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

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "82143921",
   "metadata": {},
   "source": [
    "# Predicting heart disease using machine learning\n",
    "\n",
    "This notebook looks to 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 on their medical attributes\n",
    "\n",
    "We're going to take the following approach:\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling \n",
    "6. Evaluation\n",
    "\n",
    "## 1. Problem Definition\n",
    "\n",
    "In a statement,\n",
    "> Given clinical parameters about a patient, can we predict whether or not a patient can have heart disease?\n",
    "\n",
    "## 2. Data\n",
    "\n",
    "The original data came from Cleveland data from the UCI Machine Learning Repository.\n",
    "\n",
    "There is also a version of it on Kaggle\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "> If we can reach 95% accuracy at predictng whether or not a patient has heart disease during the proof of concept, we'll pursue the project\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "This is where you will get different information about each of the features in your data. You can do this via doing your own research (such as looking at the links above) or by talking to a subject matter expert (someone who knows about the dataset)\n",
    "\n",
    "#### Create a data dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28488075",
   "metadata": {},
   "source": [
    "## Preparing our tools \n",
    "\n",
    "We're going to use pandas, Matplotlib and Numpy for data analysis and manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0388b5b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all the tools we need\n",
    "\n",
    "# Regular EDA and plotting libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# We want our plots to appear inside of the notebook\n",
    "%matplotlib inline \n",
    "\n",
    "# Models from sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model Evaluations\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, f1_score, recall_score, accuracy_score\n",
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c67dead",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f1a9fab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('heart-disease.csv')\n",
    "df.shape # rows and columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac0c470",
   "metadata": {},
   "source": [
    "## Data Exploration (EDA)\n",
    "\n",
    "The goal here is to find out more about the data and become a subject matter expert on the dataset you're working with.\n",
    "\n",
    "1. WHat questions are you trying to solve?\n",
    "2. What kind of data do we have and how do we treat different data types ?\n",
    "3. What's missing from the data and how do we deal with it?\n",
    "4. Where are the outliers and why should you care about them\n",
    "5. How can you add, change or remove features to get more out of your data ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f6f59318",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8058a7e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    165\n",
       "0    138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find out how many of each class they are\n",
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "95a7f682",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['target'].value_counts().plot(kind=\"bar\", color=[\"salmon\", 'blue']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0248df4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56487dfc",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Checking for the total null values / Are there missing values\n",
    "    \n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "55de1ecf",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dcaaa6b",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency according to Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65d20b55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "1    207\n",
       "0     96\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "66218ee4",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex colum\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e298ce7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot of crosstab\n",
    "\n",
    "pd.crosstab(df.target, df.sex).plot(kind=\"bar\",\n",
    "                                   figsize=(10,6),\n",
    "                                   color=[\"salmon\", 'blue'])\n",
    "\n",
    "plt.title(\"Heart Disease Frequence for Sex\")\n",
    "plt.xlabel(\"0 = No Heart Disease, 1 = Disease\")\n",
    "plt.ylabel('Amount')\n",
    "plt.legend(['Female', 'Male'])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8fdfb669",
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "efa3fa49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "thalach\n",
       "162    11\n",
       "160     9\n",
       "163     9\n",
       "152     8\n",
       "173     8\n",
       "       ..\n",
       "202     1\n",
       "184     1\n",
       "121     1\n",
       "192     1\n",
       "90      1\n",
       "Name: count, Length: 91, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['thalach'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dceafee0",
   "metadata": {},
   "source": [
    "### Age vs Max Heart Rate for Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19bfbdd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create another figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Scatter wth positive examples\n",
    "plt.scatter(df.age[df.target==1],\n",
    "           df.thalach[df.target==1],\n",
    "           c='salmon')\n",
    "\n",
    "# Scatter with negatve examples\n",
