end-to-end-heart-disease-classificatio.ipynb

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    "# Predicting Heart Disease using Machine Learning\n",
    "\n",
    "This notebook will introduce some foundation machine learning and data science concepts by exploring the problem of heart disease **classification**.\n",
    "\n",
    "It is intended to be an end-to-end example of what a data science and machine learning **proof of concept** might look like.\n",
    "\n",
    "We're going to take the following approach:\n",
    "\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling\n",
    "6. Experimentation\n",
    "\n",
    "## What is classification?\n",
    "\n",
    "Classification involves deciding whether a sample is part of one class or another (**single-class classification**). If there are multiple class options, it's referred to as **multi-class classification**.\n",
    "\n",
    "\n",
    "## What we'll end up with\n",
    "\n",
    "Since we already have a dataset, we'll approach the problem with the following machine learning modelling framework.\n",
    "\n",
    "| <img src=\"images/ml101-6-step-ml-framework.png\" width=500/> | \n",
    "|:--:| \n",
    "| 6 Step Machine Learning Modelling Framework |\n",
    "\n",
    "More specifically, we'll look at the following topics.\n",
    "\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?\n",
    "\n",
    "To work through these topics, we'll use pandas, Matplotlib and NumPy for data anaylsis, as well as, Scikit-Learn for machine learning and modelling tasks.\n",
    "\n",
    "| <img src=\"images/supervised-projects-6-step-ml-framework-tools-highlight.png\" width=500/> | \n",
    "|:--:| \n",
    "| Tools which can be used for each step of the machine learning modelling process. |\n",
    "\n",
    "We'll work through each step and by the end of the notebook, we'll have a handful of models, all which can predict whether or not a person has heart disease based on a number of different parameters at a considerable accuracy. \n",
    "\n",
    "You'll also be able to describe which parameters are more indicative than others, for example, sex may be more important than age."
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    "## 1. Problem Definition\n",
    "In our case, the problem we will be exploring is **binary classification** (a sample can only be one of two things). \n",
    "\n",
    "This is because we're going to be using a number of differnet **features** (pieces of information) about a person to predict whether they have heart disease or not.\n",
    "\n",
    "In a statement,\n",
    "\n",
    "> Given clinical parameters about a patient, can we predict whether or not they have heart disease?\n",
    "\n",
    "## 2. Data\n",
    "\n",
    "What you'll want to do here is dive into the data your problem definition is based on. This may involve, sourcing, defining different parameters, talking to experts about it and finding out what you should expect.\n",
    "\n",
    "The original data came from the [Cleveland database](https://archive.ics.uci.edu/ml/datasets/heart+Disease) from UCI Machine Learning Repository.\n",
    "\n",
    "Howevever, we've downloaded it in a formatted way from [Kaggle](https://www.kaggle.com/datasets/sumaiyatasmeem/heart-disease-classification-dataset).\n",
    "\n",
    "The original database contains 76 attributes, but here only 14 attributes will be used. **Attributes** (also called **features**) are the variables what we'll use to predict our **target variable**.\n",
    "\n",
    "Attributes and features are also referred to as **independent variables** and a target variable can be referred to as a **dependent variable**.\n",
    "\n",
    "> We use the independent variables to predict our dependent variable.\n",
    "\n",
    "Or in our case, the independent variables are a patients different medical attributes and the dependent variable is whether or not they have heart disease.\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "The evaluation metric is something you might define at the start of a project.\n",
    "\n",
    "Since machine learning is very experimental, you might say something like, \n",
    "\n",
    "> If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursure this project.\n",
    "\n",
    "The reason this is helpful is it provides a rough goal for a machine learning engineer or data scientist to work towards.\n",
    "\n",
    "However, due to the nature of experimentation, the evaluation metric may change over time.\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "Features are different parts of the data. During this step, you'll want to start finding out what you can about the data.\n",
    "\n",
    "One of the most common ways to do this, is to create a **data dictionary**.\n",
    "\n",
    "### Heart Disease Data Dictionary\n",
    "\n",
    "A data dictionary describes the data you're dealing with. Not all datasets come with them so this is where you may have to do your research or ask a **subject matter expert** (someone who knows about the data) for more.\n",
    "\n",
    "The following are the features we'll use to predict our target variable (heart disease or no heart disease).\n",
    "\n",
    "1. age - age in years \n",
    "2. sex - (1 = male; 0 = female) \n",
    "3. cp - chest pain type \n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "4. trestbps - resting blood pressure (in mm Hg on admission to the hospital)\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",
    "**Note:** No personal identifiable information (PPI) can be found in the dataset.\n",
    "\n",
    "It's a good idea to save these to a Python dictionary or in an external file, so we can look at them later without coming back here."
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    "## Preparing the tools\n",
    "\n",
    "At the start of any project, it's custom to see the required libraries imported in a big chunk like you can see below.\n",
    "\n",
    "However, in practice, your projects may import libraries as you go. After you've spent a couple of hours working on your problem, you'll probably want to do some tidying up. This is where you may want to consolidate every library you've used at the top of your notebook (like the cell below).\n",
    "\n",
    "The libraries you use will differ from project to project. But there are a few which will you'll likely take advantage of during almost every structured data project. \n",
    "\n",
    "* [pandas](https://pandas.pydata.org/) for data analysis.\n",
    "* [NumPy](https://numpy.org/) for numerical operations.\n",
    "* [Matplotlib](https://matplotlib.org/)/[seaborn](https://seaborn.pydata.org/) for plotting or data visualization.\n",
    "* [Scikit-Learn](https://scikit-learn.org/stable/) for machine learning modelling and evaluation."
   ]
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     "text": [
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
      "Last Updated: Sat Feb 24 22:14:59 2024\n"
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   "source": [
    "# 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",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "import time\n",
    "print(f\"Last Updated: {time.asctime()}\")\n"
   ]
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    "## Load Data\n",
    "\n",
    "There are many different kinds of ways to store data. The typical way of storing **tabular data**, data similar to what you'd see in an Excel file is in `.csv` format. `.csv` stands for comma seperated values.\n",
    "\n",
    "Pandas has a built-in function to read `.csv` files called `read_csv()` which takes the file pathname of your `.csv` file. You'll likely use this a lot."
   ]
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    "df = pd.read_csv(\"data/heart-disease.csv\")\n",
    "df.shape # (rows, columns)"
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    "## Data Exploration (exploratory data analysis or EDA)\n",
    "\n",
    "Once you've imported a dataset, the next step is to explore. There's no set way of doing this. But what you should be trying to do is become more and more familiar with the dataset.\n",
    "\n",
    "Compare different columns to each other, compare them to the target variable. Refer back to your **data dictionary** and remind yourself of what different columns mean.\n",
    "\n",
    "Your goal is to become a subject matter expert on the dataset you're working with. So if someone asks you a question about it, you can give them an explanation and when you start building models, you can sound check them to make sure they're not performing too well (**overfitting**) or why they might be performing poorly (**underfitting**).\n",
    "\n",
    "Since EDA has no real set methodolgy, the following is a short check list you might want to walk through:\n",
    "\n",
    "1. What question(s) are you trying to solve (or prove wrong)?\n",
    "2. What kind of data do you have and how do you treat different types?\n",
    "3. What’s missing from the data and how do you deal with it?\n",
    "4. Where are the outliers and why should you care about them?\n",
    "5. How can you add, change or remove features to get more out of your data?\n",
    "\n",
    "Once of the quickest and easiest ways to check your data is with the `head()` function. Calling it on any dataframe will print the top 5 rows, `tail()` calls the bottom 5. You can also pass a number to them like `head(10)` to show the top 10 rows."
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       "   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",
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       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
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       "      <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": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a012305a-5d3d-4927-9832-5eb546443d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    165\n",
       "0    138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "54b5cbb8-d341-431f-ba61-2ad81c14ac74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"target\"].value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "512e2459-145e-4b9e-9d97-c83d397ef76b",
   "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": "217bac56-2bb0-4b05-947a-9faf1e9b2aa7",
   "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": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2acbdd8a-8956-4371-abdd-b7807658b718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb29c0f0-48c5-4ad4-90b9-7dfe9cbf95c2",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency according to Gender\n",
    "\n",
    "If you want to compare two columns to each other, you can use the function `pd.crosstab(column_1, column_2)`. \n",
    "\n",
    "This is helpful if you want to start gaining an intuition about how your independent variables interact with your dependent variables.\n",
    "\n",
    "Let's compare our target column with the sex column. \n",
    "\n",
    "Remember from our data dictionary, for the target column, 1 = heart disease present, 0 = no heart disease. And for sex, 1 = male, 0 = female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c773444-f241-4fea-aa20-d06f8a727e56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "1    207\n",
       "0     96\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "92dfa560-ea1b-42d1-8e73-590bd125b442",
   "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": "markdown",
   "id": "e5fafdf3-a94e-457a-af48-c38d7d655f1a",
   "metadata": {},
   "source": [
    "What can we infer from this? Let's make a simple heuristic.\n",
    "\n",
    "Since there are about 100 women and 72 of them have a postive value of heart disease being present, we might infer, based on this one variable if the participant is a woman, there's a 75% chance she has heart disease.\n",
    "\n",
    "As for males, there's about 200 total with around half indicating a presence of heart disease. So we might predict, if the participant is male, 50% of the time he will have heart disease.\n",
    "\n",
    "Averaging these two values, we can assume, based on no other parameters, if there's a person, there's a 62.5% chance they have heart disease.\n",
    "\n",
