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

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    "## Predicting `Heart Disease` using Machine `Supervised` Learning \n",
    "\n",
    "using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes.\n",
    "\n",
    "**Looking at the following main topics;**\n",
    "1. **`Problem definition`**\n",
    "> Given clinical parameters about a patient, can we predict whether or not they have heart disease?\n",
    "2. **`Data`**\n",
    "> We use the independent variables to predict our dependent variable.\n",
    "\n",
    "    * orginally data is come from https://archive.ics.uci.edu/ml/datasets/heart+Disease\n",
    "    * used kaggle version is come from https://www.kaggle.com/datasets/sumaiyatasmeem/heart-disease-classification-dataset\n",
    "\n",
    "\n",
    "3. **`Evaluation`**\n",
    "> If we can reach 90% accuracy at predicting whether or not a patient has heart disease during the proof of concept.\n",
    "4. **`Features`**\n",
    "> create a data dictionary\n",
    "    \n",
    "    ***description of heart disease columns***\n",
    "    1. **`age`** - age in years\n",
    "    2. **`sex`** - (1 = male; 0 = female)\n",
    "    3. **`cp`** - chest pain type\n",
    "        * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "        * 1: Atypical angina: chest pain not related to heart\n",
    "        * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "        * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "    4. **`trestbps`** - resting blood pressure (in mm Hg on admission to the hospital)\n",
    "        * anything above 130-140 is typically cause for concern\n",
    "    5. **`chol`** - serum cholestoral in mg/dl\n",
    "        * serum = LDL + HDL + .2 * triglycerides\n",
    "        * above 200 is cause for concern\n",
    "    6. **`fbs`** - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "        * '>126' mg/dL signals diabetes\n",
    "    7. **`restecg`** - resting electrocardiographic results\n",
    "        * 0: Nothing to note\n",
    "        * 1: ST-T Wave abnormality\n",
    "            * can range from mild symptoms to severe problems\n",
    "            * signals non-normal heart beat\n",
    "        * 2: Possible or definite left ventricular hypertrophy\n",
    "            * Enlarged heart's main pumping chamber\n",
    "    8. **`thalach`** - maximum heart rate achieved\n",
    "    9. **`exang`** - exercise induced angina (1 = yes; 0 = no)\n",
    "    10. **`oldpeak`** - ST depression induced by exercise relative to rest\n",
    "        * looks at stress of heart during excercise\n",
    "        * unhealthy heart will stress more\n",
    "    11. **`slope`** - the slope of the peak exercise ST segment\n",
    "        * 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "        * 1: Flatsloping: minimal change (typical healthy heart)\n",
    "        * 2: Downslopins: signs of unhealthy heart\n",
    "    12. **`ca`** - number of major vessels (0-3) colored by flourosopy\n",
    "        * colored vessel means the doctor can see the blood passing through\n",
    "        * the more blood movement the better (no clots)\n",
    "    13. **`thal`** - thalium stress result\n",
    "        * 1,3: normal\n",
    "        * 6: fixed defect: used to be defect but ok now\n",
    "        * 7: reversable defect: no proper blood movement when excercising\n",
    "    14. **`target`** - have disease or not (1=yes, 0=no) (= the predicted attribute)\n",
    "\n",
    "\n",
    "5. **`Modelling`**\n",
    "> use machine learning to predict our target variable based on the 13 independent variables as per above problems\n",
    "6. **`Experimentation`**\n",
    "> we already doing it as per step 1 and step 3, the question is did you reach step 3 mentioned accuracy?\n",
    "\n",
    "***Subtopics***\n",
    "* **`Exploratory data analysis (EDA)`**: the process of going through a dataset and finding out more about it.\n",
    "* **`Model training`**: create model(s) to learn to predict a target variable based on other variables.\n",
    "* **`Model evaluation`**: evaluating a models predictions using problem-specific evaluation metrics.\n",
    "* **`Model comparison`**: comparing several different models to find the best one.\n",
    "* **`Model fine-tuning`**: once we've found a good model, how can we improve it?\n",
    "* **`Feature importance`**: since we're predicting the presence of heart disease, are there some things which are more important for prediction?\n",
    "* **`Cross-validation`**: if we do build a good model, can we be sure it will work on unseen data?\n",
    "* **`Reporting what we've found`**: if we had to present our work, what would we show someone?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc2fe34b",
   "metadata": {},
   "source": [
    "### Preparing the tools\n",
    "\n",
    "using `pandas`, `Matplotlib` and `NumPy` for **data analysis** and **manipulation**"
   ]
  },
  {
   "cell_type": "code",
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   "id": "6f0e78de",
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   "outputs": [],
   "source": [
    "# import all the tools\n",
    "\n",
    "# regular EDA \"exploratory data analysis and plotting libraries\"\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# plots inside notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import SGDClassifier\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.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import plot_roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de3b7bfe",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "741f4655",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
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       "      <th>slope</th>\n",
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      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df = pd.read_csv(\"data/heart-disease.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c5276d3",
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    "### Data Exploration (`exploratory data analysis` or `EDA`)\n",
    "\n",
    "**EDA Methodolgy**\n",
    "\n",
    "* What question(s) are you trying to solve (or prove wrong)?\n",
    "* What kind of data do you have and how do you treat different types?\n",
    "* What’s missing from the data and how do you deal with it?\n",
    "* Where are the outliers and why should you care about them?\n",
    "* How can you add, change or remove features to get more out of your data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e9059039",
   "metadata": {},
   "outputs": [
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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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "abead19d",
   "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>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# last 5 rows\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0d29e4c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many of each class\n",
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c1984f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visually show balance in target\n",