    "plt.scatter(df.age[df.target==0],\n",
    "           df.thalach[df.target==0],\n",
    "           c='blue');\n",
    "\n",
    "# add some helpful infor\n",
    "plt.title(\"Heart Disease in function of age and max heart rate\")\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Heart Rate')\n",
    "plt.legend(['Disease', 'No Disease']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "28134561",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of age column with a histogram\n",
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d04660",
   "metadata": {},
   "source": [
    "### Heart Disease frequency per chest pain type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a35383df",
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a65e4729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\",\n",
    "                                  figsize=(10,6),\n",
    "                                  color=['salmon', 'blue'])\n",
    "\n",
    "plt.title(\"Heart Disease Frequency Per Chest Pain Type\")\n",
    "plt.xlabel(\"Chest Pain Type\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"No Disease\", \"Disease\"]);\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b0881dc6",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "07af10c9",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a correlation matrix\n",
    "\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "96b9c22b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets make our correlation matrix a little pretier\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                annot=True,\n",
    "                linewidths=.5,\n",
    "                fmt=\".2f\",\n",
    "                cmap=\"winter\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "809bcc5f",
   "metadata": {},
   "source": [
    "## 5. Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f5873747",
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b211dd99",
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "0        0   0     1  \n",
       "1        0   0     2  \n",
       "2        2   0     2  \n",
       "3        2   0     2  \n",
       "4        2   0     2  \n",
       "..     ...  ..   ...  \n",
       "298      1   0     3  \n",
       "299      1   0     3  \n",
       "300      1   2     3  \n",
       "301      1   1     3  \n",
       "302      1   1     2  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data into X and y\n",
    "X = df.drop('target', axis=1)\n",
    "y = df.target\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d148131f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train and test set\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into train & test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5d22ab34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>132</th>\n",
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       "      <th>202</th>\n",
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       "      <td>150</td>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>231</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>135</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>160</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "132   42    1   1       120   295    0        1      162      0      0.0   \n",
       "202   58    1   0       150   270    0        0      111      1      0.8   \n",
       "196   46    1   2       150   231    0        1      147      0      3.6   \n",
       "75    55    0   1       135   250    0        0      161      0      1.4   \n",
       "176   60    1   0       117   230    1        1      160      1      1.4   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "188   50    1   2       140   233    0        1      163      0      0.6   \n",
       "71    51    1   2        94   227    0        1      154      1      0.0   \n",
       "106   69    1   3       160   234    1        0      131      0      0.1   \n",
       "270   46    1   0       120   249    0        0      144      0      0.8   \n",
       "102   63    0   1       140   195    0        1      179      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "132      2   0     2  \n",
       "202      2   0     3  \n",
       "196      1   0     2  \n",
       "75       1   0     2  \n",
       "176      2   2     3  \n",
       "..     ...  ..   ...  \n",
       "188      1   1     3  \n",
       "71       2   1     3  \n",
       "106      1   1     2  \n",
       "270      2   0     3  \n",
       "102      2   2     2  \n",
       "\n",
       "[242 rows x 13 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "095bd7ce",
   "metadata": {},
   "source": [
    "Now we've got our data split into training and test sets, its time to build a machine learning model\n",
    "\n",
    "We'll train it (find the patterns) on the training set.\n",
    "\n",
    "And we'll test it (use the patterns) on the test set\n",
    "\n",
    "We're going to try 3 different machine learning models \n",
    "\n",
    "1. Logistic Regression\n",
    "2. K-Nearest Neighbours\n",
    "3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c4748e92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {\"Logistic Regression\": LogisticRegression(),\n",
    "         \"KNN\": KNeighborsClassifier(),\n",
    "         \"Random Forest\": RandomForestClassifier()}\n",
    "\n",
    "# Create a function to fit and score models\n",
    "\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluates given machine learning models.\n",
    "    models: a dict of sklearn machine learning models.\n",
    "    X_train: training data (no labels).\n",
    "    X_test: testing data (no labels)\n",
    "    y_train: training labels\n",
    "    y_test: test labels\n",
    "    \"\"\"\n",
