End-to-End Heart Disease Classification ML Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting heart disease using machine learning\n",
    "\n",
    "This notebook looks into using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes.\n",
    "\n",
    "Approach:\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling\n",
    "6. Experimentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Problem Definition\n",
    "> Given clinical parameters about a patient, can we predict whether or not they have heart disease?\n",
    "\n",
    "## 2. Data\n",
    "The original data came from the Cleveland data from the UCI Machine Learning Repository. \n",
    "https://archive.ics.uci.edu/ml/datasets/heart+disease\n",
    "\n",
    "There is also a version of it available on Kaggle. \n",
    "https://www.kaggle.com/ronitf/heart-disease-uci\n",
    "\n",
    "## 3. Evaluation\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",
    "This is where you'll get different information about each of the features in your data.\n",
    "\n",
    "**Create data dictionary**\n",
    "\n",
    "* age: age (in years)\n",
    "* sex: sex (1 = male; 0 = female)\n",
    "* cp: chest pain type\n",
    "    - Value 1: typical angina\n",
    "    - Value 2: atypical angina\n",
    "    - Value 3: non-anginal pain\n",
    "    - Value 4: asymptomatic\n",
    "* trestbps: resting blood pressure (in mm Hg on admission to the hospital)\n",
    "* chol: serum cholestoral in mg/dl\n",
    "* fbs:  (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "*  restecg: resting electrocardiographic results\n",
    "    - Value 0: normal\n",
    "    - Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)\n",
    "    - Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria\n",
    "* thalach: maximum heart rate achieved\n",
    "* exang: exercise induced angina (1 = yes; 0 = no)\n",
    "* oldpeak = ST depression induced by exercise relative to rest\n",
    "* slope: the slope of the peak exercise ST segment\n",
    "    - Value 1: upsloping\n",
    "    - Value 2: flat\n",
    "    - Value 3: downsloping\n",
    "* ca: number of major vessels (0-3) colored by flourosopy\n",
    "* thal: 3 = normal; 6 = fixed defect; 7 = reversable defect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tools\n",
    "We'll use Pandas, Matplotlib, and Numpy for Data Analysis and Manipulation. Scikit-Learn for Machine Learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regular EDA (Exploratory Data Analysis) and Plotting Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# %matplotlib inline - to put plots within the notebook. We comment this out because it is already inline by default\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, RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import classification_report, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix, plot_roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "# Load the data\n",
    "\n",
    "df = pd.read_csv('heart-disease.csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration (EDA)\n",
    "\n",
    "Goal: Find out more about the data and become a subject matter expert on the data.\n",
    "    \n",
    "1. What question(s) are we trying to solve?\n",
    "> Whether or not we can find enough patterns in the data to predict heart disease.\n",
    "2. What kind of data do we have and how do we treat different types? \n",
    "> In this problem, our data is purely numerical.\n",
    "3. What's missing from the data and how do we deal with them?\n",
    "> There are no missing values.\n",
    "4. Where are the outliers and why should we care about them?\n",
    "5. How can we add, change, or remove features to get more out of our data?\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data according to label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Is our data balanced or imbalanced?\n",
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['target'].value_counts().plot(kind='bar', color=['pink', 'lightblue'])\n",
    "plt.xticks(rotation=0)\n",
    "plt.ylabel('Number of people')\n",
    "plt.xlabel('1 = with; 0 = without')\n",
    "plt.title('Number of people with and without heart disease');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data between those with and without heart disease is relatively balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing values?\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency according to Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.target, df.sex).plot.bar()\n",
    "plt.title('Heart Disease Frequency according to Sex')\n",
    "plt.xticks(rotation=0)\n",
    "plt.xlabel('1 = with heart disease; 0 = without heart disease')\n",
    "plt.ylabel('Number of people')\n",
    "plt.legend(['Female', 'Male']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease function of Age and Max Heart Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Scatter plot with positive examples\n",
    "plt.scatter(df.age[df.target==1], df.thalach[df.target==1], c='tab:red')\n",