    "df[\"target\"].value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9e4162fa",
   "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": 8,
   "id": "e3d6df9f",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e42a7c64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65040d7f",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency according to Gender (SEX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5054bb62",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5173c780",
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd069180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a plot of crosstab\n",
    "pd.crosstab(df.target, df.sex).plot(kind=\"bar\", figsize=(10, 6), color=[\"salmon\", \"lightblue\"])\n",
    "\n",
    "# add some attributes\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",
    "# keep labels on the x-axis vertical\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3143ce87",
   "metadata": {},
   "source": [
    "### Age vs Max Heart Rate for Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "50c21802",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create figure\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# scatter with filter data with target positive - heart disease\n",
    "plt.scatter(df.age[df.target==1], df.thalach[df.target==1], c=\"salmon\")\n",
    "\n",
    "# scatter with target zero - no heart disease\n",
    "plt.scatter(df.age[df.target==0], df.thalach[df.target==0], c=\"darkblue\")\n",
    "\n",
    "# add some description\n",
    "plt.title(\"Heart Disease in function of Age and Max Heart Rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Max Heart Rate\")\n",
    "plt.legend([\"Disease\", \"No Disease\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb942bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of the age column using histogram\n",
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de57a28b",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency as per Chest Pain Type\n",
    "\n",
    "* **`cp`** - chest pain type\n",
    "  * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "  * 1: Atypical angina: chest pain not related to heart\n",
    "  * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "  * 3: Asymptomatic: chest pain not showing signs of disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9c370afb",
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bbf07e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# crosstab and base plot\n",
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\",\n",
    "                                   figsize=(10, 6),\n",
    "                                   color=[\"lightblue\", \"salmon\"])\n",
    "\n",
    "# add attributes to the plot\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": 17,
   "id": "f4d13c6d",
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56a63e1a",
   "metadata": {},
   "source": [
    "### Correlation between independent variables\n",
    "\n",
    "***a big table of numbers telling us how related each variable is the other***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8f6579b5",
   "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": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e2f336b4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation matrix with Heat Map\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(15, 20))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                 annot=True,\n",
    "                 linewidths=0.5,\n",
    "                 fmt=\".2f\",\n",
    "                 cmap=\"YlGnBu\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65c8affe",
   "metadata": {},
   "source": [
    "**`Correlation`:**\n",
    "  * `Negative correlation`: is when two variables move opposite one another so that when one variable rises, the other falls.\n",
    "  * `Positive correlation`: exists when two variables operate in unison so that when one variable rises or falls, the other does the same.\n",
    "  \n",
    "* ***`Investigation of column exang`***: It is negative correlation so, if someone gets chest pain during exercise `exang=1`, their chance of having heart disease `target=0` goes down."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e019c3fe",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dd4d842b",
   "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": "markdown",
   "id": "70d1a2b7",
   "metadata": {},
   "source": [
    "#### Prepare data for applying Machine Learning algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c410652c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into X and y\n",
    "X = df.drop(\"target\", axis=1)\n",
    "y = df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "70cef7e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random seed for reproducibility\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, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "602713df",
   "metadata": {},
   "source": [
    "**`Notes`**: \n",
    "* Data splitted as training and test to build machine learning model.\n",
    "* Find pattern `train data`.\n",
    "* Use pattern `test data`.\n",
    "\n",
    "#### Model choices\n",
    "\n",
    "1. Logistic Regression - `Logistic Regression()`\n",
    "\n",
    "> Logistic regression, despite its name, is a linear model for classification rather than regression. Logistic regression is also known in the literature as logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function.\n",
    "\n",
    "2. K-Nearest Neighbors - `KNeighboursClassifier()`\n",
    "3. RandomForest - `RandomForestClassifier()`\n",
    "4. CatBoost - `CatBoostClassifier()`\n",
    "5. XgBoost - `XGBClassifier()`\n",
    "6. Naive Bayes - `GaussianNB()`\n",
    "7. Support Vector Machines - `SVC()`\n",
    "8. Stochastic Gradient Descent - `SGDClassifier()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9821519f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# models in a dictionary\n",
    "models = {\"Logistic Regression        \": LogisticRegression(max_iter=1000),\n",
    "          \"KNN                        \": KNeighborsClassifier(),\n",
    "          \"Random Forest              \": RandomForestClassifier(),\n",
    "          \"CatBoost                   \": CatBoostClassifier(),\n",
    "          \"XgBoost                    \": XGBClassifier(),\n",
    "          \"Naive Bayes                \": GaussianNB(),\n",
    "          \"Support Vector Machines    \": SVC(),\n",
    "          \"Stochastic Gradient Descent\": SGDClassifier()}\n",
    "\n",
    "# function to fit and score models\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluates given machine learning models.\n",
    "    models: dictionary of different Scikit-Learn machine learning models\n",
    "    X_train: training data (no labels)\n",
    "    X_test: testing data (no labels)\n",
    "    y_train: labels assosciated with training data\n",
    "    y_test: labels assosciated with test data\n",
    "    \"\"\"\n",
    "    # Random seed for reproducible results\n",
    "    np.random.seed(42)\n",
    "    # make a list to keep model scores\n",
