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

{
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   "cell_type": "markdown",
   "id": "optical-cyprus",
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    "# Predicting heart disease using machine learning\n",
    "\n",
    "This notebook looks into using various python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes \n",
    "\n",
    "We're going to take the following approach:\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling\n",
    "6. Experimentation\n",
    "\n",
    "\n",
    "## 1. Problem Definition\n",
    "\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",
    "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 from [UCI Machine Learning Repository ](https://archive.ics.uci.edu/ml/datasets/heart+disease)\n",
    "\n",
    "Howevever, we've downloaded it in a formatted way from [Kaggle](https://www.kaggle.com/ronitf/heart-disease-uci)\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",
    "This is where you'll get different information about each of the features in your 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",
    "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. 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.  - 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",
    "**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",
    "We're going to use Pandas, Matplotlib and Numpy for Data Analysis anad manipulation.\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 for data analysis](https://pandas.pydata.org/).\n",
    "   * [NumPy for numerical operations](https://numpy.org/).\n",
    "   * [Matplotlib/seaborn for plotting or data visualization](https://matplotlib.org)(https://seaborn.pydata.org).\n",
    "   * [Scikit-Learn for machine learning modelling and evaluation](https://scikit-learn.org/stable/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "hourly-vampire",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all the tools we need\n",
    "\n",
    "# Regular EDA (Exploratory Data Analysis) and plotting libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# We want our plot to appear inside the notebook\n",
    "%matplotlib inline \n",
    "\n",
    "# Models from Scikit-learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model Evaluations\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import plot_roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infectious-thriller",
   "metadata": {},
   "source": [
    "# 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "unlikely-tuition",
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
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     "execution_count": 5,
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   ],
   "source": [
    "df = pd.read_csv('heart-disease.csv') # 'DataFrame' shortened to 'df'\n",
    "df.shape  # (rows, columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pretty-beginning",
   "metadata": {},
   "source": [
    "## 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",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
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   "source": [
    "# Let's check the top 5 rows of our dataframe\n",
    "df.head()"
   ]
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   "cell_type": "code",
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       "     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  "
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   "source": [
    "# To check the bottom 5 rows of our dataframe\n",
    "df.tail()"
   ]
  },
  {
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       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</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",
       "5   57    1   0       140   192    0        1      148      0      0.4      1   \n",
       "6   56    0   1       140   294    0        0      153      0      1.3      1   \n",
       "7   44    1   1       120   263    0        1      173      0      0.0      2   \n",
       "8   52    1   2       172   199    1        1      162      0      0.5      2   \n",
       "9   57    1   2       150   168    0        1      174      0      1.6      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  \n",
       "5   0     1       1  \n",
       "6   0     2       1  \n",
       "7   0     3       1  \n",
       "8   0     3       1  \n",
       "9   0     2       1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And the top 10\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "developmental-equation",
   "metadata": {},
   "source": [
    "value_counts() allows you to show how many times each of the values of a **categorical** column appear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "liked-concentration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find out how many of each class there are\n",
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "changing-conversation",
   "metadata": {},
   "source": [
    "Since these two values are close to even, our target column can be considered **balanced**. An **unbalanced** target column, meaning some classes have far more samples, can be harder to model than a balanced set. Ideally, all of your target classes have the same number of samples.\n",
    "\n",
    "If you'd prefer these values in percentages, value_counts() takes a parameter, normalize which can be set to true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "typical-arrest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.544554\n",