    "This can be our very simple **baseline**, we'll try to beat it with machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d4219df-b2ff-4ca4-9020-9ff186b0bc09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df[\"target\"], df[\"sex\"]).plot(kind=\"bar\", figsize=(10, 6), color=[\"salmon\", \"lightblue\"])\n",
    "\n",
    "plt.title(\"Heart Disease Frequency for sex\")\n",
    "plt.xlabel(\"0 = No disease,  1 = Disease\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Female\", \"Male\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef44ed9a-f28b-42fd-925f-ab5c894719a1",
   "metadata": {},
   "source": [
    "### Age vs Max Heart rate for Heart Disease\n",
    "\n",
    "Let's try combining a couple of independent variables, such as, `age` and `thalach` (maximum heart rate) and then comparing them to our target variable `heart disease`.\n",
    "\n",
    "Because there are so many different values for `age` and `thalach`, we'll use a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aca551fb-3fb9-4175-891f-4407f612e736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create another figure\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Start with positve examples\n",
    "plt.scatter(df.age[df.target==1],\n",
    "            df.thalach[df.target==1],\n",
    "            c=\"salmon\"); # define it as a scatter figure\n",
    "\n",
    "# Now for negative examples, we want them on the same plot, so we call plt again\n",
    "plt.scatter(df.age[df.target==0],\n",
    "           df.thalach[df.target==0],\n",
    "           c=\"lightblue\"); # axis always come as (x, y)\n",
    "\n",
    "# Add some helpful info\n",
    "plt.title(\"Heart Diseas 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": "markdown",
   "id": "b2ef7df4-552d-4516-bad3-b1a62322e609",
   "metadata": {},
   "source": [
    "What can we infer from this?\n",
    "\n",
    "It seems the younger someone is, the higher their max heart rate (dots are higher on the left of the graph) and the older someone is, the more green dots there are. But this may be because there are more dots all together on the right side of the graph (older participants).\n",
    "\n",
    "Both of these are observational of course, but this is what we're trying to do, build an understanding of the data.\n",
    "\n",
    "Let's check the age **distribution**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bc22c9c4-21c2-47e1-b522-ae18d2f5e771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAI5GSDsz3d3d6urq6nF8x44dSkhIOOahAAAAjlZIMZOTk6OysrLAc5fLpd27d+v+++/nKw4AAMBxFdLbTH/72980YcIEjRgxQvv379f06dO1efNmnX766Xr++efDPSMAAMAhhRQzycnJ2rhxo55//nlt2LBB3d3dmj17tmbMmBF0QzAAAECkuUwf/4Ilv9+vpKQktbW1KTEx0elxEKKh977p9AgALLO1ZIrTI+AY9Obf75B2ZpYvX37Y8zNnzgzlZQEAAHotpJiZP39+0PMff/xRe/fuVWxsrOLj44kZAABw3IT0aabW1tagx+7du7Vp0yZdeuml3AAMAACOq5C/m+nX0tLSVFJS0mPXBgAAIJLCFjOSdOKJJ2rXrl3hfEkAAIDDCumemTfeeCPouTFGzc3NKi8v1/jx48MyGAAAwNEIKWauvvrqoOcul0tnnHGGLr/8cj366KPhmAsAAOCohBQz3d3d4Z4DAAAgJGG9ZwYAAOB4C2lnZsGCBUd9bWlpaSh/BAAAwFEJKWY++eQTbdiwQT/99JPOOeccSdJXX32lE088URdddFHgOpfLFZ4pAQAADiGkt5mmTp2qrKws7dixQxs2bNCGDRvU1NSkCRMm6Morr9SaNWu0Zs0avfvuu4d9nYqKCo0aNUqJiYlKTEzUuHHjtGrVqsB5Y4wKCwuVnJysuLg4ZWdnq6GhIZSRAQBAHxVSzDz66KMqLi7WqaeeGjh26qmn6qGHHurVp5kGDx6skpISrVu3TuvWrdPll1+uq666KhAsixcvVmlpqcrLy1VXVyev16ucnBy1t7eHMjYAAOiDQooZv9+vb775psfxlpaWXoXG1KlTNXnyZA0fPlzDhw/Xww8/rJNPPlkfffSRjDEqKyvTokWLlJeXp/T0dFVVVWnv3r1asWJFKGMDAIA+KKSYmTZtmm6++Wa9/PLL2rFjh3bs2KGXX35Zs2fPVl5eXkiDdHV1aeXKldqzZ4/GjRunxsZG+Xw+5ebmBq5xu93KysrS2rVrQ/ozAABA3xPSDcBPPvmk7r77bt1www368ccff36hmBjNnj1bS5Ys6dVr1dfXa9y4cdq/f79OPvlkvfbaaxoxYkQgWDweT9D1Ho9H27ZtO+TrdXR0qKOjI/Dc7/f3ah4AAGCXkGImPj5eTzzxhJYsWaKvv/5axhgNGzZM/fv37/VrnXPOOdq4caN++OEHvfLKK5o1a5Zqa2sD53/9iShjzGE/JVVcXKwHHnig13MAAAA7HdMvzWtublZzc7OGDx+u/v37yxjT69eIjY3VsGHDlJGRoeLiYl1wwQV67LHH5PV6JUk+ny/o+paWlh67Nb9UUFCgtra2wKOpqanXMwEAAHuEFDPfffedJk6cqOHDh2vy5Mlqbm6WJN1666266667jmkgY4w6OjqUmpoqr9ermpqawLnOzk7V1tYqMzPzkD/vdrsDH/U+8AAAAH1XSDFz5513ql+/ftq+fbvi4+MDx6+99lq99dZbR/069913nz744ANt3bpV9fX1WrRokd577z3NmDFDLpdL+fn5Kioq0muvvabPP/9cN910k+Lj4zV9+vRQxgYAAH1QSPfMVFdXa/Xq1Ro8eHDQ8bS0tMPenPtr33zzjW688UY1NzcrKSlJo0aN0ltvvaWcnBxJ0sKFC7Vv3z7NnTtXra2tGjt2rKqrq5WQkBDK2AAAoA8KKWb27NkTtCNzwLfffiu3233Ur/PMM88c9rzL5VJhYaEKCwt7OyIAAPiNCOltpssuu0zLly8PPHe5XOru7taSJUs0YcKEsA0HAABwJCHtzCxZskTZ2dlat26dOjs7tXDhQjU0NOj777/Xhx9+GO4ZAQAADimknZkRI0bos88+0+9+9zvl5ORoz549ysvL0yeffKKzzz473DMCAAAcUq93Zn788Ufl5ubqqaee4pfTAQAAx/V6Z6Zfv376/PPPD/tbeAEAAI6XkN5mmjlz5hE/iQQAAHA8hHQDcGdnp55++mnV1NQoIyOjx3cylZaWhmU4AACAI+lVzGzZskVDhw7V559/rosuukiS9NVXXwVdw9tPAADgeOpVzKSlpam5uVlr1qyR9PPXFzz++OOH/eJHAACASOrVPTO//lbsVatWac+ePWEdCAAAoDdCugH4gF/HDQAAwPHWq5hxuVw97onhHhkAAOCkXt0zY4zRTTfdFPgyyf379+u2227r8WmmV199NXwTAgAAHEavYmbWrFlBz2+44YawDgMAANBbvYqZysrKSM0BAAAQkmO6ARgAAMBpxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsRswAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsRswAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsJqjMVNcXKyLL75YCQkJGjhwoK6++mpt2rQp6BpjjAoLC5WcnKy4uDhlZ2eroaHBoYkBAEC0cTRmamtrNW/ePH300UeqqanRTz/9pNzcXO3ZsydwzeLFi1VaWqry8nLV1dXJ6/UqJydH7e3tDk4OAACiRYyTf/hbb70V9LyyslIDBw7U+vXrddlll8kYo7KyMi1atEh5eXmSpKqqKnk8Hq1YsUJz5sxxYmwAABBFouqemba2NknSaaedJklqbGyUz+dTbm5u4Bq3262srCytXbvWkRkBAEB0cXRn5peMMVqwYIEuvfRSpaenS5J8Pp8kyePxBF3r8Xi0bdu2g75OR0eHOjo6As/9fn+EJgYAANEganZmbr/9dn322Wd6/vnne5xzuVxBz40xPY4dUFxcrKSkpMAjJSUlIvMCAIDoEBUx85e//EVvvPGG1qxZo8GDBweOe71eSf9/h+aAlpaWHrs1BxQUFKitrS3waGpqitzgAADAcY7GjDFGt99+u1599VW9++67Sk1NDTqfmpoqr9ermpqawLHOzk7V1tYqMzPzoK/pdruVmJgY9AAAAH2Xo/fMzJs3TytWrNA///lPJSQkBHZgkpKSFBcXJ5fLpfz8fBUVFSktLU1paWkqKipSfHy8pk+f7uToAAAgSjgaMxUVFZKk7OzsoOOVlZW66aabJEkLFy7Uvn37NHfuXLW2tmrs2LGqrq5WQkLCcZ4WAABEI0djxhhzxGtcLpcKCwtVWFgY+YEAAIB1ouIGYAAAgFARMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsRswAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsRswAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsFqM0wMAABAJQ+990+kRem1ryRSnR7ASOzMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsRswAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKvFOD0Ajr+h977p9AgAAISNozsz77//vqZOnark5GS5XC69/vrrQeeNMSosLFRycrLi4uKUnZ2thoYGZ4YFAABRydGY2bNnjy644AKVl5cf9PzixYtVWlqq8vJy1dXVyev1KicnR+3t7cd5UgAAEK0cfZtp0qRJmjRp0kHPGWNUVlamRYsWKS8vT5JUVVUlj8ejFStWaM6cOcdzVAAAEKWi9gbgxsZG+Xw+5ebmBo653W5lZWVp7dq1h/y5jo4O+f3+oAcAAOi7ojZmfD6fJMnj8QQd93g8gXMHU1xcrKSkpMAjJSUlonMCAABnRW3MHOByuYKeG2N6HPulgoICtbW1BR5NTU2RHhEAADgoaj+a7fV6Jf28QzNo0KDA8ZaWlh67Nb/kdrvldrsjPh8AAIgOUbszk5qaKq/Xq5qamsCxzs5O1dbWKjMz08HJAABANHF0Z2b37t3673//G3je2NiojRs36rTTTtOZZ56p/Px8FRUVKS0tTWlpaSoqKlJ8fLymT5/u4NQAACCaOBoz69at04QJEwLPFyxYIEmaNWuWnn32WS1cuFD79u3T3Llz1draqrFjx6q6uloJCQlOjQwAAKKMyxhjnB4ikvx+v5KSktTW1qbExESnx4kKfJ0BAESnrSVTnB4havTm3++ovWcGAADgaBAzAADAasQMAACwGjEDAACsRswAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsRswAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsRswAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKvFOD0AAAD42dB733R6hF7bWjLF6RHYmQEAAHYjZgAAgNWIGQAAYDViBgAAWI2YAQAAViNmAACA1YgZAABgNWIGAABYjZgBAABWI2YAAIDV+DqDY2Tjr54GAKAvsWJn5oknnlBqaqpOOukkjRkzRh988IHTIwEAgCgR9THzwgsvKD8/X4sWLdInn3yi3//+95o0aZK2b9/u9GgAACAKRH3MlJaWavbs2br11lt13nnnqaysTCkpKaqoqHB6NAAAEAWi+p6Zzs5OrV+/Xvfee2/Q8dzcXK1du/agP9PR0aGOjo7A87a2NkmS3++PyIzdHXsj8roAANggUv++HnhdY8wRr43qmPn222/V1dUlj8cTdNzj8cjn8x30Z4qLi/XAAw/0OJ6SkhKRGQEA+C1LKovs67e3tyspKemw10R1zBzgcrmCnhtjehw7oKCgQAsWLAg87+7u1vfff68BAwYc8mciye/3KyUlRU1NTUpMTDzuf/5vFevuDNbdGay7M1j3yDLGqL29XcnJyUe8Nqpj5vTTT9eJJ57YYxempaWlx27NAW63W263O+jYKaecEqkRj1piYiJ/2R3AujuDdXcG6+4M1j1yjrQjc0BU3wAcGxurMWPGqKamJuh4TU2NMjMzHZoKAABEk6jemZGkBQsW6MYbb1RGRobGjRunpUuXavv27brtttucHg0AAESBqI+Za6+9Vt99950efPBBNTc3Kz09Xf/+9781ZMgQp0c7Km63W/fff3+Pt74QWay7M1h3Z7DuzmDdo4fLHM1nngAAAKJUVN8zAwAAcCTEDAAAsBoxAwAArEbMAAAAqxEzYVBRUaFRo0YFfnHSuHHjtGrVqsB5Y4wKCwuVnJysuLg4ZWdnq6GhwcGJ+6bi4mK5XC7l5+cHjrH24VdYWCiXyxX08Hq9gfOseeTs3LlTN9xwgwYMGKD4+HhdeOGFWr9+feA8ax9+Q4cO7fH33eVyad68eZJY82hBzITB4MGDVVJSonXr1mndunW6/PLLddVVVwX+Qi9evFilpaUqLy9XXV2dvF6vcnJy1N7e7vDkfUddXZ2WLl2qUaNGBR1n7SPj/PPPV3Nzc+BRX18fOMeaR0Zra6vGjx+vfv36adWqVfriiy/06KOPBv2Gc9Y+/Orq6oL+rh/4Ja7XXHONJNY8ahhExKmnnmqefvpp093dbbxerykpKQmc279/v0lKSjJPPvmkgxP2He3t7SYtLc3U1NSYrKwsM3/+fGOMYe0j5P777zcXXHDBQc+x5pFzzz33mEsvvfSQ51n742P+/Pnm7LPPNt3d3ax5FGFnJsy6urq0cuVK7dmzR+PGjVNjY6N8Pp9yc3MD17jdbmVlZWnt2rUOTtp3zJs3T1OmTNEVV1wRdJy1j5zNmzcrOTlZqampuu6667RlyxZJrHkkvfHGG8rIyNA111yjgQMHavTo0Vq2bFngPGsfeZ2dnXruued0yy23yOVyseZRhJgJk/r6ep188slyu9267bbb9Nprr2nEiBGBL8n89RdjejyeHl+gid5buXKlNmzYoOLi4h7nWPvIGDt2rJYvX67Vq1dr2bJl8vl8yszM1HfffceaR9CWLVtUUVGhtLQ0rV69WrfddpvuuOMOLV++XBJ/34+H119/XT/88INuuukmSax5NIn6rzOwxTnnnKONGzfqhx9+0CuvvKJZs2aptrY2cN7lcgVdb4zpcQy909TUpPnz56u6ulonnXTSIa9j7cNr0qRJgf8eOXKkxo0bp7PPPltVVVW65JJLJLHmkdDd3a2MjAwVFRVJkkaPHq2GhgZVVFRo5syZgetY+8h55plnNGnSJCUnJwcdZ82dx85MmMTGxmrYsGHKyMhQcXGxLrjgAj322GOBT3n8utJbWlp61Dx6Z/369WppadGYMWMUExOjmJgY1dbW6vHHH1dMTExgfVn7yOrfv79GjhypzZs38/c9ggYNGqQRI0YEHTvvvPO0fft2SWLtI2zbtm16++23deuttwaOsebRg5iJEGOMOjo6lJqaKq/XG7gDXvr5fdfa2lplZmY6OKH9Jk6cqPr6em3cuDHwyMjI0IwZM7Rx40adddZZrP1x0NHRoS+//FKDBg3i73sEjR8/Xps2bQo69tVXXwW+dJe1j6zKykoNHDhQU6ZMCRxjzaOIk3cf9xUFBQXm/fffN42Njeazzz4z9913nznhhBNMdXW1McaYkpISk5SUZF599VVTX19vrr/+ejNo0CDj9/sdnrzv+eWnmYxh7SPhrrvuMu+9957ZsmWL+eijj8yVV15pEhISzNatW40xrHmkfPzxxyYmJsY8/PDDZvPmzeYf//iHiY+PN88991zgGtY+Mrq6usyZZ55p7rnnnh7nWPPoQMyEwS233GKGDBliYmNjzRlnnGEmTpwYCBljfv7I5P3332+8Xq9xu93msssuM/X19Q5O3Hf9OmZY+/C79tprzaBBg0y/fv1McnKyycvLMw0NDYHzrHnk/Otf/zLp6enG7Xabc8891yxdujToPGsfGatXrzaSzKZNm3qcY82jg8sYY5zeHQIAAAgV98wAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACs9v8AFI3wqyb9tSEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"age\"].plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6659a671-0b6d-421a-999f-21219c5805ae",
   "metadata": {},
   "source": [
    "We can see it's a [**normal distribution**](https://en.wikipedia.org/wiki/Normal_distribution) but slightly swaying to the right, which reflects in the scatter plot above.\n",
    "\n",
    "Let's keep going."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f05a24-a230-4fc9-b646-032a3d70aabd",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency per Chest Pain Type\n",
    "\n",
    "Let's try another independent variable. This time, `cp` (chest pain).\n",
    "\n",
    "We'll use the same process as we did before with `sex`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "13d91857-9eb6-4bdf-a70e-3457d2fe3b41",
   "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": "ced54b46-0fc7-4027-937d-15c353803142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9+tmXz5w5U8OHD9err76qXbt2KT4+XiNGjNDs2bONawIAXB1T+wAAxn777TdZlqXy5cvnqX/37t3VqVMnSVJ8fLymTp2qDRs26JFHHtGMGTNUo0YNxcfH2/t/8MEHCg0N1a+//qqzZ8/q0qVLio2NVVhYmCSpSpUq9r5eXl5KTU01nna3YcMGzZ07V40aNZIk9ezZ074sMjJSb775ph544AGdPXtWvr6+OW4jPT1db7/9tsqUKSNJ6t+/v8aMGWNUR9YxdO7cWbNmzdKjjz4qSZozZ45Kly6t6Ohoe7+LFy9q9uzZKl26tCRp6tSpatGihSZNmqSgoCCNHTtWkyZNUmxsrCQpIiJCO3fu1DvvvKNu3boZ1wUAyB1BCgBgzLIsSZefyJcXVatWtf+3j4+P/Pz87NPmNm/erG+//TbHsLJnzx7FxMSoUaNGqlKlipo2baqYmBh16NBBRYoUMa576dKl8vX11aVLl5Senq42bdpo6tSpkqStW7cqLi5O27Zt0/Hjx5WZmSlJSk5OVsWKFXPcnre3tz1ESVJwcLDDdEATTz31lGrVqqXff/9dpUqV0qxZs+wPyMhyzz332EOUJNWpU0eZmZnavXu3XFxcdPDgQfXq1cvhvqtLly5d9wMyAAC5I0gBAIxFRUXJZrNp165deXpinpubm8N7m81mDyqZmZlq1aqVxo8fn2294OBgubi4KCkpSevWrdPKlSs1depUDR8+XD/++KMiIiKM6m7YsKFmzJghNzc3hYSE2Os6d+6cYmJiFBMTo48++kglSpRQcnKymjZtqrS0NKPjygqZpu677z5Vq1ZNH374oZo2bart27friy++uOo6WSHr7+dz5syZql27tkM/FxeX66oJAJA7ghQAwFjRokXVtGlTvfXWWxowYEC2+6ROnjyZ7T6p3NSoUUOfffaZwsPD5eqa8/+WbDab6tWrp3r16mnkyJEKCwvTokWLNHjwYLm7uysjIyNP+/Lx8VHZsmWztf/3v//VsWPHNG7cOIWGhkqSNm3alKdt5qfevXvr9ddf1++//67GjRvba8mSnJysP/74QyEhIZKk9evXq1ChQrr33nsVGBioUqVKae/everSpcstrx0A7jY8bAIAcF2mT5+ujIwMPfDAA/rss8/0v//9T7t27dKbb76pOnXq5Hk7/fr10/Hjx9WpUydt2LBBe/fu1cqVK9WzZ09lZGToxx9/VHx8vDZt2qTk5GQtXLhQR48eVYUKFSRJ4eHh+vnnn7V7924dO3bM/vAIE/fcc4/c3d01depU7d27V0uWLNHYsWONt3OjunTpot9//10zZ850uGcri6enp7p166affvpJ3333nQYMGKCOHTva7w+Li4tTQkKC3njjDf3666/avn27Zs2apcmTJ9/qQwGAOx5BCgBwXSIiIrRlyxY1bNhQQ4YMUeXKldWkSRN98803efpepiwhISH6z3/+o4yMDDVt2lSVK1fW888/r4CAABUqVEj+/v5au3atmjdvrnvvvVcvv/yyJk2apGbNmkm6fG9RuXLlVLNmTZUoUUL/+c9/jI+lRIkSSkxM1KeffqqKFStq3LhxmjhxovF2bpS/v7/at28vX1/fHKdMli1bVrGxsWrevLliYmJUuXJlh8eb9+7dW++9954SExNVpUoVNWjQQImJicZTIAEA12azrncyNwAAyHdNmjRRhQoV9Oabbzq0x8XFafHixdq2bZtzCgMAOOAeKQAACoDjx49r5cqVWrVqlaZNm+bscgAA10CQAgCgAKhRo4ZOnDih8ePHq1y5cs4uBwBwDUztAwAAAABDPGwCAAAAAAwRpAAAAADAEEEKAAAAAAwRpAAAAADAEEEKAAAAAAwRpAAAAADAEEEKAAAAAAwRpAAAAADA0P8D5u77UXj8VDkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\", figsize=(10, 6), color=[\"salmon\", \"lightblue\"])\n",
    "\n",