    "\n",
    "# Scatter plot with negative examples\n",
    "plt.scatter(df.age[df.target==0], df.thalach[df.target==0], c='royalblue')\n",
    "\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Max Heart Rate')\n",
    "plt.title('Heart Disease function of Age and Max Heart Rate')\n",
    "plt.legend(['with heart disease', 'without heart disease']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check distribution of Age with a histogram\n",
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency per Chest Pain type\n",
    "cp: chest pain type\n",
    "- Value 1: typical angina\n",
    "- Value 2: atypical angina\n",
    "- Value 3: non-anginal pain\n",
    "- Value 4: asymptomatic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target).plot.bar(figsize=(10,7),\n",
    "                                       color=['tab:red', 'royalblue'])\n",
    "plt.xticks(ticks=[0,1,2,3],\n",
    "           labels=['0: typical angina',\n",
    "                   '1: atypical angina', \n",
    "                   '2: non-anginal pain', \n",
    "                   '3: asymptomatic'], \n",
    "           rotation=0)\n",
    "plt.xlabel('Chest Pain Type')\n",
    "plt.ylabel('Number of people')\n",
    "plt.legend(['0: without heart disease',\n",
    "            '1: with heart disease'],\n",
    "           title='target')\n",
    "plt.title('Heart Disease Frequency per Chest Pain Type');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                 annot=True,\n",
    "                 cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Modelling\n",
    "\n",
    "We're going to try the following models:\n",
    "1. Logistic Regression\n",
    "2. K-Nearest Neighbors Classifier\n",
    "3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0   0     1  \n",
       "1   0     2  \n",
       "2   0     2  \n",
       "3   0     2  \n",
       "4   0     2  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split into features and labels\n",
    "X = df.drop('target', axis=1)\n",
    "y = df['target']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0      1\n",
       " 1      1\n",
       " 2      1\n",
       " 3      1\n",
       " 4      1\n",
       "       ..\n",
       " 298    0\n",
       " 299    0\n",
       " 300    0\n",
       " 301    0\n",
       " 302    0\n",
       " Name: target, Length: 303, dtype: int64,\n",
       " 303,\n",
       " 303)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y, len(X), len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(242, 61, 242, 61)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "len(X_train), len(X_test), len(y_train), len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build model/s\n",
    "models = {'Logistic Regression': LogisticRegression(),\n",
    "          'KNN Classifier': KNeighborsClassifier(),\n",
    "          'Random Forest Classifier': RandomForestClassifier()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    '''\n",
    "    Fits and evaluates machine learning models.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    models (dict) - models to fit and score\n",
    "    X_train - features training set\n",
    "    y_train - labels training set\n",
    "    X_test - features test set\n",
    "    y_test - labels test set\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    model_scores - dictionary containing the models' scores\n",
    "    '''\n",
    "    np.random.seed(42)\n",
    "    model_scores = {}\n",
    "    for model_name, model in models.items():\n",
    "        model.fit(X_train, y_train)\n",
    "        model_scores[model_name] = model.score(X_test, y_test)\n",
    "        \n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sofia\\OneDrive - University of the Philippines\\Sofia\\Data Science\\Projects\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: 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': 0.8688524590163934,\n",
       " 'KNN Classifier': 0.6557377049180327,\n",
       " 'Random Forest Classifier': 0.8852459016393442}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models, X_train, X_test, y_train, y_test)\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=['accuracy'])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tuning K-Nearest Neighbors Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5081967213114754,\n",
       " 0.47540983606557374,\n",
       " 0.5901639344262295,\n",
       " 0.6229508196721312,\n",
       " 0.6557377049180327,\n",
       " 0.6721311475409836,\n",
       " 0.6721311475409836,\n",
       " 0.6721311475409836,\n",
       " 0.6721311475409836,\n",
       " 0.6557377049180327,\n",
       " 0.639344262295082,\n",
       " 0.639344262295082,\n",
       " 0.639344262295082,\n",
       " 0.6557377049180327,\n",
       " 0.6065573770491803,\n",
       " 0.6065573770491803,\n",
       " 0.5573770491803278,\n",
       " 0.6065573770491803,\n",
       " 0.6229508196721312,\n",
       " 0.6229508196721312]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# 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",