       "0    0.455446\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "standard-british",
   "metadata": {},
   "source": [
    "We can plot the target column value counts by calling the plot() function and telling it what kind of plot we'd like, in this case, bar is good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "special-spiritual",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['target'].value_counts().plot(kind='bar', color=['salmon', 'lightblue']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abroad-climate",
   "metadata": {},
   "source": [
    "df.info() shows a quick insight to the number of missing values you have and what type of data you're working with.\n",
    "\n",
    "In our case, there are no missing values and all of our columns are numerical in nature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "behind-three",
   "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": 13,
   "id": "applied-prayer",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are there any missing values?\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "republican-hamilton",
   "metadata": {},
   "source": [
    "Another way to get some quick insights on your dataframe is to use df.describe(). describe() shows a range of different metrics about your numerical columns such as mean, max and standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "understood-brooklyn",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "valued-tribute",
   "metadata": {},
   "source": [
    "## Heart Disease Frequency according to gender\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": 15,
   "id": "characteristic-butterfly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "through-privacy",
   "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",
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       "    }\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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sharing-trademark",
   "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": "markdown",
   "id": "false-rapid",
   "metadata": {},
   "source": [
    "## Making our crosstab visual\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "physical-settle",
   "metadata": {},
   "source": [
    "You can plot the crosstab by using the plot() function and passing it a few parameters such as, kind (the type of plot you want), figsize=(width, height) (how big you want it to be) and color=[colour_1, colour_2] (the different colours you'd like to use).\n",
    "\n",
    "Different metrics are represented best with different kinds of plots. In our case, a bar graph is great. We'll see examples of more later. And with a bit of practice, you'll gain an intuition of which plot to use with different variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "everyday-temple",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot\n",
    "pd.crosstab(df.target, df.sex).plot(kind='bar',\n",
    "                                    figsize=(10, 6),\n",
    "                                    color=['salmon', 'lightblue']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "realistic-withdrawal",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-18-5f5ac11fe86f>:10: UserWarning: Legend does not support 'F' instances.\n",
      "A proxy artist may be used instead.\n",
      "See: https://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
      "  plt.legend('Female', 'Male')\n",
      "<ipython-input-18-5f5ac11fe86f>:10: UserWarning: Legend does not support 'e' instances.\n",
      "A proxy artist may be used instead.\n",
      "See: https://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
      "  plt.legend('Female', 'Male')\n",
      "<ipython-input-18-5f5ac11fe86f>:10: UserWarning: Legend does not support 'm' instances.\n",
      "A proxy artist may be used instead.\n",
      "See: https://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
      "  plt.legend('Female', 'Male')\n",
      "<ipython-input-18-5f5ac11fe86f>:10: UserWarning: Legend does not support 'a' instances.\n",
      "A proxy artist may be used instead.\n",
      "See: https://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
      "  plt.legend('Female', 'Male')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot\n",
    "pd.crosstab(df.target, df.sex).plot(kind='bar',\n",
    "                                    figsize=(10, 6),\n",
    "                                    color=['salmon', 'lightblue']);\n",
    "\n",
    "# Add some attributes\n",
    "plt.title('Heart Disease frequency for sex')\n",
    "plt.xlabel('0 = No Disease, 1 = Disease')\n",
    "plt.ylabel('Amount')\n",
    "plt.legend('Female', 'Male')\n",
    "plt.xticks(rotation = 0); # keep the labels on the x-axis vertical"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "latest-lemon",
   "metadata": {},
   "source": [
    "## Age vs Max Heart rate for Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "liable-lexington",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create another figure\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Start with positive example\n",
    "plt.scatter(df.age[df.target==1],\n",
    "            df.thalach[df.target==1],\n",
    "            color='salmon') # define it as a scatter figure\n",
    "\n",
    "# Start with a negative example, 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",
    "            color='lightblue') # Axis always comes as (x, y)\n",
    "\n",
    "# Add some helpful info\n",