    "plt.title(\"Heart Diseas Frequency per chest pain type\")\n",
    "plt.xlabel(\"Chest Pain Type\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"No diseas\", \"Disease\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "59e52bf6-5baf-4f04-aaee-74e2888a53f6",
   "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>0</th>\n",
       "      <td>63</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>56</td>\n",
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       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
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       "      <td>178</td>\n",
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       "      <td>57</td>\n",
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       "      <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",
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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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a965762-b15d-4b08-8f42-9faf5656d600",
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   "source": [
    "What can we infer from this?\n",
    "\n",
    "Remember from our data dictionary what the different levels of chest pain are.\n",
    "\n",
    "3. cp - chest pain type \n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "    \n",
    "It's interesting the atypical agina (value 1) states it's not related to the heart but seems to have a higher ratio of participants with heart disease than not.\n",
    "\n",
    "Wait...?\n",
    "\n",
    "What does atypical agina even mean?\n",
    "\n",
    "At this point, it's important to remember, if your data dictionary doesn't supply you enough information, you may want to do further research on your values. This research may come in the form of asking a **subject matter expert** (such as a cardiologist or the person who gave you the data) or Googling to find out more.\n",
    "\n",
    "According to PubMed, it seems [even some medical professionals are confused by the term](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2763472/).\n",
    "\n",
    "> Today, 23 years later, “atypical chest pain” is still popular in medical circles. Its meaning, however, remains unclear. A few articles have the term in their title, but do not define or discuss it in their text. In other articles, the term refers to noncardiac causes of chest pain.\n",
    "\n",
    "Although not conclusive, this graph above is a hint at the confusion of defintions being represented in data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83ead0ba-b773-468a-8907-6f5114b92d69",
   "metadata": {},
   "source": [
    "### Correlation between independent variables\n",
    "\n",
    "Finally, we'll compare all of the independent variables in one hit.\n",
    "\n",
    "Why?\n",
    "\n",
    "Because this may give an idea of which independent variables may or may not have an impact on our target variable.\n",
    "\n",
    "We can do this using `df.corr()` which will create a [**correlation matrix**](https://www.statisticshowto.datasciencecentral.com/correlation-matrix/) for us, in other words, a big table of numbers telling us how related each variable is the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "305e9d18-c2db-40a7-950c-7c8e65f0b4fc",
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   "outputs": [
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       "      <td>1.000000</td>\n",
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       "      <td>0.213678</td>\n",
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       "      <th>sex</th>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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       "    <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": "bbc832e8-0ec8-4ffd-91a0-59432aed27b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.heatmap(corr_matrix, annot=True, linewidths=0.5, fmt=\".2f\", cmap=\"YlGnBu\")\n",
    "\n",
    "plt.xticks(rotation=45);\n",
    "plt.yticks(rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "125508b1-3cbf-48ee-8014-62330f13444c",
   "metadata": {},
   "source": [
    "Much better. A higher positive value means a potential positive correlation (increase) and a higher negative value means a potential negative correlation (decrease)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "317ed20a-132c-47ba-a180-4d11a7164f95",
   "metadata": {},
   "source": [
    "### Enough EDA, let's model\n",
    "\n",
    "Remember, we do exploratory data analysis (EDA) to start building an intuitition of the dataset.\n",
    "\n",
    "What have we learned so far? Aside from our basline estimate using `sex`, the rest of the data seems to be pretty distributed.\n",
    "\n",
    "So what we'll do next is **model driven EDA**, meaning, we'll use machine learning models to drive our next questions.\n",
    "\n",
    "A few extra things to remember:\n",
    "\n",
    "* Not every EDA will look the same, what we've seen here is an example of what you could do for structured, tabular dataset.\n",
    "* You don't necessarily have to do the same plots as we've done here, there are many more ways to visualize data, I encourage you to look at more.\n",
    "* We want to quickly find:\n",
    "    * Distributions (`df.column.hist()`)\n",
    "    * Missing values (`df.info()`)\n",
    "    * Outliers\n",
    "\n",
    "Let's build some models.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dc25647-0dd2-4917-a691-84af00d913be",
   "metadata": {},
   "source": [
    "## 5. Modeling\n",
    "\n",
    "We've explored the data, now we'll try to use machine learning to predict our target variable based on the 13 independent variables.\n",
    "\n",
    "Remember our problem?\n",
    "\n",
    "> Given clinical parameters about a patient, can we predict whether or not they have heart disease?\n",
    "\n",
    "That's what we'll be trying to answer.\n",
    "\n",
    "And remember our evaluation metric?\n",
    "\n",
    "> If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursure this project.\n",
    "\n",
    "That's what we'll be aiming for.\n",
    "\n",
    "But before we build a model, we have to get our dataset ready.\n",
    "\n",
    "Let's look at it again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "60f76cb1-ec3d-48de-87e4-9872fa528ee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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",
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       "      <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",
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       "      <td>0.8</td>\n",
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       "      <td>1</td>\n",
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       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3503c98c-3ee4-4bf8-b563-bcdfcb51bc64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Everything except target variable\n",
    "X = df.drop(\"target\", axis=1)\n",
    "\n",
    "# Target variable\n",
    "y = df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4aea46c2-4645-44cb-9575-5e891f5cb829",
   "metadata": {},
   "outputs": [
    {
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       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "0        0   0     1  \n",
       "1        0   0     2  \n",
       "2        2   0     2  \n",
       "3        2   0     2  \n",
       "4        2   0     2  \n",
       "..     ...  ..   ...  \n",
       "298      1   0     3  \n",
       "299      1   0     3  \n",
       "300      1   2     3  \n",
       "301      1   1     3  \n",
       "302      1   1     2  \n",
       "\n",
       "[303 rows x 13 columns]"
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    "X"
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   "cell_type": "code",
   "execution_count": 23,
   "id": "795131e8-3ee2-4a0e-95ad-779bbcc03af3",
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   "outputs": [
    {
     "data": {
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       "4      1\n",
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  {
   "cell_type": "markdown",
   "id": "4bf38e17-b1e6-476e-b16b-a229987009b0",
   "metadata": {},
   "source": [
    "### Training and test split\n",
    "\n",
    "Now comes one of the most important concepts in machine learning, the **training/test split**.\n",
    "\n",
    "This is where you'll split your data into a **training set** and a **test set**.\n",
    "\n",
    "You use your training set to train your model and your test set to test it.\n",
    "\n",
    "The test set must remain separate from your training set.\n",
    "\n",
    "#### Why not use all the data to train a model?\n",
    "\n",
    "Let's say you wanted to take your model into the hospital and start using it on patients. How would you know how well your model goes on a new patient not included in the original full dataset you had?\n",
    "\n",
    "This is where the test set comes in. It's used to mimic taking your model to a real environment as much as possible.\n",
    "\n",
    "And it's why it's important to never let your model learn from the test set, it should only be evaluated on it.\n",
    "\n",
    "To split our data into a training and test set, we can use Scikit-Learn's [`train_test_split()`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) and feed it our independent and dependent variables (`X` & `y`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6d7f6fd3-d5cf-4ff4-b00a-1e130b8190b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into train & test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # X ndependent variables, Y dependent variable and percentage of data to use for test set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c38a6d-c3ec-48cf-8e7b-b3679dfa2b29",
   "metadata": {},
   "source": [
    "The `test_size` parameter is used to tell the `train_test_split()` function how much of our data we want in the test set.\n",
    "\n",
    "A rule of thumb is to use 80% of your data to train on and the other 20% to test on. \n",
    "\n",
    "For our problem, a train and test set are enough. But for other problems, you could also use a validation (train/validation/test) set or cross-validation (we'll see this in a second).\n",
    "\n",
    "But again, each problem will differ. The post, [How (and why) to create a good validation set](https://www.fast.ai/2017/11/13/validation-sets/) by Rachel Thomas is a good place to go to learn more.\n",
    "\n",
    "Let's look at our training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "94a41778-68b1-4cb8-ae9b-3b300df75b3a",
   "metadata": {},
   "outputs": [
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>160</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "132   42    1   1       120   295    0        1      162      0      0.0   \n",
       "202   58    1   0       150   270    0        0      111      1      0.8   \n",
       "196   46    1   2       150   231    0        1      147      0      3.6   \n",
       "75    55    0   1       135   250    0        0      161      0      1.4   \n",
       "176   60    1   0       117   230    1        1      160      1      1.4   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "188   50    1   2       140   233    0        1      163      0      0.6   \n",
       "71    51    1   2        94   227    0        1      154      1      0.0   \n",
       "106   69    1   3       160   234    1        0      131      0      0.1   \n",
       "270   46    1   0       120   249    0        0      144      0      0.8   \n",
       "102   63    0   1       140   195    0        1      179      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "132      2   0     2  \n",
       "202      2   0     3  \n",
       "196      1   0     2  \n",
       "75       1   0     2  \n",
       "176      2   2     3  \n",
       "..     ...  ..   ...  \n",
       "188      1   1     3  \n",
       "71       2   1     3  \n",
       "106      1   1     2  \n",
       "270      2   0     3  \n",
       "102      2   2     2  \n",
       "\n",
       "[242 rows x 13 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "705e4f4f-fe91-49b0-8ee9-dd8a06cb72f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132    1\n",
       " 202    0\n",
       " 196    0\n",
       " 75     1\n",
       " 176    0\n",
       "       ..\n",