    "    knn.fit(X_train, y_train)\n",
    "    train_scores.append(knn.score(X_train, y_train))\n",
    "    test_scores.append(knn.score(X_test, y_test))\n",
    "    \n",
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max KNN Score on the test data: 67%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize test scores\n",
    "plt.plot(neighbors, train_scores, label='Train Scores')\n",
    "plt.plot(neighbors, test_scores, label='Test Scores')\n",
    "plt.xlabel('No. of neighbors')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xticks(np.arange(1,21))\n",
    "print(f'Max KNN Score on the test data: {max(test_scores)*100:.2g}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tune LogisticRegression & RandomForestClassifier With RandomizedSearchCV**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter distribution for Logistic Regression\n",
    "log_reg_dist = {'C': [1], \n",
    "                'solver': ['lbfgs', 'liblinear'], \n",
    "                'max_iter': [100, 1000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sofia\\OneDrive - University of the Philippines\\Sofia\\Data Science\\Projects\\env\\lib\\site-packages\\sklearn\\model_selection\\_search.py:278: UserWarning: The total space of parameters 4 is smaller than n_iter=20. Running 4 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  warnings.warn(\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "C:\\Users\\Sofia\\OneDrive - University of the Philippines\\Sofia\\Data Science\\Projects\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: 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",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "C:\\Users\\Sofia\\OneDrive - University of the Philippines\\Sofia\\Data Science\\Projects\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n",
      "[CV] solver=lbfgs, max_iter=100, C=1 .................................\n",
      "[CV] .................. solver=lbfgs, max_iter=100, C=1, total=   0.1s\n",
      "[CV] solver=lbfgs, max_iter=100, C=1 .................................\n",
      "[CV] .................. solver=lbfgs, max_iter=100, C=1, total=   0.1s\n",
      "[CV] solver=lbfgs, max_iter=100, C=1 .................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sofia\\OneDrive - University of the Philippines\\Sofia\\Data Science\\Projects\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: 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",
      "C:\\Users\\Sofia\\OneDrive - University of the Philippines\\Sofia\\Data Science\\Projects\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: 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",
      "C:\\Users\\Sofia\\OneDrive - University of the Philippines\\Sofia\\Data Science\\Projects\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................. solver=lbfgs, max_iter=100, C=1, total=   0.1s\n",
      "[CV] solver=lbfgs, max_iter=100, C=1 .................................\n",
      "[CV] .................. solver=lbfgs, max_iter=100, C=1, total=   0.1s\n",
      "[CV] solver=lbfgs, max_iter=100, C=1 .................................\n",
      "[CV] .................. solver=lbfgs, max_iter=100, C=1, total=   0.1s\n",
      "[CV] solver=liblinear, max_iter=100, C=1 .............................\n",
      "[CV] .............. solver=liblinear, max_iter=100, C=1, total=   0.0s\n",
      "[CV] solver=liblinear, max_iter=100, C=1 .............................\n",
      "[CV] .............. solver=liblinear, max_iter=100, C=1, total=   0.0s\n",
      "[CV] solver=liblinear, max_iter=100, C=1 .............................\n",
      "[CV] .............. solver=liblinear, max_iter=100, C=1, total=   0.0s\n",
      "[CV] solver=liblinear, max_iter=100, C=1 .............................\n",
      "[CV] .............. solver=liblinear, max_iter=100, C=1, total=   0.0s\n",
      "[CV] solver=liblinear, max_iter=100, C=1 .............................\n",
      "[CV] .............. solver=liblinear, max_iter=100, C=1, total=   0.0s\n",
      "[CV] solver=lbfgs, max_iter=1000, C=1 ................................\n",
      "[CV] ................. solver=lbfgs, max_iter=1000, C=1, total=   0.5s\n",
      "[CV] solver=lbfgs, max_iter=1000, C=1 ................................\n",
      "[CV] ................. solver=lbfgs, max_iter=1000, C=1, total=   0.5s\n",
      "[CV] solver=lbfgs, max_iter=1000, C=1 ................................\n",
      "[CV] ................. solver=lbfgs, max_iter=1000, C=1, total=   0.3s\n",
      "[CV] solver=lbfgs, max_iter=1000, C=1 ................................\n",
      "[CV] ................. solver=lbfgs, max_iter=1000, C=1, total=   0.6s\n",
      "[CV] solver=lbfgs, max_iter=1000, C=1 ................................\n",
      "[CV] ................. solver=lbfgs, max_iter=1000, C=1, total=   0.4s\n",
      "[CV] solver=liblinear, max_iter=1000, C=1 ............................\n",
      "[CV] ............. solver=liblinear, max_iter=1000, C=1, total=   0.0s\n",
