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

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting heart disease using machine learning\n",
    "\n",
    "This notebook looks into using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes.\n",
    "\n",
    "We're going to take the following approach:\n",
    "\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling\n",
    "6. Experimentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Problem Definition\n",
    "\n",
    "In a statement,\n",
    "\n",
    "> Given clinical parameters about a patient, can we predict whether or not they have heart disease?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data\n",
    "\n",
    "The original data came from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+Disease\n",
    "\n",
    "There is also a version of it available on Kaggle. https://www.kaggle.com/ronitf/heart-disease-uci"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Evaluation\n",
    "\n",
    "> If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Features\n",
    "\n",
    "This is where you'll get different information about each of the features in your data. You can do this via doing your own research (such as looking at the links above) or by talking to a subject matter expert (someone who knows about the dataset).\n",
    "\n",
    "**Create data dictionary**\n",
    "\n",
    "1. age - age in years\n",
    "2. sex - (1 = male; 0 = female)\n",
    "3. cp - chest pain type\n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "4. trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern\n",
    "5. chol - serum cholestoral in mg/dl\n",
    "    * serum = LDL + HDL + .2 * triglycerides\n",
    "    * above 200 is cause for concern\n",
    "6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "    * '>126' mg/dL signals diabetes\n",
    "7. restecg - resting electrocardiographic results\n",
    "    * 0: Nothing to note\n",
    "    * 1: ST-T Wave abnormality\n",
    "        * can range from mild symptoms to severe problems\n",
    "        * signals non-normal heart beat\n",
    "    * 2: Possible or definite left ventricular hypertrophy\n",
    "        * Enlarged heart's main pumping chamber\n",
    "8. thalach - maximum heart rate achieved\n",
    "9. exang - exercise induced angina (1 = yes; 0 = no)\n",
    "10. oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more\n",
    "11. slope - the slope of the peak exercise ST segment\n",
    "    * 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "    * 1: Flatsloping: minimal change (typical healthy heart)\n",
    "    * 2: Downslopins: signs of unhealthy heart\n",
    "12. ca - number of major vessels (0-3) colored by flourosopy\n",
    "    * colored vessel means the doctor can see the blood passing through\n",
    "    * the more blood movement the better (no clots)\n",
    "13. thal - thalium stress result\n",
    "    * 1,3: normal\n",
    "    * 6: fixed defect: used to be defect but ok now\n",
    "    * 7: reversable defect: no proper blood movement when excercising\n",
    "14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the tools\n",
    "\n",
    "We're going to use pandas, Matplotlib and NumPy for data analysis and manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all the tools we need\n",
    "\n",
    "# Regular EDA (exploratory data analysis) and plotting libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# we want our plots to appear inside the notebook\n",
    "%matplotlib inline \n",
    "\n",
    "# Models from Scikit-Learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model Evaluations\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import plot_roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/heart-disease.csv\")\n",
    "df.shape # (rows, columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration (exploratory data analysis or EDA)\n",
    "\n",
