"red\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[3,1].hist(df1[df1.num==0].slope,color=[\"blue\"],histtype=\"step\",label=\"non-disease\",normed=True)\n",
    "axes[3,1].set_xlabel(\"slope\")\n",
    "axes[3,1].legend(prop={'size': 10},loc=\"upper right\")\n",
    "\n",
    "axes[3,2].hist(df1[df1.num>0].ca.tolist(),color=[\"red\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[3,2].hist(df1[df1.num==0].ca,color=[\"blue\"],histtype=\"step\",label=\"non-disease\",normed=True)\n",
    "axes[3,2].set_xlabel(\"ca\")\n",
    "axes[3,2].legend(prop={'size': 10},loc=\"upper right\")\n",
    "\n",
    "axes[4,0].hist(df1[df1.num>0].thal.tolist(),color=[\"red\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[4,0].hist(df1[df1.num==0].thal,color=[\"blue\"],histtype=\"step\",label=\"non-disease\",normed=True)\n",
    "axes[4,0].set_xlabel(\"thal\")\n",
    "axes[4,0].set_ylim(0.0,2.5)\n",
    "axes[4,0].legend(prop={'size': 10},loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "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>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.542088</td>\n",
       "      <td>0.676768</td>\n",
       "      <td>3.158249</td>\n",
       "      <td>131.693603</td>\n",
       "      <td>247.350168</td>\n",
       "      <td>0.144781</td>\n",
       "      <td>0.996633</td>\n",
       "      <td>149.599327</td>\n",
       "      <td>0.326599</td>\n",
       "      <td>1.055556</td>\n",
       "      <td>1.602694</td>\n",
       "      <td>0.676768</td>\n",
       "      <td>4.730640</td>\n",
       "      <td>0.946128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.049736</td>\n",
       "      <td>0.468500</td>\n",
       "      <td>0.964859</td>\n",
       "      <td>17.762806</td>\n",
       "      <td>51.997583</td>\n",
       "      <td>0.352474</td>\n",
       "      <td>0.994914</td>\n",
       "      <td>22.941562</td>\n",
       "      <td>0.469761</td>\n",
       "      <td>1.166123</td>\n",
       "      <td>0.618187</td>\n",
       "      <td>0.938965</td>\n",
       "      <td>1.938629</td>\n",
       "      <td>1.234551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  297.000000  297.000000  297.000000  297.000000  297.000000  297.000000   \n",
       "mean    54.542088    0.676768    3.158249  131.693603  247.350168    0.144781   \n",
       "std      9.049736    0.468500    0.964859   17.762806   51.997583    0.352474   \n",
       "min     29.000000    0.000000    1.000000   94.000000  126.000000    0.000000   \n",
       "25%     48.000000    0.000000    3.000000  120.000000  211.000000    0.000000   \n",
       "50%     56.000000    1.000000    3.000000  130.000000  243.000000    0.000000   \n",
       "75%     61.000000    1.000000    4.000000  140.000000  276.000000    0.000000   \n",
       "max     77.000000    1.000000    4.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  297.000000  297.000000  297.000000  297.000000  297.000000  297.000000   \n",
       "mean     0.996633  149.599327    0.326599    1.055556    1.602694    0.676768   \n",
       "std      0.994914   22.941562    0.469761    1.166123    0.618187    0.938965   \n",
       "min      0.000000   71.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "25%      0.000000  133.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    2.000000    0.000000   \n",
       "75%      2.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    3.000000    3.000000   \n",
       "\n",
       "             thal         num  \n",
       "count  297.000000  297.000000  \n",
       "mean     4.730640    0.946128  \n",
       "std      1.938629    1.234551  \n",
       "min      3.000000    0.000000  \n",
       "25%      3.000000    0.000000  \n",
       "50%      3.000000    0.000000  \n",
       "75%      7.000000    2.000000  \n",
       "max      7.000000    4.000000  "
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Descriptive Statistics Summary\n",
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 297 entries, 0 to 301\n",
      "Data columns (total 15 columns):\n",
      "age         297 non-null float64\n",
      "sex         297 non-null float64\n",
      "cp          297 non-null float64\n",
      "trestbps    297 non-null float64\n",
      "chol        297 non-null float64\n",
      "fbs         297 non-null float64\n",
      "restecg     297 non-null float64\n",
      "thalach     297 non-null float64\n",
      "exang       297 non-null float64\n",
      "oldpeak     297 non-null float64\n",