    "    model_scores = {}\n",
    "    # loop through models\n",
    "    for name, model in models.items():\n",
    "        # fit the model to the data\n",
    "        model.fit(X_train, y_train)\n",
    "        # evaluate the model and append its score to model_scores\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "abf52d90",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "987:\tlearn: 0.1007708\ttotal: 1.14s\tremaining: 13.8ms\n",
      "988:\tlearn: 0.1006476\ttotal: 1.14s\tremaining: 12.6ms\n",
      "989:\tlearn: 0.1005170\ttotal: 1.14s\tremaining: 11.5ms\n",
      "990:\tlearn: 0.1003547\ttotal: 1.14s\tremaining: 10.3ms\n",
      "991:\tlearn: 0.1002426\ttotal: 1.14s\tremaining: 9.19ms\n",
      "992:\tlearn: 0.1001458\ttotal: 1.14s\tremaining: 8.04ms\n",
      "993:\tlearn: 0.1000203\ttotal: 1.14s\tremaining: 6.89ms\n",
      "994:\tlearn: 0.0999122\ttotal: 1.14s\tremaining: 5.74ms\n",
      "995:\tlearn: 0.0998241\ttotal: 1.14s\tremaining: 4.59ms\n",
      "996:\tlearn: 0.0996733\ttotal: 1.15s\tremaining: 3.44ms\n",
      "997:\tlearn: 0.0996095\ttotal: 1.15s\tremaining: 2.3ms\n",
      "998:\tlearn: 0.0994673\ttotal: 1.15s\tremaining: 1.15ms\n",
      "999:\tlearn: 0.0993388\ttotal: 1.15s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression        ': 0.8947368421052632,\n",
       " 'KNN                        ': 0.6973684210526315,\n",
       " 'Random Forest              ': 0.8157894736842105,\n",
       " 'CatBoost                   ': 0.8157894736842105,\n",
       " 'XgBoost                    ': 0.8157894736842105,\n",
       " 'Naive Bayes                ': 0.881578947368421,\n",
       " 'Support Vector Machines    ': 0.6973684210526315,\n",
       " 'Stochastic Gradient Descent': 0.5526315789473685}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models=models, X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test)\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "136d8f21",
   "metadata": {},
   "source": [
    "#### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "43d44550",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=[\"accuracy\"])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc37bfe",
   "metadata": {},
   "source": [
    "figure out ***`classification` report, `confusion matrix`, `cross-validated precision`, `recall`, `F1 scores` and `ROC curve`***\n",
    "\n",
    "**Important sections with descriptions**\n",
    "\n",
    "* `Hyperparameter tuning`: Each model you use has a series of dials you can turn to dictate how they perform. Changing these values may increase or decrease model performance.\n",
    "* `Feature importance`: If there are a large amount of features we're using to make predictions, do some have more importance than others? For example, for predicting heart disease, which is more important, sex or age?\n",
    "* `Confusion matrix`: Compares the predicted values with the true values in a tabular way, if 100% correct, all values in the matrix will be top left to bottom right (diagnol line).\n",
    "* `Cross-validation`: Splits your dataset into multiple parts and train and tests your model on each part and evaluates performance as an average.\n",
    "* `Precision`: Proportion of true positives over total number of samples. Higher precision leads to less false positives.\n",
    "* `Recall`: Proportion of true positives over total number of true positives and false negatives. Higher recall leads to less false negatives.\n",
    "* `F1 score`: Combines precision and recall into one metric. 1 is best, 0 is worst.\n",
    "* `Classification report`: Sklearn has a built-in function called classification_report() which returns some of the main classification metrics such as precision, recall and f1-score.\n",
    "* `ROC Curve`: Receiver Operating Characterisitc is a plot of true positive rate versus false positive rate.\n",
    "* `Area Under Curve (AUC)`: The area underneath the ROC curve. A perfect model achieves a score of 1.0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d55e60fc",
   "metadata": {},
   "source": [
    "#### Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8ed26d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tune KNN\n",
    "train_score = []\n",
    "test_score = []\n",
    "\n",
    "# list of different values for n_neighbors\n",
    "neighbors = range(1, 21)\n",
    "\n",
    "# KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# loop through different n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "\n",
    "    # fit the algorithm\n",
    "    knn.fit(X_train, y_train)\n",
    "\n",
    "    # update the training score list\n",
    "    train_score.append(knn.score(X_train, y_train))\n",
    "\n",
    "    # update the test score list\n",
    "    test_score.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9f7e7221",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.7929515418502202,\n",
       " 0.7709251101321586,\n",
       " 0.7444933920704846,\n",
       " 0.7577092511013216,\n",
       " 0.73568281938326,\n",
       " 0.7268722466960352,\n",
       " 0.7136563876651982,\n",
       " 0.7224669603524229,\n",
       " 0.7136563876651982,\n",
       " 0.7312775330396476,\n",
       " 0.7136563876651982,\n",
       " 0.7092511013215859,\n",
       " 0.6960352422907489,\n",
       " 0.6960352422907489,\n",
       " 0.6784140969162996,\n",
       " 0.6960352422907489,\n",
       " 0.6607929515418502,\n",
       " 0.6916299559471366,\n",
       " 0.6784140969162996]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b55eb5aa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6052631578947368,\n",
       " 0.6052631578947368,\n",
       " 0.6710526315789473,\n",
       " 0.6973684210526315,\n",
       " 0.6973684210526315,\n",
       " 0.7105263157894737,\n",
       " 0.7236842105263158,\n",
       " 0.7236842105263158,\n",
       " 0.7105263157894737,\n",
       " 0.7368421052631579,\n",
       " 0.75,\n",
       " 0.7105263157894737,\n",
       " 0.7105263157894737,\n",
       " 0.6973684210526315,\n",
       " 0.6842105263157895,\n",
       " 0.7236842105263158,\n",
       " 0.7236842105263158,\n",
       " 0.7236842105263158,\n",
       " 0.7368421052631579,\n",
       " 0.7236842105263158]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9e5fdab3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 75.00%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors, train_score, label=\"Train score\")\n",
    "plt.plot(neighbors, test_score, label=\"Test score\")\n",
    "plt.xticks(np.arange(1, 21, 1))\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_score)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96ee7ca5",
   "metadata": {},
   "source": [
    "**KNN model**: `n_neighbors (11)`.This is maximum accuracy for KNN model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a39f4b",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning with `RandomizedSearchCV`\n",
    "\n",
    "* LogisticRegression()\n",
    "* RandomForestClassifier()\n",
    "* GaussianNB()\n",
    "\n",
    "**`LogisticRegression()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "add33e73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 10, stop = 2000, num = 11)]\n",