    "    # Set random seed\n",
    "    np.random.seed(42)\n",
    "    \n",
    "    # Make a dictionary to keep model scores\n",
    "    model_scores = {}\n",
    "    \n",
    "    # Loop thru models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(X_train, y_train)\n",
    "        \n",
    "        # Evaluate the model and append its score to the model_scores\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "64cc0d5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rakinzi/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models=models,\n",
    "                            X_train=X_train,\n",
    "                            X_test=X_test,\n",
    "                            y_train=y_train,\n",
    "                            y_test=y_test)\n",
    "\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9699482b",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ba05df91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=['accuracy'])\n",
    "\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00764fe1",
   "metadata": {},
   "source": [
    "Now we've got a baseline model and we know a model's first predictions aren't always what we should base our next steps of, What should we do ?\n",
    "\n",
    "Let's look at the following:\n",
    "\n",
    "* Hyperparameter tuning\n",
    "* Feature importance\n",
    "* Confusion matrix\n",
    "* Cross-validation \n",
    "* Precision\n",
    "* Recall\n",
    "* F1 score\n",
    "* Classification report\n",
    "* ROC curve\n",
    "* Are under the curve (AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "882e8a35",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b79073d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune KNN\n",
    "\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of different values of N neighbours\n",
    "neighbors = range(1, 21)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier() \n",
    "\n",
    "# Loop thru different n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "    \n",
    "    # Fit the algorthm\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    # Update the training scores list\n",
    "    train_scores.append(knn.score(X_train, y_train))\n",
    "    \n",
    "    # Update the test scores list\n",
    "    test_scores.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "12e849be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.8099173553719008,\n",
       " 0.7727272727272727,\n",
       " 0.743801652892562,\n",
       " 0.7603305785123967,\n",
       " 0.7520661157024794,\n",
       " 0.743801652892562,\n",
       " 0.7231404958677686,\n",
       " 0.71900826446281,\n",
       " 0.6942148760330579,\n",
       " 0.7272727272727273,\n",
       " 0.6983471074380165,\n",
       " 0.6900826446280992,\n",
       " 0.6942148760330579,\n",
       " 0.6859504132231405,\n",
       " 0.6735537190082644,\n",
       " 0.6859504132231405,\n",
       " 0.6652892561983471,\n",
       " 0.6818181818181818,\n",
       " 0.6694214876033058]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "af750ff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6229508196721312,\n",
       " 0.639344262295082,\n",
       " 0.6557377049180327,\n",
       " 0.6721311475409836,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.7049180327868853,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.7540983606557377,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.6557377049180327]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ffca9226",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 75.41\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors,  train_scores, label=\"Train score\")\n",
    "plt.plot(neighbors,  test_scores, label=\"Test score\")\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_scores)*100:.2f}\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ab4a8f",
   "metadata": {},
   "source": [
    "## Hyperparameter with RandomizedSearchCV\n",
    "\n",
    "We are going to tune:\n",
    "* LogisticRegression()\n",
    "* RandomForestClassifier()\n",
    "\n",
    "... using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bafb4070",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Hyperparameter grid for LogisticRegression\n",
    "\n",
    "log_reg_grid = {\"C\": np.logspace(-4,4,20),\n",
    "               \"solver\": ['liblinear']}\n",
    "\n",
    "# Create a hyperparameter grid for RandomForestClassifier\n",
    "rf_grid = {\"n_estimators\": np.arange(10,1000,50),\n",
    "          \"max_depth\": [None, 3, 5, 10],\n",
    "          \"min_samples_split\": np.arange(2, 20, 2),\n",
    "          \"min_samples_leaf\": np.arange(1, 20, 2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b23f78a",
   "metadata": {},
   "source": [
    "Now we've got hyperparameter grids setup for our models, let's tune them using RandomizedSearchCV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "49c6bdb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune the LogisticRegression\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(), \n",
    "                               param_distributions=log_reg_grid,\n",
    "                               cv=5,\n",
    "                               n_iter=20, \n",
    "                               verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for Logistic Regression\n",
    "rs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "beaa744c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f77bbd31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67e6d058",
   "metadata": {},
   "source": [
    "Now we have tuned LogisticRegression, let's do for RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b777d1b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup a Random Seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for RandomForest\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(), \n",