    "The goal here is to find out more about the data and become a subject matter export on the dataset you're working with. \n",
    "\n",
    "1. What question(s) are you trying to solve?\n",
    "2. What kind of data do we have and how do we treat different types?\n",
    "3. What's missing from the data and how do you deal with it?\n",
    "4. Where are the outliers and why should you care about them?\n",
    "5. How can you add, change or remove features to get more out of your data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <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  "
      ]
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     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <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",
       "      <td>0</td>\n",
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       "      <td>57</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find out how many of each class there\n",
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EfCsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"target\"].value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
      "age         303 non-null int64\n",
      "sex         303 non-null int64\n",
      "cp          303 non-null int64\n",
      "trestbps    303 non-null int64\n",
      "chol        303 non-null int64\n",
      "fbs         303 non-null int64\n",
      "restecg     303 non-null int64\n",
      "thalach     303 non-null int64\n",
      "exang       303 non-null int64\n",
      "oldpeak     303 non-null float64\n",
      "slope       303 non-null int64\n",
      "ca          303 non-null int64\n",
      "thal        303 non-null int64\n",
      "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": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are there any missing values?\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency according to Sex "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kkiRplY3le9BH2upcGwOZJElaJRMnTmTBggWGskFUFQsWLGDixImrtJ0fDCtJklbJ5MmTmTdvHvPnz29dylpp4sSJTJ48eZW2MZBJkqRVssEGG7Djjju2LmOd4pSlJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMZGLJAl+UySXyS5oa9tyyQXJ7m1+3eLrj1JPpbktiTXJ3nBSNUlSZK0thnJEbKzgJcOaDsJuKSqdgIu6ZYBXgbs1D2OBz4xgnVJkiStVUYskFXVZcADA5pnAmd3z88GDu1r/1z1XA5snmTbkapNkiRpbTLa95BtU1X3dc9/DmzTPd8euKev37yu7UmSHJ9kTpI58+fPH7lKJUmSRkmzm/qrqoBaje3OqKrpVTV90qRJI1CZJEnS6BrtQHb/0qnI7t9fdO33Ak/v6ze5a5MkSVrnjXYguxA4qnt+FHBBX/ufd++23AdY2De1KUmStE6bMFI7TnIOsC+wdZJ5wCnAB4CvJDkWuAt4bdf9W8DBwG3Ar4G/GKm6JEmS1jYjFsiq6ojlrNp/kL4F/O1I1SJJkrQ285P6JUmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKmxCa0LkCRpLPvqzfe1LmGt86qdt21dwpjjCJkkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUWJNAluStSW5MckOSc5JMTLJjkiuS3Jbky0k2bFGbJEnSaBv1QJZke+DNwPSq2g1YHzgc+CDw0ap6NvAr4NjRrk2SJKmFVlOWE4CNkkwAngrcB+wHnNutPxs4tFFtkiRJo2rUA1lV3Qt8GLibXhBbCFwNPFhVS7pu84DtR7s2SZKkFlpMWW4BzAR2BLYDNgZeugrbH59kTpI58+fPH6EqJUmSRk+LKcsDgDuqan5VLQa+CrwI2LybwgSYDNw72MZVdUZVTa+q6ZMmTRqdiiVJkkZQi0B2N7BPkqcmCbA/cBNwKfCars9RwAUNapMkSRp1Le4hu4Lezfs/An7c1XAG8C7gbUluA7YCzhzt2iRJklqYsPIuw6+qTgFOGdB8O7BXg3IkSZKa8pP6JUmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIam9C6AEnS2LD4fW9vXcLa6fB3tK5A6wBHyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxlYayJJcMpQ2SZIkrZ4Jy1uRZCLwVGDrJFsA6VY9Ddh+FGqTJEkaF5YbyIA3AG8BtgOu5reB7CHg4yNclyRJ0rix3EBWVacBpyV5U1XNGsWaJEmSxpUVjZABUFWzkvw+MKW/f1V9bgTrkiRJGjdWGsiSfB54FnAt8HjXXICBTJIkaRisNJAB04FdqqpGuhhJkqTxaCifQ3YD8L9GuhBJkqTxaigjZFsDNyW5EvifpY1VdciIVSVJkjSODCWQnTrSRUiSJI1nQ3mX5XdHoxBJkqTxaijvsnyY3rsqATYENgAeraqnjWRhkiRJ48VQRsg2Xfo8SYCZwD4jWZQkSdJ4MpR3WS5TPV8DDlqTgybZPMm5SX6SZG6SFybZMsnFSW7t/t1iTY4hSZI0VgxlyvJVfYvr0ftcskVreNzTgP+sqtck2ZDel5i/B7ikqj6Q5CTgJOBda3gcSZKktd5Q3mX5ir7nS4A76U1brpYkmwF/CBwNUFW/AX6TZCawb9ftbGA2BjJJkjQODOUesr8Y5mPuCMwHPpvk+cDVwAnANlV1X9fn58A2g22c5HjgeIBnPOMZw1yaJEnS6FvpPWRJJic5P8kvusd5SSavwTEnAC8APlFV04BH6U1PLtN9TdOgX9VUVWdU1fSqmj5p0qQ1KEOSJGntMJSb+j8LXAhs1z2+3rWtrnnAvKq6ols+l15Auz/JtgDdv79Yg2NIkiSNGUMJZJOq6rNVtaR7nAWs9tBUVf0cuCfJzl3T/sBN9ELfUV3bUcAFq3sMSZKksWQoN/UvSHIkcE63fASwYA2P+ybgi907LG8H/oJeOPxKkmOBu4DXruExJEmSxoShBLJjgFnAR+nd1/UDegFqtVXVtfQ+PmOg/ddkv5IkSWPRUN5leRdwyCjUIkmSNC4N5YNhd6Q3xTilv39VGdIkSZKGwVCmLL8GnEnv3ZVPjGw5kiRJ489QAtmiqvrYiFciSZI0Tg0lkJ2W5BTgIuB/ljZW1Y9GrCpJkqRxZCiBbHfg9cB+/HbKsrplSZIkraGhBLLDgGd2XwIuSZKkYTaUT+q/Adh8pAuRJEkar4YyQrY58JMkV/Hbe8iqqmaOXFmSJEnjx1AC2Sl9zwP8AXD4yJQjSZI0/qx0yrKqvgs8BLwcOIvezfynj2xZkiRJ48dyR8iSPIfeF4kfAfwS+DKQqnrJKNUmSZI0LqxoyvInwPeAl1fVbQBJ3joqVUmSJI0jK5qyfBVwH3Bpkk8l2Z/ePWSSJEkaRssNZFX1tao6HHgucCnwFuD3knwiyYGjVaAkSdK6big39T9aVf9eVa8AJgPXAO8a8cokSZLGiaF8MOwyVfWrqjqjqvYfqYIkSZLGm1UKZJIkSRp+BjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaaxbIkqyf5Jok3+iWd0xyRZLbknw5yYatapMkSRpNLUfITgDm9i1/EPhoVT0b+BVwbJOqJEmSRlmTQJZkMvAnwKe75QD7Aed2Xc4GDm1RmyRJ0mhrNUL2r8A7gSe65a2AB6tqSbc8D9i+RWGSJEmjbdQDWZKXA7+oqqtXc/vjk8xJMmf+/PnDXJ0kSdLoazFC9iLgkCR3Al+iN1V5GrB5kgldn8nAvYNtXFVnVNX0qpo+adKk0ahXkiRpRI16IKuqd1fV5KqaAhwO/FdV/RlwKfCarttRwAWjXZskSVILa9PnkL0LeFuS2+jdU3Zm43okSZJGxYSVdxk5VTUbmN09vx3Yq2U9kiRJLaxNI2SSJEnjkoFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhqb0LoADb/F73t76xLWToe/o3UFkiQNyhEySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqbFRD2RJnp7k0iQ3JbkxyQld+5ZJLk5ya/fvFqNdmyRJUgstRsiWAG+vql2AfYC/TbILcBJwSVXtBFzSLUuSJK3zRj2QVdV9VfWj7vnDwFxge2AmcHbX7Wzg0NGuTZIkqYWm95AlmQJMA64Atqmq+7pVPwe2Wc42xyeZk2TO/PnzR6VOSZKkkdQskCXZBDgPeEtVPdS/rqoKqMG2q6ozqmp6VU2fNGnSKFQqSZI0spoEsiQb0AtjX6yqr3bN9yfZtlu/LfCLFrVJkiSNthbvsgxwJjC3qj7St+pC4Kju+VHABaNdmyRJUgsTGhzzRcDrgR8nubZrew/wAeArSY4F7gJe26A2SZKkUTfqgayq/hvIclbvP5q1SJIkrQ38pH5JkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWpsrQpkSV6a5OYktyU5qXU9kiRJo2GtCWRJ1gf+DXgZsAtwRJJd2lYlSZI08taaQAbsBdxWVbdX1W+ALwEzG9ckSZI04tamQLY9cE/f8ryuTZIkaZ02oXUBqyrJ8cDx3eIjSW5uWY/GkFM/sjXwy9ZlSFrH+LtFQ7fD8lasTYHsXuDpfcuTu7bfUVVnAGeMVlFadySZU1XTW9chad3i7xYNh7VpyvIqYKckOybZEDgcuLBxTZIkSSNurRkhq6olSd4IfBtYH/hMVd3YuCxJkqQRt9YEMoCq+hbwrdZ1aJ3lVLekkeDvFq2xVFXrGiRJksa1tekeMkmSpHHJQKZ1nl/JJWkkJPlMkl8kuaF1LRr7DGRap/mVXJJG0FnAS1sXoXWDgUzrOr+SS9KIqKrLgAda16F1g4FM6zq/kkuStNYzkEmSJDVmINO6bkhfySVJUksGMq3r/EouSdJaz0CmdVpVLQGWfiXXXOArfiWXpOGQ5Bzgh8DOSeYlObZ1TRq7/KR+SZKkxhwhkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZNI4kuSlSW5OcluSk4Zhf1OSVJI39bV9PMnRq7CPU5Pcm+TaJLcm+Wr/F8An+fRY+kL4JIcluTHJE0mmD9M+z0pyR5LrktyS5HNJJvet/1aSzYfjWJLaMJBJ40SS9YF/A14G7AIcMUxB5xfACd0H766uj1bVHlW1E/Bl4L+STAKoquOq6qZhqHO03AC8CrhsmPd7YlU9H9gZuIbeNdoQoKoOrqoHh/l4kkaRgUwaP/YCbquq26vqN8CXgJnDsN/5wCXAUQNXJNkjyeVJrk9yfpItVrazqvoycBHwp90+Zi8daUpyYJIfJvlRkv+XZJOu/QNJbuqO8+GubVKS85Jc1T1e1LXv1e3jmiQ/SLJz175rkiu7kbrrk+zUtR/Z1/7JLtiuqP65VXXz0C/fqqmejwI/pxeuSXJnkq2TbJzkm91I2g1JXtet3zPJd5NcneTbSbbt2v+yuzbXddfqqV37Yd321yW5rGtbP8mHuv7XJ3nDSJ2jNB4ZyKTxY3vgnr7leV3b70hyYhc+Bj4+toJ9fxB4xyBh5XPAu6pqKvBj4JQh1voj4LkD6toa+HvggKp6ATAHeFuSrYBXArt2x/nHbpPT6I28zQBeDXy6a/8J8AdVNQ14L/BPXftfAadV1R7AdGBekucBrwNe1LU/DvzZEM9huZJsupxrfO0qjFo+6RoBLwV+VlXPr6rdgP9MsgEwC3hNVe0JfAZ4f9f/q1U1oxt5mwss/aT59wIHde2HdG3HAgu76zkD+MskO67G6UsaxITWBUhau1TVh4APrXWGsBMAAANkSURBVOI2tye5gm5UCyDJZsDmVfXdruls4P8NcZcZpG0felOt308CsCG9r61ZCCwCzkzyDeAbXf8DgF26vgBP60bUNgPO7kbACtigW/9D4O+6e7O+WlW3Jtkf2BO4qtvPRvSmaNdIVT0M7LGGuxnsGv0Y+JckHwS+UVXfS7IbsBtwcXcO6wP3df13S/KPwObAJvS+Ygzg+8BZSb4CfLVrOxCYmuQ13fJmwE7AHWt4HpIwkEnjyb3A0/uWJ3dtvyPJiQw+CnRZVb15Bfv/J+Bc4Lsr6DNU0+iNgP1OacDFVXXEwM5J9gL2B15D77tL96M3A7BPVS0a0PfjwKVV9cokU4DZAFX1712o/BPgW92UXICzq+rdw3BO/TVsCnxvOav/dIj3zE2jN1W8TFXdkuQFwMHAPya5BDgfuLGqXjjIPs4CDq2q69J7I8a+3X7+Ksne9K7F1Un2pHct3lRV3x5kP5LWkFOW0vhxFbBTkh27m8EPBy4c2KmqPtTdYD/wsaIwRlX9BLgJeEW3vBD4VZI/6Lq8niGEtSSvpjcac86AVZcDL0ry7K7fxkmes3TUq6q+BbwVeH7X/yKg/92fS0ekNuO3QfTovvXPBG6vqo8BFwBT6QWe1yT5va7Plkl26J5/rguCq6yqHl7ONd5jZWEsPW8GtgX+c8C67YBfV9UX6I1yvgC4GZiU5IVdnw2S7NptsilwXzet+Wd9+3lWVV1RVe+ld4/g0+mNnv1115fu2m+8Oucv6ckcIZPGiapakuSN9P6wrg98pqpuHObDvJ/eOwCXOgo4vbtZ/HbgL5az3VuTHAlsTO9divtV1fwB9c/vRnHOSfKUrvnvgYeBC5JMpDeK87Zu3ZuBf0tyPb3fdZfRu0/sn+lNWf498M2+Q7wWeH2SxfRumP+nqnqg63dRkvWAxcDfAnfRC2w/G3giSV5J756tScA3k1xbVQct94oN3YeSnAw8lV44fUn35ox+u3f9nuhq/euq+k03zfixbhp5AvCvwI3AycAV9ELXFfQC2tJj7UTvel4CXAdcD0wBfpTe3Od84NBhOC9JQKqqdQ2SNKYkeRpwZlUd1roWSesGA5kkSVJj3kMmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNfb/ATGgs1D8K0hVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot of crosstab\n",