    "\n",
    "# Number of features to consider at every split\n",
    "max_features = ['sqrt']\n",
    "\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(80, 210, num = 3)]\n",
    "max_depth.append(None)\n",
    "\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [int(x) for x in np.linspace(2, 110, num = 3)]\n",
    "\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [int(x) for x in np.linspace(2, 110, num = 3)]\n",
    "\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "\n",
    "# hyperparameter grid for LogisticRegression\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 20),\n",
    "                \"max_iter\": np.arange(1000, 5000, 1000),\n",
    "                \"solver\": ['liblinear'],\n",
    "                \"penalty\": ['l1', 'l2']}\n",
    "\n",
    "# hyperparameter grid for RandomForestClassifier\n",
    "rf_grid ={'n_estimators': n_estimators,\n",
    "          'max_features': max_features,\n",
    "          'max_depth': max_depth,\n",
    "          'min_samples_split': min_samples_split,\n",
    "          'min_samples_leaf': min_samples_leaf,\n",
    "          'bootstrap': bootstrap}\n",
    "\n",
    "# hyperparameter grid for GaussianNB\n",
    "nb_grid = {'var_smoothing': np.logspace(0, -9, 100)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "28671515",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n"
     ]
    }
   ],
   "source": [
    "# setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# setup random hyperparameter search for logisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                                param_distributions=log_reg_grid,\n",
    "                                cv=StratifiedKFold(),\n",
    "                                n_iter=40,\n",
    "                                verbose=True)\n",
    "\n",
    "# fit random hyperparameter search model\n",
    "rs_log_reg.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "eb885166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear',\n",
       " 'penalty': 'l2',\n",
       " 'max_iter': 3000,\n",
       " 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "be5d5eee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8947368421052632"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0c0413",
   "metadata": {},
   "source": [
    "**`RandomForestClassifier()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a58cf141",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "                   estimator=RandomForestClassifier(), n_iter=50, n_jobs=-1,\n",
       "                   param_distributions={&#x27;bootstrap&#x27;: [True, False],\n",
       "                                        &#x27;max_depth&#x27;: [80, 145, 210, None],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [2, 56, 110],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 56, 110],\n",
       "                                        &#x27;n_estimators&#x27;: [10, 209, 408, 607, 806,\n",
       "                                                         1005, 1204, 1403, 1602,\n",
       "                                                         1801, 2000]},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "                   estimator=RandomForestClassifier(), n_iter=50, n_jobs=-1,\n",
       "                   param_distributions={&#x27;bootstrap&#x27;: [True, False],\n",
       "                                        &#x27;max_depth&#x27;: [80, 145, 210, None],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [2, 56, 110],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 56, 110],\n",
       "                                        &#x27;n_estimators&#x27;: [10, 209, 408, 607, 806,\n",
       "                                                         1005, 1204, 1403, 1602,\n",
       "                                                         1801, 2000]},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "                   estimator=RandomForestClassifier(), n_iter=50, n_jobs=-1,\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [80, 145, 210, None],\n",
       "                                        'max_features': ['sqrt'],\n",
       "                                        'min_samples_leaf': [2, 56, 110],\n",
       "                                        'min_samples_split': [2, 56, 110],\n",
       "                                        'n_estimators': [10, 209, 408, 607, 806,\n",
       "                                                         1005, 1204, 1403, 1602,\n",
       "                                                         1801, 2000]},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# setup random hyperparameter search for RandomForestClassifier\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                           param_distributions=rf_grid,\n",
    "                           cv=StratifiedKFold(),\n",
    "                           n_iter=50,\n",
    "                           verbose=True,\n",
    "                           n_jobs=-1)\n",
    "\n",
    "# fit random hyperparameter search model\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "58442a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 209,\n",
       " 'min_samples_split': 56,\n",
       " 'min_samples_leaf': 2,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 210,\n",
       " 'bootstrap': False}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find best parameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bb0f173b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8421052631578947"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate the randomized search RandomForestClassifier model\n",
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "30ac1020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression        ': 0.8947368421052632,\n",
       " 'KNN                        ': 0.6973684210526315,\n",
       " 'Random Forest              ': 0.8157894736842105,\n",
       " 'CatBoost                   ': 0.8157894736842105,\n",
       " 'XgBoost                    ': 0.8157894736842105,\n",
       " 'Naive Bayes                ': 0.881578947368421,\n",
       " 'Support Vector Machines    ': 0.6973684210526315,\n",
       " 'Stochastic Gradient Descent': 0.5526315789473685}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "170e8427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02631578947368418"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how much increased as per base model\n",
    "rs_rf_improve = (rs_rf.score(X_test, y_test) - model_scores.get('Random Forest              '))\n",
    "rs_rf_improve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7137aeb5",
   "metadata": {},
   "source": [
    "**`GaussianNB()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8cf196c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n"
     ]
    }
   ],
   "source": [
    "# setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# setup random hyperparameter search for logisticRegression\n",
    "rs_nb = RandomizedSearchCV(GaussianNB(),\n",
    "                                param_distributions=nb_grid,\n",
    "                                cv=StratifiedKFold(),\n",
    "                                n_iter=40,\n",