    "plt.title('Heart Disease in function of Age and Max Heart Rate')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Max Heart Rate')\n",
    "plt.legend(['Disease', 'No Disease']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pending-wallet",
   "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": 20,
   "id": "applicable-breed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of the Age column with a Histogram \n",
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incredible-means",
   "metadata": {},
   "source": [
    "## Heart Disease Frequency per Chest Pain Type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "plastic-paint",
   "metadata": {},
   "source": [
    "3. 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",
    "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": 21,
   "id": "bulgarian-jungle",
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "desperate-helicopter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the crosstab more visual\n",
    "pd.crosstab(df.cp, df.target).plot(kind='bar',\n",
    "                                   figsize=(10, 6),\n",
    "                                   color=['salmon', 'lightblue'])\n",
    "\n",
    "# Add attributes to the plot to make it more readable\n",
    "plt.title('Heart Diseaese Frequency Per Chest Pain Type')\n",
    "plt.xlabel('Chest Pain Type')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend(['No Disease', 'Disease'])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "considered-extreme",
   "metadata": {},
   "source": [
    "What can we infer from this?\n",
    "\n",
    "Remember from our data dictionary what the different levels of chest pain are.\n",
    "\n",
    "1. 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.\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": "code",
   "execution_count": 23,
   "id": "noticed-philosophy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "emerging-immunology",
   "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": 24,
   "id": "incorporate-crest",
   "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",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a correlation matrix\n",
    "corr_matrix= df.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "positive-indie",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's make the correlation matrix look a little prettier\n",
    "corr_matrix=df.corr()\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.heatmap(corr_matrix,\n",
    "            annot=True,\n",
    "            linewidth=0.5,\n",
    "            fmt='.2f',\n",
    "            cmap='YlGnBu');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "great-coffee",
   "metadata": {},
   "source": [
    "## 5. Modelling\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": 26,
   "id": "structured-programmer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quarterly-junction",
   "metadata": {},
   "source": [
    "We're trying to predict our target variable using all of the other variables.\n",
    "\n",
    "To do this, we'll split the target variable from the rest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "monthly-lucas",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into X(features) and y(labels)\n",
    "X = df.drop('target', axis=1)\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "persistent-coach",
   "metadata": {},
   "outputs": [
    {
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       "      <td>241</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <th>300</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>57</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "0        0   0     1  \n",
       "1        0   0     2  \n",
       "2        2   0     2  \n",
       "3        2   0     2  \n",
       "4        2   0     2  \n",
       "..     ...  ..   ...  \n",
       "298      1   0     3  \n",
       "299      1   0     3  \n",
       "300      1   2     3  \n",
       "301      1   1     3  \n",
       "302      1   1     2  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Independent variables (no target column)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "interracial-gauge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "298    0\n",
       "299    0\n",
       "300    0\n",
       "301    0\n",
       "302    0\n",
       "Name: target, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Target\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unauthorized-worcester",
   "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()` and feed it our independent and dependent variables (X & y)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "spoken-sustainability",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,  # Independent variable\n",
    "                                                    y,  # Depenedent variable\n",
    "                                                    test_size=0.2)  # Percentage of data to use for test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "domestic-draft",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "informational-cattle",
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train, len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "approximate-hartford",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
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       "  </thead>\n",
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      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "179   57    1   0       150   276    0        0      112      1      0.6   \n",
       "228   59    1   3       170   288    0        0      159      0      0.2   \n",