       " 188    0\n",
       " 71     1\n",
       " 106    1\n",
       " 270    0\n",
       " 102    1\n",
       " Name: target, Length: 242, dtype: int64,\n",
       " 242)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train, len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1da124b-82cf-457b-9d72-7bab68c9829f",
   "metadata": {},
   "source": [
    "### Model choices\n",
    "\n",
    "Now we've got our data prepared, we can start to fit models. We'll be using the following and comparing their results.\n",
    "\n",
    "1. Logistic Regression - [`LogisticRegression()`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)\n",
    "2. K-Nearest Neighbors - [`KNeighboursClassifier()`](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)\n",
    "3. RandomForest - [`RandomForestClassifier()`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f3b26d-e2c3-496d-89e6-5bd9d60c3a38",
   "metadata": {},
   "source": [
    "#### Why these?\n",
    "\n",
    "If we look at the [Scikit-Learn algorithm cheat sheet](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html), we can see we're working on a classification problem and these are the algorithms it suggests (plus a few more).\n",
    "\n",
    "| <img src=\"images/sklearn-ml-map-cheatsheet-heart-disease-ensemble.png\" alt=\"an example classification path using the Scikit-Learn machine learning model map\" width=500/> | \n",
    "|:--:| \n",
    "| An example path we can take using the Scikit-Learn Machine Learning Map |\n",
    "\n",
    "\"Wait, I don't see Logistic Regression and why not use LinearSVC?\"\n",
    "\n",
    "Good questions. \n",
    "\n",
    "I was confused too when I didn't see Logistic Regression listed as well because when you read the Scikit-Learn documentation on it, you can see it's [a model for classification](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression).\n",
    "\n",
    "And as for LinearSVC, let's pretend we've tried it, and it doesn't work, so we're following other options in the map.\n",
    "\n",
    "For now, knowing each of these algorithms inside and out is not essential.\n",
    "\n",
    "Machine learning and data science is an iterative practice. These algorithms are tools in your toolbox.\n",
    "\n",
    "In the beginning, on your way to becoming a practioner, it's more important to understand your problem (such as, classification versus regression) and then knowing what tools you can use to solve it.\n",
    "\n",
    "Since our dataset is relatively small, we can experiment to find algorithm performs best.\n",
    "\n",
    "All of the algorithms in the Scikit-Learn library use the same functions, for training a model, `model.fit(X_train, y_train)` and for scoring a model `model.score(X_test, y_test)`. `score()` returns the ratio of correct predictions (1.0 = 100% correct).\n",
    "\n",
    "Since the algorithms we've chosen implement the same methods for fitting them to the data as well as evaluating them, let's put them in a dictionary and create a which fits and scores them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aa32215b-a25d-4c6b-9e66-16820d749f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {\"Logistic Regression\": LogisticRegression(), \"KNN\": KNeighborsClassifier(), \"Random Forest\": RandomForestClassifier()}\n",
    "\n",
    "# Create function to fit and score models\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluates given machine learning models.\n",
    "    models : a dict of different Scikit-Learn machine learning models\n",
    "    X_train : training data\n",
    "    X_test : testing data\n",
    "    y_train : labels assosciated with training data\n",
    "    y_test : labels assosciated with test data\n",
    "    \"\"\"\n",
    "    # Set random seed\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": 28,
   "id": "4133eee2-3124-4b61-a2c0-1e381b72255b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jonatha/miniconda3/envs/MachineLearning/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 28,
     "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": "655094a4-964c-46a2-816d-788b012d2f6b",
   "metadata": {},
   "source": [
    "Beautiful! Since our models are fitting, let's compare them visually."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d22a93c0-26f6-4885-a9c7-80f0e97ed5e1",
   "metadata": {},
   "source": [
    "## Model Comparison\n",
    "\n",
    "Since we've saved our models scores to a dictionary, we can plot them by first converting them to a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "73ef64a7-85ed-4078-b73d-122c4ecc0ff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=[\"Accuracy\"])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b145f8c-1db7-4031-80d7-a0376e3aed8c",
   "metadata": {},
   "source": [
    "Beautiful! We can't really see it from the graph but looking at the dictionary, the [LogisticRegression()](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) model performs best.\n",
    "\n",
    "Since you've found the best model. Let's take it to the boss and show her what we've found.\n",
    "\n",
    "> **You:** I've found it!\n",
    "\n",
    "> **Her:** Nice one! What did you find?\n",
    "    \n",
    "> **You:** The best algorithm for prediting heart disease is a LogisticRegrssion!\n",
    "\n",
    "> **Her:** Excellent. I'm surprised the hyperparameter tuning is finished by now.\n",
    "\n",
    "> **You:** *wonders what **hyperparameter tuning** is*\n",
    "    \n",
    "> **You:** Ummm yeah, me too, it went pretty quick.\n",
    "    \n",
    "> **Her:** I'm very proud, how about you put together a **classification report** to show the team, and be sure to include a **confusion matrix**, and the **cross-validated precision**, **recall** and **F1 scores**. I'd also be curious to see what **features are most important**. Oh and don't forget to include a **ROC curve**.\n",
    "    \n",
    "> **You:** *asks self, \"what are those???\"*\n",
    "    \n",
    "> **You:** Of course! I'll have to you by tomorrow.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ebef69a-0d28-44f7-8901-5f13f0928e65",
   "metadata": {},
   "source": [
    "Alright, there were a few words in there which could sound made up to someone who's not a budding data scientist like yourself. But being the budding data scientist you are, you know data scientists make up words all the time.\n",
    "\n",
    "Let's briefly go through each before we see them in action.\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**](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) - 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**](https://scikit-learn.org/stable/modules/cross_validation.html) - Splits your dataset into multiple parts and train and tests your model on each part and evaluates performance as an average. \n",
    "* [**Precision**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html#sklearn.metrics.precision_score) - Proportion of true positives over total number of samples. Higher precision leads to less false positives.\n",
    "* [**Recall**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score) - Proportion of true positives over total number of true positives and false negatives. Higher recall leads to less false negatives.\n",
    "* [**F1 score**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score) - Combines precision and recall into one metric. 1 is best, 0 is worst.\n",
    "* [**Classification report**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html) - 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**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_score.html) - [Receiver Operating Characterisitc](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) is a plot of true positive rate versus false positive rate.\n",
    "* [**Area Under Curve (AUC)**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html) - The area underneath the ROC curve. A perfect model achieves a score of 1.0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a18a486-a17f-43cd-83dc-bca920fa44e3",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning and cross-validation\n",
    "\n",
    "To cook your favourite dish, you know to set the oven to 180 degrees and turn the grill on. But when your roommate cooks their favourite dish, they set use 200 degrees and the fan-forced mode. Same oven, different settings, different outcomes.\n",
    "\n",
    "The same can be done for machine learning algorithms. You can use the same algorithms but change the settings (hyperparameters) and get different results.\n",
    "\n",
    "But just like turning the oven up too high can burn your food, the same can happen for machine learning algorithms. You change the settings and it works so well, it **overfits** (does too well) the data.\n",
    "\n",
    "We're looking for the goldilocks model. One which does well on our dataset but also does well on unseen examples.\n",
    "\n",
    "To test different hyperparameters, you could use a **validation set** but since we don't have much data, we'll use **cross-validation**.\n",
    "\n",
    "The most common type of cross-validation is *k-fold*. It involves splitting your data into *k-fold's* and then testing a model on each. For example, let's say we had 5 folds (k = 5). This what it might look like.\n",
    "\n",
    "| <img src=\"images/sklearn-cross-validation.png\" width=500/> | \n",
    "|:--:| \n",
    "| Normal train and test split versus 5-fold cross-validation |\n",
    "\n",
    "We'll be using this setup to tune the hyperparameters of some of our models and then evaluate them. We'll also get a few more metrics like **precision**, **recall**, **F1-score** and **ROC** at the same time.\n",
    "\n",
    "Here's the game plan:\n",
    "1. Tune model hyperparameters, see which performs best\n",
    "2. Perform cross-validation\n",
    "3. Plot ROC curves\n",
    "4. Make a confusion matrix\n",
    "5. Get precision, recall and F1-score metrics\n",
    "6. Find the most important model features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a83ce5a9-f168-452e-98d6-7a309e1a0af7",
   "metadata": {},
   "source": [
    "### Tune KNeighborsClassifier (K-Nearest Neighbors or KNN) by hand\n",
    "\n",
    "There's one main hyperparameter we can tune for the K-Nearest Neighbors (KNN) algorithm, and that is number of neighbours. The default is 5 (`n_neigbors=5`).\n",
    "\n",
    "What are neighbours?\n",
    "\n",
    "Imagine all our different samples on one graph like the scatter graph we have above. KNN works by assuming dots which are closer together belong to the same class. If `n_neighbors=5` then it assume a dot with the 5 closest dots around it are in the same class.\n",
    "\n",
    "We've left out some details here like what defines close or how distance is calculated but I encourage you to research them.\n",
    "\n",
    "For now, let's try a few different values of `n_neighbors`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8ae86089-52ce-4f8e-b1c2-65113a29ca3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of train scores\n",
    "train_scores = []\n",
    "# Create a list of test scores\n",
    "test_scores = []\n",
    "# Create a list of different values for n_neighbors\n",
    "neighbors = range(1, 21)# 1 to 20\n",
    "\n",
    "# Setup algorithm\n",
    "knn = KNeighborsClassifier()\n",
    "# Loop through different neighbors values\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i) # set neighbors value\n",
    "\n",
    "    # Fit the algorithm\n",
    "    knn.fit(X_train, y_train)\n",
    "\n",
    "    # Update the training scores\n",
    "    train_scores.append(knn.score(X_train, y_train))\n",
    "\n",
    "    # Update the test scores\n",
    "    test_scores.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "60af0333-195e-4907-903b-d59f38aa963f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.8099173553719008,\n",
       " 0.7727272727272727,\n",
       " 0.743801652892562,\n",
       " 0.7603305785123967,\n",
       " 0.7520661157024794,\n",
       " 0.743801652892562,\n",
       " 0.7231404958677686,\n",
       " 0.71900826446281,\n",
       " 0.6942148760330579,\n",
       " 0.7272727272727273,\n",
       " 0.6983471074380165,\n",
       " 0.6900826446280992,\n",
       " 0.6942148760330579,\n",
       " 0.6859504132231405,\n",
       " 0.6735537190082644,\n",
       " 0.6859504132231405,\n",
