end-to-end-heart-disease-classification.ipynb

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    "# 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 or not someone has heart disease based on their medical attributes.\n",
    "\n",
    "We're going to take the following approach:\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling\n",
    "6. Experimentation\n",
    "\n",
    "## 1. Problem Definition\n",
    "\n",
    "In a statement,\n",
    "> Given clinical parameters about a patient, can we predict whether or not they have heart disease?\n",
    "\n",
    "## 2. Data\n",
    "\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",
    "\n",
    "There is also a version of it available on Kaggle. https://www.kaggle.com/datasets/sumaiyatasmeem/heart-disease-classification-dataset\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "> If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project.\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "This is where you'll get different information about each of the features in your data. You can do this via doing your own research (such as looking at the links above) or by talking to a subject matter expert (someone who knows about the dataset).\n",
    "\n",
    "**Create data dictionary**\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) anything above 130-140 is typically cause for concern\n",
    "5. chol - serum cholestoral in mg/dl\n",
    "    * serum = LDL + HDL + .2 * triglycerides\n",
    "    * above 200 is cause for concern\n",
    "6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "    * '>126' mg/dL signals diabetes\n",
    "7. restecg - resting electrocardiographic results\n",
    "    * 0: Nothing to note\n",
    "    * 1: ST-T Wave abnormality\n",
    "        * can range from mild symptoms to severe problems\n",
    "        * signals non-normal heart beat\n",
    "    * 2: Possible or definite left ventricular hypertrophy\n",
    "        * Enlarged heart's main pumping chamber\n",
    "8. thalach - maximum heart rate achieved\n",
    "9. exang - exercise induced angina (1 = yes; 0 = no)\n",
    "10. oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more\n",
    "11. slope - the slope of the peak exercise ST segment\n",
    "    * 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "    * 1: Flatsloping: minimal change (typical healthy heart)\n",
    "    * 2: Downslopins: signs of unhealthy heart\n",
    "12. ca - number of major vessels (0-3) colored by flourosopy\n",
    "    * colored vessel means the doctor can see the blood passing through\n",
    "    * the more blood movement the better (no clots)\n",
    "13. thal - thalium stress result\n",
    "    * 1,3: normal\n",
    "    * 6: fixed defect: used to be defect but ok now\n",
    "    * 7: reversable defect: no proper blood movement when excercising\n",
    "14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)ntation"
   ]
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   "source": [
    "## Preparing the tools\n",
    "\n",
    "We're going to use pandas, Matplotlib and NumPy for data analysis and manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "d4ad9b69-5794-48d7-b089-fddb5994f662",
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   "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 Evaluations\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import RocCurveDisplay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e394219-54ce-4c60-ae82-2f122063b3ba",
   "metadata": {},
   "source": [
    "## Load  data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5120e233-dbb6-4ae4-ba9d-f2a26168a90b",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.read_csv(\"heart-disease.csv\")\n",
    "df.shape # (rows, columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e578f3e3-6aff-4daf-9ed3-4896ebe6d6e8",
   "metadata": {},
   "source": [
    "## Data Exploration (exploratory data analysis or EDA)\n",
    "\n",
    "The goal here is to find out more about the data and become a subject matter export on the dataset you're working with. \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 you deal with it?\n",
    "4. Where are the outliers and why should you care about them?\n",
    "5. How can you add, change or remove features to get more out of your data?"