      "[CV] solver=liblinear, max_iter=1000, C=1 ............................\n",
      "[CV] ............. solver=liblinear, max_iter=1000, C=1, total=   0.0s\n",
      "[CV] solver=liblinear, max_iter=1000, C=1 ............................\n",
      "[CV] ............. solver=liblinear, max_iter=1000, C=1, total=   0.0s\n",
      "[CV] solver=liblinear, max_iter=1000, C=1 ............................\n",
      "[CV] ............. solver=liblinear, max_iter=1000, C=1, total=   0.0s\n",
      "[CV] solver=liblinear, max_iter=1000, C=1 ............................\n",
      "[CV] ............. solver=liblinear, max_iter=1000, C=1, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    2.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': [1], 'max_iter': [100, 1000],\n",
       "                                        'solver': ['lbfgs', 'liblinear']},\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune Logistic Regression\n",
    "np.random.seed(42)\n",
    "\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(), log_reg_dist,\n",
    "                                n_iter=20, cv=5, verbose=2)\n",
    "\n",
    "rs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'max_iter': 100, 'C': 1}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8688524590163934,\n",
       " 'KNN Classifier': 0.6557377049180327,\n",
       " 'Random Forest Classifier': 0.8852459016393442}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter distribution for RandomForestClassifier\n",
    "rf_grid = {'n_estimators': np.arange(100, 1000, 100),\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": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n",
      "[CV] n_estimators=600, min_samples_split=6, min_samples_leaf=11, max_depth=3 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=600, min_samples_split=6, min_samples_leaf=11, max_depth=3, total=   2.1s\n",
      "[CV] n_estimators=600, min_samples_split=6, min_samples_leaf=11, max_depth=3 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=600, min_samples_split=6, min_samples_leaf=11, max_depth=3, total=   2.1s\n",
      "[CV] n_estimators=600, min_samples_split=6, min_samples_leaf=11, max_depth=3 \n",
      "[CV]  n_estimators=600, min_samples_split=6, min_samples_leaf=11, max_depth=3, total=   2.3s\n",
      "[CV] n_estimators=600, min_samples_split=6, min_samples_leaf=11, max_depth=3 \n",
      "[CV]  n_estimators=600, min_samples_split=6, min_samples_leaf=11, max_depth=3, total=   2.3s\n",
      "[CV] n_estimators=600, min_samples_split=6, min_samples_leaf=11, max_depth=3 \n",
      "[CV]  n_estimators=600, min_samples_split=6, min_samples_leaf=11, max_depth=3, total=   2.1s\n",
      "[CV] n_estimators=700, min_samples_split=2, min_samples_leaf=9, max_depth=None \n",
      "[CV]  n_estimators=700, min_samples_split=2, min_samples_leaf=9, max_depth=None, total=   2.6s\n",
      "[CV] n_estimators=700, min_samples_split=2, min_samples_leaf=9, max_depth=None \n",
      "[CV]  n_estimators=700, min_samples_split=2, min_samples_leaf=9, max_depth=None, total=   2.5s\n",
      "[CV] n_estimators=700, min_samples_split=2, min_samples_leaf=9, max_depth=None \n",
      "[CV]  n_estimators=700, min_samples_split=2, min_samples_leaf=9, max_depth=None, total=   2.5s\n",
      "[CV] n_estimators=700, min_samples_split=2, min_samples_leaf=9, max_depth=None \n",
      "[CV]  n_estimators=700, min_samples_split=2, min_samples_leaf=9, max_depth=None, total=   2.5s\n",
      "[CV] n_estimators=700, min_samples_split=2, min_samples_leaf=9, max_depth=None \n",
      "[CV]  n_estimators=700, min_samples_split=2, min_samples_leaf=9, max_depth=None, total=   2.6s\n",
      "[CV] n_estimators=700, min_samples_split=6, min_samples_leaf=17, max_depth=3 \n",
      "[CV]  n_estimators=700, min_samples_split=6, min_samples_leaf=17, max_depth=3, total=   2.7s\n",
      "[CV] n_estimators=700, min_samples_split=6, min_samples_leaf=17, max_depth=3 \n",
      "[CV]  n_estimators=700, min_samples_split=6, min_samples_leaf=17, max_depth=3, total=   2.7s\n",
      "[CV] n_estimators=700, min_samples_split=6, min_samples_leaf=17, max_depth=3 \n",
      "[CV]  n_estimators=700, min_samples_split=6, min_samples_leaf=17, max_depth=3, total=   2.4s\n",
      "[CV] n_estimators=700, min_samples_split=6, min_samples_leaf=17, max_depth=3 \n",
      "[CV]  n_estimators=700, min_samples_split=6, min_samples_leaf=17, max_depth=3, total=   2.6s\n",
      "[CV] n_estimators=700, min_samples_split=6, min_samples_leaf=17, max_depth=3 \n",
      "[CV]  n_estimators=700, min_samples_split=6, min_samples_leaf=17, max_depth=3, total=   2.7s\n",
      "[CV] n_estimators=300, min_samples_split=8, min_samples_leaf=13, max_depth=5 \n",
      "[CV]  n_estimators=300, min_samples_split=8, min_samples_leaf=13, max_depth=5, total=   1.3s\n",
      "[CV] n_estimators=300, min_samples_split=8, min_samples_leaf=13, max_depth=5 \n",
      "[CV]  n_estimators=300, min_samples_split=8, min_samples_leaf=13, max_depth=5, total=   1.1s\n",
