Complete Machine Learning and Data Science - Zero to Mastery - AN/11.Heart Disease Project/Milestone Project - Heart Disease Classification.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Predicting Heart Disease using Machine Learning\n",
    "\n",
    "This notebook uses various Python based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has a Heart Disease based on their medical attributes.\n",
    "\n",
    "We're going to take the following approach:\n",
    "1. [Problem Definition](#definition)\n",
    "2. [Data](#data) \n",
    "3. [Evaluation](#evaluation)\n",
    "4. [Features](#features)\n",
    "5. [Modelling](#modelling)\n",
    "6. [Experimentation](#experimentation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"definition\">1. Problem Definition</a>\n",
    "\n",
    "In a statement, \n",
    "> Given clinical parameters about a patient, can we predict whether or not a they have a heart disease or not?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"data\">2. Data</a>\n",
    "\n",
    "[Heart Disease UCI - Original Version](https://archive.ics.uci.edu/ml/datasets/heart+disease)\n",
    "\n",
    "\n",
    "[Heart Disease UCI - Kaggle Version](https://www.kaggle.com/ronitf/heart-disease-uci)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"evaluation\">3.Evaluation</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> If we can reach 95% of accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"features\">4.Features</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following are the features we'll use to predict our target variable (heart disease or no heart disease).\n",
    "\n",
    "1. age - age in years \n",
    "2. sex - (1 = male; 0 = female) \n",
    "3. cp - chest pain type \n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "4. trestbps - resting blood pressure (in mm Hg on admission to the hospital)\n",
    "    * anything above 130-140 is typically cause for concern\n",
    "5. chol - serum cholestoral in mg/dl \n",
    "    * serum = LDL + HDL + .2 * triglycerides\n",
    "    * above 200 is cause for concern\n",
    "6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) \n",
    "    * '>126' mg/dL signals diabetes\n",
    "7. restecg - resting electrocardiographic results\n",
    "    * 0: Nothing to note\n",
    "    * 1: ST-T Wave abnormality\n",
    "        - can range from mild symptoms to severe problems\n",
    "        - signals non-normal heart beat\n",
    "    * 2: Possible or definite left ventricular hypertrophy\n",
    "        - Enlarged heart's main pumping chamber\n",
    "8. thalach - maximum heart rate achieved \n",
    "9. exang - exercise induced angina (1 = yes; 0 = no) \n",
    "10. oldpeak - ST depression induced by exercise relative to rest \n",
    "    * looks at stress of heart during excercise\n",
    "    * unhealthy heart will stress more\n",
    "11. slope - the slope of the peak exercise ST segment\n",
    "    * 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "    * 1: Flatsloping: minimal change (typical healthy heart)\n",
    "    * 2: Downslopins: signs of unhealthy heart\n",
    "12. ca - number of major vessels (0-3) colored by flourosopy \n",
    "    * colored vessel means the doctor can see the blood passing through\n",
    "    * the more blood movement the better (no clots)\n",
    "13. thal - thalium stress result\n",
    "    * 1,3: normal\n",
    "    * 6: fixed defect: used to be defect but ok now\n",
    "    * 7: reversable defect: no proper blood movement when excercising \n",
    "14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)\n",
    "\n",
    "**Note:** No personal identifiable information (PPI) can be found in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regular EDA and plotting libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Models from scikit-learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model Evaluations\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import plot_roc_curve, plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/heart-disease.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
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       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
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    "df.head()"
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   "source": [
    "--------"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Exploratory Data Analysis (EDA)\n",
    "\n",
    "1. What question(s) are we trying to solve?\n",
    "2. What kind of data do we have and how do we treat different types?\n",
    "3. What are the missing data and how are we going to handle them?\n",
    "4. What are the outliers, why we care about them and how are we going to handle them?\n",
    "5. How can we add, change or remove features to get more out of the data?"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if there is any missing data\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many classes are in target variable?\n",
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visulaiztion of classes\n",
    "# sns.countplot(x=df['target']);\n",
    "df['target'].value_counts().plot.bar(color=['salmon', 'lightblue']);\n",
    "plt.xlabel('0: No Disease, 1: Heart Disease')\n",
    "plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seem like there are 2 categories and pretty balanced dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding Patterns in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>303.0</td>\n",
       "      <td>54.366337</td>\n",
       "      <td>9.082101</td>\n",
       "      <td>29.0</td>\n",
