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

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   "id": "97e99911",
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   "source": [
    "# Predicting heart disease using machine learning\n",
    "\n",
    "This notebook looks into using various Python-based machine learning and data science libraries in an attempt to a build machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes.\n",
    "\n",
    "We're going to take the following approach:\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling\n",
    "6. Experimentation\n",
    "\n",
    "## 1. Problem Definition\n",
    "\n",
    "In a statement\n",
    "> Given clinical parameter about a patient, can we predict whether or not they have heart disease?\n",
    "\n",
    "# 2. Data\n",
    "\n",
    "The orignal data came from the UCI Machine Learnrning Repository.\n",
    "\n",
    "There is also a version available on Kaggle: https://www.kaggle.com/c/heart-disease-uci/data\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "> If we can reach 95% accuracy wheather 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. The age of the patient.\n",
    "\n",
    "sex. The gender of the patient. (1 = male, 0 = female).\n",
    "\n",
    "cp. Type of chest pain. (1 = typical angina, 2 = atypical angina, 3 = non — anginal pain, 4 = asymptotic).\n",
    "\n",
    "trestbps. Resting blood pressure in mmHg.\n",
    "\n",
    "chol. Serum Cholestero in mg/dl.\n",
    "\n",
    "fbs. Fasting Blood Sugar. (1 = fasting blood sugar is more than 120mg/dl, 0 = otherwise).\n",
    "\n",
    "restecg. Resting ElectroCardioGraphic results (0 = normal, 1 = ST-T wave abnormality, 2 = left ventricular hyperthrophy).\n",
    "\n",
    "thalach. Max heart rate achieved.\n",
    "\n",
    "exang. Exercise induced angina (1 = yes, 0 = no).\n",
    "\n",
    "oldpeak. ST depression induced by exercise relative to rest.\n",
    "\n",
    "slope. Peak exercise ST segment (1 = upsloping, 2 = flat, 3 = downsloping).\n",
    "\n",
    "ca. Number of major vessels (0–3) colored by flourosopy.\n",
    "\n",
    "thal. Thalassemia (3 = normal, 6 = fixed defect, 7 = reversible defect).\n",
    "\n",
    "num. Diagnosis of heart disease (0 = absence, 1, 2, 3, 4 = present).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c84a43",
   "metadata": {},
   "source": [
    "## Preparing the tools\n",
    "\n",
    "We're going to use pandas, Matplotlib and numpy for data analysis and manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe5a3f32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all the tools we need\n",
    "\n",
    "# Regualar 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",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8770dcb",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4846bba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"21882974-heart-disease.csv\")\n",
    "df.shape # (rows, colomns) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ae380fb",
   "metadata": {},
   "source": [
    "## Data Exploration (exploratory data analysis or EDA)\n",
    "\n",
    "The here is to find out more about the data and become a subject matter expert on the dataset you're working with.\n",
    "\n",
    "1. What 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 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": 4,
   "id": "d0ff0db5",
   "metadata": {},
   "outputs": [
    {
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       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
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       "      <th>slope</th>\n",
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       "      <td>1.4</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.6</td>\n",
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
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     "execution_count": 4,
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "02bca007",
   "metadata": {},
   "outputs": [
    {
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       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
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       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