       " 0.6652892561983471,\n",
       " 0.6818181818181818,\n",
       " 0.6694214876033058]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9db8e0ef-f106-4ead-9b07-83d7a963b348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6229508196721312,\n",
       " 0.639344262295082,\n",
       " 0.6557377049180327,\n",
       " 0.6721311475409836,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.7049180327868853,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.7540983606557377,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.6557377049180327]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e2952ba0-0ac1-4445-874d-bf0b9aff741a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 75.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors, train_scores, label=\"Train score\")\n",
    "plt.plot(neighbors, test_scores, label=\"Test score\")\n",
    "plt.xticks(np.arange(1, 21, 1))\n",
    "plt.xlabel(\"Number if neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3319bb3-e60f-4397-9a5f-a4ee1bcc6dc5",
   "metadata": {},
   "source": [
    "Looking at the graph, `n_neighbors = 11` seems best.\n",
    "\n",
    "Even knowing this, the `KNN`'s model performance didn't get near what `LogisticRegression` or the `RandomForestClassifier` did.\n",
    "\n",
    "Because of this, we'll discard `KNN` and focus on the other two.\n",
    "\n",
    "We've tuned `KNN` by hand but let's see how we can `LogisticsRegression` and `RandomForestClassifier` using [`RandomizedSearchCV`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html).\n",
    "\n",
    "Instead of us having to manually try different hyperparameters by hand, `RandomizedSearchCV` tries a number of different combinations, evaluates them and saves the best.\n",
    "\n",
    "### Tuning models with with [`RandomizedSearchCV`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html)\n",
    "\n",
    "Reading the Scikit-Learn documentation for [`LogisticRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV), we find there's a number of different hyperparameters we can tune.\n",
    "\n",
    "The same for [`RandomForestClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html).\n",
    "\n",
    "Let's create a hyperparameter grid (a dictionary of different hyperparameters) for each and then test them out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4108b098-aee8-4c02-ada6-d68b8072a4d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Different LogisticRegression hyperparameters\n",
    "log_red_grid = {\"C\": np.logspace(-4, 4, 20), \"solver\": [\"liblinear\"]}\n",
    "# Different RandomForestClassifier hyperparameters\n",
    "rf_grid = {\"n_estimators\": np.arange(10, 1000, 50), \"max_depth\": [None, 3, 5, 10], \"min_samples_split\": np.arange(2, 20, 2), \"min_samples_leaf\": np.arange(1, 20, 2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc777855-3155-4d0f-b817-b6132f0fd057",
   "metadata": {},
   "source": [
    "Now let's use `RandomizedSearchCV` to try and tune our `LogisticRegression` model.\n",
    "\n",
    "We'll pass it the different hyperparameters from `log_reg_grid` as well as set `n_iter = 20`. This means, `RandomizedSearchCV` will try 20 different combinations of hyperparameters from `log_reg_grid` and save the best ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "df94dc59-48ee-4da1-9ada-0a0aed839a9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 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(), param_distributions=log_red_grid, cv=5, n_iter=20, verbose=True)\n",
    "# Fit random hyperparameter search model\n",
    "rs_log_reg.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e4402f2f-304c-4780-b97f-aa32a03e5d8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8e89a3fd-61cb-4169-aca3-a0e45ca2c013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6677beaf-ed7c-4b91-8180-35023a84fed0",
   "metadata": {},
   "source": [
    "Now we've tuned `LogisticRegression` using `RandomizedSearchCV`, we'll do the same for `RandomForestClassifier`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2d0a21c0-319a-4ac2-8bba-9aa39e05f98c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-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=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-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=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-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=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 38,
     "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(), param_distributions=rf_grid, cv=5, n_iter=20, verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d1a88d0c-af8a-4fb2-b63b-ee2d8d09f884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best parameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9a28cf15-a475-442b-8e73-49d4c2ee71ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the randomized search random forest model\n",
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "425979e3-fccc-4d04-b2bb-a172dbc068d5",
   "metadata": {},
   "source": [
    "Excellent! Tuning the hyperparameters for each model saw a slight performance boost in both the `RandomForestClassifier` and `LogisticRegression`.\n",
    "\n",
    "This is akin to tuning the settings on your oven and getting it to cook your favourite dish just right.\n",
    "\n",
    "But since `LogisticRegression` is pulling out in front, we'll try tuning it further with [`GridSearchCV`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html).\n",
    "\n",
    "### Tuning a model with [`GridSearchCV`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)\n",
    "\n",
    "The difference between `RandomizedSearchCV` and `GridSearchCV` is where `RandomizedSearchCV` searches over a grid of hyperparameters performing `n_iter` combinations, `GridSearchCV` will test every single possible combination.\n",
    "\n",
    "In short:\n",
    "* `RandomizedSearchCV` - tries `n_iter` combinations of hyperparameters and saves the best.\n",
    "* `GridSearchCV` - tries every single combination of hyperparameters and saves the best.\n",
    "\n",
    "Let's see it in action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d7a0d33f-f911-44ad-9896-1413cda80252",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 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=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "             verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "             verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         'solver': ['liblinear']},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different LogisticRegression hyperparameters\n",
    "log_red_grid = {\"C\": np.logspace(-4, 4, 30), \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(), param_grid=log_red_grid, cv=5, verbose=True)\n",
    "\n",
    "# Fit grid hyperparameter search model\n",
    "gs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9cc81dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, '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": "e4af0342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ffa21f",
   "metadata": {},
   "source": [
    "In this case, we get the same results as before since our grid only has a maximum of 20 different hyperparameter combinations.\n",
    "\n",
    "**Note:** If there are a large amount of hyperparameters combinations in your grid, `GridSearchCV` may take a long time to try them all out. This is why it's a good idea to start with `RandomizedSearchCV`, try a certain amount of combinations and then use `GridSearchCV` to refine them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da53be2c",
   "metadata": {},
   "source": [
    "## Evaluating a classification model, beyond accuracy\n",
    "\n",
    "Now we've got a tuned model, let's get some of the metrics we discussed before.\n",
    "\n",
    "We want:\n",
    "* ROC curve and AUC score - [`RocCurveDisplay()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.RocCurveDisplay.html) \n",
    "    * **Note:** This was previously `sklearn.metrics.plot_roc_curve()`, as of Scikit-Learn version 1.2+, it is `sklearn.metrics.RocCurveDisplay()`.\n",
    "* Confusion matrix - [`confusion_matrix()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html)\n",
    "* Classification report - [`classification_report()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html)\n",
    "* Precision - [`precision_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html)\n",
    "* Recall - [`recall_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html)\n",
    "* F1-score - [`f1_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html)\n",
    "\n",
    "Luckily, Scikit-Learn has these all built-in.\n",
    "\n",
    "To access them, we'll have to use our model to make predictions on the test set. You can make predictions by calling `predict()` on a trained model and passing it the data you'd like to predict on.\n",
    "\n",
    "We'll make predictions on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9761167d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make preidctions on test data\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a48534eb",
   "metadata": {},
   "source": [
    "Let's see them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d98bfb0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2391c89c",
   "metadata": {},
   "source": [
    "They look like our original test data labels, except different where the model has predicred wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "90365fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179    0\n",
       "228    0\n",
       "111    1\n",
       "246    0\n",
       "60     1\n",
       "      ..\n",
       "249    0\n",
       "104    1\n",
       "300    0\n",
       "193    0\n",
       "184    0\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b051f6a",
   "metadata": {},
   "source": [
    "Since we've got our prediction values we can find the metrics we want.\n",
    "\n",
    "Let's start with the ROC curve and AUC scores.\n",
    "\n",
    "### ROC Curve and AUC Scores\n",
    "\n",
    "What's a ROC curve?\n",
    "\n",
    "It's a way of understanding how your model is performing by comparing the true positive rate to the false positive rate.\n",
    "\n",
    "In our case...\n",
    "\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",
    "Scikit-Learn implements a function `RocCurveDisplay` (previously called `plot_roc_curve` in Scikit-Learn versions > 1.2) which can help us create a ROC curve as well as calculate the area under the curve (AUC) metric.\n",
    "\n",
    "Reading the documentation on the [`RocCurveDisplay`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.RocCurveDisplay.html) function we can see it has a class method called [`from_estimator(estimator, X, y)`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay.from_estimator) as inputs. \n",
    "\n",
    "Where `estiamator` is a fitted machine learning model and `X` and `y` are the data you'd like to test it on.\n",
    "\n",
    "In our case, we'll use the GridSearchCV version of our `LogisticRegression` estimator, `gs_log_reg` as well as the test data, `X_test` and `y_test`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0c6c0395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Before Scikit-Learn 1.2.0 (will error with versions 1.2+)\n",
    "# from sklearn.metrics import plot_roc_curve \n",
    "# plot_roc_curve(gs_log_reg, X_test, y_test);\n",
    "\n",
    "# Scikit-Learn 1.2.0 or later\n",
    "# from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "RocCurveDisplay.from_estimator(estimator=gs_log_reg, X=X_test, y=y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c5513c",
   "metadata": {},
   "source": [
    "This is great, our model does far better than guessing which would be a line going from the bottom left corner to the top right corner, AUC = 0.5. But a perfect model would achieve an AUC score of 1.0, so there's still room for improvement.\n",
    "\n",
    "Let's move onto the next evaluation request, a confusion matrix.\n",
    "\n",
    "### Confusion matrix \n",
    "\n",
    "A confusion matrix is a visual way to show where your model made the right predictions and where it made the wrong predictions (or in other words, got confused).\n",
    "\n",
