heart_disease_prediction.ipynb

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   "source": [
    "# Predicting Heart Disease Using Machine Learning\n",
    "\n",
    "This notebook looks into using various Python-based machine learning and data science libraries in an attempt to build a machine-learning model capable of predicting whether someone has heart disease based on their medical information.\n",
    "\n",
    "We are going to take the following approach:\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modeling\n",
    "6. Experimentation\n",
    "\n",
    "## 1. Problem Definition\n",
    "> Given clinical parameters about a patient, can we predict whether they have heart disease?\n",
    "\n",
    "## 2. Data\n",
    "- The original data came from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+Disease\n",
    "- There is also a version of it available on Kaggle. https://www.kaggle.com/ronitf/heart-disease-uci\n",
    "\n",
    "## 3. Evaluation\n",
    "> If we can reach 95% accuracy at predicting whether a patient has heart disease during the proof of concept.\n",
    "\n",
    "## 4. Features\n",
    "**Heart Disease Data Dictionary**\n",
    "\n",
    "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 cholesterol 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 electrocardiograph 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 exercise\n",
    "    - unhealthy heart will stress more\n",
    "11. slope - the slope of the peak exercise ST segment\n",
    "    - 0: Upsloping: better heart rate with exercise (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 fluoroscopy\n",
    "    - colored vessels means the doctor can see the blood passing through\n",
    "    - the more blood movement the better (no clots)\n",
    "13. thal - thallium 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 exercising\n",
    "14. target: have disease or not (1 = yes, 0 = no)\n",
    "\n"
   ],
   "id": "6be87b5f5e59f42b"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Preparing the tools\n",
    "We're going to use pandas, matplotlib, and numpy for data analysis and manipulation."
   ],
   "id": "11cdbcc9ed24ec90"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:47.744538Z",
     "start_time": "2024-07-29T07:11:45.422203Z"
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   "cell_type": "code",
   "source": [
    "# Import all the tools we need\n",
    "\n",
    "# Regular EDA (exploratory data analysis) 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",
    "# We want our plots to appear inside the notebook\n",
    "%matplotlib inline\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 Evaluation\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 RocCurveDisplay"
   ],
   "id": "b765cd33159e19d3",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Load Data",
   "id": "56114a3315545f7c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:47.757391Z",
     "start_time": "2024-07-29T07:11:47.746423Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Create our heart disease DataFrame\n",
    "df = pd.read_csv('./data/heart-disease.csv')\n",
    "df.shape  # (rows, columns)"
   ],
   "id": "987489d4ffbc3f9e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Data Exploration (exploratory data analysis or EDA)\n",
    "\n",
    "The goal is to find out more about the data\n",
    "\n",
    "1. What question(s) are you trying to solve?\n",
    "2. What kind of data do we have and how do we treat different types?\n",
    "3. What's missing from the data and how do we deal with it?\n",
    "4. Where are the outliers and why we should care about them?\n",
    "5. How can we add, change, or remove features to get more out of the data?"