      "[CV] n_estimators=300, min_samples_split=8, min_samples_leaf=13, max_depth=5 \n",
      "[CV]  n_estimators=300, min_samples_split=8, min_samples_leaf=13, max_depth=5, total=   1.1s\n",
      "[CV] n_estimators=300, min_samples_split=8, min_samples_leaf=13, max_depth=5 \n",
      "[CV]  n_estimators=300, min_samples_split=8, min_samples_leaf=13, max_depth=5, total=   1.1s\n",
      "[CV] n_estimators=300, min_samples_split=8, min_samples_leaf=13, max_depth=5 \n",
      "[CV]  n_estimators=300, min_samples_split=8, min_samples_leaf=13, max_depth=5, total=   1.3s\n",
      "[CV] n_estimators=400, min_samples_split=4, min_samples_leaf=19, max_depth=10 \n",
      "[CV]  n_estimators=400, min_samples_split=4, min_samples_leaf=19, max_depth=10, total=   1.5s\n",
      "[CV] n_estimators=400, min_samples_split=4, min_samples_leaf=19, max_depth=10 \n",
      "[CV]  n_estimators=400, min_samples_split=4, min_samples_leaf=19, max_depth=10, total=   1.5s\n",
      "[CV] n_estimators=400, min_samples_split=4, min_samples_leaf=19, max_depth=10 \n",
      "[CV]  n_estimators=400, min_samples_split=4, min_samples_leaf=19, max_depth=10, total=   1.8s\n",
      "[CV] n_estimators=400, min_samples_split=4, min_samples_leaf=19, max_depth=10 \n",
      "[CV]  n_estimators=400, min_samples_split=4, min_samples_leaf=19, max_depth=10, total=   1.6s\n",
      "[CV] n_estimators=400, min_samples_split=4, min_samples_leaf=19, max_depth=10 \n",
      "[CV]  n_estimators=400, min_samples_split=4, min_samples_leaf=19, max_depth=10, total=   1.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:   50.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=5,\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([100, 200, 300, 400, 500, 600, 700, 800, 900])},\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune RandomForestClassifier\n",
    "\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(), rf_grid,\n",
    "                                n_iter=5, cv=5, verbose=2)\n",
    "\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 700,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 9,\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 18 candidates, totalling 90 fits\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=100 .......\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=100, total=   0.5s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=100 .......\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=700, total=   3.1s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=700, total=   2.8s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=700, total=   2.8s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=700, total=   2.9s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=700, total=   2.8s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=1200, total=   5.1s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=1200, total=   4.9s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=1200, total=   4.8s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=1200, total=   5.1s\n",
      "[CV] min_samples_leaf=1, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=2, n_estimators=1200, total=   5.3s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=100, total=   0.6s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=100, total=   0.5s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=700, total=   2.9s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=700, total=   2.7s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=700, total=   2.8s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=700, total=   3.0s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=700, total=   2.7s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=1200, total=   4.9s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=1200, total=   4.9s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=1200, total=   5.0s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=1200, total=   4.9s\n",
      "[CV] min_samples_leaf=1, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=1, min_samples_split=3, n_estimators=1200, total=   4.9s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=700, total=   2.7s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=700, total=   2.6s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=700, total=   2.7s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=700, total=   2.8s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=700, total=   2.6s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=1200, total=   4.6s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=1200, total=   4.5s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=1200, total=   4.6s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=1200, total=   4.7s\n",