    "Scikit-Learn allows us to create a confusion matrix using [`confusion_matrix()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html) and passing it the true labels and predicted labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8b72f759",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0078757b",
   "metadata": {},
   "source": [
    "As you can see, Scikit-Learn's built-in confusion matrix is a bit bland. For a presentation you'd probably want to make it visual.\n",
    "\n",
    "Let's create a function which uses Seaborn's [`heatmap()`](https://seaborn.pydata.org/generated/seaborn.heatmap.html) for doing so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "608c85be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import Seaborn\n",
    "import seaborn as sns\n",
    "sns.set_theme(font_scale=1.5) # Increase font size\n",
    "\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a confusion matrix using Seaborn's heatmap().\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds), annot=True, cbar=False)\n",
    "    plt.xlabel(\"Predicted label\") # predictions go on the x-axis\n",
    "    plt.ylabel(\"True label\") # true labels go on the y-axis \n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26043b8f",
   "metadata": {},
   "source": [
    "Beautiful! That looks much better. \n",
    "\n",
    "You can see the model gets confused (predicts the wrong label) relatively the same across both classes. In essence, there are 4 occasaions where the model predicted 0 when it should've been 1 (false negative) and 3 occasions where the model predicted 1 instead of 0 (false positive)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aa25f8f",
   "metadata": {},
   "source": [
    "### Classification report\n",
    "\n",
    "We can make a classification report using [`classification_report()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html) and passing it the true labels as well as our models predicted labels. \n",
    "\n",
    "A classification report will also give us information of the precision and recall of our model for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "947ab1db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Show classification report\n",
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f268f3df",
   "metadata": {},
   "source": [
    "What's going on here?\n",
    "\n",
    "Let's get a refresh.\n",
    "\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).\n",
    "\n",
    "Ok, now we've got a few deeper insights on our model. But these were all calculated using a single training and test set.\n",
    "\n",
    "What we'll do to make them more solid is calculate them using cross-validation.\n",
    "\n",
    "How?\n",
    "\n",
    "We'll take the best model along with the best hyperparameters and use [`cross_val_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html) along with various `scoring` parameter values.\n",
    "\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",
    "Let's remind ourselves of the best hyperparameters and then see them in action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3e2a4ad6",
   "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>LogisticRegression(C=0.20433597178569418, 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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.20433597178569418, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.20433597178569418, solver='liblinear')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hyperparameters\n",
    "gs_log_reg.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f0b2ef1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(C=0.20433597178569418, solver='liblinear')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a205795",
   "metadata": {},
   "source": [
    "Now we've got an instantiated classifier, let's find some cross-validated metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "19d37942",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.8852459 , 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated accuracy score\n",
    "ccv_acc = cross_val_score(clf, X, y, cv=5, scoring=\"accuracy\")\n",
    "ccv_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c10435d",
   "metadata": {},
   "source": [
    "Since there are 5 metrics here, we'll take the average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3d83c34b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8479781420765027"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccv_acc = np.mean(ccv_acc)\n",
    "ccv_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e6259e6",
   "metadata": {},
   "source": [
    "Now we'll do the same for other classification metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bd92ff1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8215873015873015"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_precision = cross_val_score(clf, X, y, cv=5, scoring=\"precision\")\n",
    "cv_precision = np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b209afec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9272727272727274"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated recall score\n",
    "cv_recall = np.mean(cross_val_score(clf, X, y, cv=5, scoring=\"recall\"))\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "72127195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8705403543192143"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated F1 score\n",
    "cv_f1 = np.mean(cross_val_score(clf, X, y, cv=5, scoring=\"f1\"))\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "014ae193",
   "metadata": {},
   "source": [
    "Okay, we've got cross validated metrics, now what?\n",
    "\n",
    "Let's visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "459dd9ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x8ZJkver3b+XKlcvzehbnvQTHQ4iBodSvX1+SdObMGV28eLFI33bPnz+vtLQ0+fj4FNheUKfIS5cuSdI192/55m+5neTi4qJFixYpKChI33//vXXW1WPHjmnp0qUqV66cRo0apXHjxslkMknK/aBasWKFPvvsM23evFmpqanasmWLtmzZopkzZ6pt27YKDAxUo0aNJEkTJ04scCr4qlWrat68eXmWtWrVSo0aNVJkZKRCQ0OtV10sc8P07NnTegwW+/fv1//+9z/r1SULLy8v+fv769ChQ4qNjb3q63I1KSkpknJDYGH+edvjn44dO6Z33nlHW7ZsyRNUypUrp3bt2ikhIaFIE/xZXLx4UVLurarExMQi1V6rVq0iHUNBV2hs5ebmpq5duyokJETr16/X2LFjlZmZqZ9//llVqlS5aud2y9+wVLRbf5bXoqhsCRD//NJxtdfsn4rzXoLjIcTAUO6//37NnDlT2dnZ2rZtm7p3737NbYKDgzV37lzVq1dPISEhV738blG2bFlJ1/6H3fJhZllfyv3gefLJJ/Xkk0/qzJkz2rZtm7Zv365ff/1V586d0/vvvy93d3fr0FUp98rD3LlzlZmZqf3792v79u36448/tGfPHu3evVsjRozQhg0b5OnpqfDwcOu3538qrN9M//79NXv2bIWEhGjAgAHKysrSjz/+KEn5ZuiNiorSsGHDlJGRoUaNGql///5q2rSpGjZsaL3dMmjQoOsKMZUqVZKU90P23wq6HZSYmKjHH39ciYmJqlWrlgYOHKjmzZurQYMG8vHxkclk0vPPP29TiLHcnhw5cmSeYfU36hiKo1evXnlCzB9//KGkpCQNHTr0qqPDLMcm5d5OvNqVrxvtn7VYAn9RFOe9BMdCnxgYSp06ddSyZUtJuR0FzWbzVde/cuWKli9fLklq0KBBkQKMZV1JOnz4cKG3Ji5duqTjx49L+r9+LcnJydq3b5/1/n2NGjXUt29fzZw5U5s3b7Z21LT0R8nOztaJEye0c+dOSbm3Idq1a6cJEyYoKChIQUFBMplMOnv2rHUIcFhYmCIiIvL99895T/6pb9++cnV11c6dO3X27Fn9+eefSkxMVP369dWuXbs86y5ZskQZGRlq0KCBVqxYoZEjR6pjx455+otYbgsUl+Vq2pEjRwo9f5GRkfmWrVy5UomJiapUqZJWrlypp59+Wv7+/qpTp471m7ittVlqudrzgS5cuKDdu3fr9OnT1not2x0+fLjAbcxms3U+o+t17733qnz58jp06JBOnjypn376SdK1J7irUKGCvL29JRX8elpERETo8OHDN7SDbIUKFaydnwt7rRMSEjRw4EBNmjRJFy9etPm9BMdEiIHhvPLKKzKZTNq7d+81H+747rvvKjY2Vk5OTtZHEBTFfffdJxcXF509e7bQB+wtXbpUWVlZ8vDwsHZQfeWVV/TYY4/ps88+y7e+q6urdT1LB9G///5bPXr00PDhwwu8RdS6dWvrVZ7i9kGpXLmyOnfurJycHG3cuNHaSbSg5yRZrvA0bNgwz7dni99//12nT5/Ocwy26tq1q1xdXRUfH69Nmzblaz979myBo6ksV39q1apV4EiYyMhI7du3r8DaLCHn36HJ8kH4559/Fho63n33XQ0ZMkRPPPGE9RxY5owJCwsrMDj98ssvNj/9uTCWW0pS7jOgNm3apJo1axbYIf3fOnfuLCn3b7Wgv5+LFy9q+PDh6tu3r5YsWZKnrbDXrLjuu+8+Sf83+ujf1q9fr/3792v//v0qX768ze8lOCZCDAynVatW1tE6H3zwgZ5//vl83+5iY2M1ZcoULV68WJI0YcIEtWjRosi/o2bNmho4cKAkafr06XlGh+Tk5Oibb77Rhx9+KCn3+U2WvjOWkSzfffedVq9enecD4O+//7aOvLA8abpp06by9fVVdna2Jk+erDNnzljXv3Llit577z1dunRJnp6e+a6a2MISWNavX6+NGzfK1dVVjz76aL71LFcYfv/9d+3atcu63HILatKkSdZlxb1dUqlSJY0cOVKSNG3aNP3555/WttOnT2v8+PHWDrr/ZLk6duTIEesEaFLuh+yvv/6qUaNGWUd2/XvklCUIJiQkKCsry7q8Xbt2uvfee5WVlaXRo0dbJ3iTcl//+fPnKzg4WJI0evRo6+2bzp07q02bNkpLS9O4ceMUExNj3W7Xrl2aNm1aMV6ZwvXq1UuS9PnnnyslJUW9evUqUj+QMWPGyNPTU7t379YLL7yg8+fPW9tOnTqlMWPG6MKFCypfvryGDh2aZ1tL3xVLaL1eo0aNUpkyZbRr1y4FBgbmOUe//vqr3nvvPUnSU089Jcn29xIcE31iYEiTJk1SpUqVNGfOHP3444/68ccfVbVqVdWoUUMpKSk6ceKEpNxvbM8++6xGjx5t8++YOnWq9WrBs88+q2rVqqlGjRqKiYnRhQsXJOUOC/3nvnv06KGBAwdq+fLleumllzR79mzVrFlTly5d0smTJ2U2m3XnnXdq3Lhx1m3ee+89DRo0SDt27FC3bt3k4+MjDw8PxcbGKiUlRc7OzgoMDLyuuXHuvfdeVa9eXdu2bbPWWdCjDEaOHKkff/xRFy5c0NChQ1WvXj2VLVtWsbGxSk5Olqenp1q3bq29e/fmCVy2euaZZxQdHa0NGzZoxIgRqlevnjw9PXX06FE5OTnJ398/3+ikgIAAffPNNzpx4oQmTpyo2rVry8vLS3FxcUpMTLR+O9+xY0e+qyOWmWRPnTqlHj16qFq1avr2229lMpk0Z84cjR07Vvv379fgwYPl4+OjihUrKiYmxtrnafjw4Ro0aJB1f05OTnr33Xc1atQoHTp0SA888IB8fX2Vnp6u48ePy8fHR9WrVy/0dpOtOnXqpAoVKljrKeqzkm677Ta9//77mjRpkn788UeFhoaqUaNGyszM1PHjx5WVlSVPT099+umn+f4emjdvrp07dyowMFDffvuthgwZcl1POW/UqJFmz56tF198UUFBQVq1apUaNGigxMRE6y2jfv36aciQIZKK916C4+FKDAzrySef1Lp16/TUU0+pRYsWunz5sg4dOqRz586pWbNmGjlypNatW1esACPlXsb/+OOP9d577+mee+7RlStXdPjwYXl4eOjBBx/UV199penTp+f7Rvzf//5XM2fOlJ+fn3JychQREaGkpCS1bdtWr732mr755ps8I4IaNWqkVatWafDgwapdu7ZOnz6tyMhIVahQQf3799cPP/yghx566LpeK2dn5zxXXgr7MKpVq5bWrFmjwYMHq169eoqLi1N0dLS8vb31xBNPaM2aNXruueck5c5VUtAVk6Jwc3PTBx98oJkzZ6p169Y6d+6cYmJi1KFDB3377bd5nnNlUa5cOa1YsUJjxoxR48aNdf78ef39998qV66c+vfvr5UrV1qfk3XkyJE8VxDuvvtuvfjii6pdu7YSEhIUGxurc+fOScoddRUUFKTAwEC1b99eFy9eVEREhFxcXOTv76/58+fnebTEP1+r7777ThMnTlT9+vUVHR2tS5cuqX///vruu++snX9Lgpubm7p16yYpN5jYclXR399fa9eu1YgRI1S3bl1FR0frxIkTql27toYMGaI1a9aoTZs2+babMWOGOnXqJBcXF0VHR1v7f12PXr166YcfftCAAQPk5eWliIgIXbx4UX5+fnr//fc1c+bMPO8nW99LcDwmc0nd8AQAAChFXIkBAACGRIgBAACGRIgBAACGRIgBAACGRIgBAACGRIgBAACGRIgBAACGdEvP2Gs2m5WT43jT4Dg5mRzyuB0V59uxcL4diyOebycnU5EeqyHd4iEmJ8es8+eL/tj3W4GLi5O8vMoqJSVNWVnFe2AgjIPz7Vg4347FUc935cpl5exctBDD7SQAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBILvYuAACMxsnJJCcnU6n/Xmdnpzz/W9pycszKyTHb5XcDBSHEAIANnJxMqlTJ025BQpIqVPCwy+/Nzs5RUlIaQQY3DUIMANjAyckkZ2cn/S9ot2LjL9q7nFLjU728pgxtKycnEyEGNw1CDAAUQ2z8RUWdSrZ3GYBDo2MvAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJEIMAAAwJBd7FwAAwM3MyckkJydTqf9eZ2enPP9b2nJyzMrJMdvldxcVIQYAgEI4OZlUqZKn3YKEJFWo4GGX35udnaOkpLSbOsgQYgAAKISTk0nOzk76X9BuxcZftHc5pcanenlNGdpWTk4mQgwAAEYWG39RUaeS7V0G/oWOvQAAwJAIMQAAwJAIMQAAwJAIMQAAwJDo2HuDMK8AAAA3FiHmBmBegZt7XgEAwK2BEHMDMK/AzT2vAADg1kCIuYGYVwAAgBuHjr0AAMCQCDEAAMCQCDEAAMCQCDEAAMCQCDEAAMCQCDEAAMCQGGINlABmaAaA0keIAa4TMzQzQzMA+yhWiImOjtbHH3+s3bt3KzExUTVq1FCvXr00duxYeXp62rSvffv2aeHChdqzZ49SU1NVpUoVdezYUU8//bTq1q1bnPKAUsUMzczQDMA+bA4xBw4c0PDhw5WWlqY777xTLVq00J49e7RgwQL98ssv+uabb1SuXLki7eunn37S888/r+zsbN1+++2qVauWIiIi9P333ys0NFSLFy/WnXfeafNBAfbADM0AULpsuv6dlZWlyZMnKy0tTW+//baCg4M1b948bdy4UV27dlVERITmzp1bpH2lp6frtddeU05Ojt599119//33+uijj7R+/XqNGDFCqampmjZtWrEOCgAA3PpsCjFr165VTEyMOnTooICAAOtyd3d3zZgxQ56enlq+fLmSk6/9bXTfvn1KSUlR48aN1adPH+tyZ2dnTZo0Sc7Ozjp69KjOnz9vS4kAAMBB2BRiwsLCJEndu3fP1+bl5SU/Pz9lZmbqt99+u+a+nJ2dJUnnz5/XlStX8rRduHBB2dnZcnV1LfKtKQAA4FhsCjFHjx6VJDVp0qTA9kaNGkmSjhw5cs19tWjRQpUrV9a5c+f0/PPP69ixY8rIyND+/fs1YcIESdKIESPk5uZmS4kAAMBB2NSxNyEhQZJUvXr1AturVauWZ72r8fDw0EcffaRJkyZpw4YN2rBhg7XN3d1dgYGBeuyxx2wpr0AuLqU/7NWeQ21vBo52/I52vP/maMfvaMf7b452/I52vP92sx+/TSEmLS1NUm7IKIhluWW9a6lfv74efvhhffHFF2ratKlq1Kiho0ePKiYmRosXL1bz5s3VokULW0rMw8nJJC+vssXeHsVjrzlLYB+cb8fC+XYsN/v5tinEODs7KycnRybT1WcmNZuvPWdEXFycHn/8cV24cEGLFi1Shw4drNsuWbJEM2fO1JNPPqmQkBDVrFnTljKtcnLMSkkpWqAqSc7OTjf9ib+RUlLSlZ2dY+8ySg3nm/PtSDjfjsUe57tCBY8iXwGyKcSULVtWSUlJSk9PL7A9IyNDUu6tomt57733FBsbq6lTp1oDjCSZTCaNGDFC4eHhCgkJ0eLFizV16lRbyswjK8tx3mw3i+zsHF53B8L5diycb8dys59vm252Wfq8nD17tsB2S18Yy3pX88cff0iS7r333gLbO3fuLEkKDw+3pUQAAOAgbAoxllFJkZGRBbZblhc2eumfLHPJuLgUfDHIMgQ7KyvLlhIBAICDsCnE+Pv7S5JCQ0PztV24cEHbt2+Xq6urOnXqdM19NWzYUNL/zT3zb1u3bpUkNWvWzJYSAQCAg7ApxHTv3l21atXS1q1bFRQUZF2ekZGhadOmKS0tTQEBAfL29ra2ZWZmKioqSlFRUcrMzLQuHzp0qCTpo48+0s6dO/P8nhUrVmjlypVydXXV448/XqwDAwAAtzabOva6u7tr1qxZGjNmjAIDA7Vy5Ur5+Pho7969SkhIUPPmzTVlypQ828THx6t3796SpE2bNsnHx0eSNGDAAP3111/67rvv9Pjjj6tFixaqUaOGIiMjFR0dLVdXV7399tvWCfQAAAD+yeanWPv5+Sk4OFgfffSRduzYocjISPn4+CggIEBPPfWUTY8JCAwM1H333advv/1W4eHhOnz4sLy8vNSnTx+NGjWKW0kAAKBQNocYSfL19dW8efOKtK6Pj48iIiIKbe/WrZu6detWnDIAAIADu7nnEwYAACgEIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABgSIQYAABiSS3E2io6O1scff6zdu3crMTFRNWrUUK9evTR27Fh5enratK+0tDR98cUXCg0N1cmTJ+Xk5KRmzZpp2LBh6tmzZ3HKAwAADsDmKzEHDhxQv379FBISIm9vb3Xu3FlpaWlasGCBBg0apEuXLhV5X+fOndOAAQP04Ycf6vz587rnnnvUpEkT7dmzR88++6wWL15sa3kAAMBB2BRisrKyNHnyZKWlpentt99WcHCw5s2bp40bN6pr166KiIjQ3Llzi7y/adOmKTIyUg888IDCwsL08ccfa9myZfriiy/k6uqqd955R6dPn7b5oAAAwK3PphCzdu1axcTEqEOHDgoICLAud3d314wZM+Tp6anly5crOTn5mvs6cOCANm/erLp162rOnDkqU6aMta1jx47q16+fatSoofDwcFtKBAAADsKmEBMWFiZJ6t69e742Ly8v+fn5KTMzU7/99ts19/XTTz9Jkp544ok8AcYiMDBQYWFh6tGjhy0lAgAAB2FTx96jR49Kkpo0aVJge6NGjfTLL7/oyJEj6tOnz1X3ZbnC0qpVK6WlpSk0NFR//fWXsrOz1aJFC/Xp00fu7u62lAcAAByITSEmISFBklS9evUC26tVq5Znvas5fvy4JOnChQvq06ePTp06ZW1btmyZPvnkEy1YsECNGze2pcR8XFxKfxS5s7Njj1x3tON3tOP9N0c7fkc73n9ztON3tOP9t5v9+G0KMWlpaZJU6BUSy3LLeldjGcU0ZcoU1apVS0uXLlWzZs0UGxur//3vf/rtt980evRo/fjjjypXrpwtZVo5OZnk5VW2WNui+CpU8LB3CShFnG/Hwvl2LDf7+bYpxDg7OysnJ0cmk+mq65nN5mvu6/Lly5IkNzc3ffXVV6pYsaIkqWnTplqwYIEeffRRHT16VMuWLdOoUaNsKdMqJ8eslJRrB6qS5uzsdNOf+BspJSVd2dk59i6j1HC+Od+OhPPtWOxxvitU8CjyFSCbQkzZsmWVlJSk9PT0AtszMjIkSR4e1z7h7u7uSk1NVd++fa0BxlqUi4sGDRqkwMBA/fnnn8UOMZKUleU4b7abRXZ2Dq+7A+F8OxbOt2O52c+3TTe7LH1ezp49W2C7pS+MZb2r8fb2liT5+PgU2G5ZfuHCBVtKBAAADsKmEGMZlRQZGVlgu2V5YaOXCtpXfHx8ge2WoFS5cmVbSgQAAA7CphDj7+8vSQoNDc3XduHCBW3fvl2urq7q1KnTNffVuXNnSbnzxWRmZuZr//XXXyVJ7du3t6VEAADgIGwKMd27d1etWrW0detWBQUFWZdnZGRo2rRpSktLU0BAgPVWkSRlZmYqKipKUVFRecJK79695ePjo+PHjyswMDBPW3BwsEJDQ1WxYkX179//eo4PAADcomzq2Ovu7q5Zs2ZpzJgxCgwM1MqVK+Xj46O9e/cqISFBzZs315QpU/JsEx8fr969e0uSNm3aZO3r4uHhoQ8++ECjRo3S8uXLtXnzZrVs2VInTpzQ0aNHVaZMGc2aNUtVqlQpoUMFAAC3EptnsfHz81NwcLAeeOABnT59Wps3b1b58uU1fvx4ff311zbN6XLHHXcoJCRETzzxhNzc3LR582ZduHBBDz74oJYvX66uXbvaWh4AAHAQNl2JsfD19dW8efOKtK6Pj48iIiIKba9atapeffVVvfrqq8UpBQAAOKibez5hAACAQhBiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRFiAACAIRUrxERHR2vKlCnq0qWL7rzzTvXo0UPvvfee0tLSrrug2bNnq0mTJvrwww+ve18AAODWZXOIOXDggPr166eQkBB5e3urc+fOSktL04IFCzRo0CBdunSp2MX8/vvv+vLLL4u9PQAAcBw2hZisrCxNnjxZaWlpevvttxUcHKx58+Zp48aN6tq1qyIiIjR37txiFXL+/Hm99NJLMpvNxdoeAAA4FptCzNq1axUTE6MOHTooICDAutzd3V0zZsyQp6enli9fruTkZJsLeeWVV3ThwgW1adPG5m0BAIDjsSnEhIWFSZK6d++er83Ly0t+fn7KzMzUb7/9ZlMRQUFB+uWXXzRhwgTdcccdNm0LAAAck00h5ujRo5KkJk2aFNjeqFEjSdKRI0eKvM+///5bs2fPVps2bTR27FhbygEAAA7MxZaVExISJEnVq1cvsL1atWp51ruWy5cva/LkyXJ1ddWcOXPk7OxsSzlF4uJS+qPInZ0de+S6ox2/ox3vvzna8Tva8f6box2/ox3vv93sx29TiLEMoXZ3dy+w3bK8qEOt33nnHR09elSzZ8+Wj4+PLaUUiZOTSV5eZUt8v7i6ChU87F0CShHn27Fwvh3LzX6+bQoxzs7OysnJkclkuup6RRlhtHnzZi1dulS9e/dW3759bSmjyHJyzEpJuf65a2zl7Ox005/4GyklJV3Z2Tn2LqPUcL45346E8+1Y7HG+K1TwKPIVIJtCTNmyZZWUlKT09PQC2zMyMiRJHh5XP+Hnzp3T1KlTVbNmTf33v/+1pQSbZWU5zpvtZpGdncPr7kA4346F8+1YbvbzbVOIqVatmpKSknT27FnVqVMnX7ulL4ylb0xh5s+fr/Pnz6tZs2YKDAzM03bw4EFJ0oYNG3TixAk1bNhQTz/9tC1lAgAAB2BTiGnSpImOHj2qyMjIAudziYyMtK53NZY+M4cPH9bhw4cLXOfo0aM6evSo2rdvT4gBAAD52NTt2N/fX5IUGhqar+3ChQvavn27XF1d1alTp6vuZ9asWYqIiCjwv2HDhkmSnnnmGUVEROjrr7+2pUQAAOAgbAox3bt3V61atbR161YFBQVZl2dkZGjatGlKS0tTQECAvL29rW2ZmZmKiopSVFSUMjMzS65yAADg0Gy6neTu7q5Zs2ZpzJgxCgwM1MqVK+Xj46O9e/cqISFBzZs315QpU/JsEx8fr969e0uSNm3adEOGUgMAAMdj8yw2fn5+Cg4O1gMPPKDTp09r8+bNKl++vMaPH6+vv/5a5cqVuxF1AgAA5GHTlRgLX19fzZs3r0jr+vj4KCIiosj7njZtmqZNm1acsgAAgAO5uecTBgAAKAQhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGBIhBgAAGJJLcTaKjo7Wxx9/rN27dysxMVE1atRQr169NHbsWHl6etq0r82bN2vp0qUKDw/XxYsXValSJbVp00ajRo1Sy5Yti1MeAABwADZfiTlw4ID69eunkJAQeXt7q3PnzkpLS9OCBQs0aNAgXbp0qcj7mjt3rsaOHautW7eqdu3a6ty5sypUqKANGzZo8ODBWrVqla3lAQAAB2HTlZisrCxNnjxZaWlpevvttxUQECBJysjI0KRJkxQWFqa5c+fqtddeu+a+du3apYULF8rDw0MLFy6Un5+ftW3ZsmV6/fXX9dprr8nPz0+1atWy8bAAAMCtzqYrMWvXrlVMTIw6dOhgDTCS5O7urhkzZsjT01PLly9XcnLyNfe1YsUKSdKoUaPyBBhJGjRokPz9/XXlyhWFhobaUiIAAHAQNoWYsLAwSVL37t3ztXl5ecnPz0+ZmZn67bffrrkvd3d3+fr66u677y6wvUGDBpKk+Ph4W0oEAAAOwqYQc/ToUUlSkyZNCmxv1KiRJOnIkSPX3Ncbb7yhkJAQtWvXrsD2/fv3S5Jq1KhhS4kAAMBB2NQnJiEhQZJUvXr1AturVauWZ73iCgsL0549e+Tq6lrgVR9buLiU/ihyZ2fHHrnuaMfvaMf7b452/I52vP/maMfvaMf7bzf78dsUYtLS0iTl3goqiGW5Zb3iiIiI0NSpUyXl9pepXbt2sffl5GSSl1fZYm+P4qlQwcPeJaAUcb4dC+fbsdzs59umEOPs7KycnByZTKarrmc2m4tVzIEDBzRmzBglJSWpS5cumjhxYrH2Y5GTY1ZKSvEDVXE5Ozvd9Cf+RkpJSVd2do69yyg1nG/OtyPhfDsWe5zvChU8inwFyKYQU7ZsWSUlJSk9Pb3A9oyMDEmSh4ftJ3z9+vV6+eWXlZ6erm7duum9996Tk9P1X8bKynKcN9vNIjs7h9fdgXC+HQvn27Hc7OfbppRg6fNy9uzZAtstfWEs6xXVxx9/rOeee07p6ekaMmSI5s2bJzc3N5v2AQAAHItNIcYyKikyMrLAdsvywkYv/VtOTo5efvllzZs3T05OTpo2bZpef/11OTs721IWAABwQDaFGH9/f0kqcAK6CxcuaPv27XJ1dVWnTp2KtL9XX31Vq1atkqenpz755BMNGzbMlnIAAIADsynEdO/eXbVq1dLWrVsVFBRkXZ6RkaFp06YpLS1NAQEB8vb2trZlZmYqKipKUVFRyszMtC5fvXq1Vq5cKRcXF82fP98akAAAAIrCpo697u7umjVrlsaMGaPAwECtXLlSPj4+2rt3rxISEtS8eXNNmTIlzzbx8fHq3bu3JGnTpk3y8fFRdna23n//fUlSlSpVtHLlSq1cubLA33nPPfeob9++th8ZAAC4pdkUYiTJz89PwcHB+uijj7Rjxw5FRkbKx8dHAQEBeuqpp1SuXLlr7iMiIkJxcXGSckNOSEhIoetWqFCBEAMAAPKxOcRIkq+vr+bNm1ekdX18fBQREZFnWfPmzfMtAwAAsMXNPZ8wAABAIQgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkAgxAADAkFyKs1F0dLQ+/vhj7d69W4mJiapRo4Z69eqlsWPHytPT06Z9xcfHa/78+frjjz905swZeXt7q2vXrpowYYIqV65cnPIAAIADsPlKzIEDB9SvXz+FhITI29tbnTt3VlpamhYsWKBBgwbp0qVLRd5XTEyM+vfvr2XLlsnd3V1dunSRs7Ozli5dqkcffVRnzpyxtTwAAOAgbAoxWVlZmjx5stLS0vT2228rODhY8+bN08aNG9W1a1dFRERo7ty5Rd7fyy+/rLNnz2rChAkKCQnRvHnzFBoaqkGDBunMmTN6/fXXbT4gAADgGGwKMWvXrlVMTIw6dOiggIAA63J3d3fNmDFDnp6eWr58uZKTk6+5r127dmnXrl2qV6+ennnmGetyZ2dnvfrqq6pVq5Y2b96syMhIW0oEAAAOwqYQExYWJknq3r17vjYvLy/5+fkpMzNTv/32W5H3df/998vJKW8Zrq6u6tq1qyRp06ZNtpQIAAAchE0h5ujRo5KkJk2aFNjeqFEjSdKRI0eKvC9fX9/r3hcAAHA8No1OSkhIkCRVr169wPZq1arlWa+09lUYJyeTKlcuW+zti8tkyv3fN0Z3UFZ2Tqn/fntxcc7NxBUreshstnMxpYjzzfl2BJxvzndpcXIyFXldm0JMWlqapNw+MAWxLLesV5R9eXh4XPe+CmMymeTsXPQXo6RVKl/Gbr/bnv59e9BRcL4dC+fbsXC+b042Vefs7CwpNxxcjbkIsa0k9wUAAByPTSGmbNncWzPp6ekFtmdkZEgq/OrKjdoXAABwPDaFGEs/lbNnzxbYbum/YlmvtPYFAAAcj00hxjIqqbC5WyzLCxu9dKP2BQAAHI9NIcbf31+SFBoamq/twoUL2r59u1xdXdWpU6ci7+vnn39WTk7eHt+ZmZnW+WG6dOliS4kAAMBB2BRiunfvrlq1amnr1q0KCgqyLs/IyNC0adOUlpamgIAAeXt7W9syMzMVFRWlqKgoZWZmWpe3adNGLVq0UFRUlObOnWvtwJudna23335bcXFxuu+++9SsWbPrPUYAAHALMpltHP6zfft2jRkzRhkZGbr99tvl4+OjvXv3KiEhQc2bN9fXX3+tcuXKWdePjY3V/fffLyl39l0fHx9r299//63HH39cSUlJatCggRo3bqzDhw/r5MmTql27tr799ttC55EBAACOzeYB4H5+fgoODtYDDzyg06dPa/PmzSpfvrzGjx+fL8BcS+PGjfX999+rX79+unjxon755RdJ0hNPPKHly5cTYAAAQKFsvhIDAABwM7i5p+IDAAAoBCEGAAAYEiEGAAAYEiEGAAAYEiEGAAAYEiHGoP7++297lwAAgF0xxNqgmjZtqpYtWyogIEAPPvigPD097V0SAAClihBjUA8//LCOHj0qk8kkDw8P9e7dWwEBAWrVqpW9SwMAoFQQYgwsPDxcwcHBWrdunS5evCiTyaRGjRqpf//+euSRR+Tl5WXvEgHY4ODBg9e1/e23315ClQDGQIi5BVy+fFnr16/XihUrtGvXLpnNZrm5uen+++9XQEBAkZ4qDuM4fvy4lixZoiNHjig9PV3Z2dkFrmcymbRmzZpSrg7Xo2nTpjKZTMXa1mQy6dChQyVcEXBzI8TcYmJjY7V+/Xr9/PPP+uuvv2Q2m1WzZk0NHDhQAwcOVOXKle1dIq5DRESEhgwZorS0NF3rrWsymXT48OFSqgwloWvXrte1fVhYWAlVAhiDi70LQMlycXGRi4uLTCaTzGazzGazTp8+rffff18LFizQuHHjNG7cOHuXiWL6+OOPlZqaqlatWmnQoEGqXr26nJ2d7V0WSgghxLG89dZb17X9q6++WkKVGBdXYm4Bly9fVmhoqFatWqXt27dbv6H7+flp4MCBuuuuu7R27Vp99tlnOn/+vCZMmKBnnnnGzlWjOO6++265ubnp559/VpkyZexdDoDr0Lp1a2VkZFh/tuXjmCutuQgxBrZnzx6tWrVKP/30k1JTU2U2m+Xt7a1HH31UAwcOVJ06dfKsf/jwYT366KPy9vbW1q1b7VQ1rkfLli3l7++vefPm2bsU3ABJSUnXtX2lSpVKpA6UjuPHj2v8+PE6duyY6tSpo0ceecSm7fkyyu0kw+rZs6dOnDghs9ksJycnderUSY899pi6dOkiF5eCT2uzZs1UpkwZZWZmlnK1KCl169ZVfHy8vcvADdKhQ4dib0vHXuOpV6+evv76aw0ePFgxMTG644471LlzZ3uXZSjM2GtQx48fV7Vq1fT0009r48aN+vzzz9W9e/dCA4yUe9vpkUce0ZQpU0qxUpSkRx99VH/99Zd2795t71JwA1j6sRXnv5ycHHuXj2KoUqWKPvjgA7m6uiowMFBXrlyxd0mGwu0kgwoLC1Pnzp3l5EQOdSQ5OTl69tlntW3bNg0fPlxt2rRRhQoVCh2Wy7whgDHMmTNHixYt0ssvv6wRI0bYuxzDIMQY3L59+3Ts2DH169fPumzXrl36+uuvNWjQoOu6PI2bzx133CFJysrKuuZ8ItxeAIwjJSVFX331lWrUqKGAgAB7l2MY9IkxsFmzZmnJkiVq3LhxnhATFRWl0NBQbdiwQSNGjNBLL71kxypRkqpVq2bvEnATOn/+vDZt2qQBAwbYuxQUU4UKFeioWwyEGINat26dFi9erEqVKuUJMJLUrVs3ZWVl6aOPPtLixYvVqlUrPfDAA3aqFCWJeUQcT0hIiL766ivFxsbqypUreYbhms1mXblyxdofhhADR8PtJIMaMmSIwsPDtXr1ajVo0KDAdY4dO6ZHHnlErVq10tdff13KFQK4Xlu2bNHYsWOvuZ6np6c6dOigjz/+uBSqQklp3769evfurTfeeMPepRgWV2IMKjo6Wn5+foUGGElq0KCB2rVrp3379pVeYSgVWVlZ+umnn7Rt2zYlJCTIzc1NVapU0d13363777+fifBuEUFBQZKkxx57TIMHD1ZoaKgWLlyo4OBgmUwmbdmyRQsWLJCXl5feeecdO1cLW6WkpCgtLa3AtmHDhqlTp05FCrGOjBBjUFeuXJGbm9s11ytXrpxNs0Di5hcZGakJEybo5MmT+c5tcHCw6tSpo3nz5qlp06Z2qhAlJTw8XDVr1tTrr78uJycnXb58WZ988oni4uLUrVs3NW/eXLfddpsmT56sL7/8kj4Vt5AdO3aoRo0a9i7jpsf4XIO67bbbtGvXrkJTvJQ7L8yePXvyzdwL4zp//rxGjhypEydOyNfXV5MnT9bcuXM1d+5cPffcc2rUqJFOnjypcePGXffsr7C/lJQUNW3a1DqVQqNGjSQpz6iz3r17q27dutqyZYtdagTsiRBjUL1791ZycrImT56slJSUfO2pqal64YUXdP78efXq1csOFeJG+Pzzz5WQkKChQ4dq9erVGjNmjHr37q3evXtr3LhxWrNmjQYPHqwzZ87oq6++sne5uE4eHh555oIqW7asKleurKioqDzrNWnSRHFxcaVdHmB33E4yqCeeeEJr1qzR5s2b5e/vr7Zt26pWrVqSpDNnzliv0jRu3JiJk24hYWFhql69uqZOnVrgPDEmk0mvvPKKNm3apJ9//lkTJ060Q5UoKXXq1FFERESeZXXr1tXBgwfzLEtPT1dqampplgbcFLgSY1BlypTR4sWL1atXL12+fFlbt27V8uXLtXz5cv36669KT09Xt27dtGTJEnl6etq7XJSQ06dPq2XLlld9vISrq6tatWql2NjYUqwMN8K9996rU6dO6c0339TFixclSS1atNCpU6e0ceNGSbmPINm5c6dq1qxpz1IBu+BKjIFVrlxZ7733nhITE7Vt2zbFx8crKytL1apVU7t27eTj42PvElHC3NzcivSNOzU1Vc7OzqVQEW6k4cOH6/vvv9c333yjmJgYffrppxo8eLCWLl2q5557Ts2aNVNkZKSuXLmi7t2727tcoNQRYm4BVapU0YMPPmjvMlAKfH19tWvXLp06dUq1a9cucJ1Tp05p586d1kcUwLgqV66sb775Ru+8846qV68uKXfqhKlTp2r27Nn666+/JOVenRkzZow9SwXsgsnubgHp6en5ZvLMycnRlStXdPbsWW3cuFGTJk2yY4UoKd9//71eeeUVNWjQQHPmzMn3gMdDhw7phRde0LFjx/Tmm2/yDJZb2OnTp7Vv3z55eXnJz8+Ph8EaUNOmTa/5DLTC8Gy0XIQYA1u4cKGWLFmiCxcuXHPdw4cPl0JFKA1jx47Vli1bZDKZVLduXettw9jYWOvcMffdd58WLlxY7H8gcfOJj4+3Xo2RcjvwR0RE6J577uHWoUFd71xOR44cKaFKjIsQY1Br1qzRiy++eM31vL291bt3b73yyiulUBVKQ2ZmpubNm6elS5cqPT09T5uHh4eGDh2qZ599Vq6urnaqECXpwIEDevHFF1WlShXrDL5S7jOVXnzxRdWuXVvvvvuuWrZsaccqURynTp26ru0Lu6XsSAgxBjVs2DDt3LlTzz33nIYMGaI1a9bo7bff1ubNm1WmTBlt2bJFM2bMkMlkUkhIiLy9ve1dMkrYlStX9NdffykhIUFms1nVqlVTixYteOTALSQqKkr9+/dXRkaG2rZtmyfEbNu2TXPmzNHBgwdVtmxZrVq1SnXr1rVjtUDp4yaqQUVERKhu3boaO3asypcvr9atWysnJ0e7du1SxYoV9fDDD+u9997ThQsXtGjRInuXixvAzc1Nbdu2Va9evdS7d2+1a9eOAHOLWbBggTIyMvTSSy/lCTCSdPfdd2vlypV6+eWXlZqaqvnz59upSsB+GJ1kUKmpqbrrrrusPzds2FAmk0lHjhxR7969JUkdOnRQ48aNtX37dnuVieu0YcMGSdI999wjT09P689F1aNHjxtRFkrJ3r171bRpUz355JOFrjNixAitXLlSW7duLcXKgJsDIcagypYtq6ysLOvPZcqUkbe3tyIjI/OsV79+fe3cubO0y0MJmThxokwmk9atW6f69etbfy4qOnQb29mzZ4s0VL5BgwbatGlTKVQE3FwIMQZVv359HTx4UNnZ2daRCbfddpvCw8PzrJecnKzLly/bo0SUAMvVNg8Pjzw/wzF4e3vne05SQWJiYuTl5VUKFQE3F0KMQXXt2lVz587VxIkTNWXKFNWvX19t27bV7t279c0332jIkCHavXu3du3apYYNG9q7XBTT119/fdWfcWvr2LGjVqxYoZUrV6p///4FrrNu3TodOnRIffr0KeXqAPtjdJJBpaamasCAATp27Jj8/f21cOFCxcfHq1u3bsrKypKXl5d1/pgXXnhBI0eOtHPFAGxlGZ105coV9erVS/fff79q1qwpk8mkuLg4bdmyRSEhIXJ2dtaKFSvk6+tr75KBUkWIMbDk5GQtWLBA5cuX1/jx4yVJoaGhmjZtmi5duiQpt2Pnu+++y5wht5gDBw6oRo0aqlatmqTcmXrff/99xcXF6c4779R//vMf1ahRw85VoiRs3LhRU6dO1cWLF/P1hzKbzfLw8NA777zDs5PgkAgxBpWVlVXok4zT09P1999/y8vLS3Xq1CnlynAjXblyRRMnTtSWLVs0a9YsPfLII0pMTFTPnj116dIl66MnatasqVWrVqlSpUr2LRgl4ty5c1q1apW2bdumhIQEZWVlqWrVqmrfvr0GDBiQZyZfwJHQJ8aghgwZojp16ujdd9/N1+bh4aE777zTDlXhRvv222+1efNmeXt7WztyLlu2TBcvXlTbtm317LPPKiQkRMHBwfr88881ZcoUO1eMkuDt7a3Ro0dr9OjR9i4FuKkw2Z1BHT16tEjPTMKtZd26dfLw8NCKFSt03333Scq93WAymfTCCy+offv2CgwMVO3atRUWFmbnalHSTpw4oT///NP64L+UlBQ7VwTYFyHGoDw9PcWdQMdz7NgxtW/f3trf5dy5czp8+LAqVKigVq1aScp9um2zZs10+vRpO1aKkhQUFKQuXbqoZ8+eGjlypBYvXiwpdx6hUaNG6ezZs/YtELATQoxBjRw5Utu3b9fy5csJMw4kKysrz6MFfv/9d0n554+5cuVKqdaFG+fll1/WW2+9pbi4OHl5eeV5v8fHx2vr1q16/PHHdfHiRTtWCdgHfWIMKjMzU/Xq1dPrr7+u2bNnq3HjxqpYsWKBs7maTCZ98skndqgSJa127dqKiIiw/vzLL7/IZDLpnnvusS67dOmS9u/fLx8fH3uUiBL0ww8/aPXq1WrSpInefvtt3XHHHWratKm1ffHixXrxxRe1Y8cOBQUFady4cXasFih9hBiD+uCDD6z/PzU1Vfv27St0XVumqcfN7a677tKyZcv00ksvqVq1atqwYYOcnZ2tw2v37dund999VykpKXrsscfsXC2u1/Lly+Xh4aHPPvvMOpz+n6pXr66PP/5YXbp0UWhoKCEGDocQY1AzZ860dwmwg6efflphYWH64YcfrMvGjBmjKlWqSJKeeeYZnTt3Ts2bN2ckyy3gyJEjateuXYEBxqJcuXJq06aN9u7dW4qVATcHQoxBPfroo/YuAXZQrVo1rV69Wt9++63Onj0rPz8/9ezZ09p+3333qWbNmnrqqafk6elpx0pREv75bLRrrZeZmVkKFQE3F0IMYDBeXl7WGZr/bcaMGaVcDW6kunXr6q+//lJGRobc3d0LXCctLU3h4eFMbAmHRIgxqC+//NKm9Z988skbVAmAG+WBBx7Qhx9+qP/+978KDAzM9/iQzMxMBQYGKjk5WUOHDrVTlYD98NgBg2ratGmROuyazWaZTCYdPny4FKpCSXvooYeso8tq166thx56qMjbmkwmrVmz5gZWhxstNTVVAQEBOn78uHx8fNS2bVutXr1azZo1U+vWrfX777/r5MmTql27tlatWqXy5cvbu2SgVHElxqD69u1bYIjJzs5WcnKywsPDlZiYqD59+qhdu3Z2qBAl4e+//5bJZLLO+/L3338XeVtGpRlf2bJltWTJEk2ZMkU7duxQTEyMJOnw4cPWLyYtWrTQ3LlzCTBwSFyJuUVlZWXpzTff1A8//KAVK1aoUaNG9i4JxbBjxw5JUsuWLVWmTBnrz0XVvn37G1EW7OCvv/7Sn3/+qbi4OOXk5Khq1ary8/OzTnR45coVubm52blKoHQRYm5hWVlZ6tKli1q3bq158+bZuxwAN8iff/6pN954Q6GhofYuBShVPHbgFubi4qJWrVpp+/bt9i4FJWzt2rX69NNP8yz77bffFBAQoBUrVtipKpSEy5cva9GiRRoyZIh69+6tyZMnF9qnLSUlRa+88opGjhypkydPlnKlgP3RJ+YWl5iYqMuXL9u7DJSQrKwsPf/889qwYYMaNGigMWPGWNtOnjyp8PBwTZ8+XVu3btX7779vv0JRLBkZGRo+fLgOHDhgfUbSsWPHtGnTJn366afy8/Ozrrt+/Xq99dZbSkxMlNlsVrNmzexVNmA3XIm5hX3//ffas2ePGjRoYO9SUEJWrlyp0NBQ1a1bVxMnTszTNmDAAC1YsEC33XabQkNDtXz5cjtVieJasmSJ9u/frzJlyujJJ5/U9OnT1alTJ12+fFmvvfaapNzO+9OnT9ekSZN07tw5lSlTRpMnT+YKHBwSfWIM6mpDbXNycnT+/HklJSVJkt544w2eo3OLsAy3Xb9+vby9vQtcJyEhQT179lT9+vW1cuXKUq4Q1yMgIEAHDx7UokWL1LFjR+vy559/XuvWrdM333yjL7/8Uj///LPMZrM6dOigwMBAJrqDw+J2kkEVZaitm5ubnnjiCQLMLeTkyZO66667Cg0wUu6jCdq2baudO3eWYmUoCSdPnlSjRo3yBBhJGjFihNauXavZs2dr37598vDw0NSpUzVw4EA7VQrcHAgxBvXVV18V2ubk5CRPT0/Vr19fHh4epVgVSkNOTs4113FxcWGeGANKTU3Vbbfdlm+55Zbw/v37VadOHS1cuJDbxIAIMYbF/B+OqUGDBtq5c6fOnz+vypUrF7hOcnKydu3axYecAWVnZxf44M6yZctKkpydnfX5558XGHQAR0THXoNjqK1j6du3r9LS0jRu3DhFR0fna4