   ],
   "id": "7de4aa2bff2f73a5"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:47.782965Z",
     "start_time": "2024-07-29T07:11:47.762418Z"
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   },
   "cell_type": "code",
   "source": "df.head()",
   "id": "8eb3c87ec7006140",
   "outputs": [
    {
     "data": {
      "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",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "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",
       "4   0     2       1  "
      ],
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       "    }\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",
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       "      <td>1</td>\n",
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       "      <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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
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     "start_time": "2024-07-29T07:11:47.784697Z"
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   },
   "cell_type": "code",
   "source": [
    "# Find out how many of each class there are\n",
    "df['target'].value_counts()"
   ],
   "id": "fdc1afdf1c0bf27b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    165\n",
       "0    138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:48.083697Z",
     "start_time": "2024-07-29T07:11:47.796076Z"
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   },
   "cell_type": "code",
   "source": [
    "ax = df['target'].value_counts().plot(\n",
    "    kind = 'bar',\n",
    "    color = [\n",
    "        'salmon',\n",
    "        'lightblue',\n",
    "    ],\n",
    ")\n",
    "\n",
    "# Adding a title and labels\n",
    "plt.title('Distribution of Target Variable')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Heart Disease Status')\n",
    "\n",
    "# Customizing the x-axis labels\n",
    "ax.set_xticklabels(['Does have heart disease', 'Does not have heart disease'], rotation = 0)\n",
    "\n",
    "# Display the plot\n",
    "plt.show();"
   ],
   "id": "48e7445da05f9a0e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:56.903607Z",
     "start_time": "2024-07-29T07:12:56.892916Z"
    }
   },
   "cell_type": "code",
   "source": "df.info();",
   "id": "4ccf7c8498463b7b",
   "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"
     ]
    }
   ],
   "execution_count": 60
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:48.110082Z",
     "start_time": "2024-07-29T07:11:48.100710Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Are there any missing values?\n",
    "df.isna().sum()"
   ],
   "id": "6638c89243a371d0",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:48.162052Z",
     "start_time": "2024-07-29T07:11:48.112953Z"
    }
   },
   "cell_type": "code",
   "source": "df.describe()",
   "id": "7983af433d789b47",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ],
      "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>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</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>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</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>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Heart Disease Frequency According to Sex",
   "id": "d0e8e2172d3f9657"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:48.171351Z",
     "start_time": "2024-07-29T07:11:48.164005Z"
    }
   },
   "cell_type": "code",
   "source": "df['sex'].value_counts()",
   "id": "2c1448909a5ccb4b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "1    207\n",
       "0     96\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:48.191661Z",
     "start_time": "2024-07-29T07:11:48.173340Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(\n",
    "    df['target'],\n",
    "    df['sex'],\n",
    ")  # target is displayed vertically, sex horizontally"
   ],
   "id": "6b3679832928a43a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
      ],
      "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>sex</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</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>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:48.494299Z",
     "start_time": "2024-07-29T07:11:48.193580Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Create a plot of crosstab\n",
    "\n",
    "pd.crosstab(df['target'], df['sex']).plot(\n",
    "    kind = 'bar',\n",
    "    figsize = (10, 6),\n",
    "    color = ['salmon', 'lightblue']\n",
    ")\n",
    "\n",
    "plt.title('Heart Disease Frequency Among Male and Female')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('0 = No Heart Disease, 1 = Heart Disease')\n",
    "plt.legend(['Female', 'Male'])\n",
    "plt.xticks(rotation = 0)\n",
    "\n",
    "# Customizing the x-axis labels\n",
    "ax.set_xticklabels(['Does have heart disease', 'Does not have heart disease'], rotation = 0);"
   ],