    "pd.crosstab(df.target, df.sex).plot(kind=\"bar\",\n",
    "                                    figsize=(10, 6),\n",
    "                                    color=[\"salmon\", \"lightblue\"])\n",
    "\n",
    "plt.title(\"Heart Disease Frequency for Sex\")\n",
    "plt.xlabel(\"0 = No Diesease, 1 = Disease\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Female\", \"Male\"]);\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age vs. Max Heart Rate for Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create another figure\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Scatter with postivie examples\n",
    "plt.scatter(df.age[df.target==1],\n",
    "            df.thalach[df.target==1],\n",
    "            c=\"salmon\")\n",
    "\n",
    "# Scatter with negative examples\n",
    "plt.scatter(df.age[df.target==0],\n",
    "            df.thalach[df.target==0],\n",
    "            c=\"lightblue\")\n",
    "\n",
    "# Add some helpful info\n",
    "plt.title(\"Heart Disease in function of Age and Max Heart Rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Max Heart Rate\")\n",
    "plt.legend([\"Disease\", \"No Disease\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of the age column with a histogram\n",
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency per Chest Pain Type\n",
    "\n",
    "3. cp - chest pain type\n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the crosstab more visual\n",
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\",\n",
    "                                   figsize=(10, 6),\n",
    "                                   color=[\"salmon\", \"lightblue\"])\n",
    "\n",
    "# Add some communication\n",
    "plt.title(\"Heart Disease Frequency Per Chest Pain Type\")\n",
    "plt.xlabel(\"Chest Pain Type\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"No Disease\", \"Disease\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14.5, -0.5)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's make our correlation matrix a little prettier\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                 annot=True,\n",
    "                 linewidths=0.5,\n",
    "                 fmt=\".2f\",\n",
    "                 cmap=\"YlGnBu\");\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Modelling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into X and y\n",
    "X = df.drop(\"target\", axis=1)\n",
    "\n",
    "y = df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
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       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <td>68</td>\n",
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      "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "298    0\n",
       "299    0\n",
       "300    0\n",
       "301    0\n",
       "302    0\n",
       "Name: target, Length: 303, dtype: int64"
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     "execution_count": 26,
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    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train and test sets\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into train & test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
       "      <td>160</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
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       "      <td>2</td>\n",
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       "      <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",
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       "      <td>1</td>\n",