    "                                verbose=True)\n",
    "\n",
    "# fit random hyperparameter search model\n",
    "rs_nb.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7c063e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8947368421052632"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_nb.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4efeb068",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning with GridSearchCV\n",
    "\n",
    "* LogisticRegression\n",
    "* RandomForestClassifier\n",
    "* CatboostClassifier\n",
    "* GaussianNB\n",
    "\n",
    "***Experiments with GridSearchCV for above best models***\n",
    "\n",
    "**`LogisticRegression`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "10dbf1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 800 candidates, totalling 4000 fits\n"
     ]
    }
   ],
   "source": [
    "# hyperparameter grid for LogisticRegression\n",
    "# log_reg_grid = {\"penalty\": ['l1', 'l2', 'elasticnet', 'none'],\n",
    "#                 \"C\": np.logspace(-4, 4, 20),\n",
    "#                 \"solver\": ['liblinear', 'lbfgs', 'newton-cg', 'sag', 'saga'],\n",
    "#                 'max_iter': [5000]\n",
    "#                 }\n",
    "\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 50),\n",
    "                \"max_iter\": np.arange(1000, 5000, 500),\n",
    "                \"solver\": ['liblinear'],\n",
    "                \"penalty\": ['l1', 'l2']}\n",
    "\n",
    "# setup random hyperparameter search for logisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                          param_grid=log_reg_grid,\n",
    "                          cv=StratifiedKFold(),\n",
    "                          verbose=True,\n",
    "                          n_jobs=-1)\n",
    "\n",
    "# fit grid hyperparameter search model\n",
    "gs_log_reg.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d53e85a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.18420699693267145,\n",
       " 'max_iter': 1000,\n",
       " 'penalty': 'l2',\n",
       " 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the best parameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fd8cf199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8947368421052632"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e4dff537",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.895\n"
     ]
    }
   ],
   "source": [
    "print(f'Accuracy: {gs_log_reg.score(X_test, y_test):.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1e3c066",
   "metadata": {},
   "source": [
    "**`RandomForestClassifier`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7d4fd22d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 216 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;bootstrap&#x27;: [True, False],\n",
       "                         &#x27;max_depth&#x27;: [80, 145, 210, None],\n",
       "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [56, 83, 110],\n",
       "                         &#x27;min_samples_split&#x27;: [56, 83, 110],\n",
       "                         &#x27;n_estimators&#x27;: [10, 1005, 2000]},\n",
       "             verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;bootstrap&#x27;: [True, False],\n",
       "                         &#x27;max_depth&#x27;: [80, 145, 210, None],\n",
       "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [56, 83, 110],\n",
       "                         &#x27;min_samples_split&#x27;: [56, 83, 110],\n",
       "                         &#x27;n_estimators&#x27;: [10, 1005, 2000]},\n",
       "             verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={'bootstrap': [True, False],\n",
       "                         'max_depth': [80, 145, 210, None],\n",
       "                         'max_features': ['sqrt'],\n",
       "                         'min_samples_leaf': [56, 83, 110],\n",
       "                         'min_samples_split': [56, 83, 110],\n",
       "                         'n_estimators': [10, 1005, 2000]},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 10, stop = 2000, num = 3)]\n",
    "\n",
    "# Number of features to consider at every split\n",
    "max_features = ['sqrt']\n",
    "\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(80, 210, num = 3)]\n",
    "max_depth.append(None)\n",
    "\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [int(x) for x in np.linspace(56, 110, num = 3)]\n",
    "\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [int(x) for x in np.linspace(56, 110, num = 3)]\n",
    "\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "\n",
    "# hyperparameter grid for RandomForestClassifier\n",
    "rf_grid ={'n_estimators': n_estimators,\n",
    "          'max_features': max_features,\n",
    "          'max_depth': max_depth,\n",
    "          'min_samples_split': min_samples_split,\n",
    "          'min_samples_leaf': min_samples_leaf,\n",
    "          'bootstrap': bootstrap}\n",
    "\n",
    "# setup GridSearchCV hyperparameter for RandomForestClassifier\n",
    "gs_rf = GridSearchCV(RandomForestClassifier(),\n",
    "                     param_grid=rf_grid,\n",
    "                     cv=StratifiedKFold(),\n",
    "                     verbose=True,\n",
    "                     n_jobs=-1)\n",
    "\n",
    "# fit grid hyperparameter\n",
    "gs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5d56c7d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(bootstrap=False, max_depth=210, min_samples_leaf=56,\n",
       "                       min_samples_split=110, n_estimators=1005)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(bootstrap=False, max_depth=210, min_samples_leaf=56,\n",
       "                       min_samples_split=110, n_estimators=1005)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(bootstrap=False, max_depth=210, min_samples_leaf=56,\n",
       "                       min_samples_split=110, n_estimators=1005)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_rf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c35a36c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': False,\n",
       " 'max_depth': 210,\n",
       " 'max_features': 'sqrt',\n",
       " 'min_samples_leaf': 56,\n",
       " 'min_samples_split': 110,\n",
       " 'n_estimators': 1005}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1caf7486",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9078947368421053"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f845a3e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.908\n"
     ]
    }
   ],
   "source": [
    "print(f'Accuracy: {gs_rf.score(X_test, y_test):.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "09bcfd26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0921052631578948"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how much increased as per base model\n",
    "gs_rf_improve = (gs_rf.score(X_test, y_test) - model_scores.get('Random Forest              '))\n",
    "gs_rf_improve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d8c9063",
   "metadata": {},
   "source": [
    "**`CatboostClassifier`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cedaa6ec",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 125 candidates, totalling 625 fits\n",