   "id": "7f28a3ab39985e51",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Age vs Max Heart Rate for Heart Disease",
   "id": "44f639ce7049293"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:49.043351Z",
     "start_time": "2024-07-29T07:11:48.503008Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Create another figure\n",
    "plt.figure(\n",
    "    figsize = (10, 6)\n",
    ")\n",
    "\n",
    "# Scatter with positive examples\n",
    "plt.scatter(\n",
    "    df.age[df.target == 1],\n",
    "    df.thalach[df.target == 1],\n",
    "    c = 'salmon'\n",
    ")\n",
    "\n",
    "# Scatter with negative examples\n",
    "plt.scatter(\n",
    "    df.age[df.target == 0],\n",
    "    df.thalach[df.target == 0],\n",
    "    c = 'lightblue',\n",
    ")\n",
    "\n",
    "# Add some helpful information\n",
    "plt.title('Heart Disease in Function of Age and Max Heart Rate')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Heart Rate')\n",
    "plt.legend(['Disease', 'No Heart Disease']);"
   ],
   "id": "3a226838ce96010d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:49.348403Z",
     "start_time": "2024-07-29T07:11:49.045408Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Check the distribution of the age column with a histogram\n",
    "df.age.plot.hist()\n",
    "\n",
    "# Adding a title and labels\n",
    "plt.title('Age Distributions of Patients')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Display the plot\n",
    "plt.show();"
   ],
   "id": "d55f9f278aba1e05",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Heart Disease Frequency per Chest Pain Type\n",
    "\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"
   ],
   "id": "6b1bee9320c1687"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:49.374323Z",
     "start_time": "2024-07-29T07:11:49.351198Z"
    }
   },
   "cell_type": "code",
   "source": "pd.crosstab(df.cp, df.target)",
   "id": "a0555afea27c712a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target    0   1\n",
       "cp             \n",
       "0       104  39\n",
       "1         9  41\n",
       "2        18  69\n",
       "3         7  16"
      ],
      "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>cp</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>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:49.691301Z",
     "start_time": "2024-07-29T07:11:49.376203Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Make the crosstab more visual\n",
    "pd.crosstab(df.cp, df.target).plot(\n",
    "    kind = 'bar',\n",
    "    figsize = (10, 6),\n",
    "    color = [\n",
    "        'lightblue',\n",
    "        'salmon'\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Add some communication\n",
    "plt.title('Heart Disease Frequency per Chest Pain Type')\n",
    "plt.xlabel('Chest Pain Type')\n",
    "plt.ylabel('Count')\n",
    "plt.legend(['No Disease', 'Disease'])\n",
    "plt.xticks(rotation = 0);"
   ],
   "id": "205abfa36a75296e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:49.716808Z",
     "start_time": "2024-07-29T07:11:49.693254Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Make correlation matrix\n",
    "df.corr()"
   ],
   "id": "70e6917324f1cbcd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ],
      "text/html": [
       "<div>\n",
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       "<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>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:50.845775Z",
     "start_time": "2024-07-29T07:11:49.719574Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Let's make our correlation matrix a little prettier\n",
    "\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize = (15, 10))\n",
    "sns.heatmap(\n",
    "    corr_matrix,\n",
    "    annot = True,\n",
    "    linewidths = 0.5,\n",
    "    fmt = '.2f',\n",
    "    cmap = 'YlGnBu',\n",
    ");"
   ],
   "id": "6cff27016437615f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 5. Modeling",
   "id": "9842dfed47a7c64a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:50.860333Z",
     "start_time": "2024-07-29T07:11:50.847872Z"
    }
   },
   "cell_type": "code",
   "source": "df.head()",
   "id": "c070442284651ecd",
   "outputs": [
    {
     "data": {
      "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",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "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",
       "4   0     2       1  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:50.871547Z",
     "start_time": "2024-07-29T07:11:50.863511Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Split data into X and y\n",
    "X = df.drop('target', axis = 1)\n",
    "y = df['target']"
   ],
   "id": "d3495934f149e3e4",
   "outputs": [],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:50.881753Z",
     "start_time": "2024-07-29T07:11:50.874061Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Split data into train and test sets\n",
    "np.random.seed(42)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)"
   ],