       "      <td>179</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "  </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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132    1\n",
       " 202    0\n",
       " 196    0\n",
       " 75     1\n",
       " 176    0\n",
       "       ..\n",
       " 188    0\n",
       " 71     1\n",
       " 106    1\n",
       " 270    0\n",
       " 102    1\n",
       " Name: target, Length: 242, dtype: int64, 242)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train, len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got our data split into training and test sets, it's time to build a machine learning model.\n",
    "\n",
    "We'll train it (find the patterns) on the training set.\n",
    "\n",
    "And we'll test it (use the patterns) on the test set.\n",
    "\n",
    "We're going to try 3 different machine learning models:\n",
    "1. Logistic Regression \n",
    "2. K-Nearest Neighbours Classifier\n",
    "3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {\"Logistic Regression\": LogisticRegression(),\n",
    "          \"KNN\": KNeighborsClassifier(),\n",
    "          \"Random Forest\": RandomForestClassifier()}\n",
    "\n",
    "# Create a function to fit and score models\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluates given machine learning models.\n",
    "    models : a dict of differetn Scikit-Learn machine learning models\n",
    "    X_train : training data (no labels)\n",
    "    X_test : testing data (no labels)\n",
    "    y_train : training labels\n",
    "    y_test : test labels\n",
    "    \"\"\"\n",
    "    # Set random seed\n",
    "    np.random.seed(42)\n",
    "    # Make a dictionary to keep model scores\n",
    "    model_scores = {}\n",
    "    # Loop through models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(X_train, y_train)\n",
    "        # Evaluate the model and append its score to model_scores\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 31,
     "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",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P27UnsKmfqvploHdnQ1V9qu8itM2+u4W2XYGzGCxfYaDvuL4BrAJOZNCRGvMwg87VyHHIpSNJ3s1gnv7fAT8Ya6+qL/ZWlLZJkj2A1zNYPfQK4H1VdX+/VWkySXauqo3D53sDB1bVV3ouqxf20LsztmDZ+DUkCvjFHmrRNkiyD/BG4JUMVg09oqoe7LcqbYNrk5zIIM9uBe5PcmNVjVwv3UDvSFW9oO8atO2SvBd4OYOrCw+vqu/0XJK23V5V9VCSs4CPVNXvJRnJHrrTFjuSZK8kf5xk1fDxviR79V2XJvUmYH/gbcA3kjw0fDyc5KGea9PUzE3yZOBU4B/7LqZPBnp3LmVwMubU4eMh4EO9VqRJVdVOVfUzVbVHVe057rFHVe3Zd32akosYrDX1/6rqliQ/B3y955p64UnRjiS5raqeOVmbJE0Xe+jd+X6S549tDC80+n6P9WgKxoZWhl8fHrf9vSQjOZd5tknytCT/nOTfhtuLk7yt77r6YA+9I0meyWCGxF4MrhJ9ADijqr7ca2HaJkl2B84Dfh24qqre1HNJmkSSfwHeAlxSVUuHbf9WVYf1W9nMc5ZLR6rqNmBJkj2H255Qm0WS/CzwW8CvAZcBz6oq77o1O+xaVV+YcF/6kfx0ZaBvpySvqqqPJXnjhHYAquqPeylMU5JkPwYzXV7B4MT20qra0G9V2kbfSvJUBtd9kORXgW/2W1I/DPTtt9vw6x69VqHH6z+AdQxmJH0POHN8T88/yLPCeQyuI/hvSe4D7mZwkdjIcQxdIy3J7zPs2W1BVdVFM1iOtlGSnYBfraorkuwG7FRVD/ddV18M9I4k+SPgnQxmtnwaWAy8oao+1mthekxJDqyqe7ey7yVVNdIXqswGSVZV1bLJj2yf0xa788vDE6EvAe4Bfp7BmXft2K5NsnBiY5LlwJ/NeDV6PK5L8uYkBybZZ+zRd1F9cAy9O2M/yxcDf1tVGyacddeO6Y3APyV5cVV9HSDJW4H/AfxCr5Vpql4x/HreuLYCRu7mMgZ6d/4xyZ0Mhlx+I8l84JGea9IkqurqJD8APpXkZAbroB8FHOOKi7NDVS3qu4YdhWPoHRp+zNtQVY8m2RXYs6r+q++6NLkk/x24CrgROLWq/GM8SyTZGfgN4Jhh02cZXGS0sbeiemKgd2R4U9pPV9XDw8uOjwDe6Q0udmxJHmbw8TzAE4CNDG4hOHZPWBfo2sEl+QtgZwZXagO8Gni0qs7qr6p+GOgdSfKVqlo8XM/lncB7gbdX1bMn+VZJ2yHJl6tqyWRto8BZLt0ZuzH0i4EVVfVJYJce65FGxaPDK0UBGC6fO5I3avekaHfuS3IJcBzwniRPwD+Y0kx4C3B9krsYDJU9BVjeb0n9cMilI8OToMcDt1fV14d3UDm8qv6p59Kk5g07UE8fbq6uqh881vGtsgfZkar6HnA/MLYm+iZG9K4p0kxI8gfjNo+pqq8MHyMZ5mAPvTNJfg9YBjy9qp6WZH8GFxg9r+fSpCYl+WJVHTHx+Sizh96dlwEnAt8FqKpv4AqMkmaQJ0W788OqqiRjazLvNtk3SNouTxzehyDjnm82iksfG+jduWI4y+Vnk5wNvBb4855rklr25/z4U/D45yPLMfQOJTkO+GUGPYZrqurankuSNEIM9A4kmQNcV1Uv6LsWSaPLk6IdqKpHgR8l2avvWiSNLsfQu/Md4PYk1zKc6QJQVf+zv5IkjRIDvTt/N3xImkFJfhb4NWAh4zJtFDtTjqFLmtWS3AjcDNwO/Gisvar+aqvf1CgDvSNJbuen7x6/AVjFYF309TNfldQ+rxL9MQO9I0n+iMGSnZcNm04DdgX+C3h+Vb20r9qkliV5A4NzWP8IbF7Hpaoe6K2onhjoHdlSL2GsLcntVXV4X7VJLUtyHvAu4Nv8+FNyVZU3idbjNifJUVX1BYAkzwLmDPdt6q8sqXlvAn6+qr7VdyF9M9C7cxZwaZLdh9sPA2cN13T5w/7Kkpq3Bvhe30XsCBxy6djYxUVVtaHvWqRRkOQq4BnA9fzkGPrITVu0h96RJE8C/gDYv6pOSHIo8Nyq+sueS5Na9/fDx8izh96RJJ8CPgT8TlUtSTIX+JInQ6Xpl2QX4GnDzdVVtbHPevriWi7d2a+qrmB4YUNVbWJE7zwuzaQkxzK43ePFwAeBf09yTK9F9cQhl+58N8m+DKdNJXkOgwuLJE2v9wG/XFWrAZI8Dfhr4Mheq+qBgd6dNwIrgacm+TwwHzil35KkkbDzWJgDVNW/J9m5z4L64hh6h4bj5k9ncIOLkR3Hk2ZSkksZDHV+bNj0SmBOVb22v6r6YaBPk+Hdiy6oquP6rkVqWZInAOcBzx82fQ74YFX9YOvf1SYDfTsl+UXgfwP7M5g69R4Gs10CvKuqXFJX0oww0LdTki8BbwBuAk5g8LHvwqr6X70WJjVuKyucblZVi2ewnB2Cgb6dJi7KlWR1VT29z5qkUZDkKcOn5w2/fnT49VUMFue6cOar6peBvp2S3AW8eVzTe4G3jG045CJNryRfqqqlE9pGco10py1uv38Bxq91fsO47cLb0knTLUmeV1WfH24czYheNGkPXdKsluRI4FJgLwaTER4EXltVX+y1sB4Y6JKa4EqnBrqkWW44D/1XgIWMG0auqov6qqkvjqFLmu3+gcG6Sbcybj30UWQPvSPD+xp+vKq+PdzeGzi9qj7Yb2VS25L8W1Ud1ncdO4KRPBM8Tc4eC3OAqnoQOLvHeqRRcWMS7zuAQy5dmpMkNfzIk2QOsEvPNUmj4PnAGUnuZjDkEgYXFo3claIGenc+DfxNkkuG278+bJM0vU7ou4AdhWPoHUmyE4MQf+Gw6VrgL6rKuxZJMyDJE4F5Y9tV9Z89ltMLA13SrJbkRAZ3LdofuB94CnBHVT2j18J64JDLdkpyRVWdurWV30ZxHE+aYe8AngNcV1VLk7yAwQJdI8dA336vH359Sa9VSKNrY1WtT7JTkp2q6vokf9p3UX1w2uJ2qqpvDp++rqr+Y/wDeF2ftUkj4ttJdmewMN7Hk/wZ8N2ea+qFY+gd2dJynUm+4pCLNL2S7AZ8n0EH9ZUMFun6eFWt77WwHhjo2ynJbzDoiT8VWDNu1x7A56tqJMfypL4MZ5ydXlUf77uWmWagb6fhCm97A38IjL9DysNV9UA/VUntS7Ing7sVHQCsZDBV+DwGN5z5clWd1GN5vTDQO5LkqcDaqvpBkmOBxcBHxi8HIKk7Sf6BwdrnNzG4/uOJDK4SfX1V3dZnbX0x0DuS5DZgGYMlPK9msALcM6rqRX3WJbUqye1Vdfjw+Rzgm8BBVfVIv5X1x1ku3flRVW0CXg58oKreAjy555qklm0cezK8InvtKIc5OA+9SxuTnA78Gj++p+jOPdYjtW5JkoeGzwP8zHB7bHGuPfsrrR8GeneWA+cC76qqu5MsAj7ac01Ss6pqTt817GgcQ5ekRthD306u5SJpR2EPfTsleXJVfTPJU7a0f7gEgCRNOwNdkhrhkEtHkjzMTw+5bABWAW+qqrtmvipJo8RA786fAmuByxhMmzqNwfouXwQuBY7trTJJI8Ehl44k+XJVLZnQdltVPXNL+ySpa14p2p3vJTl1bJH9JKcCY1et+VdT0rSzh96RJD8H/Bnw3GHTTcAbgPuAI6vq//ZVm6TRYKBLUiMcculIkgVJrkpy//BxZZIFfdclaXQY6N35EINF9vcfPv7PsE2SZoRDLh0Zm9EyWZskTRd76N1Zn+RVSeYMH68CRu4mtZL6Yw+9I8O1XD7AYJZLATcCv1lV9/ZamKSRYaBPoyS/VVV/2ncdkkaDgT6NkvxnVR3Udx2SRoNj6NMrfRcgaXQY6NPLjz+SZoyrLW6nrSybC8Ob1s5wOZJGmGPoktQIh1wkqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSI/4/vuSSGZRDvUwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=[\"accuracy\"])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got a baseline model... and we know a model's first predictions aren't always what we should based our next steps off. What should we do?\n",
    "\n",
    "Let's look at the following:\n",
    "* Hypyterparameter tuning\n",
    "* Feature importance\n",
    "* Confusion matrix\n",
    "* Cross-validation\n",
    "* Precision\n",
    "* Recall\n",
    "* F1 score\n",
    "* Classification report\n",
    "* ROC curve\n",
    "* Area under the curve (AUC)\n",
    "\n",
    "### Hyperparameter tuning (by hand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune KNN\n",
    "\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of differnt values for n_neighbors\n",
    "neighbors = range(1, 21)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "    \n",
    "    # Fit the algorithm\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    # Update the training scores list\n",
    "    train_scores.append(knn.score(X_train, y_train))\n",
    "    \n",
    "    # Update the test scores list\n",
    "    test_scores.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 75.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors, train_scores, label=\"Train score\")\n",
    "plt.plot(neighbors, test_scores, label=\"Test score\")\n",
    "plt.xticks(np.arange(1, 21, 1))\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning with RandomizedSearchCV\n",
    "\n",
    "We're going to tune:\n",
    "* LogisticRegression()\n",
    "* RandomForestClassifier()\n",
    "\n",
    "... using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a hyperparameter grid for LogisticRegression\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 20),\n",