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       "      <td>57</td>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a125e0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find out how many of each class there are\n",
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "87ab21c5",
   "metadata": {},
   "outputs": [
    {
     "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": 8,
   "id": "dc5f5fd4",
   "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": 9,
   "id": "553212a9",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are there any missing values?\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1592b28",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed61bd6",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency according to Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d01c7ad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a98a43e",
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6d633e54",
   "metadata": {
    "scrolled": false
   },
   "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",
   "id": "3092e1fd",
   "metadata": {},
   "source": [
    "### Age vs. Max Heart Rate for heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5af9d1c5",
   "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],\n",
    "           c=\"salmon\")\n",
    "# Scatter with negative examples\n",
    "plt.scatter(df.age[df.target==0],\n",
    "           df.thalach[df.target==0],\n",
    "           c=\"lightblue\")\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([\"Diseae\", \"No Disease\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "30b4ae2b",
   "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 colunm with a histogtam\n",
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcaea38f",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency per Chest Pain Type\n",
    "cp. Type of chest pain.\n",
    "\n",
    "     * 0 = typical angina, \n",
    "     * 1 = atypical angina, \n",
    "     * 2 = non — anginal pain, \n",
    "     * 3 = asymptotic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7961e25f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "cp             \n",
       "0       104  39\n",
       "1         9  41\n",
       "2        18  69\n",
       "3         7  16"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4597bd75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ceeSRvOc97+EnP/kJ++23H9/97nfJTCZOnMjXvva1nu10Nwxkq8nq7hy5KprpWClJUimDBw/mIx/5CJdccgmf+MQnANhtt9246qqrOProo5k2bRrvfe97u13PbbfdxtSpU5kxY8brxi9dupQ//elPfOhDH2KXXXbhne98JwD77rsvF110Ef/4j/8IwOzZsxk7diyLFy9miy22AKonOds9+uijbLXVVpx66qk8+uijPPDAA+y7774cfPDBnH766QwdOpRnn32WJUuWsOWWW67SMbEPmSRJarkzzjiDp59+evnwhRdeyGWXXcaYMWP4/ve/zwUXXNDhcldffTVjx45l66235qtf/SrXXXfdG1rIlixZwoEHHsiYMWN4//vfv/whggsvvJCZM2cyZswYRo8ezZQpUwA466yzOPvss9l9991f10H/6quvZvvtt2fs2LHMnTuXY445htGjR3Peeeex7777MmbMGPbZZx8WLFiwyscjOrr3uqYYP358zpw5s3QZgC1kkqS+ra2t7Q3BRb2no+MdEbMyc3xH89tCJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgrrtUAWEZdGxMKIeLBh3OCI+HlEzKt/b9Iw7eyI+E1EPBwR+/VWXZIkqYwBAwYwduxYtttuO3bYYQe+8Y1v8NprrwEwc+bMpr+/cm3Um2/qvxy4CPhew7jJwPTM/HpETK6HPxsRo4EjgO2AzYGbI2LrzFz1r0+XJElvcP3Dq/4y00aHbjOs23nWW289Zs+eDcDChQs58sgjWbx4Meeeey7jx49n/PgOX9HVL/RaC1lm3g48u8Log4Er6s9XAIc0jL8qM1/OzN8CvwF27q3aJElSWUOHDmXq1KlcdNFFZCa33nrr8i8Bv+222xg7dixjx45l3LhxLFmyBIB/+Zd/YcKECYwZM4Zzzjln+boOOeQQdtppJ7bbbjumTp0KwKuvvsqxxx7L9ttvz7vf/e7lb+t/5JFH2H///dlpp5143/vex9y5c1u85x1r9XdZbpaZCwAyc0FEtH89+hbALxvmm1+Pe4OImARMAnjHO97Ri6VKkqTetNVWW/Haa6+xcOHC140///zzufjii9l9991ZunQpgwYN4qabbmLevHncfffdZCYHHXQQt99+O3vssQeXXnopgwcP5sUXX2TChAkcdthhPPbYYzz55JM8+GDVc+q5554DYNKkSUyZMoVRo0bxq1/9ik9+8pPccsstrd71N+grXy7e0Ve6d/idTpk5FZgK1Vcn9WZRkiSpd3X0FY677747n/nMZzjqqKM49NBDGT58ODfddBM33XQT48aNA6ovEJ83bx577LEHF154ITfccAMATzzxBPPmzWObbbbh0Ucf5ZRTTuGAAw5g3333ZenSpdx5550cfvjhy7f18ssvt2ZHu9HqQPZURAyrW8eGAe2ReD7w9ob5hgO/b3FtkiSphR599FEGDBjA0KFDaWtrWz5+8uTJHHDAAfz0pz9ll1124eabbyYzOfvssznxxBNft45bb72Vm2++mbvuuov111+fPffck5deeolNNtmE+++/n5/97GdcfPHFXHPNNXzrW99i4403Xt6PrS9p9WsvbgQm1p8nAj9uGH9ERKwbESOBUcDdLa5NkiS1yKJFizjppJM4+eSTiXj9jbJHHnmEd7/73Xz2s59l/PjxzJ07l/32249LL72UpUuXAvDkk0+ycOFCFi9ezCabbML666/P3Llz+eUvqx5QTz/9NK+99hqHHXYYX/7yl7n33nvZaKONGDlyJD/84Q+BqnXu/vvvb+2Od6LXWsgi4gfAnsCmETEfOAf4OnBNRBwPPA4cDpCZcyLiGuAh4BXgUz5hKUnS2uXFF19k7NixLFu2jIEDB3L00Ufzmc985g3zfetb32LGjBkMGDCA0aNH88EPfpB1112XtrY2dt11VwA22GADrrzySvbff3+mTJnCmDFj2Gabbdhll12AKrAdd9xxy1+r8bWvfQ2AadOm8Q//8A+cd955LFu2jCOOOIIddtihRUegc9HRvds1xfjx43PmzJmlywBg2blnlC5huXXO+dfSJUiS+pi2tja23Xbb0mX0Gx0d74iYlZkdvtvDN/VLkiQVZiCTJEkqzEAmSZJUmIFMkqR+Yk3uN74mWZnjbCCTJKkfGDRoEM8884yhrJdlJs888wyDBg3q0XJ95U39kiSpFw0fPpz58+ezaNGi0qWs9QYNGsTw4cN7tIyBTJKkfmCdddZh5MiRpctQJ7xlKUmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYUUCWUScHhFzIuLBiPhBRAyKiMER8fOImFf/3qREbZIkSa3W8kAWEVsApwLjM3N7YABwBDAZmJ6Zo4Dp9bAkSdJar9Qty4HAehExEFgf+D1wMHBFPf0K4JAypUmSJLVWywNZZj4JnA88DiwAFmfmTcBmmbmgnmcBMLTVtUmSJJVQ4pblJlStYSOBzYG3RMTHe7D8pIiYGREzFy1a1FtlSpIktUyJW5YfAH6bmYsycxlwPbAb8FREDAOofy/saOHMnJqZ4zNz/JAhQ1pWtCRJUm8pEcgeB3aJiPUjIoC9gTbgRmBiPc9E4McFapMkSWq5ga3eYGb+KiKuBe4FXgHuA6YCGwDXRMTxVKHt8FbXJkmSVELLAxlAZp4DnLPC6JepWsskSZL6Fd/UL0mSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIGli5AkrRmuP7hBaVLWO7QbYaVLkFarWwhkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRUJZBGxcURcGxFzI6ItInaNiMER8fOImFf/3qREbZIkSa1WqoXsAuC/MvNdwA5AGzAZmJ6Zo4Dp9bAkSdJar+WBLCI2AvYALgHIzD9n5nPAwcAV9WxXAIe0ujZJkqQSug1kETG9mXE9sBWwCLgsIu6LiO9GxFuAzTJzAUD9e+gqbEOSJGmN0Wkgi4hBETEY2DQiNqn7eA2OiBHA5quwzYHAjsC3M3Mc8AI9uD0ZEZMiYmZEzFy0aNEqlCFJktQ3dNVCdiIwC3hX/bv958fAxauwzfnA/Mz8VT18LVVAeyoihgHUvxd2tHBmTs3M8Zk5fsiQIatQhiRJUt/QaSDLzAsycyRwZmZulZkj658dMvOild1gZv4BeCIitqlH7Q08BNwITKzHTaQKfpIkSWu9gd3NkJn/FhG7ASMa58/M763Cdk8BpkXEm4FHgeOowuE1EXE88Dhw+CqsX5IkaY3RbSCLiO8Dfw3MBl6tRyew0oEsM2cD4zuYtPfKrlOSJGlN1W0gowpOozMze7sYSZKk/qiZ95A9CPxVbxciSZLUXzXTQrYp8FBE3A283D4yMw/qtaokSZL6kWYC2Rd7uwhJkqT+rJmnLG9rRSGSJEn9VTNPWS6heqoS4M3AOsALmblRbxYmSZLUXzTTQrZh43BEHALs3FsFSZIk9TfNPGX5Opn5I+BvVn8pkiRJ/VMztywPbRh8E9V7yXwnmSRJ0mrSzFOWH274/ArwGHBwr1QjSZLUDzXTh+y4VhQiSZLUX3XbhywihkfEDRGxMCKeiojrImJ4K4qTJEnqD5rp1H8ZcCOwObAF8O/1OEmSJK0GzQSyIZl5WWa+Uv9cDgzp5bokSZL6jWYC2dMR8fGIGFD/fBx4prcLkyRJ6i+aCWSfAD4C/AFYAPxdPU6SJEmrQTNPWT4OHNSCWiRJkvqlZl4MOxI4BRjROH9mGtIkSZJWg2ZeDPsj4BKqpytf69VqJEmS+qFmAtlLmXlhr1ciSZLUTzUTyC6IiHOAm4CX20dm5r29VpUkSVI/0kwgezdwNPA3/OWWZdbDkiRJWkXNBLK/BbbKzD/3djGSJEn9UTPvIbsf2LiX65AkSeq3mmkh2wyYGxH38Jc+ZJmZB/deWZIkSf1HM4HsnIbPAbwX+FjvlCNJktT/dHvLMjNvAxYDBwCXA3sDU3q3LEmSpP6j0xayiNgaOIKqNewZ4GogMnOvFtUmSZLUL3R1y3Iu8N/AhzPzNwARcXpLqpIkSepHurpleRjwB2BGRHwnIvam6kMmSZKk1ajTQJaZN2TmR4F3AbcCpwObRcS3I2LfFtUnSZK01mumU/8LmTktMw8EhgOzgcm9XZgkSVJ/0cyLYZfLzGcz8/9mpl+bJEmStJr0KJBJkiRp9TOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVNrDUhiNiADATeDIzD4yIwcDVwAjgMeAjmfnHUvVJ/cX1Dy8oXQIAh24zrHQJklRMyRay04C2huHJwPTMHAVMr4clSZLWekUCWUQMBw4Avtsw+mDgivrzFcAhLS5LkiSpiFItZN8CzgJeaxi3WWYuAKh/D+1owYiYFBEzI2LmokWLer1QSZKk3tbyQBYRBwILM3PWyiyfmVMzc3xmjh8yZMhqrk6SJKn1SnTq3x04KCI+BAwCNoqIK4GnImJYZi6IiGHAwgK1SZIktVzLA1lmng2cDRARewJnZubHI+JfgInA1+vfP251bWuLvvLUHPjknCRJzehL7yH7OrBPRMwD9qmHJUmS1nrF3kMGkJm3ArfWn58B9i5ZjyRJUgl9qYVMkiSpXzKQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKqzlgSwi3h4RMyKiLSLmRMRp9fjBEfHziJhX/96k1bVJkiSVUKKF7BXgjMzcFtgF+FREjAYmA9MzcxQwvR6WJEla67U8kGXmgsy8t/68BGgDtgAOBq6oZ7sCOKTVtUmSJJVQtA9ZRIwAxgG/AjbLzAVQhTZgaCfLTIqImRExc9GiRS2rVZIkqbcUC2QRsQFwHfDpzHy+2eUyc2pmjs/M8UOGDOm9AiVJklqkSCCLiHWowti0zLy+Hv1URAyrpw8DFpaoTZIkqdVKPGUZwCVAW2Z+o2HSjcDE+vNE4Metrk2SJKmEgQW2uTtwNPDriJhdj/sc8HXgmog4HngcOLxAbZIkSS3X8kCWmb8AopPJe7eyFkmSpL6gRAuZJKlJy849o3QJf3HEmaUrkNZafnWSJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFeZTlpIkabW7/uEFpUsA4NBthpUuoSm2kEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqbCBpQuQ+qNl555RuoS/OOLM0hVIUr9nC5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQV5lOWkiStJXyCe81lC5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqbA+F8giYv+IeDgifhMRk0vXI0mS1Nv6VCCLiAHAxcAHgdHAxyJidNmqJEmSelefCmTAzsBvMvPRzPwzcBVwcOGaJEmSelVfC2RbAE80DM+vx0mSJK21IjNL17BcRBwO7JeZJ9TDRwM7Z+YpDfNMAibVg9sAD7e80L5vU+Dp0kVojeH1omZ5ragnvF7eaMvMHNLRhIGtrqQb84G3NwwPB37fOENmTgWmtrKoNU1EzMzM8aXr0JrB60XN8lpRT3i99Exfu2V5DzAqIkZGxJuBI4AbC9ckSZLUq/pUC1lmvhIRJwM/AwYAl2bmnMJlSZIk9ao+FcgAMvOnwE9L17GG85auesLrRc3yWlFPeL30QJ/q1C9JktQf9bU+ZJIkSf2OgWwt41dPqVkRcWlELIyIB0vXor4tIt4eETMioi0i5kTEaaVrUt8UEYMi4u6IuL++Vs4tXdOawluWa5H6q6f+B9iH6hUi9wAfy8yHihamPiki9gCWAt/LzO1L16O+KyKGAcMy896I2BCYBRzi3y1aUUQE8JbMXBoR6wC/AE7LzF8WLq3Ps4Vs7eJXT6lpmXk78GzpOtT3ZeaCzLy3/rwEaMNvUVEHsrK0Hlyn/rHlpwkGsrWLXz0lqVdFxAhgHPCrwqWoj4qIARExG1gI/DwzvVaaYCBbu0QH4/yfiaTVIiI2AK4DPp2Zz5euR31TZr6amWOpvm1n54iwS0QTDGRrl26/ekqSVkbdH+g6YFpmXl+6HvV9mfkccCuwf9lK1gwGsrWLXz0labWrO2pfArRl5jdK16O+KyKGRMTG9ef1gA8Ac4sWtYYwkK1FMvMVoP2rp9qAa/zqKXUmIn4A3AVsExHzI+L40jWpz9odOBr4m4iYXf98qHRR6pOGATMi4gGqRoKfZ+Z/FK5pjeBrLyRJkgqzhUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJKiYi/ioiroqIRyLioYj4aURsHRF7RsQqPypfr2e3TqYdGxGL6lc4PBQRf9/Nur4bEaOb3O5xDa+H+HNE/Lr+/PWV2Q9Ja7+BpQuQ1D/VLxu9AbgiM4+ox40FNluNm9kTWArc2cn0qzPz5IgYCsyJiBsz86mOZszME5rdaGZeBlwGEBGPAXtl5tM9KVxS/2ILmaRS9gKWZeaU9hGZOTsz/7se3CAiro2IuRExrQ5wRMROEXFbRMyKiJ9FxLB6/Kl1S9cDdavbCOAk4PS6dep9nRWSmQuBR4AtI+LbETEzIuZExLnt80TErRExvv68NCK+EhH3R8QvI6LbEBkRx0fENxuG/z4ivhERI+p9vKKu/dqIWL+rfZW09jGQSSple2BWF9PHAZ8GRgNbAbvX36f4b8DfZeZOwKXAV+r5JwPjMnMMcFJmPgZMAb6ZmWMbgt4bRMRW9TZ+A/yvzBwPjAHeHxFjOljkLcAvM3MH4Hagy9udtauAg+p9ADiOuhUN2AaYWtf+PPDJbvZV0lrGW5aS+qq7M3M+QETMBkYAz1EFuZ/XDWYDgAX1/A8A0yLiR8CPmtzGRyPivcDLwImZ+WxEnBQRk6j+fhxGFQgfWGG5PwPtfdxmAft0t6HMfCEibgEOjIg2YJ3M/HXdkvdEZt5Rz3olcCrwX13sq6S1jIFMUilzgL/rYvrLDZ9fpfr7KoA5mblrB/MfAOwBHAT8U0Rs10QNV2fmye0DETESOBOYkJl/jIjLgUEdLLcs//K9c+21NeO7wOeovmz5sobxK36HXdL1vkpay3jLUlIptwDrNj7dGBETIuL9XSzzMDAkInat518nIraLiDcBb8/MGcBZwMbABsASYMMe1LQR8AKwuO4X9sGe7FB3MvNXwNuBI4EfNEx6R/s+AR8DfkEn+7o665HUdxjIJBVRtzD9LbBP/dqLOcAXgd93scyfqVrV/jki7gdmA7tR3c67MiJ+DdxH1W/sOeDfgb/trlN/w/rvr5efQ9Vn646ul1gp1wB3ZOYfG8a1ARMj4gFgMPDtLvZV0loo/tLqLknqbfX71b6ZmdPr4RHAf2Tm9kULk1SULWSS1AIRsXFE/A/wYnsYk6R2tpBJkiQVZguZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKuz/Aybzsg1zNUjrAAAAAElFTkSuQmCC\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 Frequency Per Chest Pain Type\")\n",
    "plt.xlabel(\"Chest Pain Type\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"No Disease\", \"Disease\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d6f5af01",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "95ce6de4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's make our correlation matrix a little prettier\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                annot=True,\n",
    "                linewidths=0.5,\n",
    "                fmt=\".2f\",\n",
    "                cmap=\"YlGnBu\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a2c0ef4",
   "metadata": {},
   "source": [
    "## 5. Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e7737a46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "70e2eae5",
   "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": 22,
   "id": "2bbdf1fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "298    0\n",
       "299    0\n",
       "300    0\n",
       "301    0\n",
       "302    0\n",
       "Name: target, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb84e93a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train and test sets\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into train and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                   y,\n",
    "                                                   test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6661d986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>295</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>231</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>135</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>160</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "132   42    1   1       120   295    0        1      162      0      0.0   \n",
       "202   58    1   0       150   270    0        0      111      1      0.8   \n",
       "196   46    1   2       150   231    0        1      147      0      3.6   \n",
       "75    55    0   1       135   250    0        0      161      0      1.4   \n",
       "176   60    1   0       117   230    1        1      160      1      1.4   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "188   50    1   2       140   233    0        1      163      0      0.6   \n",
       "71    51    1   2        94   227    0        1      154      1      0.0   \n",
       "106   69    1   3       160   234    1        0      131      0      0.1   \n",
       "270   46    1   0       120   249    0        0      144      0      0.8   \n",
       "102   63    0   1       140   195    0        1      179      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "132      2   0     2  \n",
       "202      2   0     3  \n",
       "196      1   0     2  \n",
       "75       1   0     2  \n",
       "176      2   2     3  \n",
       "..     ...  ..   ...  \n",
       "188      1   1     3  \n",
       "71       2   1     3  \n",
       "106      1   1     2  \n",
       "270      2   0     3  \n",