   ]
  },
  {
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   "execution_count": 11,
   "id": "b10612b6-026d-459f-a26c-a56e0a20b7b1",
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       "      <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",
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       "      <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",
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      "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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     "execution_count": 11,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22e54675-eba0-4a92-8b91-8f589b023911",
   "metadata": {},
   "outputs": [
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       "      <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",
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       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>57</td>\n",
       "      <td>1</td>\n",
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       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ebb50e45-2d74-4fdb-8ea4-6f767316d0e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    165\n",
       "0    138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find out how many of each class there are\n",
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d079a7f5-ca86-4124-ae68-cf1a8aa01e49",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"target\"].value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5b10f617-eea9-41ef-80eb-827009026a64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2b2b6b9c-f792-4b18-bb11-9ad2b5aa012c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are there any missing values?\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ddb2cce5-9123-4da0-912d-53552230a0c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>"
      ],
      "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  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2b2e93d-e79e-456f-b1ad-312ac32c182c",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency according to Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fd025117-6720-45a9-bf6b-446cfc56d008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "1    207\n",
       "0     96\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7b4d9ac2-e463-429e-9cff-09e59cb2b694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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>"
      ],
      "text/plain": [
       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "addb10b4-253c-4334-99a1-40dc16daa9f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot of crosstab\n",
    "pd.crosstab(df.target, df.sex).plot(kind=\"bar\",\n",
    "                                    figsize=(10, 6),\n",
    "                                    color=[\"salmon\", \"lightblue\"])\n",
    "\n",
    "plt.title(\"Heart Disease Frequency for Sex\")\n",
    "plt.xlabel(\"0 = No Disease, 1 = Disease\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Female\", \"Male\"]);\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df977ad6-4555-45da-af74-678d9fc5d239",
   "metadata": {},
   "source": [
    "### Age vs. Max Heart Rate for Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "67aca978-4c3b-4d06-a07e-f89f8e072e79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create another figure\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Scatter with postive examples\n",
    "plt.scatter(df.age[df.target==1],\n",
    "            df.thalach[df.target==1],\n",
    "            color=\"salmon\")\n",
    "\n",
    "# Scatter with negative examples\n",
    "plt.scatter(df.age[df.target==0],\n",
    "            df.thalach[df.target==0],\n",
    "            color=\"lightblue\")\n",
    "\n",
    "# Add some helpful info\n",
    "plt.title(\"Heart Disease in function of Age and Max Heart Rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Max Heart Rate\")\n",
    "plt.legend([\"Disease\", \"No Disease\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7a35c8ab-7f3f-4194-9972-f7f9649564ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of the age column with a histogram\n",
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9493f69f-f308-44a8-8b6b-6ff49460f72e",
   "metadata": {},
   "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e96a11a0-fea3-4432-b0d7-511d7d4c57d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>"
      ],
      "text/plain": [
       "target    0   1\n",
       "cp             \n",
       "0       104  39\n",
       "1         9  41\n",
       "2        18  69\n",
       "3         7  16"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5053e35a-9a06-4b68-96ce-997da9705a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3]),\n",
       " [Text(0, 0, '0'), Text(1, 0, '1'), Text(2, 0, '2'), Text(3, 0, '3')])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the crosstab more visual\n",
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\",\n",
    "                                   figsize=(10, 6),\n",
    "                                   color=[\"salmon\", \"lightblue\"])\n",
    "\n",
    "# Add some communication\n",
    "plt.title(\"Heart Disease Frequency Per Chest Pain Type\")\n",
    "plt.xlabel(\"Chest Pain Type\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"No Disease\", \"Disease\"])\n",
    "plt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "63cb5f06-1ca7-4405-b3e2-d369f90053b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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>"
      ],
      "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  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9d8c0c4b-333f-40f3-95a4-6d236f5dde99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>"
      ],
      "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  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7e29d7d5-3586-40f9-9155-1eb9a29e57c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's make our correlation matrix a little prettier\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                 annot=True,\n",
    "                 linewidths=0.5,\n",
    "                 fmt=\".2f\",\n",
    "                 cmap=\"YlGnBu\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a16a10ec-aadb-4b83-94f4-9785051c79af",
   "metadata": {},
   "source": [