    "                \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Create a hyperparameter grid for RandomForestClassifier\n",
    "rf_grid = {\"n_estimators\": np.arange(10, 1000, 50),\n",
    "           \"max_depth\": [None, 3, 5, 10],\n",
    "           \"min_samples_split\": np.arange(2, 20, 2),\n",
    "           \"min_samples_leaf\": np.arange(1, 20, 2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got hyperparameter grids setup for each of our models, let's tune them using RandomizedSearchCV..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisticRegression\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                                param_distributions=log_reg_grid,\n",
    "                                cv=5,\n",
    "                                n_iter=20,\n",
    "                                verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for LogisticRegression\n",
    "rs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've tuned LogisticRegression(), let's do the same for RandomForestClassifier()..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:  1.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for RandomForestClassifier\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(), \n",
    "                           param_distributions=rf_grid,\n",
    "                           cv=5,\n",
    "                           n_iter=20,\n",
    "                           verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for RandomForestClassifier()\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best hyperparameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the randomized search RandomForestClassifier model\n",
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparamter Tuning with GridSearchCV\n",
    "\n",
    "Since our LogisticRegression model provides the best scores so far, we'll try and improve them again using GridSearchCV..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    1.0s finished\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,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best hyperparmaters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the grid search LogisticRegression model\n",
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluting our tuned machine learning classifier, beyond accuracy\n",
    "\n",
    "* ROC curve and AUC score\n",
    "* Confusion matrix\n",
    "* Classification report\n",
    "* Precision\n",
    "* Recall\n",
    "* F1-score\n",
    "\n",
    "... and it would be great if cross-validation was used where possible.\n",
    "\n",
    "To make comparisons and evaluate our trained model, first we need to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions with tuned model\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179    0\n",
       "228    0\n",
       "111    1\n",
       "246    0\n",
       "60     1\n",
       "      ..\n",
       "249    0\n",
       "104    1\n",
       "300    0\n",
       "193    0\n",
       "184    0\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1205579d0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve and calculate and calculate AUC metric\n",
    "plot_roc_curve(gs_log_reg, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 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",
    "    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",
    "    bottom, top = ax.get_ylim()\n",
    "#     ax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got a ROC curve, an AUC metric and a confusion matrix, let's get a classification report as well as cross-validated precision, recall and f1-score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics using cross-validation\n",
    "\n",
    "We're going to calculate accuracy, precision, recall and f1-score of our model using cross-validation and to do so we'll be using `cross_val_score()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                         solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.86885246, 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated accuracy\n",