      "slope       297 non-null float64\n",
      "ca          297 non-null float64\n",
      "thal        297 non-null float64\n",
      "num         297 non-null int64\n",
      "num2        297 non-null int64\n",
      "dtypes: float64(13), int64(2)\n",
      "memory usage: 47.1 KB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shimingshen/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    160\n",
       "1    137\n",
       "Name: num2, dtype: int64"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let us convert 'Disease' and 'Non-Disease' to 0 and 1 respectively so it will be easier for distingish\n",
    "df1['num2']=np.where(df1['num']==0,0,1)\n",
    "df1.head()\n",
    "\n",
    "#Frequency Comparison between 'Disease' & 'Non-Disease'\n",
    "sns.countplot(df1['num2'])\n",
    "df1.info()\n",
    "df1['num2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.356234\n",
      "         Iterations 7\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                  297\n",
      "Model:                          Logit   Df Residuals:                      284\n",
      "Method:                           MLE   Df Model:                           12\n",
      "Date:                Mon, 02 Jul 2018   Pseudo R-squ.:                  0.4838\n",
      "Time:                        13:58:00   Log-Likelihood:                -105.80\n",
      "converged:                       True   LL-Null:                       -204.97\n",
      "                                        LLR p-value:                 7.166e-36\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "age           -0.0454      0.020     -2.223      0.026      -0.085      -0.005\n",
      "sex            0.9617      0.451      2.134      0.033       0.078       1.845\n",
      "cp             0.3951      0.174      2.271      0.023       0.054       0.736\n",
      "trestbps       0.0154      0.010      1.553      0.120      -0.004       0.035\n",
      "chol           0.0027      0.004      0.729      0.466      -0.005       0.010\n",
      "fbs           -0.8086      0.532     -1.519      0.129      -1.852       0.234\n",
      "restecg        0.2708      0.183      1.482      0.138      -0.087       0.629\n",
      "thalach       -0.0385      0.008     -4.837      0.000      -0.054      -0.023\n",
      "exang          0.8409      0.407      2.066      0.039       0.043       1.639\n",
      "oldpeak        0.2657      0.208      1.276      0.202      -0.142       0.674\n",
      "slope          0.2701      0.339      0.796      0.426      -0.395       0.935\n",
      "ca             1.2641      0.259      4.881      0.000       0.756       1.772\n",
      "thal           0.3485      0.101      3.443      0.001       0.150       0.547\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "#Logistics Regression setup\n",
    "from sklearn import datasets\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import statsmodels.api as sm\n",
    "\n",
    "X = df1[df1.columns[0:13]]\n",
    "#df1[0:13] = df1[0:13].apply(pd.to_numeric， errors='ignore')\n",
    "y = df1['num2'].values\n",
    "logit_model=sm.Logit(y,X)\n",
    "\n",
    "#logit_model=sm.Logit(y,df[[0:13]])\n",
    "result=logit_model.fit()\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Interpretation:\n",
    "The coefficient means that when holding the other coefficients consistent, the result, which is\n",
    "log-odds value on the dependent variable would be increased if the coefficient is positive. The log-odds value\n",
    "means the probability of the dependent variable. Therefore, the larger the coefficient on the independent variable,\n",
    "the higher probability a person would get the heart disease.\n",
    "\n",
    "Conclusion:\n",
    "From the distribution graphs, we can see that the major group is concentrated in age between 50-65. However, it is\n",
    "very interesting to see that the coefficient of age is negative. This means the larger the age, the less risk would\n",
    "happen on the heart disease. So we should set up another model to consider the age effects on heart disease.\n",
    "Furthermore, based on the interpretation, we three major variable we should focus on is ca (number of major vessels \n",
    "colored by fluoroscopy), sex and exang (exercise induced angina) for the further analysis.\n",
    "\n",
    "Reference:\n",
    "https://github.com/LucDemortier/HeartDiseaseStudy/blob/master/HeartDiseaseProject.ipynb\n",
    "'''"
   ]
  }
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