       "102      2   2     2  \n",
       "\n",
       "[242 rows x 13 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "459a5591",
   "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,\n",
       " 242)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train, len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb6aa927",
   "metadata": {},
   "source": [
    "Now we've got our data split into training and test sets, it's time to build a machine learning model.\n",
    "\n",
    "We'll train it (find the patterns) on the training set.\n",
    "\n",
    "And we'll test it (use the patterns) on the test set.\n",
    "\n",
    "We're going to try 3 different machine learning models.\n",
    "1. Logistic Regression\n",
    "2. K-Nearest Neighbours Classifier\n",
    "3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0de3f0d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {\"Logistic Regression\": LogisticRegression(),\n",
    "         \"KNN\": KNeighborsClassifier(),\n",
    "         \"Random Forest\": RandomForestClassifier()}\n",
    "# Create a function to fit and score models\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fit and evaluate given machine learning models.\n",
    "    models : a dict of different Scikit-learn machine learning models\n",
    "    X_train : training data (no labels)\n",
    "    X_test : testing data (no labels)\n",
    "    y_train : training labels\n",
    "    y_test : test labels\n",
    "    \"\"\"\n",
    "    # Set random seed\n",
    "    np.random.seed(42)\n",
    "    # Make a dictionary to keep model scores\n",
    "    model_scores = {}\n",
    "    # Loop through models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(X_train, y_train)\n",
    "        # Evaluate the model and append its score to model\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1244f89f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Skill course\\ML\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: 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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models=models,\n",
    "                            X_train=X_train,\n",
    "                            X_test=X_test,\n",
    "                            y_train=y_train,\n",
    "                            y_test=y_test)\n",
    "\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dc5b8ac",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a45f5ce5",
   "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=[\"accurcay\"])\n",
    "model_compare.T.plot.bar()\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199f9b2f",
   "metadata": {},
   "source": [
    "Now we've got a baseline model.. and we know a model's first predictions aren't always what we should base our next step of. What should we do?\n",
    "\n",
    "Let's look at the following:\n",
    "* Hyperparameter tuning\n",
    "* Feature importance\n",
    "* Confusion matrix\n",
    "* Cross-validation\n",
    "* Precision\n",
    "* Recall\n",
    "* F1 score\n",
    "* Classification report\n",
    "* ROC curve\n",
    "* Area under the curve (AUC)\n",
    "\n",
    "## Hyperparameter tuning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b63bc9ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune\n",
    "\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of different values for n_neighbours\n",
    "neighbors = range(1, 21)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different n_neighbours\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\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))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "86977cf8",
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "76f4def6",
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "50bc616f",
   "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.legend()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d00f12f",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning with RandomizedSearchCV\n",
    "\n",
    "We're going to tune:\n",
    "* LogisticRegression()\n",
    "* RandomForestClassier()\n",
    "\n",
    ". . . using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "969fe000",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a hyperparameter grid for LogisticRegression\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 20),\n",