      "0:\tlearn: 0.6737615\ttotal: 2.37ms\tremaining: 68.7ms\n",
      "1:\tlearn: 0.6531845\ttotal: 4.6ms\tremaining: 64.4ms\n",
      "2:\tlearn: 0.6413713\ttotal: 6.63ms\tremaining: 59.6ms\n",
      "3:\tlearn: 0.6222068\ttotal: 7.88ms\tremaining: 51.2ms\n",
      "4:\tlearn: 0.5962512\ttotal: 8.31ms\tremaining: 41.6ms\n",
      "5:\tlearn: 0.5847062\ttotal: 10.2ms\tremaining: 41ms\n",
      "6:\tlearn: 0.5686715\ttotal: 10.8ms\tremaining: 35.5ms\n",
      "7:\tlearn: 0.5579293\ttotal: 12.8ms\tremaining: 35.1ms\n",
      "8:\tlearn: 0.5496587\ttotal: 14.7ms\tremaining: 34.4ms\n",
      "9:\tlearn: 0.5363895\ttotal: 16ms\tremaining: 32ms\n",
      "10:\tlearn: 0.5236062\ttotal: 16.8ms\tremaining: 29.1ms\n",
      "11:\tlearn: 0.5157541\ttotal: 18.9ms\tremaining: 28.3ms\n",
      "12:\tlearn: 0.5048443\ttotal: 20.8ms\tremaining: 27.2ms\n",
      "13:\tlearn: 0.4913515\ttotal: 22ms\tremaining: 25.2ms\n",
      "14:\tlearn: 0.4839496\ttotal: 23.9ms\tremaining: 23.9ms\n",
      "15:\tlearn: 0.4766455\ttotal: 25.9ms\tremaining: 22.6ms\n",
      "16:\tlearn: 0.4697709\ttotal: 27.7ms\tremaining: 21.2ms\n",
      "17:\tlearn: 0.4598366\ttotal: 28.3ms\tremaining: 18.9ms\n",
      "18:\tlearn: 0.4536907\ttotal: 30.4ms\tremaining: 17.6ms\n",
      "19:\tlearn: 0.4452292\ttotal: 31.7ms\tremaining: 15.8ms\n",
      "20:\tlearn: 0.4398087\ttotal: 33.8ms\tremaining: 14.5ms\n",
      "21:\tlearn: 0.4317993\ttotal: 35.7ms\tremaining: 13ms\n",
      "22:\tlearn: 0.4252789\ttotal: 37.6ms\tremaining: 11.4ms\n",
      "23:\tlearn: 0.4185602\ttotal: 39.5ms\tremaining: 9.88ms\n",
      "24:\tlearn: 0.4137007\ttotal: 41.5ms\tremaining: 8.29ms\n",
      "25:\tlearn: 0.4091604\ttotal: 43.4ms\tremaining: 6.67ms\n",
      "26:\tlearn: 0.4053587\ttotal: 45.3ms\tremaining: 5.04ms\n",
      "27:\tlearn: 0.3996797\ttotal: 47.3ms\tremaining: 3.38ms\n",
      "28:\tlearn: 0.3938241\ttotal: 49.2ms\tremaining: 1.7ms\n",
      "29:\tlearn: 0.3891675\ttotal: 51.1ms\tremaining: 0us\n"
     ]
    }
   ],
   "source": [
    "depth = [int(x) for x in np.linspace(6, 11, num = 5)]\n",
    "\n",
    "learning_rate = [float(x) for x in np.linspace(0.04, 0.09, num = 5)]\n",
    "\n",
    "iterations = [int(x) for x in np.linspace(10, 50, num = 5)]\n",
    "\n",
    "cb_grid = {'depth': depth,\n",
    "           'learning_rate': learning_rate,\n",
    "           'iterations': iterations}\n",
    "\n",
    "# setup hyperparameter for CatBoost Classifier\n",
    "gs_cb = GridSearchCV(CatBoostClassifier(),\n",
    "                     param_grid=cb_grid,\n",
    "                     cv=StratifiedKFold(),\n",
    "                     verbose=True,\n",
    "                     n_jobs=-1)\n",
    "\n",
    "# fit grid hyperparameter\n",
    "gs_cb.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7734cc16",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Results from Grid Search \n",
      "\n",
      " The best parameters across ALL searched params:\n",
      " {'depth': 9, 'iterations': 30, 'learning_rate': 0.09}\n",
      "\n",
      " Accuracy: 0.829\n"
     ]
    }
   ],
   "source": [
    "print(\" Results from Grid Search \" )\n",
    "print(\"\\n The best parameters across ALL searched params:\\n\",gs_cb.best_params_)\n",
    "print(f'\\n Accuracy: {gs_cb.score(X_test, y_test):.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9cac1fc",
   "metadata": {},
   "source": [
    "**`GaussianNB`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e700e040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
     ]
    }
   ],
   "source": [
    "# hyperparameter grid for GaussianNB\n",
    "nb_grid = {'var_smoothing': np.logspace(-4, -9, 100)}\n",
    "\n",
    "# setup hyperparameter for GaussianNB\n",
    "gs_nb = GridSearchCV(GaussianNB(),\n",
    "                     param_grid=nb_grid,\n",
    "                     cv=StratifiedKFold(),\n",
    "                     verbose=True,\n",
    "                     n_jobs=-1)\n",
    "\n",
    "# fit grid hyperparameter\n",
    "gs_nb.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e215f686",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Results from Grid Search \n",
      "\n",
      " The best parameters across ALL searched params:\n",
      " {'var_smoothing': 6.280291441834246e-05}\n",
      "\n",
      " Accuracy: 0.895\n"
     ]
    }
   ],
   "source": [
    "print(\" Results from Grid Search \" )\n",
    "print(\"\\n The best parameters across ALL searched params:\\n\",gs_nb.best_params_)\n",
    "print(f'\\n Accuracy: {gs_nb.score(X_test, y_test):.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc5638e",
   "metadata": {},
   "source": [
    "evaluate our classification model for TEAM.\n",
    "\n",
    "### Evaluating the `BEST classification model`, beyond accuracy\n",
    "\n",
    "* **ROC curve and AUC score**: `plot_roc_curve()`\n",
    "* **Confusion matrix**: `confusion_matrix()`\n",
    "* **Classification report**: `classification_report()`\n",
    "* **Precision**: `precision_score()`\n",
    "* **Recall**: `recall_score()`\n",
    "* **F1-score**: `f1_score()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1e403e8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions with tuned model\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5b4f264f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>True Label</th>\n",
       "      <th>Predict Label</th>\n",
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       "</table>\n",
       "<p>76 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     True Label  Predict Label\n",
       "179           0              0\n",
       "228           0              1\n",
       "111           1              1\n",
       "246           0              0\n",
       "60            1              1\n",
       "..          ...            ...\n",
       "22            1              1\n",
       "258           0              0\n",
       "56            1              1\n",
       "242           0              0\n",
       "114           1              1\n",
       "\n",
       "[76 rows x 2 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'True Label': y_test, 'Predict Label': y_preds})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4869f91",
   "metadata": {},
   "source": [
    "### `1. ROC curve and AUC score`\n",
    "\n",
    "**ROC curve**: performing by comparing the `true positive` rate to the `false positive` rate.\n",
    "> To get an appropriate example in a real-world problem, consider a diagnostic test that seeks to determine whether a person has a certain disease. A false positive in this case occurs when the person tests positive, but does not actually have the disease. A false negative, on the other hand, occurs when the person tests negative, suggesting they are healthy, when they actually do have the disease.\n",
    "\n",
    "**AUC score**: area under the curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "07005656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Plot ROC curve and calculate AUC metric\n",
    "plot_roc_curve(gs_rf, X_test, y_test);\n",
    "\n",