    "## 5. Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7ebc55ed-a037-4b3e-be4b-12be2e7cfe88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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>"
      ],
      "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  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fa863072-2926-492c-8a5f-2e1c13cd6bf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into X and y \n",
    "X = df.drop(\"target\", axis=1)\n",
    "y = df[\"target\"]"
   ]
  },
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    {
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   "execution_count": 69,
   "id": "e26014e0-2550-4613-8246-9aea93b9b8c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train and test sets\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into train & test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y,\n",
    "                                                    test_size=0.2)"
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   "execution_count": 70,
   "id": "3cf74036-d286-4bd8-be4b-92fb6c11a0e2",
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      "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",
       "75       1   0     2  \n",
       "176      2   2     3  \n",
       "..     ...  ..   ...  \n",
       "188      1   1     3  \n",
       "71       2   1     3  \n",
       "106      1   1     2  \n",
       "270      2   0     3  \n",
       "102      2   2     2  \n",
       "\n",
       "[242 rows x 13 columns]"
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     "execution_count": 70,
     "metadata": {},
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   ],
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   "execution_count": 72,
   "id": "8d47036b-6d02-435b-af1e-8a8624dc5c75",
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   "outputs": [
    {
     "data": {
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       "(132    1\n",
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       " 196    0\n",
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       " 176    0\n",
       "       ..\n",
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   "source": [
    "Now we've got our data split into training and test sets, it's time to build a machine learning model.\n",
    "\n",
    "We'll train it (find the patterns) on the training set.\n",
    "\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",
    "1. Logistic Regression \n",
    "2. K-Nearest Neighbours Classifier\n",
    "3. Random Forest Classifierfier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1c29791e-2383-4ca2-a111-45566fd60435",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {\"Logistic Regression\": LogisticRegression(),\n",
    "          \"KNN\": KNeighborsClassifier(),\n",
    "          \"Random Forest\": RandomForestClassifier()}\n",
    "\n",
    "# Create a function to fit and score models\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluates given machine learning models.\n",
    "    models : a dict of different Scikit-Learn machine learning models\n",
    "    X_train : training data (no labels)\n",
    "    X_test : testing data (no labels)\n",
    "    y_train : training labels\n",
    "    y_test : test labels\n",
    "    \"\"\"\n",
    "    # set Random seed\n",
    "    np.random.seed(42)\n",
    "    # Make a dictionary to keep model scores\n",
    "    model_scores = {}\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",
    "        # Evaluate the model and append its score to model_scores\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "61080be6-263a-4ba0-9b7f-0d1758d872f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Desktop\\sample_project\\heart-disease-project\\env\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models=models,\n",
    "                             X_train=X_train,\n",
    "                             X_test=X_test,\n",
    "                             y_train=y_train,\n",
    "                             y_test=y_test)\n",
    "\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "150b85fa-0c44-4d4e-8110-d7c8873e3b65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=[\"accuracy\"])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c7387c8-23b1-44bc-a2bb-8d36a5c276f0",
   "metadata": {},
   "source": [
    "Now we've got a baseline model... and we know a model's first predictions aren't always what we should based our next steps off. What should we do?\n",
    "\n",
    "Let's look at the following:\n",
    "* Hypyterparameter 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",
    "### Hyperparameter tuning (by hand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3401cda2-8d95-4f6a-a330-4ac8a515f0a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune KNN\n",
    "\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of different values for n_neighbors\n",
    "neighbors = range(1, 21)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "\n",
    "    # Fit the algorithm\n",
    "    knn.fit(X_train, y_train)\n",
    "\n",
    "    # Update the training scores list\n",
    "    train_scores.append(knn.score(X_train, y_train))\n",
    "\n",
    "    # Update the test scores list\n",
    "    test_scores.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "edd6904c-585b-40d9-a0ba-6b380fe09a3c",
   "metadata": {},
   "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": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "439e706e-b1a7-428b-a179-17446c875120",
   "metadata": {},
   "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": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "813a9e25-5356-4a91-878d-811b99cfa509",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 75.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors, train_scores, label=\"Test score\")\n",
    "plt.plot(neighbors, test_scores, label=\"Test score\")\n",
    "plt.xticks(np.arange(1, 21, 1))\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be7e7ee6-11ef-4c8c-8074-4765d868ca78",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning with RandomizedSearchCV\n",
    "\n",
    "We're going to tune:\r\n",
    "* LogisticRegression()\r\n",
    "* RandomForestClassifier()\r\n",
    "\r\n",