    "cv_acc = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"accuracy\")\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446994535519124"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8207936507936507"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated precision\n",
    "cv_precision = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"precision\")\n",
    "cv_precision=np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212121212121213"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated recall\n",
    "cv_recall = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"recall\")\n",
    "cv_recall = np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673007976269721"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated f1-score\n",
    "cv_f1 = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"f1\")\n",
    "cv_f1 = np.mean(cv_f1)\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\": cv_acc,\n",
    "                           \"Precision\": cv_precision,\n",
    "                           \"Recall\": cv_recall,\n",
    "                           \"F1\": cv_f1},\n",
    "                          index=[0])\n",
    "\n",
    "cv_metrics.T.plot.bar(title=\"Cross-validated classification metrics\",\n",
    "                      legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit an instance of LogisticRegression\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                         solver=\"liblinear\")\n",
    "\n",
    "clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00320769, -0.86062049,  0.66001432, -0.01155971, -0.00166496,\n",
       "         0.04017236,  0.31603405,  0.02458922, -0.60470171, -0.56795456,\n",
       "         0.45085392, -0.63733328, -0.67555094]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.0032076883508599633,\n",
       " 'sex': -0.8606204883695241,\n",
       " 'cp': 0.660014324982524,\n",
       " 'trestbps': -0.01155970600550047,\n",
       " 'chol': -0.0016649614843449207,\n",
       " 'fbs': 0.040172360271308105,\n",
       " 'restecg': 0.31603405294617176,\n",
       " 'thalach': 0.02458922341328129,\n",
       " 'exang': -0.604701713592625,\n",
       " 'oldpeak': -0.5679545646616215,\n",
       " 'slope': 0.4508539209693025,\n",
       " 'ca': -0.6373332766360461,\n",
       " 'thal': -0.6755509369619848}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match coef's of features to columns\n",
    "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "feature_df = pd.DataFrame(feature_dict, index=[0])\n",
    "feature_df.T.plot.bar(title=\"Feature Importance\", legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "sex            \n",
       "0        24  72\n",
       "1       114  93"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target   0    1\n",
       "slope          \n",
       "0       12    9\n",
       "1       91   49\n",
       "2       35  107"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"slope\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "slope - the slope of the peak exercise ST segment\n",
    "* 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "* 1: Flatsloping: minimal change (typical healthy heart)\n",
    "* 2: Downslopins: signs of unhealthy heart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Experimentation\n",
    "\n",
    "If you haven't hit your evaluation metric yet... ask yourself...\n",
    "\n",
    "* Could you collect more data?\n",
    "* Could you try a better model? Like CatBoost or XGBoost?\n",
    "* Could you improve the current models? (beyond what we've done so far)\n",
    "* If your model is good enough (you have hit your evaluation metric) how would you export it and share it with others?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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