    "warnings.filterwarnings(\"default\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aa0e015",
   "metadata": {},
   "source": [
    "**`AUC`**: Calcuated Area Under Curve, maximum value is `1`, model is good as per how much you close to maximum value. \n",
    "\n",
    "###  `2. Confusion matrix`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c7a386f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[30  5]\n",
      " [ 3 38]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0d20551f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot matrix by custom\n",
    "sns.set(font_scale=1.5)\n",
    "\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a nice looking confusion matrix using Seaborn's heatmap()\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot=True,\n",
    "                     cbar=False)\n",
    "    \n",
    "    plt.xlabel(\"Predicted label\")\n",
    "    plt.ylabel(\"True label\")\n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "994ed23b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1b96af35900>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot matrix by sklearn library\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "# matrix for predictions\n",
    "ConfusionMatrixDisplay.from_predictions(y_true=y_test, y_pred=y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b48469a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix for estimator\n",
    "ConfusionMatrixDisplay.from_estimator(estimator=gs_rf, X=X, y=y);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9281b69",
   "metadata": {},
   "source": [
    "### `3. Classification report`\n",
    "\n",
    "#### Due to Normal Train and Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cd91c28c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.86      0.88        35\n",
      "           1       0.88      0.93      0.90        41\n",
      "\n",
      "    accuracy                           0.89        76\n",
      "   macro avg       0.90      0.89      0.89        76\n",
      "weighted avg       0.90      0.89      0.89        76\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96e5b80",
   "metadata": {},
   "source": [
    "**Key Descriptions**\n",
    "* **`Precision`**: Indicates the proportion of positive identifications (model predicted class 1) which were actually correct. A model which produces no false positives has a precision of 1.0.\n",
    "* **`Recall`**: Indicates the proportion of actual positives which were correctly classified. A model which produces no false negatives has a recall of 1.0.\n",
    "* **`F1 score`**: A combination of precision and recall. A perfect model achieves an F1 score of 1.0.\n",
    "* **`Support`**: The number of samples each metric was calculated on.\n",
    "* **`Accuracy`**: The accuracy of the model in decimal form. Perfect accuracy is equal to 1.0.\n",
    "* **`Macro avg`**: Short for macro average, the average precision, recall and F1 score between classes. Macro avg doesn’t class imbalance into effort, so if you do have class imbalances, pay attention to this metric.\n",
    "* **`Weighted avg`**: Short for weighted average, the weighted average precision, recall and F1 score between classes. Weighted means each metric is calculated with respect to how many samples there are in each class. This metric will favour the majority class (e.g. will give a high value when one class out performs another due to having more samples)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "595e144d",
   "metadata": {},
   "source": [
    "### Due to Cross-validation, calculate evaluation metrics\n",
    "**`cross_val_score()`**: works by taking an estimator (machine learning model) along with data and labels. It then evaluates the machine learning model on the data and labels using `cross-validation` and a defined `scoring` parameter.\n",
    "\n",
    "* **cross validated metrics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "06f62d54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.18420699693267145,\n",
       " 'max_iter': 1000,\n",
       " 'penalty': 'l2',\n",
       " 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "878f61ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8947368421052632"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# final score\n",
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a9d160e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate best model with best hyperparameters\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                         max_iter=1000,\n",
    "                         penalty='l2',\n",
    "                         solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d5b58f4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.86885246, 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross-validated accuracy score\n",
    "cv_acc = cross_val_score(clf, X, y, cv=StratifiedKFold(), scoring='accuracy')\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9ebe6615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446994535519124"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate average of cross-validation\n",
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c18d3d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8207936507936507"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross-validated precision score\n",
    "cv_precision = cross_val_score(clf, X, y, cv=StratifiedKFold(), scoring='precision')\n",
    "cv_precision = np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "51a66ded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212121212121213"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross-validated recall score\n",
    "cv_recall = cross_val_score(clf, X, y, cv=StratifiedKFold(), scoring='recall')\n",
    "cv_recall = np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ce44a61a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673007976269721"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross-validated F1 score\n",
    "cv_f1 = cross_val_score(clf, X, y, cv=StratifiedKFold(), scoring='f1')\n",
    "cv_f1 = np.mean(cv_f1)\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "47c90256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\": cv_acc,\n",
    "                           \"Precision\": cv_precision,\n",
    "                           \"Recall\": cv_recall,\n",
    "                           \"F1\": cv_f1}, index=[0])\n",
    "\n",
    "cv_metrics.T.plot.bar(title=\"Cross-validated Metrics\", legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0894acb8",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "Feature importance is another way of asking, `which features contributing most to the outcomes of the model?`\n",
    "\n",
    "> for our problem, trying to predict heart disease using a patient's medical characterisitcs, `which charateristics contribute most to a model predicting whether someone has heart disease or not?`\n",
    "\n",
    "***Finding feature importance is different for each machine learning model***\n",
    "\n",