    "               \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Create a hyperparameter grid for RandomForrestClassifier\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)}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d34fc82a",
   "metadata": {},
   "source": [
    "Now we've got hyperparameters grids setup for each of our models,\n",
    "let's tune them using RandomizedSearchedCV..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "06f8e176",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisticRegression\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRregession\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                               param_distributions=log_reg_grid,\n",
    "                               cv=5,\n",
    "                               n_iter=20,\n",
    "                               verbose=True)\n",
    "# Fit random hyperparameter search model for LogisticRegression\n",
    "rs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b236ec24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "394f3b73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e47694d",
   "metadata": {},
   "source": [
    "Now we've tuned LogisticRegression(), let's do the same for RandomForestClassier()..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6f6ba2e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "# Setup random hyperparameters search for RandomForestClassier\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": 38,
   "id": "aa2afc7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best hyperparameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "286b36a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the randomized search RandomForestClassier model\n",
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dc7e7a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa326d58",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning with GridSearchCV\n",
    "\n",
    "Since our LogisticRegression model provides the best scores so far, we'll try and improve them again using GridSearchCV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "299bc727",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         'solver': ['liblinear']},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different hyperparameters for our LogisticRegression model\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 30),\n",
    "               \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                         param_grid=log_reg_grid,\n",
    "                         cv=5,\n",
    "                         verbose=True)\n",
    "# Fit grid hyperparameter search model\n",
    "gs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "32045040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e6f08021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the grid search LogisticRegression model\n",
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "519aa00e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8a462e",
   "metadata": {},
   "source": [
    "## Evaluate our tuned machine learning classifier, beyond accuracy\n",
    "\n",
    "* ROC curve and AUC score\n",
    "* Confusion matrix\n",
    "* Classification report\n",
    "* Precision\n",
    "* Recall\n",
    "* F1-score\n",
    "\n",
    "... and it would be great if cross-validation was used where possible.\n",
    "\n",
    "To make comaparisons and e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9bc99337",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions with tuned model\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "44b01b77",
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0b1d3f15",
   "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "30f0603c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Skill course\\ML\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1fab85ab1c0>"
      ]
     },
     "execution_count": 49,
     "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 AUC metric\n",
    "plot_roc_curve(gs_log_reg, X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a1d6f3e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8c60dc7b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plot a nice looking confusion matrix using Seaborn's heatmap()\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                    annot=True,\n",