    "... using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4c1c4140-696b-4756-bcbe-c44aceeb54a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a hyperparameter grid for LogisticRegression\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 20),\n",
    "                \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Create a hyperparameter grid for RandomForestClassifier\n",
    "rf_grid = {\"n_estimators\": np.arange(10, 1000, 50),\n",
    "           \"max_depth\": [None, 3, 5, 10],\n",
    "           \"min_samples_split\": np.arange(2, 20, 2),\n",
    "           \"min_samples_leaf\": np.arange(1, 20, 2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8569973-a0ba-4830-89ae-158a8755d592",
   "metadata": {},
   "source": [
    "Now we've got hyperparameter grids setup for each of our models, let's tune them using RandomizedSearchCV..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "c24d9d8d-c401-4c54-8d69-c7eba4d16992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\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 fitted 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 fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.23357214690901212, solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.23357214690901212, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisticRegression\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                                param_distributions=log_reg_grid,\n",
    "                                cv=5,\n",
    "                                n_iter=20,\n",
    "                                verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for LogisticRegression\n",
    "rs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "3becc63c-4713-46f0-b8b6-300702108033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "664048a4-c933-49b7-9eb6-a3517563baa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d149993-891c-42cf-a50d-ebb35a873f01",
   "metadata": {},
   "source": [
    "Now we've tuned LogisticRegression(), let's do the same for RandomForestClassifier()..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "265703a2-b842-4912-ba5c-2815d3816d70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" 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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=3, min_samples_leaf=19, min_samples_split=4,\n",
       "                       n_estimators=210)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=3, min_samples_leaf=19, min_samples_split=4,\n",
       "                       n_estimators=210)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for RandomForestClassifier\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                           param_distributions=rf_grid,\n",
    "                           cv=5,\n",
    "                           n_iter=20,\n",
    "                           verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for RandomForestClassifier()\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "19003e29-27fc-43fd-a0b1-163b66f63636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best hyperparameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "379cbdb7-63fa-46bc-9c87-ddb9ff7b9823",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the randomized search RandomRandomForestClassifier model\n",
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "974ca763-e5a2-4669-a7af-5ec2fb7d28d1",
   "metadata": {},
   "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..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "3928fc43-5195-44a8-999e-8385105e99d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    }
   ],
   "source": [
    "# Different hyperparameters for our LogisticRegression model\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 30),\n",
    "                \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                          param_grid=log_reg_grid,\n",
    "                          cv=5,\n",
    "                          verbose=True)\n",
    "\n",
    "# Fit grid hyperparameter search model\n",
    "gs_log_reg.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "5e7007ed-e203-4658-a6b0-7b1d2e4fbc5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best hyperparameter\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9fb034d1-52ee-454b-b3c2-f2551e11f44b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the grid search LogisLogisticRegression model\n",
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed926f7-e85d-4e98-92b3-06a3668227ce",
   "metadata": {},
   "source": [
    "## Evaluting 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",
    "... and it would be great if cross-validation was used where possible.\n",
    "\n",
    "To make comparisons and evaluate our trained model, first we need to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "88c6fe0f-ef5b-4c9a-8f0c-24720c25fca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions with tuned model\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d949ac12-80d9-4659-b6ad-7686071d6fce",
   "metadata": {},
   "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], dtype=int64)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "5a5efe59-45d2-46fc-87a6-6f8f271d7790",
   "metadata": {},
   "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": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "9deb13ec-9812-452f-b12a-dce6e80e3adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x159b7cf98b0>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve and calculate and calculate AUC metric\n",
    "RocCurveDisplay.from_estimator(gs_log_reg, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "fdf9196c-4c92-434e-a90e-bd1dc10d7d61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "d48e5fef-5465-4309-bbdd-25501ab9afec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import Seaborn\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=1.5) # Increase font size\n",
    " \n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a confusion matrix using Seaborn's heatmap().\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot=True, # Annotate the boxes\n",
    "                     cbar=False)\n",
    "    plt.xlabel(\"Predicted label\") # predictions go on the x-axis\n",
    "    plt.ylabel(\"True label\") # true labels go on the y-axis \n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b395f39-841a-418a-9bab-4f055b041969",
   "metadata": {},
   "source": [