       "111   57    1   2       150   126    1        1      173      0      0.2   \n",
       "246   56    0   0       134   409    0        0      150      1      1.9   \n",
       "60    71    0   2       110   265    1        0      130      0      0.0   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "249   69    1   2       140   254    0        0      146      0      2.0   \n",
       "104   50    1   2       129   196    0        1      163      0      0.0   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "193   60    1   0       145   282    0        0      142      1      2.8   \n",
       "184   50    1   0       150   243    0        0      128      0      2.6   \n",
       "\n",
       "     slope  ca  thal  \n",
       "179      1   1     1  \n",
       "228      1   0     3  \n",
       "111      2   1     3  \n",
       "246      1   2     3  \n",
       "60       2   1     2  \n",
       "..     ...  ..   ...  \n",
       "249      1   3     3  \n",
       "104      2   0     2  \n",
       "300      1   2     3  \n",
       "193      1   2     3  \n",
       "184      1   0     3  \n",
       "\n",
       "[61 rows x 13 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "quick-french",
   "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,\n",
       " 61)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test, len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brilliant-metro",
   "metadata": {},
   "source": [
    "Now we've got out data split into training and test sets, it's time to build our machine learning model.\n",
    "\n",
    "We'll train it (find the patterns) on the training set.\n",
    "\n",
    "And we'll test (use the patterns) on the test set.\n",
    "\n",
    "We're going to try 3 different machine learning models;\n",
    "1. Logistic Regression\n",
    "2. K-Nearest Neighbors\n",
    "3. RandomForestClassfier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "hundred-digest",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {'KNN': KNeighborsClassifier(),  # KNN, Logistic Regression, Random Forest are the names(Keys), the KNeighborsClassifier() \n",
    "          'Logistic Regression': LogisticRegression(), # LogisticRegression(), RandomForestClassifier()(Values) are the models\n",
    "          'Random Forest': RandomForestClassifier()}\n",
    "\n",
    "# Create functions to fit and score the models\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    fits and evaluate given machine learning model.\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 associated with training data\n",
    "    y_test: label associated with test data\n",
    "    \"\"\"\n",
    "    # Random seed for reproducible result\n",
    "    np.random.seed(42)\n",
    "    # Make a dictionary to keep model scores\n",
    "    model_scores = {}\n",
    "    # Loop through models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the training data\n",
    "        model.fit(X_train, y_train)\n",
    "        \n",
    "        # Evaluate the model and append its scores to model_scores\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "graduate-fraction",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Desktop\\sample_project_1\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: 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": [
       "{'KNN': 0.6885245901639344,\n",
       " 'Logistic Regression': 0.8852459016393442,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores =  fit_and_score(models=models,\n",
    "                              X_train=X_train,\n",
    "                              X_test=X_test,\n",
    "                              y_train=y_train,\n",
    "                              y_test=y_test)\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "circular-sensitivity",
   "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": 37,
   "id": "sealed-community",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare= pd.DataFrame(model_scores,  index=['Accuracy'])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "immune-summer",
   "metadata": {},
   "source": [
    "Now we've got a baseline model... and we know that a model's first prediction aren't always what we should base our next step \n",
    "off. What should we do??\n",
    "\n",
    "\n",
    "\n",
    "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 look at the following before we see them in action( These are some of the things we should pay attention to when working on a classification model).\n",
    "\n",
    "1. **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",
    "\n",
    "2. **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",
    "\n",
    "3. **Confusion matrix** - Compares the predicted values with the true values in a tabular way, if 100% correct, all values in the matrix will be top left to bottom right (diagnol line).\n",
    "\n",
    "4. **Cross-validation** - Splits your dataset into multiple parts and train and tests your model on each part and evaluates performance as an average.\n",
    "\n",
    "5. **Precision** - Proportion of true positives over total number of samples. Higher precision leads to less false positives.\n",
    "\n",
    "6. **Recall** - Proportion of true positives over total number of true positives and false negatives. Higher recall leads to less false negatives.\n",
    "\n",
    "7. **F1 score** - Combines precision and recall into one metric. 1 is best, 0 is worst.\n",
    "\n",