       "      <td>47.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>303.0</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>94.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>303.0</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>126.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>274.5</td>\n",
       "      <td>564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>303.0</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>71.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>153.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>303.0</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>303.0</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>303.0</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.544554</td>\n",
       "      <td>0.498835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count        mean        std    min    25%    50%    75%    max\n",
       "age       303.0   54.366337   9.082101   29.0   47.5   55.0   61.0   77.0\n",
       "sex       303.0    0.683168   0.466011    0.0    0.0    1.0    1.0    1.0\n",
       "cp        303.0    0.966997   1.032052    0.0    0.0    1.0    2.0    3.0\n",
       "trestbps  303.0  131.623762  17.538143   94.0  120.0  130.0  140.0  200.0\n",
       "chol      303.0  246.264026  51.830751  126.0  211.0  240.0  274.5  564.0\n",
       "fbs       303.0    0.148515   0.356198    0.0    0.0    0.0    0.0    1.0\n",
       "restecg   303.0    0.528053   0.525860    0.0    0.0    1.0    1.0    2.0\n",
       "thalach   303.0  149.646865  22.905161   71.0  133.5  153.0  166.0  202.0\n",
       "exang     303.0    0.326733   0.469794    0.0    0.0    0.0    1.0    1.0\n",
       "oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2\n",
       "slope     303.0    1.399340   0.616226    0.0    1.0    1.0    2.0    2.0\n",
       "ca        303.0    0.729373   1.022606    0.0    0.0    0.0    1.0    4.0\n",
       "thal      303.0    2.313531   0.612277    0.0    2.0    2.0    3.0    3.0\n",
       "target    303.0    0.544554   0.498835    0.0    0.0    1.0    1.0    1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart disease frequency according to Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are about 207 male and 96 females. So we have more male population, so we need to keep that in back of our mind\n",
    "\n",
    "(1 = male; 0 = female)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "sex            \n",
       "0        24  72\n",
       "1       114  93"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['sex'], df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.75, 0.4492753623188406)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "72/(24+72), 93/(114+93)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that based on existing data, there are 75% chances that female can have a heart disease. For male, there are 45% chance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the data\n",
    "# pd.crosstab(df['sex'], df['target']).plot(kind='bar', color=['salmon', 'lightblue']);\n",
    "\n",
    "pd.crosstab(df['sex'], df['target']).plot(kind='bar');\n",
    "plt.title('Heart disease frequency by Sex')\n",
    "plt.xlabel('0: No Disease, 1: Heart Disease ')\n",
    "plt.ylabel('Count')\n",
    "plt.legend(['Female', 'Male']);\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age Vs Max. Heart Rate for people who have Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "# positive cases\n",
    "sns.scatterplot(data=df, x=df.age[df.target==1], y=df.thalach[df.target==1], color='salmon', s=50, alpha=0.8);\n",
    "\n",
    "# negative cases\n",
    "sns.scatterplot(data=df, x=df.age[df.target==0], y=df.thalach[df.target==0], color='lightblue', s=50, alpha=0.8)\n",
    "\n",
    "plt.title('Heart Disease in function of Age and Max Heart Rate')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Max Heart Rate');\n",
    "plt.legend(['Heart Disease', 'No Disease']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df, x=df['age'], bins=30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency per Chest Pain level\n",
    "\n",
    "cp - chest pain type \n",
    "   * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "   * 1: Atypical angina: chest pain not related to heart\n",
    "   * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "   * 3: Asymptomatic: chest pain not showing signs of disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>cp</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>41</td>\n",
       "      <td>69</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cp        0   1   2   3\n",
       "target                 \n",
       "0       104   9  18   7\n",
       "1        39  41  69  16"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['target'], df['cp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df['cp'], df['target']).plot(kind='bar', color=['lightblue', 'salmon']);\n",
    "\n",
    "plt.title('Heart Disease Frequency per Chest Pain level')\n",
    "plt.xlabel('Chest Pain Level')\n",
    "plt.ylabel('Count')\n",
    "plt.legend(['No Diease', 'Heart Disease'])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation between indepedent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age        -0.225439\n",
       "sex        -0.280937\n",
       "cp          0.433798\n",
       "trestbps   -0.144931\n",
       "chol       -0.085239\n",
       "fbs        -0.028046\n",
       "restecg     0.137230\n",
       "thalach     0.421741\n",
       "exang      -0.436757\n",
       "oldpeak    -0.430696\n",
       "slope       0.345877\n",
       "ca         -0.391724\n",