    "                    cbar=False)\n",
    "    plt.xlabel(\"Predicted label\")\n",
    "    plt.ylabel(\"True label\")\n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61ac50d7",
   "metadata": {},
   "source": [
    "Now we've got ROC curve, an AUC metric and a confusion matrix, let's get a classification report as well as cross-validation precision, recall and f1-score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "57bd928f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02cb65ad",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics using cross validation\n",
    "\n",
    "We're going to calculate accuracy, precision, recall and f1-score of our model using cross-validation and to do so we'll be using `cross_validation_score()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "37e46bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4d78611b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                        solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "72d28b0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.86885246, 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 62,
     "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": 64,
   "id": "bc92c693",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446994535519124"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1e20fe14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8207936507936507"
      ]
     },
     "execution_count": 67,
     "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": 68,
   "id": "87368470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212121212121213"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated recall\n",
    "# Cross-validated precision\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": 69,
   "id": "98cec3d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673007976269721"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated f1-score\n",
    "# Cross-validated precision\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": 75,
   "id": "818411bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3]),\n",
       " [Text(0, 0, 'Accuracy'),\n",
       "  Text(1, 0, 'Precision'),\n",
       "  Text(2, 0, 'Recall'),\n",
       "  Text(3, 0, 'F1')])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
    "cv_metrics.T.plot.bar(title=\"Cross-validated classification metrics\",\n",
    "                   legend=False);\n",
    "plt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fa3589a",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "Feature importance is another way 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": 78,
   "id": "cb68bcb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit an instance of LogisticRegression\n",
    "gs_log_reg.best_params_\n",
    "\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                        solver=\"liblinear\")\n",
    "\n",
    "clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "60f0db49",
   "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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a44a7c39",
   "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": 81,
     "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": 83,
   "id": "ebecf400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature Importance'}>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": 86,
   "id": "4c8bf24b",
   "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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "75cc4f3f",
   "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": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"slope\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8fc711e",
   "metadata": {},
   "source": [
    "### 6. Experimentation\n",
    "\n",
    "If you haven't hit your evaluation metric yet... ask yourself...\n",
    "\n",
    "* Could you collect more data?\n",
    "* Could you try a better model? Like CatBoost or XGBoost?\n",
    "* Could you improve the current models? (beyond what we've done so far)\n",
    "* If your model is good enough (you have hit your evaluation metric) How would you export it and share it with others?\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
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}