End-to-End- Heart-disease-classification.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "# Predicting heart disease using Machine learning\n",
    "This notebook looks into using various Python 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 are going to take the following approach:\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features \n",
    "5. Modelling\n",
    "6. Experimentation\n",
    "\n",
    "## 1. Problem Definition\n",
    "\n",
    "In a statement,\n",
    "> Given clinical parameters about a patient, can we predict whether or not they have heart disease?\n",
    "\n",
    "## 2. Data \n",
    "\n",
    "The original data cone from the Clevland data from the UCI Machine learning Repository .\n",
    "https://archive.ics.uci.edu/ml/datasets/heart+disease\n",
    "\n",
    "There is also a version of it available on kaggle. \n",
    "https://www.kaggle.com/ronitf/heart-disease-uci\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "> If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project.\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "This is where you'll get different information about each of the features in your data.\n",
    "\n",
    "**Create data dictionary **\n",
    "\n",
    "* age in years\n",
    "* sex ( 1 = male; 0 = female)\n",
    "* chest pain type (4 values)\n",
    "* resting blood pressure\n",
    "* serum cholestoral in mg/dl\n",
    "* fasting blood sugar > 120 mg/dl\n",
    "* resting electrocardiographic results (values 0,1,2)\n",
    "* maximum heart rate achieved\n",
    "* exercise induced angina(1 = yes' 0 = no)\n",
    "* oldpeak = ST depression induced by exercise relative to rest\n",
    "* the slope of the peak exercise ST segment\n",
    "* number of major vessels (0-3) colored by flourosopy\n",
    "* thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\n",
    "* target 1 or 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the tools\n",
    "we're going to use Pandas, matplotlib and Numpy for data analysis and manipulations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"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?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>63</td>\n",
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       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>0</td>\n",
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       "      <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",
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       "      <th>3</th>\n",
       "      <td>56</td>\n",
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       "      <td>0.8</td>\n",
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       "      <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  "
      ]
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ASUVORK5CYII=\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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are there any missing values?\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency according to Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target,df.sex)"
   ]
  },
  {
   "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 a plot of crosstab\n",
    "pd.crosstab(df.target,df.sex).plot(kind =\"bar\",\n",
    "                                   figsize=(10,6),\n",
    "                                   color=[\"salmon\",\"lightblue\"])\n",
    "plt.title(\"Heart Disease Frequency for Sex\")\n",
    "plt.xlabel(\"0= No Disease, 1 = Disease\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Female\",\"Male\"]);\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age Vs Max Heart Rate for Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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 positive examples\n",
    "plt.scatter(df.age[df.target==1],\n",
    "           df.thalach[df.target==1],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": 42,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp,df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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",
    "# Add some communication\n",
    "plt.title(\"Heart Disease Frequence 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": 45,
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14.5, -0.5)"
      ]
     },
     "execution_count": 47,
     "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",
    "                linewidth=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": 48,
   "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 50,
   "metadata": {},
   "outputs": [
    {
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       "      <th>298</th>\n",
       "      <td>57</td>\n",
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       "      <td>241</td>\n",
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       "      <td>264</td>\n",
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       "     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",
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       "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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
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  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
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       "3      1\n",
       "4      1\n",
       "      ..\n",
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       "Name: target, Length: 303, dtype: int64"
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    "y"
   ]
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  {
   "cell_type": "code",
   "execution_count": 52,
   "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>63</td>\n",
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       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got our data split into training and test set and it's time to build a machine learning model\n",
    "\n",
    "we'll train it(find 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 Neighbors classification\n",
    "3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = { \"Logistic Regression\": LogisticRegression(),\n",
    "            \"KNN\":KNeighborsClassifier(),\n",
    "             \"Random Forest\": RandomForestClassifier()}\n",
    "\n",
    "# Create a function to fit and score models\n",
    "\n",
    "def fit_and_score(models, X_train,X_test,y_train,y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluates given machine learning models.\n",
    "    models: a dict of different SCikit-Learn machine learning models\n",
    "    X_train : training data(no labels)\n",
    "    X_test: testing data (no labels)\n",
    "    y_train: training labels\n",
    "    y_test: test labels\n",
    "    \"\"\"\n",