      "[CV] min_samples_leaf=9, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=2, n_estimators=1200, total=   4.5s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=700, total=   2.8s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=700, total=   2.7s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=700, total=   2.7s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=700, total=   2.7s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=700, total=   2.8s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=1200, total=   4.9s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=1200 ......\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=1200, total=   4.6s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=1200, total=   4.8s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=1200, total=   4.8s\n",
      "[CV] min_samples_leaf=9, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=9, min_samples_split=3, n_estimators=1200, total=   4.9s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=100, total=   0.5s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=100, total=   0.5s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=700, total=   3.2s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=700, total=   2.8s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=700, total=   2.7s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=700, total=   2.8s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=700, total=   2.9s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=1200, total=   4.8s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=1200, total=   5.0s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=1200, total=   5.0s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=1200, total=   4.9s\n",
      "[CV] min_samples_leaf=5, min_samples_split=2, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=2, n_estimators=1200, total=   5.1s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=100, total=   0.5s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=100, total=   0.5s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=100, total=   0.5s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=100 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=100, total=   0.4s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=700, total=   3.3s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=700, total=   3.0s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=700, total=   3.4s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=700, total=   3.4s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=700 .......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=700, total=   3.2s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=1200, total=   6.2s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=1200, total=   4.5s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=1200, total=   4.6s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=1200, total=   4.9s\n",
      "[CV] min_samples_leaf=5, min_samples_split=3, n_estimators=1200 ......\n",
      "[CV]  min_samples_leaf=5, min_samples_split=3, n_estimators=1200, total=   5.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  90 out of  90 | elapsed:  4.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={'min_samples_leaf': [1, 9, 5],\n",
       "                         'min_samples_split': [2, 3],\n",
       "                         'n_estimators': [100, 700, 1200]},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tuning RandomForestClassifier\n",
    "\n",
    "rf_grid = {'n_estimators': [100, 700, 1200],\n",
    "           'min_samples_split': [2, 3],\n",
    "           'min_samples_leaf': [1,9,5]}\n",
    "\n",
    "gs_rf = GridSearchCV(RandomForestClassifier(), rf_grid, cv=5, verbose=2)\n",
    "gs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_leaf': 9, 'min_samples_split': 3, 'n_estimators': 100}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "[CV] C=0.1, max_iter=100, solver=liblinear ...........................\n",
      "[CV] ............ C=0.1, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=0.1, max_iter=100, solver=liblinear ...........................\n",
      "[CV] ............ C=0.1, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=0.1, max_iter=100, solver=liblinear ...........................\n",
      "[CV] ............ C=0.1, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=0.1, max_iter=100, solver=liblinear ...........................\n",
      "[CV] ............ C=0.1, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=0.1, max_iter=100, solver=liblinear ...........................\n",
      "[CV] ............ C=0.1, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=0.1, max_iter=700, solver=liblinear ...........................\n",
      "[CV] ............ C=0.1, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=0.1, max_iter=700, solver=liblinear ...........................\n",
      "[CV] ............ C=0.1, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=0.1, max_iter=700, solver=liblinear ...........................\n",
      "[CV] ............ C=0.1, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=0.1, max_iter=700, solver=liblinear ...........................\n",
      "[CV] ............ C=0.1, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=0.1, max_iter=700, solver=liblinear ...........................\n",