       "thal       -0.344029\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['target'][:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "corr_matrix = df.corr()\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(corr_matrix, annot=True, linewidth=0.5, fmt='.2f', cmap='viridis_r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per the heatmap above, `Chest pain (cp)` has the highest positive correlation with Target variable among the features, followed by `thalach (Maximum Heart Rate)` variable.\n",
    "\n",
    "On the other hand, `exang - exercise induced angina` and `oldpeak - ST depression induced by exercise relative to rest` have the lowest correlation with Target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"modelling\">5. Modelling</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split features and labels\n",
    "X = df.drop('target', axis=1)\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "\n",
       "   ca  thal  \n",
       "0   0     1  \n",
       "1   0     2  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into training, testing datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((242, 13), (61, 13), (242,), (61,))"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there is no missing data and no values to convert from categorical to numerical values, we will continue to build model and train them ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We will try 3 different models.\n",
    "\n",
    "1. Logistic Regression\n",
    "2. K-Nearest Neighbours Classifier\n",
    "3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# put models in dictionary\n",
    "\n",
    "models = {\n",
    "    'LogisticRegression': LogisticRegression(max_iter=1000),\n",
    "    'KNN': KNeighborsClassifier(),\n",
    "    'RandomForestClassifer': RandomForestClassifier()\n",
    "}\n",
    "\n",
    "# create function to fit and score model\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evalute given machine learning models.\n",
    "    models: a dictionary of different scikit learn machine learning models\n",
    "    X_train: training date (no labels)\n",
    "    X_test: testing data (no labels)\n",
    "    y_train: training labels\n",
    "    y_test : testing labels\n",
    "    returns model scores dictionary.\n",
    "    \"\"\"\n",
    "    \n",
    "    # set random seed\n",
    "    np.random.seed(42)\n",
    "    \n",
    "    # make dictonary to keep scores\n",
    "    model_scores = {}\n",
    "    \n",
    "    # loop through models to fit and score\n",
    "    for model_name, model in models.items():\n",
    "        model.fit(X_train, y_train) # fit model\n",
    "        score = model.score(X_test, y_test) # get score\n",
    "        model_scores[model_name] = score # put score for each model\n",
    "        \n",
    "    return model_scores        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'RandomForestClassifer': 0.8360655737704918}"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit and score\n",
    "model_scores = fit_and_score(models, X_train, X_test, y_train, y_test)\n",
    "\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>LogisticRegression</th>\n",
       "      <th>KNN</th>\n",
       "      <th>RandomForestClassifer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.885246</td>\n",
       "      <td>0.688525</td>\n",
       "      <td>0.836066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          LogisticRegression       KNN  RandomForestClassifer\n",
       "accuracy            0.885246  0.688525               0.836066"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=['accuracy'])\n",
    "\n",
    "model_compare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare.T.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"experimentation\">6.Experimentation</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning or Improving our models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got baseline models. and we might want to experiment to improve the results.\n",
    "\n",
    "We will be doing:\n",
    "* Hyperparameter tuning\n",
    "* Feature Importance\n",
    "* Confusion Matrix\n",
    "* Cross Validation\n",
    "* Precision\n",
    "* Recall\n",
    "* F1 Score\n",
    "* Classification Report\n",
    "* ROC curve\n",
    "* Area Under the Curve (AUC)\n",
    "\n",
    "\n",
    "### Hyperparameter Tuning\n",
    "1. [Hyperparameter Tuning - Manually](#manually)\n",
    "2. [Hyperparameter Tuning - using RandomizedSearchCV](#randomized)\n",
    "3.[Hyperparameter Tuning - using GridSearchCV](#grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='manually'>Hyperparameter Tuning - Manually</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_scores = []\n",
    "test_scores = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a different values of  parameters\n",
    "neighbours = range(1, 21)\n",
    "\n",
    "# instantiate instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# loop through different n_neighbors\n",
    "for i in neighbours:\n",
    "    # set param\n",
    "    knn.set_params(n_neighbors=i)\n",
    "    \n",
    "    # fit model\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    # get score\n",
    "    train_scores.append(knn.score(X_train, y_train))\n",
    "    test_scores.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 75.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbours, train_scores, label='Train Score')\n",
    "plt.plot(neighbours, test_scores, label='Test Score');\n",
    "\n",
    "plt.xticks(np.arange(1,21,1))\n",
    "plt.legend();\n",
    "plt.xlabel('n_neighbor')\n",
    "plt.ylabel('score');\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_scores) * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='randomized'>Hyperparameter Tuning - using RandomizedSearchCV</a>\n",