   "id": "ae9be2cc0748761",
   "outputs": [],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:50.904598Z",
     "start_time": "2024-07-29T07:11:50.885984Z"
    }
   },
   "cell_type": "code",
   "source": "X_train",
   "id": "ba29803763c3505b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "132   42    1   1       120   295    0        1      162      0      0.0   \n",
       "202   58    1   0       150   270    0        0      111      1      0.8   \n",
       "196   46    1   2       150   231    0        1      147      0      3.6   \n",
       "75    55    0   1       135   250    0        0      161      0      1.4   \n",
       "176   60    1   0       117   230    1        1      160      1      1.4   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "188   50    1   2       140   233    0        1      163      0      0.6   \n",
       "71    51    1   2        94   227    0        1      154      1      0.0   \n",
       "106   69    1   3       160   234    1        0      131      0      0.1   \n",
       "270   46    1   0       120   249    0        0      144      0      0.8   \n",
       "102   63    0   1       140   195    0        1      179      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "132      2   0     2  \n",
       "202      2   0     3  \n",
       "196      1   0     2  \n",
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       "..     ...  ..   ...  \n",
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       "106      1   1     2  \n",
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       "\n",
       "[242 rows x 13 columns]"
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      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:50.916589Z",
     "start_time": "2024-07-29T07:11:50.907962Z"
    }
   },
   "cell_type": "code",
   "source": "y_train",
   "id": "bac7bd719c5a9bef",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132    1\n",
       "202    0\n",
       "196    0\n",
       "75     1\n",
       "176    0\n",
       "      ..\n",
       "188    0\n",
       "71     1\n",
       "106    1\n",
       "270    0\n",
       "102    1\n",
       "Name: target, Length: 242, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Now we've got our data split into training and test sets, it's time to create an ML model\n",
    "\n",
    "* We'll train it (find the patterns) on the training set\n",
    "* And we'll test it (use the patterns) on the test set\n",
    "\n",
    "We're going to try 3 different machine learning models\n",
    "\n",
    "1. Logistic Regression\n",
    "2. K-Nearest Neighbours Classifier\n",
    "3. Random Forest Classifier"
   ],
   "id": "f1c073415df22d4"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:16:29.121991Z",
     "start_time": "2024-07-29T07:16:29.111036Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "# Put models in a dict\n",
    "models = {\n",
    "    'Logistic Regression': LogisticRegression(\n",
    "        solver = 'liblinear',\n",
    "        max_iter = 1000,\n",
    "    ),\n",
    "    'KNN': KNeighborsClassifier(),\n",
    "    'Random Forest': RandomForestClassifier()\n",
    "}\n",
    "\n",
    "\n",
    "# Create a function to fit and score models\n",
    "def fit_and_score(\n",
    "        models: dict,\n",
    "        X_train: DataFrame,\n",
    "        X_test: DataFrame,\n",
    "        y_train: DataFrame,\n",
    "        y_test: DataFrame\n",
    ") -> dict:\n",
    "    \"\"\"\n",
    "    Fits and evaluates given machine learning models\n",
    "    :param models: a dict of different Scikit-Learn machine learning models\n",
    "    :param X_train: training data (no labels)\n",
    "    :param X_test: testing data (no labels)\n",
    "    :param y_train: training labels\n",
    "    :param y_test: testing labels\n",
    "    \"\"\"\n",
    "\n",
    "    # Set random seed\n",
    "    np.random.seed(42)\n",
    "\n",
    "    # Make a dict to keep model scores\n",
    "    model_scores = {}\n",
    "\n",
    "    # Loop through models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(X_train, y_train)\n",
    "\n",
    "        # Eval the model and append its score to model_scores\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "\n",
    "    return model_scores"
   ],
   "id": "35c1a117d8da75dc",
   "outputs": [],
   "execution_count": 63
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:16:30.291355Z",
     "start_time": "2024-07-29T07:16:30.112847Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model_scores = fit_and_score(models, X_train, X_test, y_train, y_test)\n",
    "model_scores"
   ],
   "id": "f0265e986994870c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8688524590163934,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 64
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Model Comparison",
   "id": "98ba3a0ee1084d45"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:51.355060Z",
     "start_time": "2024-07-29T07:11:51.117941Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model_compare = pd.DataFrame(model_scores, index = ['Accuracy'])\n",