      "[CV] ............ C=0.1, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=1, max_iter=100, solver=liblinear .............................\n",
      "[CV] .............. C=1, max_iter=100, solver=liblinear, total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=1, max_iter=100, solver=liblinear .............................\n",
      "[CV] .............. C=1, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=1, max_iter=100, solver=liblinear .............................\n",
      "[CV] .............. C=1, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=1, max_iter=100, solver=liblinear .............................\n",
      "[CV] .............. C=1, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=1, max_iter=100, solver=liblinear .............................\n",
      "[CV] .............. C=1, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=1, max_iter=700, solver=liblinear .............................\n",
      "[CV] .............. C=1, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=1, max_iter=700, solver=liblinear .............................\n",
      "[CV] .............. C=1, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=1, max_iter=700, solver=liblinear .............................\n",
      "[CV] .............. C=1, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=1, max_iter=700, solver=liblinear .............................\n",
      "[CV] .............. C=1, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=1, max_iter=700, solver=liblinear .............................\n",
      "[CV] .............. C=1, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=10, max_iter=100, solver=liblinear ............................\n",
      "[CV] ............. C=10, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=10, max_iter=100, solver=liblinear ............................\n",
      "[CV] ............. C=10, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=10, max_iter=100, solver=liblinear ............................\n",
      "[CV] ............. C=10, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=10, max_iter=100, solver=liblinear ............................\n",
      "[CV] ............. C=10, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=10, max_iter=100, solver=liblinear ............................\n",
      "[CV] ............. C=10, max_iter=100, solver=liblinear, total=   0.0s\n",
      "[CV] C=10, max_iter=700, solver=liblinear ............................\n",
      "[CV] ............. C=10, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=10, max_iter=700, solver=liblinear ............................\n",
      "[CV] ............. C=10, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=10, max_iter=700, solver=liblinear ............................\n",
      "[CV] ............. C=10, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=10, max_iter=700, solver=liblinear ............................\n",
      "[CV] ............. C=10, max_iter=700, solver=liblinear, total=   0.0s\n",
      "[CV] C=10, max_iter=700, solver=liblinear ............................\n",
      "[CV] ............. C=10, max_iter=700, solver=liblinear, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [0.1, 1, 10], 'max_iter': [100, 700],\n",
       "                         'solver': ['liblinear']},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tuning Logistic Regression\n",
    "log_reg_grid = {'solver': ['liblinear'], 'max_iter': [100, 700], 'C': [0.1, 1, 10]}\n",
    "\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(), log_reg_grid, cv=5, verbose=2)\n",
    "gs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'max_iter': 100, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating model, beyond accuracy\n",
    "- ROC curve and AUC score\n",
    "- Confusion matrix\n",
    "- Classification report\n",
    "- Precision\n",
    "- Recall\n",
    "- F1 score\n",
    "\n",
    "*Cross-validation will be used where possible.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the model with best parameters\n",
    "clf = LogisticRegression(C=1, max_iter=100, solver='liblinear')\n",
    "\n",
    "# Fit the model\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Plot ROC curve\n",
    "plot_roc_curve(clf, X_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145    1\n",
       "42     1\n",
       "65     1\n",
       "60     1\n",
       "92     1\n",
       "      ..\n",
       "236    0\n",
       "67     1\n",
       "90     1\n",
       "17     1\n",
       "2      1\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15,  5],\n",
       "       [ 2, 39]], dtype=int64)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(clf, X_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.75      0.81        20\n",
      "           1       0.89      0.95      0.92        41\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.88      0.85      0.86        61\n",