    "* `LogisticRegression`: coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3fe678fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.20433597178569418, max_iter=1000, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.20433597178569418, max_iter=1000, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.20433597178569418, max_iter=1000, solver='liblinear')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit an instance of LogisticRegression\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d8d0181f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00503922, -0.76947823,  0.68812983, -0.00991718, -0.00127897,\n",
       "         0.00226276,  0.40721864,  0.02147257, -0.60142004, -0.54531813,\n",
       "         0.45519223, -0.6569426 , -0.70184842]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check coefficient\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cb976513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.005039219028197717,\n",
       " 'sex': -0.7694782261251526,\n",
       " 'cp': 0.6881298272584871,\n",
       " 'trestbps': -0.009917184184719494,\n",
       " 'chol': -0.0012789704596882492,\n",
       " 'fbs': 0.002262758498285967,\n",
       " 'restecg': 0.40721864391970897,\n",
       " 'thalach': 0.02147257412019282,\n",
       " 'exang': -0.6014200434372448,\n",
       " 'oldpeak': -0.5453181276533761,\n",
       " 'slope': 0.4551922319697744,\n",
       " 'ca': -0.6569426022962225,\n",
       " 'thal': -0.7018484174338481}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# match features to column\n",
    "features_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "features_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "af3b541f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "features_df = pd.DataFrame(features_dict, index=[0])\n",
    "features_df.T.plot.bar(title=\"Feature Importance\", legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0458704",
   "metadata": {},
   "source": [
    "***The larger the value (bigger bar), the more the `feature contributes` to the models decision.***\n",
    "\n",
    "> For example, the sex attribute has a negative value of -0.904, which means as the value for sex increases, the target value decreases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "77f4b3c7",
   "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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3224655",
   "metadata": {},
   "source": [
    "* when sex is 0 (female), there are almost 3 times as many `(72 vs. 24)` people with heart disease `(target = 1)` than without.\n",
    "\n",
    "* when sex increases to 1 (male), the ratio goes down to almost 1 to 1 `(114 vs. 93)` of people who have heart disease and who don't.\n",
    "\n",
    ">  it seems if the patient is female, they're more likely to have heart disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ab2568ba",
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Contrast slope \"positive coefficient\" with target\n",
    "pd.crosstab(df[\"slope\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4612f4be",
   "metadata": {},
   "source": [
    "***This positive correlation means our model is picking up the pattern that as slope increases, so does the target value.***\n",
    "\n",
    "**Keys**\n",
    "* **`Finding out more`**: If some of the correlations and feature importances are confusing, a subject matter expert may be able to shed some light on the situation and help you figure out more.\n",
    "* **`Redirecting efforts`**: If some features offer far more value than others, this may change how you collect data for different problems. See point 3.\n",
    "* **`Less but better`**: Similar to above, if some features are offering far more value than others, you could reduce the number of features your model tries to find patterns in as well as improve the ones which offer the most. This could potentially lead to saving on computation, by having a model find patterns across less features, whilst still achieving the same performance levels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60b74d47",
   "metadata": {},
   "source": [
    "### Saving and Loading Model\n",
    "\n",
    "**`joblib`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "68d6f3d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d8d79384",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_preds(y_true, y_preds):\n",
    "    \"\"\"\n",
    "    Performs evaluation comparison on y_true labels vs. y_pred labels\n",
    "    in classification.\n",
    "    \"\"\"\n",
    "    accuracy = accuracy_score(y_true, y_preds)\n",
    "    precision = precision_score(y_true, y_preds)\n",
    "    recall = recall_score(y_true, y_preds)\n",
    "    f1 = f1_score(y_true, y_preds)\n",
    "    metric_dict = {\"accuracy\": round(accuracy, 2),\n",
    "                   \"precision\": round(precision, 2), \n",
    "                   \"recall\": round(recall, 2),\n",
    "                   \"f1\": round(f1, 2)}\n",
    "    print(f\"Acc: {accuracy * 100:.2f}%\")\n",
    "    print(f\"Precision: {precision:.2f}\")\n",
    "    print(f\"Recall: {recall:.2f}\")\n",
    "    print(f\"F1 score: {f1:.2f}\")\n",
    "\n",
    "    return metric_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "61f9cf4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/heart_disease_LogisticRegression_classification_model_1.joblib']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "\n",
    "# save model to file\n",
    "dump(gs_log_reg, filename=\"models/heart_disease_LogisticRegression_classification_model_1.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2b88528c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import a saved joblib model\n",
    "loaded_job_model = load(filename=\"models/heart_disease_LogisticRegression_classification_model_1.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7d8a53e6",
   "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>True Label</th>\n",
       "      <th>Predict Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     True Label  Predict Label\n",
       "179           0              0\n",
       "228           0              1\n",
       "111           1              1\n",
       "246           0              0\n",
       "60            1              1\n",
       "..          ...            ...\n",
       "22            1              1\n",
       "258           0              0\n",
       "56            1              1\n",
       "242           0              0\n",
       "114           1              1\n",
       "\n",
       "[76 rows x 2 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make and evaluate joblib predictions\n",
    "joblib_y_preds = loaded_job_model.predict(X_test)\n",
    "pd.DataFrame({'True Label': y_test, 'Predict Label': joblib_y_preds})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3ef71587",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 89.47%\n",
      "Precision: 0.88\n",
      "Recall: 0.93\n",
      "F1 score: 0.90\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.89, 'precision': 0.88, 'recall': 0.93, 'f1': 0.9}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_preds(y_test, joblib_y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d442e23",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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