    "\n",
    "We are going to tune the following models using RandomizedSearchCV.\n",
    "\n",
    "* Logistic Regression\n",
    "\n",
    "* RandomForest Classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  help(LogisticRegression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04])"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(-4, 4, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# help(RandomForestClassifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Hyperparameter Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create hyperparameter grid for Logistic Regression\n",
    "log_reg_grid = {\n",
    "    'C': np.logspace(-4, 4, 20),\n",
    "    'solver': ['liblinear']\n",
    "}\n",
    "\n",
    "# create hyperparameter grid for Random Forest Classifier\n",
    "rf_grid = {\n",
    "    'n_estimators': np.arange(10, 1000, 50),\n",
    "    'max_depth': [None, 3, 5, 10],\n",
    "    'min_samples_split': np.arange(2, 20, 2),\n",
    "    'min_samples_leaf': np.arange(1, 20, 2)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create RandomizedSearchCV with created Hyperparameter Grid (Logistic Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "# set up random hyperparameter search for Logistic Regression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(), \n",
    "                                                        log_reg_grid, \n",
    "                                                        cv=5, \n",
    "                                                        n_iter=20, \n",
    "                                                        verbose=True)\n",
    "\n",
    "# fit random hyperparameter search model for Logistic Regression\n",
    "rs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check best parameters\n",
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the score\n",
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'RandomForestClassifer': 0.8360655737704918}"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparing with baseline scores\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create RandomizedSearchCV with created Hyperparameter Grid (Random Forest Classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:  1.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "# set up random hyperparameter search for RandomForestClassifier\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(), rf_grid, cv=5, n_iter=20, verbose=True)\n",
    "\n",
    "# fit random hyperparamter search model\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check best parameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the score\n",
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'RandomForestClassifer': 0.8360655737704918}"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparing with baseline scores\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can see that between LogisticRegression and RandomForestClassifier using RandomizedSearchCV, LogisticRegression score is better.**\n",
    "\n",
    "**So we will explore using LogisticRegression with GridSearchCV to further improve the performance.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='grid'>Hyperparameter Tuning - using GridSearchCV</a>\n",
    "\n",
    "We are going to tune the following models using GridSearchCV.\n",
    "\n",
    "* Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                         'solver': ['liblinear']},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create hyperparameter grid for Logistic Regression\n",
    "log_reg_grid = {\n",
    "    'C': np.logspace(-4, 4, 20),\n",
    "    'solver': ['liblinear']\n",
    "}\n",
    "\n",
    "# set up grid hyperparameter search for Logistic Regression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(), \n",
    "                                          log_reg_grid, \n",
    "                                          cv=5, \n",
    "                                          verbose=True)\n",
    "\n",
    "# train the model\n",
    "gs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get best parameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the score\n",
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating Models\n",
    "\n",
    "Evaluating our tuned machine learning classifiers, beyond accuracy\n",
    "\n",
    "* ROC and AUC score\n",
    "* Confusion Matrix, Plot Confusion Matrix\n",
    "* Classification Report\n",
    "* Precision\n",
    "* Recall\n",
    "* F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve and AUC\n",
    "plot_roc_curve(gs_log_reg, X_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25,  4],\n",
       "       [ 3, 29]], dtype=int64)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(gs_log_reg, X_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE: As the above `classification report` only covers ONE train test split set of Test data.**\n",
    "\n",
    "**So we may want to use cross validated precision, recall, f1 score to get the whole idea.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate evaluation metrics using Cross Validated Precision, Recall and F1 score\n",
    "\n",
    "- we will use `cross_val_score` for this with different `scoring` parameter.\n",
    "- we will create new model and validated on whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check current best parameter\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new classifier with current best parameter\n",
    "clf = LogisticRegression(C=0.23357214690901212, solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.8852459 , 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross Validated Accuracy\n",