    "model_compare.T.plot.bar();"
   ],
   "id": "aee26fce8b763cf6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Let's look at the following:\n",
    "\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 on KNN"
   ],
   "id": "510bfd61a8fe0dc"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:51.693839Z",
     "start_time": "2024-07-29T07:11:51.357229Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Let's tune KNN\n",
    "\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of different values for n_neighbours\n",
    "neighbours = range(1, 21)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different n_neighbours\n",
    "for i in neighbours:\n",
    "    knn.set_params(n_neighbors = i)\n",
    "\n",
    "    # Fit the algorithm\n",
    "    knn.fit(X_train, y_train)\n",
    "\n",
    "    # Update training scores lists\n",
    "    train_scores.append(knn.score(X_train, y_train))\n",
    "\n",
    "    # Update the test scores lists\n",
    "    test_scores.append(knn.score(X_test, y_test))"
   ],
   "id": "8e18a56043dce7ba",
   "outputs": [],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:51.702748Z",
     "start_time": "2024-07-29T07:11:51.696354Z"
    }
   },
   "cell_type": "code",
   "source": "train_scores",
   "id": "c7ef6c430000d0dd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.8099173553719008,\n",
       " 0.7727272727272727,\n",
       " 0.743801652892562,\n",
       " 0.7603305785123967,\n",
       " 0.7520661157024794,\n",
       " 0.743801652892562,\n",
       " 0.7231404958677686,\n",
       " 0.71900826446281,\n",
       " 0.6942148760330579,\n",
       " 0.7272727272727273,\n",
       " 0.6983471074380165,\n",
       " 0.6900826446280992,\n",
       " 0.6942148760330579,\n",
       " 0.6859504132231405,\n",
       " 0.6735537190082644,\n",
       " 0.6859504132231405,\n",
       " 0.6652892561983471,\n",
       " 0.6818181818181818,\n",
       " 0.6694214876033058]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:51.713059Z",
     "start_time": "2024-07-29T07:11:51.706773Z"
    }
   },
   "cell_type": "code",
   "source": "test_scores",
   "id": "98344c1f1993958f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6229508196721312,\n",
       " 0.639344262295082,\n",
       " 0.6557377049180327,\n",
       " 0.6721311475409836,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.7049180327868853,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.7540983606557377,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.6557377049180327]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:52.243969Z",
     "start_time": "2024-07-29T07:11:51.715568Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.plot(neighbours, train_scores, label = 'Train score')\n",
    "plt.plot(neighbours, test_scores, label = 'Test score')\n",
    "plt.xticks(np.arange(1, 21, 1))\n",
    "plt.xlabel('Number of neighbours')\n",
    "plt.ylabel('Model score')\n",
    "plt.legend()\n",
    "\n",
    "print(f'Maximum KNN score on the test data {max(test_scores) * 100:.2f}%')"
   ],
   "id": "ce26dced944f6d4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data 75.41%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Hyperparameter Tuning with RandomizedSearchCV\n",
    "\n",
    "We're going to tune:\n",
    "* LogisticRegression()\n",
    "* RandomForestClassifier()\n",
    "\n",
    "Using RandomizedSearchCV"
   ],
   "id": "84eec1f784a9e0a6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:52.253894Z",
     "start_time": "2024-07-29T07:11:52.246752Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Create a hyperparameter grid for LogisticRegression\n",
    "log_reg_grid = {\n",
    "    'C': np.logspace(\n",
    "        -4,\n",
    "        4,\n",
    "        20,\n",
    "    ),\n",
    "    'solver': ['liblinear']\n",
    "}\n",
    "\n",
    "# Create hyperparameters grid for RandomForestClassifier\n",
    "rf_grid = {\n",
    "    'n_estimators': np.arange(\n",
    "        10,\n",
    "        1000,\n",
    "        50\n",
    "    ),\n",
    "    'max_depth': [\n",
    "        None,\n",
    "        3,\n",
    "        5,\n",
    "        10,\n",
    "    ],\n",
    "    'min_samples_split': np.arange(\n",
    "        2,\n",
    "        20,\n",
    "        2\n",
    "    ),\n",
    "    'min_samples_leaf': np.arange(\n",
    "        1,\n",
    "        20,\n",
    "        2\n",
    "    ),\n",
    "}"
   ],
   "id": "221efb31728b851f",
   "outputs": [],
   "execution_count": 30
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Now we've got hyperparameter grids setup for each of our models, let's tune them using RandomizedSearchCV",
   "id": "c1c104cdfa4d692a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:13:35.334170Z",
     "start_time": "2024-07-29T07:13:34.623004Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Tune Logistic Regression\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(\n",