    "                            param_distributions=rf_grid,\n",
    "                            cv=5,\n",
    "                          n_iter=20,\n",
    "                          verbose=True)\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "977f2c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0d8fe1ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the randomized search RandomForestClassifier model\n",
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c24fb771",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning with GridSearchCV\n",
    "\n",
    "Since our LogisticRegression model provides the best scores so far, we'll try and improve again using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "57c91f94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    }
   ],
   "source": [
    "# Different Hyperparameters for our LogisticRegression Model\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 30),\n",
    "               \"solver\": ['liblinear']}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                         param_grid=log_reg_grid,\n",
    "                         cv=5,\n",
    "                         verbose=True)\n",
    "\n",
    "# Fit grid hyperparameter search model\n",
    "gs_log_reg.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c09ce06f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best hyperparams\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ee8f4d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the grid search params for LogisticRegression\n",
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c19b2e",
   "metadata": {},
   "source": [
    "## Evaluating our tuned machine learning classifier, beyond accuracy\n",
    "\n",
    "* ROC Curve and AUC score\n",
    "* Confusion matrix\n",
    "* Classification report\n",
    "* Precision\n",
    "* Recall\n",
    "* F1 score\n",
    "\n",
    "... and it would be great if cross-validation was used where possible.\n",
    "\n",
    "To make comparisons and evaluate our trained model, first we need to make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "84548bd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions with tuned model\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "11f61c50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c8779810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.        , 0.13793103, 1.        ]),\n",
       " array([0.     , 0.90625, 1.     ]),\n",
       " array([inf,  1.,  0.]))"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot ROC curve and calculate AUC metric\n",
    "roc_curve(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2750fcde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a nice looking confusion matrix using Seaborn's heatmap\n",
    "    \"\"\"\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(3,3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                    annot=True,\n",
    "                    cbar=False)\n",
    "    plt.xlabel(\"True label\")\n",
    "    plt.ylabel(\"Predicted label\")\n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "876f8816",
   "metadata": {},
   "source": [
    "Now we've got a ROC Curve and a Confusion matrix let's get a classification report as well as cross-validated precision, recall and f1-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "81fef142",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5edb0642",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                         solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad80a973",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics using cross-validation\n",
    "\n",
    "We're going to calculate precision, recall and f1-score of our model using cross-validation and to do so we will be using `cross_valscore()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "df8450b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8446994535519124,\n",
       " 'precision': 0.8207936507936507,\n",
       " 'recall': 0.9212121212121213,\n",
       " 'f1': 0.8673007976269721}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "    \n",
    "    accuracy = cross_val_score(clf, X, y, cv=5, scoring='accuracy')\n",
    "    precision = cross_val_score(clf, X, y, cv=5, scoring='precision')\n",
    "    recall = cross_val_score(clf, X, y, cv=5, scoring='recall')\n",
    "    f1 = cross_val_score(clf, X, y, cv=5, scoring='f1')\n",
    "    \n",
    "    metric_dict = {\n",
    "        \"accuracy\": np.mean(accuracy),\n",
    "        \"precision\": np.mean(precision),\n",
    "        \"recall\": np.mean(recall),\n",
    "        \"f1\": np.mean(f1)\n",
    "    }\n",
    "    \n",
    "    metric_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "54c17b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_metrics = pd.DataFrame(metrics,\n",
    "                         index=[0])\n",
    "cv_metrics.T.plot.bar(title=\"Cross-validated classification metrics\", \n",
    "                     legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be237e79",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "Feature importance is another as asking, \"which features contributed most to the outcomes of the model and how did they contribute?\"\n",
    "\n",
    "Finding feature importance is different for each machine learning model. One way to find feature importance is to search for \"(MODEL NAME) feature importance\".\n",
    "\n",
    "Let's find the feature importance for our LogisticRegression model..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7649dea9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
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}