      "weighted avg       0.89      0.89      0.88        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cross-validated Accuracy, Precision, Recall, and F1 scores**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = ['accuracy', 'precision', 'recall', 'f1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to compute cross-validated Accuracy, Precision, Recall, and F1 scores\n",
    "\n",
    "def compute_metric_scores(estimator, metrics, X, y, num_of_models=5):\n",
    "    metric_scores = {}\n",
    "    for metric in metrics:\n",
    "        metric_scores[metric] = cross_val_score(estimator, X, y,\n",
    "                                                cv=num_of_models, \n",
    "                                                scoring=metric)\n",
    "    return metric_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': array([0.92307692, 0.66666667, 0.91666667, 0.91666667, 0.75      ]),\n",
       " 'precision': array([1.        , 0.75      , 0.88888889, 0.88888889, 0.77777778]),\n",
       " 'recall': array([0.88888889, 0.75      , 1.        , 1.        , 0.875     ]),\n",
       " 'f1': array([0.94117647, 0.75      , 0.94117647, 0.94117647, 0.82352941])}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric_scores = compute_metric_scores(clf, metrics, X_test, y_test)\n",
    "metric_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_metric_mean_scores(scores):\n",
    "    mean_scores = {}\n",
    "    for metric, array in scores.items():\n",
    "        mean_scores[metric] = np.mean(array)\n",
    "    return mean_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8346153846153846,\n",
       " 'precision': 0.861111111111111,\n",
       " 'recall': 0.9027777777777779,\n",
       " 'f1': 0.8794117647058822}"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric_mean_scores = compute_metric_mean_scores(metric_scores)\n",
    "metric_mean_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize cross-validated metrics\n",
    "cv_metrics = pd.DataFrame(metric_mean_scores.values(), index=metric_mean_scores.keys())\n",
    "cv_metrics.plot.bar(legend=False)\n",
    "plt.xticks(rotation=0)\n",
    "plt.title('Cross-validated classification metrics');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "Feature importance is another way of 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature importance of Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.02002651, -1.57125991,  0.70974503, -0.01646397, -0.00347072,\n",
       "        -0.21431278,  0.64147185,  0.02983723, -0.92053141, -0.54160945,\n",
       "         0.35626279, -0.71295858, -0.77906756]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.020026505764564245,\n",
       " 'sex': -1.5712599136736796,\n",
       " 'cp': 0.709745033063706,\n",
       " 'trestbps': -0.016463967664244333,\n",
       " 'chol': -0.003470720504041314,\n",
       " 'fbs': -0.21431277757644476,\n",
       " 'restecg': 0.6414718519765883,\n",
       " 'thalach': 0.029837230906337085,\n",
       " 'exang': -0.9205314068367085,\n",
       " 'oldpeak': -0.5416094476293489,\n",
       " 'slope': 0.3562627879170945,\n",
       " 'ca': -0.7129585841617135,\n",
       " 'thal': -0.7790675613654283}"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match coef of features to columns\n",
    "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.020027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-1.571260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>0.709745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>-0.016464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>-0.003471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>-0.214313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>0.641472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>0.029837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>-0.920531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>-0.541609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>0.356263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>-0.712959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>-0.779068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "age       0.020027\n",
       "sex      -1.571260\n",
       "cp        0.709745\n",
       "trestbps -0.016464\n",
       "chol     -0.003471\n",
       "fbs      -0.214313\n",
       "restecg   0.641472\n",
       "thalach   0.029837\n",
       "exang    -0.920531\n",
       "oldpeak  -0.541609\n",
       "slope     0.356263\n",
       "ca       -0.712959\n",
       "thal     -0.779068"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "feature_df = pd.DataFrame(feature_dict.values(), index=feature_dict.keys())\n",
    "feature_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_df.plot.bar(title='Feature Importance', legend=False, figsize=(10,6))\n",
    "plt.style.use('seaborn');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most important features on this data are sex, exang, cp, thal, ca.\n",
    "\n",
    "The least important features are age, trestbps, chol, thalach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}