    "    LogisticRegression(),\n",
    "    param_distributions = log_reg_grid,\n",
    "    cv = 5,\n",
    "    n_iter = 20,\n",
    "    verbose = True,\n",
    ")\n",
    "\n",
    "# Fit random hyperparameter search  model for LogisticRegression\n",
    "rs_log_reg.fit(X_train, y_train);"
   ],
   "id": "9767451f2715ec1a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    }
   ],
   "execution_count": 61
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:52.918500Z",
     "start_time": "2024-07-29T07:11:52.912588Z"
    }
   },
   "cell_type": "code",
   "source": "rs_log_reg.best_params_",
   "id": "c49f0a19c8757800",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:11:52.927693Z",
     "start_time": "2024-07-29T07:11:52.920300Z"
    }
   },
   "cell_type": "code",
   "source": "rs_log_reg.score(X_test, y_test)",
   "id": "e0bb5e095621c5bd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Let's tune RandomForestClassifier()",
   "id": "2f1860bac0f43bda"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:54.498442Z",
     "start_time": "2024-07-29T07:11:52.929506Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter for RandomForestClassifier\n",
    "rs_rf = RandomizedSearchCV(\n",
    "    RandomForestClassifier(),\n",
    "    param_distributions = rf_grid,\n",
    "    cv = 5,\n",
    "    n_iter = 20,\n",
    "    verbose = True,\n",
    ")\n",
    "\n",
    "# Fit random hyperparameter search model for RandomForestClassifier()\n",
    "rs_rf.fit(X_train, y_train)"
   ],
   "id": "a1c8ee679595128c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\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)"
      ],
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: 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)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: 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)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:54.505662Z",
     "start_time": "2024-07-29T07:12:54.500331Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Find the best hyperparameters\n",
    "rs_rf.best_params_"
   ],
   "id": "3237a22a37867197",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:54.535791Z",
     "start_time": "2024-07-29T07:12:54.508210Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Evaluate the randomized search RandomForestClassifier model\n",
    "rs_rf.score(X_test, y_test)"
   ],
   "id": "4d4dbae35f0afa7c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 36
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Hyperparameter Tuning with GridSearchCV\n",
    "\n",
    "Since our LogisticRegression model provides the best scores so far, we'll try and improve them again using GridSearchCV"
   ],
   "id": "ded8a381664ebdd8"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.341057Z",
     "start_time": "2024-07-29T07:12:54.538762Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Different hyperparameters for our LogisticRegression model\n",
    "\n",
    "log_reg_grid = {\n",
    "    'C': np.logspace(-4, 4, 30),\n",
    "    'solver': ['liblinear'],\n",
    "}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression \n",
    "\n",
    "gs_log_reg = GridSearchCV(\n",
    "    LogisticRegression(),\n",
    "    param_grid = log_reg_grid,\n",
    "    cv = 5,\n",
    "    verbose = True,\n",
    ")\n",
    "\n",
    "# Fit the model\n",
    "gs_log_reg.fit(X_train, y_train);"
   ],
   "id": "7928dc97fd7536da",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    }
   ],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.349113Z",
     "start_time": "2024-07-29T07:12:55.343779Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Check the best hyperparameters\n",
    "gs_log_reg.best_params_"
   ],
   "id": "1830c7274efa670b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.372552Z",
     "start_time": "2024-07-29T07:12:55.362478Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Evaluate the model\n",
    "gs_log_reg.score(X_test, y_test)"
   ],
   "id": "68b23071ad332101",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 39
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.382719Z",
     "start_time": "2024-07-29T07:12:55.374516Z"
    }
   },
   "cell_type": "code",
   "source": "model_scores",
   "id": "1796cdd636b2866",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 40
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Evaluating our tuned machine learning classifier, beyond accuracy\n",
    "\n",
    "* ROC curve and AUC score\n",
    "* Confusion matrix\n",
    "* Classification report\n",
    "* Precision\n",
    "* Recall\n",
    "* F1-score\n",
    "\n",
    "To make comparisons and evaluate our trained model, first we need to make predictions"
   ],
   "id": "90e942f72d3f77db"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.393088Z",
     "start_time": "2024-07-29T07:12:55.385613Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Make predictions with tuned model\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ],
   "id": "ccded9d46803aac0",