    "8. **Classification report** - Sklearn has a built-in function called classification_report() which returns some of the main classification metrics such as precision, recall and f1-score.\n",
    "\n",
    "9. **ROC Curve** - Receiver Operating Characterisitc is a plot of true positive rate versus false positive rate.\n",
    "\n",
    "10. **Area Under Curve (AUC)** - The area underneath the ROC curve. A perfect model achieves a score of 1.0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "angry-wireless",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning and 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",
    "\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": "theoretical-breeding",
   "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": 38,
   "id": "brilliant-daily",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of train scores\n",
    "train_scores = []\n",
    "\n",
    "# Create a list of test scores\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of different values for n_neighbors\n",
    "neighbors = range(1, 21) # 1 to 20\n",
    "\n",
    "# Setup Algorithm(model)\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different n_neighbors values\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)  # set neighbors value\n",
    "    \n",
    "    # Fit the algorithm(model)\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    # Update the training scores list\n",
    "    train_scores.append(knn.score(X_train, y_train))\n",
    "    \n",
    "    # Update the test scores list\n",
    "    test_scores.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "institutional-massage",
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "recovered-stock",
   "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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "pressed-inside",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 75.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors, train_scores, label='Train score')\n",
    "plt.plot(neighbors, test_scores, label='Test score')\n",
    "plt.xticks(np.arange(1, 21, 1))\n",
    "plt.xlabel('Number of neighbors')\n",
    "plt.ylabel('Model score')\n",
    "plt.legend()\n",
    "\n",
    "print(f'Maximum KNN score on the test data: {max(test_scores)*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "associate-covering",
   "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 tune `LogisticsRegression` and `RandomForestClassifier` using `RandomizedSearchCV`.\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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "premier-treasure",
   "metadata": {},
   "source": [
    "## Tuning models(hyperparameter) with RandomizedSearchCV\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": 42,
   "id": "urban-postage",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Different LogisticRegression hyperparameters\n",
    "log_reg_grid = {'C': np.logspace(-4, 4, 20),\n",
    "                'solver': ['liblinear']}\n",
    "\n",
    "# Different RandomForestClassifier hyperparameters\n",
    "rf_grid = {'n_estimators': np.arange(10, 1000, 50),\n",
    "           'max_depth': [None, 3, 5, 10],\n",
    "           'min_samples_split': np.arange(2, 20, 2),\n",
    "           'min_samples_leaf': np.arange(1, 20, 2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unavailable-rendering",
   "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": 43,
   "id": "ranking-penguin",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisticRegression\n",
    "\n",
    "# Setup Random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                                param_distributions = log_reg_grid,\n",
    "                                cv = 5,\n",
    "                                n_iter = 20,\n",
    "                                verbose =  True)\n",
    "# Fit random hyperparameter search model for LogisticRegression\n",
    "rs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cellular-genome",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best parameter\n",
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "coral-chicken",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "logical-walker",
   "metadata": {},
   "source": [
    "Now we've tuned `LogisticRegression` using `RandomizedSearchCV`, we'll do the same for `RandomForestClassifier`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "authentic-rochester",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune RandomForestClassifier\n",
    "\n",
    "# Setup Random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for RandomForestClassifier\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                           param_distributions = rf_grid,\n",
    "                           cv = 5,\n",
    "                           n_iter = 20,\n",
    "                           verbose = True)\n",
    "\n",
    "# Fit random hyperparameter search model for RandomForestClassifier\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "three-tracy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best parameter\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cubic-alpha",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "engaging-operator",
   "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`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fifth-execution",
   "metadata": {},
   "source": [
    "## Tuning a model with GridSearchCV\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",
    "\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": 49,