+JidGECRN06dIl9e3bt/QLxA3l5+dHgAH+gSsxBsVQW8c0cOBArVu3Tjt27FDv3r1Vr1491apVS5J05swZRUdHKycnR3fddZcGDx5s52pR0gq7+gY4Kq7EGBRDbR2Tk5OTPv/8c40aNUrly5dXdHS0fv/9d/3++++KioqSm5ubhg8frs8//1wuLnxHudXQzwnIi3/lDCo4OFjlypVTUFBQvpEqbm5u6ty5s5o3b66ePXvqu+++YxTDLcTNzU1TpkzRc889p/DwcMXHxys7O1tVq1ZVixYt5O7ubu8SAaBUME+MQbVv315t27bVJ598ctX1Ro8erZ07d2rfvn2lUxiAYmvatKk8PT3l5eWVr+306dPy8PAosE3KvUqzcePGG10icFPhSoyBMdTWcV25ckVr1qzRtm3bFBcXp/bt2+vZZ59VUFCQbr/9drVq1creJaKY0tLSlJaWZnMb73M4IkKMQTHU1nH99ddfmjhxos6cOSOz2SyTyaTatWtLyu0r9dZbb2nKlCl66qmn7FwpbHW1+Z8A5EeIMai+ffvqjTfe0Lhx4zR79mzVr18/T3tMTIymTp3KUNtbTFxcnEaNGqXk5GT5+/vL399fgYGB1vaOHTvq6NGj+t///qeWLVuqXbt2dqwWtmL+J8A2hBiDYqitY1qwYIGSk5M1ffp0DR06VJLyhJgpU6aoZcuW+s9//qMvv/ySEAPglkaIMSjLUNt58+Zp+fLlio6OzjP5mbu7ux577DFNnjyZoba3kN9++00NGza0BpiCdO/eXc2aNdPhw4dLsTIAKH18uhkYQ20dz9mzZ9W1a9drrlenTh1FRUWVQkUAYD+EmFtAYmJintEocXFx2r59u+655x45OzvbrzCUuAoVKujUqVPXXC82Nlbly5cvhYoAwH6YsdfADhw4oJ49e2ry5Ml5lu/atUtjx47VAw88oP3799upOtwIrVq10qFDh7Rr165C19mxY4cOHTrEMGsAtzxCjEFFRUVp2LBhOn78eL62qlWr6vbbb1dsbKxGjhypkydPln6BuCGefPJJmc1mTZgwQcHBwYqNjbW2nT9/Xt9//70mTpwok8mkJ554wo6VAsCNx4y9BvXCCy8oJCREL730kp588skC11m8eLFmzZqlvn37atasWaVcIW6UL774QnPmzMmzzGQyyfJWtoSc//znP/YoDwBKDSHGoLp166Zy5cpp9erVV13voYce0oULF7R169bSKQylYseOHfr888+1c+dOpaenS5JcXV3VunVrPfXUU/L397dzhQBw49Gx16DOnj2rO+6445rrNWjQQJs2bSqFilAaEhMTVaVKFbVv317t27dXTk6OkpKSlJOTo0qVKjGcHoBD4V88g/L29i7SENqYmJhCHxgH4xk2bJgqVaqkoKAgSbnzBRX22AkAuNXRsdegOnbsqMjISK1cubLQddatW6dDhw7Jz8+vFCvDjRQTE8PQaQD4/7gSY1AjRoxQSEiIpk+frj/++EP333+/atasKZPJpLi4OG3ZskUhISFyc3PTmDFj7F0uSkjlypWVlJRk7zIA4KZAx14D27hxo6ZOnaqLFy/KZDLlaTObzfLw8NA777yj7t2726lClLQffvhBU6dO1ejRozV8+HBuJQFwaIQYgzt37pxWrVqlbdu2KSEhQVlZWapatarat2+vAQMGqHr16oqPj1f16tXtXSpKwPTp07Vz506dOHFCUm7fqIoVK+YLsVLusOs1a9aUdokAUGoIMbewbdu26ZtvvlFYWJjCw8PtXQ5KQNOmTYu8rslk4iGQAG5p9Im5xaSmpmr16tX65ptvdOzYMZnN5gK/pcOYvvrqK3uXAAA3DULMLSIqKkpBQUH64YcflJaWJrPZLCcnJ3Xo0EH9+/e3d3koAbGxsSpTpoxq1aqlqlWr2rscALA7QoyB5eTkaOPGjQoKCtKOHTsk5Xbo9fHx0aOPPqp+/fqpZs2adq4S1+vIkSN69dVXdfDgQeuyjh076q233uL8AnBo9IkxoMTERH333Xf67rvvlJCQILPZrDJlysjFxUVpaWn0g7iFxMXFqW/fvkpOTs7XVrt2ba1evZp5YwA4LCa7M5A9e/bo+eefV+fOnfXhhx8qPj5ejRs31quvvqrffvtNzZs3t3eJKGGLFy9WcnKy2rdvr9WrV2vv3r1atmyZmjdvrtOnT+vbb7+1d4kAYDfcTjKIRx99VEeOHJHZbFa5cuXUt29fBQQEqGXLlvYuDTfQH3/8IS8vL3366adyd3eXJLVq1UqfffaZunTpoi1btjCZIQCHRYgxiMOHD8vd3V1PPfWURo8ebf1Aw60tLi5O7dq1y3e+q1SpojvvvLNIz88CgFsVt5MMonLlysrIyND8+fPVu3dvzZkzhw8wB5Cenq6yZcsW2FatWjVdvHixlCsCgJsHIcYgfv31V33wwQfq2LGjzpw5o0WLFqlPnz4aPHiwvv/+e6Wnp9u7RNwA2dnZcnZ2LrDN2dlZ2dnZpVwRANw8uJ1kEC4uLnrggQf0wAMP6PTp0woODtaqVau0d+9e7du3T2+//XahH3YAANyKuBJjQLVq1dKzzz6rsLAwLViwQF26dNHly5eVkpIiSerSpYvee+89bjcBAG5phBgDc3JyUufOnTV//nxt3rxZkyZNUp06dRQXF6dPP/1Uffr0Ub9+/bRkyRJ7lwoAQIljsrtb0J9//qnly5dr06ZNunLlCg8CNLCmTZvK09NTXl5e+douXLig9PR01apVq8BtTSaTNm7ceKNLBAC7IcTcwpKSkrR69WqtWLFCP/74o73LQTHY8tTqfyO8ArjVEWKAm5jlmVjF1b59+xKqBABuPoQYAABgSHTsBQAAhkSIAQAAhkSIAQAAhkSIAQAAhkSIAQAAhkSIAQAAhkSIAQAAhvT/ALAnC95CcExBAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\": ccv_acc,\n",
    "                            \"Precision\": cv_precision,\n",
    "                            \"Recall\": cv_recall,\n",
    "                            \"F1\": cv_f1},\n",
    "                          index=[0])\n",
    "cv_metrics.T.plot.bar(title=\"Cross-Validated Metrics\", legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a8964c2",
   "metadata": {},
   "source": [
    "Great! This looks like something we could share. An extension might be adding the metrics on top of each bar so someone can quickly tell what they were.\n",
    "\n",
    "What now?\n",
    "\n",
    "The final thing to check off the list of our model evaluation techniques is feature importance.\n",
    "\n",
    "## Feature importance\n",
    "\n",
    "Feature importance is another way of asking, \"which features contributing most to the outcomes of the model?\"\n",
    "\n",
    "Or 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",
    "Unlike some of the other functions we've seen, because how each model finds patterns in data is slightly different, how a model judges how important those patterns are is different as well. This means for each model, there's a slightly different way of finding which features were most important.\n",
    "\n",
    "You can usually find an example via the Scikit-Learn documentation or via searching for something like \"[MODEL TYPE] feature importance\", such as, \"random forest feature importance\".\n",
    "\n",
    "Since we're using `LogisticRegression`, we'll look at one way we can calculate feature importance for it.\n",
    "\n",
    "To do so, we'll use the `coef_` attribute. Looking at the [Scikit-Learn documentation for `LogisticRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html), the `coef_` attribute is the coefficient of the features in the decision function.\n",
    "\n",
    "We can access the `coef_` attribute after we've fit an instance of `LogisticRegression`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "50132572",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit an instance of LogisticRegression (taken from above)\n",
    "clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a65547e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00316728, -0.86044681,  0.66067028, -0.01156993, -0.00166374,\n",
       "         0.04386098,  0.31275871,  0.02459362, -0.60413098, -0.56862784,\n",
       "         0.45051633, -0.63609911, -0.67663374]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01cb3626",
   "metadata": {},
   "source": [
    "Looking at this it might not make much sense. But these values are how much each feature contributes to how a model makes a decision on whether patterns in a sample of patients health data leans more towards having heart disease or not.\n",
    "\n",
    "Even knowing this, in it's current form, this `coef_` array still doesn't mean much. But it will if we combine it with the columns (features) of our dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7be17765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.0031672836648050503,\n",
       " 'sex': -0.8604468104930325,\n",
       " 'cp': 0.6606702797887507,\n",
       " 'trestbps': -0.01156993266606034,\n",
       " 'chol': -0.0016637447948541165,\n",
       " 'fbs': 0.043860983413115645,\n",
       " 'restecg': 0.31275871271392713,\n",
       " 'thalach': 0.024593615341531826,\n",
       " 'exang': -0.6041309838702352,\n",
       " 'oldpeak': -0.5686278420079321,\n",
       " 'slope': 0.45051633355228077,\n",
       " 'ca': -0.6360991091618156,\n",
       " 'thal': -0.6766337375624075}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match features to columns\n",
    "features_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "features_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec50c481",
   "metadata": {},
   "source": [
    "Now we've match the feature coefficients to different features, let's visualize them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "de6ded86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": "91c9e8af",
   "metadata": {},
   "source": [
    "You'll notice some are negative and some are positive.\n",
    "\n",
    "The larger the value (bigger bar), the more the feature contributes to the models decision.\n",
    "\n",
    "If the value is negative, it means there's a negative correlation. And vice versa for positive values. \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.\n",
    "\n",
    "We can see this by comparing the `sex` column to the `target` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5e701bc2",
   "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": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb6aaae5",
   "metadata": {},
   "source": [
    "You can see, 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",
    "And then as `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",
    "What does this mean?\n",
    "\n",
    "It means the model has found a pattern which reflects the data. Looking at these figures and this specific dataset, it seems if the patient is female, they're more likely to have heart disease.\n",
    "\n",
    "How about a positive correlation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d37993d8",
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Contrast slope (positive coefficient) with target\n",
    "pd.crosstab(df[\"slope\"], df[\"target\"])"
   ]
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    "Looking back the data dictionary, we see `slope` is the \"slope of the peak exercise ST segment\" where:\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",
    "    \n",
    "According to the model, there's a positive correlation of 0.470, not as strong as `sex` and `target` but still more than 0.\n",
    "\n",
    "This positive correlation means our model is picking up the pattern that as `slope` increases, so does the `target` value.\n",
    "\n",
    "Is this true?\n",
    "\n",
    "When you look at the contrast (`pd.crosstab(df[\"slope\"], df[\"target\"]`) it is. As `slope` goes up, so does `target`. \n",
    "\n",
    "What can you do with this information?\n",
    "\n",
    "This is something you might want to talk to a subject matter expert about. They may be interested in seeing where machine learning model is finding the most patterns (highest correlation) as well as where it's not (lowest correlation). \n",
    "\n",
    "Doing this has a few benefits:\n",
    "1. **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",
    "2. **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",
    "3. **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."
   ]
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    "## 6. Experimentation\n",
    "\n",
    "Well we've completed all the metrics your boss requested. You should be able to put together a great report containing a confusion matrix, a handful of cross-valdated metrics such as precision, recall and F1 as well as which features contribute most to the model making a decision.\n",
    "\n",
    "But after all this you might be wondering where step 6 in the framework is, experimentation.\n",
    "\n",
    "Well the secret here is, as you might've guessed, the whole thing is experimentation.\n",
    "\n",
    "From trying different models, to tuning different models to figuring out which hyperparameters were best.\n",
    "\n",
    "What we've worked through so far has been a series of experiments.\n",
    "\n",
    "And the truth is, we could keep going. But of course, things can't go on forever.\n",
    "\n",
    "So by this stage, after trying a few different things, we'd ask ourselves did we meet the evaluation metric?\n",
    "\n",
    "Remember we defined one in step 3.\n",
    "\n",
    "> If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursure this project.\n",
    "\n",
    "In this case, we didn't. The highest accuracy our model achieved was below 90%.\n",
    "\n",
    "#### What next?\n",
    "\n",
    "You might be wondering, what happens when the evaluation metric doesn't get hit?\n",
    "\n",
    "Is everything we've done wasted?\n",
    "\n",
    "No.\n",
    "\n",
    "It means we know what doesn't work. In this case, we know the current model we're using (a tuned version of `LogisticRegression`) along with our specific data set doesn't hit the target we set ourselves.\n",
    "\n",
    "This is where step 6 comes into its own.\n",
    "\n",
    "A good next step would be to discuss with your team or research on your own different options of going forward.\n",
    "\n",
    "* Could you collect more data?\n",
    "\n",
    "* Could you try a better model? If you're working with structured data, you might want to look into [CatBoost](https://catboost.ai/) or [XGBoost](https://xgboost.ai/).\n",
    "\n",
    "* Could you improve the current models (beyond what we've done so far)?\n",
    "* If your model is good enough, how would you export it and share it with others? (Hint: check out [Scikit-Learn's documentation on model persistance](https://scikit-learn.org/stable/modules/model_persistence.html))\n",
    "\n",
    "The key here is to remember, your biggest restriction will be time. Hence, why it's paramount to minimise your times between experiments.\n",
    "\n",
    "The more you try, the more you figure out what doesn't work, the more you'll start to get a hang of what does."
   ]
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