   "outputs": [],
   "execution_count": 41
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.402124Z",
     "start_time": "2024-07-29T07:12:55.395461Z"
    }
   },
   "cell_type": "code",
   "source": "y_preds",
   "id": "baac5a6b099d857d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 42
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.415959Z",
     "start_time": "2024-07-29T07:12:55.405316Z"
    }
   },
   "cell_type": "code",
   "source": "y_test",
   "id": "c3f01bb54cfadefe",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179    0\n",
       "228    0\n",
       "111    1\n",
       "246    0\n",
       "60     1\n",
       "      ..\n",
       "249    0\n",
       "104    1\n",
       "300    0\n",
       "193    0\n",
       "184    0\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 43
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.698931Z",
     "start_time": "2024-07-29T07:12:55.418158Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Plot ROC curve and calculate and calculate AUC metric\n",
    "RocCurveDisplay.from_estimator(gs_log_reg, X_test, y_test);"
   ],
   "id": "7e14972d32bf518e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 44
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.711717Z",
     "start_time": "2024-07-29T07:12:55.701685Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Confusion matrix\n",
    "print(confusion_matrix(y_test, y_preds))"
   ],
   "id": "16471762f250ed3b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "execution_count": 45
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.849309Z",
     "start_time": "2024-07-29T07:12:55.714258Z"
    }
   },
   "cell_type": "code",
   "source": [
    "sns.set(font_scale = 1.5)\n",
    "\n",
    "def plot_confusion_matrix(y_test: DataFrame, y_preds: DataFrame):\n",
    "    \"\"\"\n",
    "    Plots a nice looking confusion matrix using Seaborn's heatmap()\n",
    "    \"\"\"\n",
    "    \n",
    "    plt.subplots(figsize = (3, 3))\n",
    "    sns.heatmap(\n",
    "        confusion_matrix(y_test, y_preds),\n",
    "        annot = True,\n",
    "        cbar = False,\n",
    "    )\n",
    "    plt.xlabel(\"Predicted label\")\n",
    "    plt.ylabel(\"True label\")\n",
    "plot_confusion_matrix(y_test, y_preds)"
   ],
   "id": "81b417e2442511e8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 46
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Classification report and cross-validated precision, recall, and F1 score",
   "id": "b70973c2c3210b9c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.867392Z",
     "start_time": "2024-07-29T07:12:55.851820Z"
    }
   },
   "cell_type": "code",
   "source": "print(classification_report(y_test, y_preds))",
   "id": "d372e0a89d0e0aa0",
   "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"
     ]
    }
   ],
   "execution_count": 47
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Calculate evaluation metrics using cross-validation\n",
    "\n",
    "We're going to calculate accuracy, precision, recall, and F1-score of our model using cross validation and to do so, we'll be using `cross_val_score()`"
   ],
   "id": "b721d29d1896b947"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.876240Z",
     "start_time": "2024-07-29T07:12:55.870171Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ],
   "id": "17069d8b024a4259",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 48
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.882586Z",
     "start_time": "2024-07-29T07:12:55.878996Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Create a new classifier with the best params\n",
    "clf = LogisticRegression(\n",
    "    C = 0.20433597178569418,\n",
    "    solver = 'liblinear',\n",
    ")"
   ],
   "id": "22d0103d9531d4eb",
   "outputs": [],
   "execution_count": 49
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.921998Z",
     "start_time": "2024-07-29T07:12:55.884737Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Cross-validated accuracy\n",
    "cv_acc = cross_val_score(\n",
    "    clf,\n",
    "    X,\n",
    "    y,\n",
    "    cv = 5,\n",
    "    scoring = 'accuracy'\n",
    ")\n",
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ],
   "id": "66a9760caa7b412b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446994535519124"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 50
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:55.982717Z",
     "start_time": "2024-07-29T07:12:55.923945Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Cross-validated precision\n",
    "cv_precision = cross_val_score(\n",
    "    clf,\n",
    "    X,\n",
    "    y,\n",
    "    cv = 5,\n",
    "    scoring = 'precision'\n",
    ")\n",
    "cv_precision = np.mean(cv_precision)\n",
    "cv_precision"
   ],
   "id": "f02e9aed9437c8a2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8207936507936507"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 51
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:56.036551Z",