    "    # set random seeed\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": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\13095\\Machinelearning\\Heart-disease-Project\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 56,
     "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",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=[\"accuracy\"])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Now we 've got a baseline model... and we know a model's first predictions and always what we should based our next steps off.What should we do?\n",
    " \n",
    " Let's look at the following:\n",
    " \n",
    "* Hyper parameter 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# Let's tune KNN\n",
    "train_scores=[]\n",
    "test_scores= [] \n",
    "\n",
    "# create a list of different values for n_nieghbors\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": 63,
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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",
    "...using RandomizedSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a hyperparameter grid for LogisticRegression\n",
    "\n",
    "log_reg_grid = {\"C\":np.logspace(-4, 4, 20),\n",
    "               \"solver\":[\"liblinear\"]}\n",
    "\n",
    "# Create a hyperparameter grid for RandomForestClassifier\n",
    "rf_grid = {\"n_estimators\": np.arange(10, 1000, 50),\n",
    "           \"max_depth\": [None, 3, 5, 10],\n",
    "           \"min_samples_split\": np.arange(2, 20, 2),\n",
    "           \"min_samples_leaf\": np.arange(1, 20, 2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we 've got hyperparameter griids setup for each of our models,let tune them using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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:    0.2s 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": 87,
     "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": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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:   37.4s 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": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# 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": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best hyperparameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 93,
     "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",
    "Since our LogisticRegression model provides the best scores so far, we'll try and improve them again using GridSearchCV..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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:    0.3s finished\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 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": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best hyperparmaters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 96,
     "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",
    " * ROC curve and AUC score\n",
    " * Confusion matrix\n",
    " * Classification report\n",
    " * Precision\n",
    " * Recall\n",
    " * F1-score\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": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Make predictions with tuned model\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x2aad7bc9d60>"
      ]
     },
     "execution_count": 100,
     "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": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Confusion matrix\n",
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import Seaborn\n",
    "import seaborn as sns\n",
    "\n",
    "# Increase font size\n",
    "sns.set(font_scale=1.5) \n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a confusion matrix using Seaborn's heatmap().\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot=True, # Annotate the boxes\n",
    "                     cbar=False)\n",
    "    plt.xlabel(\"Predicted label\") # predictions go on the x-axis\n",
    "    plt.ylabel(\"True label\") # true labels go on the y-axis \n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "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": 103,
   "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": [
    "\n",
    "### Calculate evaluation metrics using cross-validation\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": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                         solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.86885246, 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 107,
     "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": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446994535519124"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8207936507936507"
      ]
     },
     "execution_count": 109,
     "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": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212121212121213"
      ]
     },
     "execution_count": 110,
     "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": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673007976269721"
      ]
     },
     "execution_count": 111,
     "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": 112,
   "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",
    "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": 113,
   "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": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00316728, -0.86044674,  0.66067031, -0.01156993, -0.00166375,\n",
       "         0.04386101,  0.31275865,  0.02459362, -0.60413094, -0.56862789,\n",
       "         0.45051632, -0.63609908, -0.67663375]])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.0031672830780218957,\n",
       " 'sex': -0.8604467440762573,\n",
       " 'cp': 0.6606703120090932,\n",
       " 'trestbps': -0.011569932037408597,\n",
       " 'chol': -0.00166374523064295,\n",
       " 'fbs': 0.043861009724542044,\n",
       " 'restecg': 0.3127586507840532,\n",
       " 'thalach': 0.024593615555173243,\n",
       " 'exang': -0.6041309439103262,\n",
       " 'oldpeak': -0.5686278914396258,\n",
       " 'slope': 0.4505163222528207,\n",
       " 'ca': -0.6360990763634887,\n",
       " 'thal': -0.6766337475895309}"
      ]
     },
     "execution_count": 116,
     "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": 117,
   "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": 118,
   "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": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"slope\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "slope - the slope of the peak exercise ST segment\n",
    "\n",
    " 0: Upsloping: better heart rate with excercise (uncommon)\n",
    " 1: Flatsloping: minimal change (typical healthy heart)\n",
    " 2: Downslopins: signs of unhealthy heart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}