    "cv_accuracy = cross_val_score(clf, X, y, scoring='accuracy', cv=5)\n",
    "cv_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8479781420765027"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean of cross valided accuracy\n",
    "cv_accuracy = np.mean(cv_accuracy)\n",
    "cv_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8215873015873015"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross Validated Precision\n",
    "cv_precision = cross_val_score(clf, X, y, scoring='precision', cv=5)\n",
    "\n",
    "cv_precision = np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9272727272727274"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross Validated Recall\n",
    "cv_recall = cross_val_score(clf, X, y, scoring='recall', cv=5)\n",
    "\n",
    "cv_recall = np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8705403543192143"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross Validated F1\n",
    "cv_f1 = cross_val_score(clf, X, y, scoring='f1', cv=5)\n",
    "\n",
    "cv_f1 = np.mean(cv_f1)\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({'Accuracy': cv_accuracy,\n",
    "                                            'Precision': cv_precision,\n",
    "                                            'Recall': cv_recall,\n",
    "                                            'F1': cv_f1},\n",
    "                                             index=[0])\n",
    "\n",
    "cv_metrics.T.plot.bar(legend=False);\n",
    "\n",
    "plt.title('Cross Validated Classification Metrics')\n",
    "plt.xticks(rotation=30);  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance\n",
    "\n",
    "Feature Importance is another as asking, \"which features contributed most to the outcomes of the model and how did they contribute?\n",
    "\n",
    "Finding Feature Importance is different for each machine learning model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding Feature Importance for Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.23357214690901212, solver='liblinear')"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(C=0.23357214690901212, solver='liblinear')\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00369922, -0.90424085,  0.67472828, -0.0116134 , -0.00170364,\n",
       "         0.04787689,  0.33490184,  0.02472939, -0.63120401, -0.57590906,\n",
       "         0.47095113, -0.65165351, -0.699842  ]])"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check Coefficient of features\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  "
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.003699218314634231,\n",
       " 'sex': -0.9042408481315317,\n",
       " 'cp': 0.6747282826858118,\n",
       " 'trestbps': -0.011613404239724763,\n",
       " 'chol': -0.0017036448048050072,\n",
       " 'fbs': 0.04787689050301493,\n",
       " 'restecg': 0.33490183888358416,\n",
       " 'thalach': 0.024729385199310817,\n",
       " 'exang': -0.6312040056168838,\n",
       " 'oldpeak': -0.5759090611742839,\n",
       " 'slope': 0.47095113183576787,\n",
       " 'ca': -0.6516535125690047,\n",
       " 'thal': -0.6998419969792693}"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match coef's of features to columns name\n",
    "feature_dict = dict(zip(df.columns, list(model.coef_[0])))\n",
    "\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE: Unlike correlation which is done during EDA, cofficient is model driven.**\n",
    "\n",
    "We got those coef_ values after we have the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize Feature Importance\n",
    "feature_df = pd.DataFrame(feature_dict, index=[0])\n",
    "\n",
    "feature_df.T.plot.bar(title='Feature Importance of Logistic Regression', legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target   0    1\n",
       "slope          \n",
       "0       12    9\n",
       "1       91   49\n",
       "2       35  107"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['slope'], df['target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "based on the coef_, the higher the value of slope, the model tends to predict higher value (which is 0 to 1: meaning likely to have heart disease)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "sex            \n",
       "0        24  72\n",
       "1       114  93"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['sex'], df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "72/24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8157894736842105"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "93/114"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "based on the coef_, the higher the value of sex (0 => 1), the model tends to predict lower value.\n",
    "\n",
    "Example: \n",
    "\n",
    "For Sex 0 (female), Target changes from 0 => 1 is 72/24 = 3.0\n",
    "\n",
    "For Sex 1 (male), Target changes from 0 => 1 is 93/114 = 0.8157894736842105\n",
    "\n",
    "So the value got decrease from 3.0 to 0.8157894736842105."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Experimentation\n",
    "To improve our evaluation metrics, we can\n",
    "\n",
    "* collect more data.\n",
    "* try different models like XGBoost or CatBoost.\n",
    "* improve the current model with additional hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    " # save the model\n",
    "from joblib import dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model/mdl_logistic_regression']"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(clf, 'model/mdl_logistic_regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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