     "start_time": "2024-07-29T07:12:55.984804Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Cross-validated recall\n",
    "cv_recall = cross_val_score(\n",
    "    clf,\n",
    "    X,\n",
    "    y,\n",
    "    cv = 5,\n",
    "    scoring = 'recall'\n",
    ")\n",
    "cv_recall = np.mean(cv_recall)\n",
    "cv_recall"
   ],
   "id": "3b6f9da2553b99a6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212121212121213"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 52
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:56.083203Z",
     "start_time": "2024-07-29T07:12:56.038223Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Cross-validated F1\n",
    "cv_f1 = cross_val_score(\n",
    "    clf,\n",
    "    X,\n",
    "    y,\n",
    "    cv = 5,\n",
    "    scoring = 'f1'\n",
    ")\n",
    "cv_f1 = np.mean(cv_f1)\n",
    "cv_f1"
   ],
   "id": "78d38fb5385a9163",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673007976269721"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 53
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:56.393751Z",
     "start_time": "2024-07-29T07:12:56.085237Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Visualize cross-validated metrics\n",
    "\n",
    "cv_metrics = pd.DataFrame(\n",
    "    {\n",
    "        'Accuracy': cv_acc,\n",
    "        'Precision': cv_precision,\n",
    "        'Recall': cv_recall,\n",
    "        'F1': cv_f1\n",
    "    },\n",
    "    index = [0]\n",
    ")\n",
    "\n",
    "cv_metrics.T.plot.bar(\n",
    "    title = 'Cross-validated classification metrics',\n",
    "    legend = False,\n",
    ");"
   ],
   "id": "6a452fca68e53c20",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 54
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Feature Importance\n",
    "\n",
    "Which features contributed most to the outcome of the model, and how did they contribute?\n",
    "\n",
    "Finding feature importance is different for each ML model\n",
    "\n",
    "Let's find the feature importance for our LogisticRegression model"
   ],
   "id": "c89303fc25ca8987"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:56.406097Z",
     "start_time": "2024-07-29T07:12:56.395405Z"
    }
   },
   "cell_type": "code",
   "source": "df.head()",
   "id": "bfd3e2da8d7ff77d",
   "outputs": [
    {
     "data": {
      "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",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "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",
       "4   0     2       1  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 55
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:56.417707Z",
     "start_time": "2024-07-29T07:12:56.408761Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Fit an instance of LogisticRegression\n",
    "clf = LogisticRegression(\n",
    "    C = 0.20433597178569418,\n",
    "    solver = 'liblinear',\n",
    ")\n",
    "\n",
    "clf.fit(X_train, y_train);"
   ],
   "id": "d4aef596e4da1e21",
   "outputs": [],
   "execution_count": 56
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:56.426284Z",
     "start_time": "2024-07-29T07:12:56.419709Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ],
   "id": "65bac24737f03da7",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00320769, -0.86062049,  0.66001432, -0.01155971, -0.00166496,\n",
       "         0.04017236,  0.31603405,  0.02458922, -0.60470171, -0.56795456,\n",
       "         0.45085392, -0.63733328, -0.67555094]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 57
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:56.434792Z",
     "start_time": "2024-07-29T07:12:56.428634Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Match coef's of features to columns\n",
    "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "feature_dict"
   ],
   "id": "74725e1c860f6e92",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.0032076883508599633,\n",
       " 'sex': -0.8606204883695241,\n",
       " 'cp': 0.660014324982524,\n",
       " 'trestbps': -0.01155970600550047,\n",
       " 'chol': -0.0016649614843449207,\n",
       " 'fbs': 0.040172360271308105,\n",
       " 'restecg': 0.31603405294617176,\n",
       " 'thalach': 0.02458922341328129,\n",
       " 'exang': -0.604701713592625,\n",
       " 'oldpeak': -0.5679545646616215,\n",
       " 'slope': 0.4508539209693025,\n",
       " 'ca': -0.6373332766360461,\n",
       " 'thal': -0.6755509369619848}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 58
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-29T07:12:56.886292Z",
     "start_time": "2024-07-29T07:12:56.437142Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Visualize feature importance\n",
    "feature_df = pd.DataFrame(feature_dict, index = [0])\n",
    "feature_df.T.plot.bar(\n",
    "    title = 'Feature Importance',\n",
    "    legend = False,\n",
    ");"
   ],
   "id": "5ba235ee51703a06",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 59
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}