    "Now we've got a ROC curve, an AUC metric and a confusion matrix, let's get a classification report as well as cross-validated precision, recall and f1-score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "e72f78c2-9e64-4b18-a89b-0c51435f4b8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a4ba5b4-0397-4221-9b16-651c63a215c9",
   "metadata": {},
   "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()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c2fdde1b-264e-4b4c-8881-b9d89ae6508f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "fd45fb78-9579-4e46-8351-06d13d7131dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                         solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "5d08d4cc-6653-47b1-aa15-2c63459ecc32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.86885246, 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated accuracy\n",
    "cv_acc = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"accuracy\")\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "abf8bb5d-0fe2-46db-acc3-538576fe7106",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446994535519124"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "c84ba125-2b98-4d15-b36e-9212b97193fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8207936507936507"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated precision\n",
    "cv_precision = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"precision\")\n",
    "cv_precision=np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "79c37565-a536-477d-9a34-1c0bea72b483",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212121212121213"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated recall\n",
    "cv_recall = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"recall\")\n",
    "cv_recall=np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "54d71fd0-a630-4493-a107-faccb1e45505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673007976269721"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated f1-score\n",
    "cv_f1 = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"f1\")\n",
    "cv_f1=np.mean(cv_f1)\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "d2111c01-a6ad-46a6-a8e7-2828b1008474",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\": cv_acc,\n",
    "                           \"Precision\": cv_precision,\n",
    "                           \"Recall\": cv_recall,\n",
    "                           \"F1\": cv_f1},\n",
    "                          index=[0])\n",
    "\n",
    "cv_metrics.T.plot.bar(title=\"Cross-validated classification metrics\",\n",
    "                      legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d1f1ce-f5c0-479f-aeb4-8e70255d2356",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "Feature importance is another as asking, \"which features contributed most to the outcomes of the model and how did they contribute?\"\n",
    "\n",
    "Finding feature importance is different for each machine learning model. One way to find feature importance is to search for \"(MODEL NAME) feature importance\".\n",
    "\n",
    "Let's find the feature importance for our LogisticRegression model..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "3bcc7057-0b67-4951-87d0-f4db905d2f11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit an instance of LogiLogisticRegression\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                         solver=\"liblinear\")\n",
    "\n",
    "clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f6031c0-8d13-4e7c-9adc-133e86e07060",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "6aea557f-570c-4232-9d9e-c9b8638204a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00316727, -0.86044582,  0.66067073, -0.01156993, -0.00166374,\n",
       "         0.04386131,  0.31275787,  0.02459361, -0.60413038, -0.56862852,\n",
       "         0.45051617, -0.63609863, -0.67663375]])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "cde2e771-052d-4bdd-8a60-82c5683257f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.0031672721856887734,\n",
       " 'sex': -0.860445816920919,\n",
       " 'cp': 0.6606707303492849,\n",
       " 'trestbps': -0.011569930902919925,\n",
       " 'chol': -0.001663741604035976,\n",
       " 'fbs': 0.04386130751482091,\n",
       " 'restecg': 0.3127578715206996,\n",
       " 'thalach': 0.02459360818122666,\n",
       " 'exang': -0.6041303799858143,\n",
       " 'oldpeak': -0.5686285194546157,\n",
       " 'slope': 0.4505161679452401,\n",
       " 'ca': -0.6360986316921434,\n",
       " 'thal': -0.6766337521354281}"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match coef's of features to columns\n",
    "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "e321ae75-8b2c-4cfa-a105-7e4624229c65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "feature_df = pd.DataFrame(feature_dict, index=[0])\n",
    "feature_df.T.plot.bar(title=\"Feature Importance\", legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "d74c5995-0038-430d-93e6-c5bad97927d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "sex            \n",
       "0        24  72\n",
       "1       114  93"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "0d7143be-1ea5-4e70-93d3-4743cdb01182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target   0    1\n",
       "slope          \n",
       "0       12    9\n",
       "1       91   49\n",
       "2       35  107"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"slope\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ad43a8-42d5-4bad-a84a-c551f3f36e08",
   "metadata": {},
   "source": [
    "slope - the slope of the peak exercise ST segment\n",
    "\n",
    "* 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "* 1: Flatsloping: minimal change (typical healthy heart)\n",
    "* 2: Downslopins: signs of unhealthy heart"
   ]
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    "## 6. Experimentation\n",
    "\n",
    "If you haven't hit your evaluation metric yet... ask yourself...\n",
    "\n",
    "* Could you collect more data?\n",
    "* Could you try a better model? Like CatBoost or XGBoost?\n",
    "* Could you improve the current models? (beyond what we've done so far)\n",
    "* If your model is good enough (you have hit your evaluation metric) how would you export it and share it with others?"
   ]
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