   "id": "interracial-trinidad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                         'solver': ['liblinear']},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different LogisticRegression hyperparameters\n",
    "log_reg_grid = {'C': np.logspace(-4, 4, 20),\n",
    "                'solver': ['liblinear']}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV (LogisticRegression(),\n",
    "                            param_grid = log_reg_grid,\n",
    "                            cv = 5,\n",
    "                            verbose = True)\n",
    "# Fit grid hyperparamater search model\n",
    "gs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "intended-execution",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best hyperparameter\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "designing-extent",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the grid search LogisticRegression model\n",
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "stuck-captain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'KNN': 0.6885245901639344,\n",
       " 'Logistic Regression': 0.8852459016393442,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cooked-seattle",
   "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": "stock-editor",
   "metadata": {},
   "source": [
    "## Evaluating our tuned machine learning classifier 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",
    "\n",
    "* ROC curve and AUC score - `plot_roc_curve()`\n",
    "* Confusion matrix - `confusion_matrix()`\n",
    "* Classification report - `classification_report()``\n",
    "* Precision - `precision_score()`\n",
    "* Recall - `recall_score()`\n",
    "* F1-score - `f1_score()`\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. To make comparisons and evaluate our trained model, first we need to make predictions\n",
    "\n",
    "We'll make predictions on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "therapeutic-rocket",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making predictions on the test data\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "vanilla-alaska",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "registered-gazette",
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "designed-ebony",
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "minute-ukraine",
   "metadata": {},
   "source": [
    "### 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 `plot_roc_curve` 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 `plot_roc_curve` function we can see it takes (estimator, X, y) as inputs. Where estimator 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": 63,
   "id": "separate-degree",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import ROC curve function from metrics module\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "\n",
    "# Plot ROC curve and calculate AUC metric\n",
    "plot_roc_curve(gs_log_reg, X_test, y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tamil-bennett",
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interior-selling",
   "metadata": {},
   "source": [
    "### Confusion Matrix\n",
    "\n",
    " 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() and passing it the true labels and predicted labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "handmade-fellowship",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "# Display Confusion Matrix\n",
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "knowing-sweden",
   "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()` for doing so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "economic-cable",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOQAAADfCAYAAADm6n/jAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAfFUlEQVR4nO3deVxVdf7H8RdcFkVcwNwFVBJUllIsRXMBRcUlc8ENcdw3VFxTGjUbZyzNcrTUGLfSnwuUa245LjBlEZNZjopbhKAgLiwKKOv5/eFwJwLsAvdyD/J5Ph49Ht1zvvfcN1fenHvWa6IoioIQQhVMjR1ACPE/UkghVEQKKYSKSCGFUBEppBAqYmbsAIby+OAqY0d4bjT0DzF2hOdKWvovJc6TNaQQKiKFFEJFpJBCqIgUUggVkUIKoSJSSCFURAophIpIIYVQESmkECoihRRCRaSQQqiIFFIIFZFCCqEiUkghVEQKKYSKSCGFUBEppBAqIoUUQkWkkEKoiBRSCBWRQgqhIlJIIVRECimEikghhVARKaQQKiKFFEJFpJBCqIgUUggVkUIKoSJSSCFURAophIpIIYVQkef2C1vV4turt9h06ieib9/HxMQEd/v6BPb2wN2hvnaM/0cHuRR/v8hze7o1Y3VAj4qMW+m5uDgT/vUBPli9kfdWrDN2nFKTQhrQD78kErj1Kxwb2BDYuz15+fmEfRfNhE+OsHVaf9zs66EoCr8mpeHl4kBPt2aFnt/Ixto4wSspjUbDxpBVWFhYGDtKmUkhDej9LyNpWLsGO2a8TnWLp2/1AI+WDFr9BR9/9QMhk3xJSEknMzuH7i729Gv3opETV25z50+jVeuWxo5RLrINaSAPM7O4lpiMj3sLbRkB6tasjkeLRvwcexeAX+6kANCifh1jxHxutHFxYsGb01m1cr2xo5SL0deQt2/f5tdffyU9PR1TU1Nq1qxJ8+bNadiwobGjlUuNauYcmD+0UBkLpGY8wUxjAsAvSU8L2fy/hXycnUN1C/MKy/k80Gg0bNi4ivAz3xK25wBLls41dqQyM1ohT5w4wdq1a4mJiUFRlELzTExMcHBwYPbs2fTp08dICctHY2qKQ73aRaZfS0zmp5tJdHJqCsCNpBRqWJrzwZff89XPv5KZnUNT25rM6ONBn5cdKzp2pTRn7hRaODZj1IipmJlpjB2nXIxSyAMHDrBo0SJ8fX2ZOXMmDg4O1KhRA0VRyMjI4ObNm3z11VfMmTOHnJwcBgwYYIyYepeZlcPiPREAjOvuDsAvd1LJyMrh0ZNs/jqiKw8fZ7Prm0ss2hVObl4+/T0q9zaRobVq3ZI3F81gwbx3SEi4g719E2NHKhejFPIf//gHI0eO5O233y52fps2bfD19WXZsmWEhIQ8F4V8nJ1L0Kf/5FpiMuO9XqK9YyMAhnRwJk9RGNGpjXZsn5dbMPSDfaw58m982zqiMZVN/eKYmpqyYeNKIr87x2efhho7jl6UWMh///vfZVrgK6+88odjbt++Tc+ePf9wXI8ePdi/f3+ZcqjJw8dZzNr2T36KTeKNV5yY2cdDO8/Ps3WR8dXMzejX7kVCTp4nJimVlo1sKzJupRE0exKubq3o4zMc27o2ANSp83Qzwap6dWzr2pCSnFpkk0jNSixkQEAAJiYmOi9IURRMTEyIjo7+w7F2dnZ88803dO7c+ZnjwsPDK/3OneT0x0zbfJyrCckM6eDM4sGddXpfba2rAZCZnWPoiJVWD5+uWFpacuZfB4rMC5ozmaA5k3Fr05W4uNsVH66MSizku+++a7AXnTp1KgsWLODu3bv06tWL5s2bY21tjYmJCenp6dptyMOHD/POO+8YLIehZTzJ1pZxdBcX5g/oWGh+UloG0zYfp7d7C6b4tC00L/ZeGgBNbGtWWN7KZnHwu9SpU6vQtHr1X2Dz1jXs3rWfPbv2kZR0z0jpyqbEQg4aNMhgL9q/f380Gg1r1qzhyJEjRdYYiqLQtGlTVqxYYdAchvbuge+4mpDMqNeKlhGgQe0apD/JZl/UVfy7uGBd7ekZJndS0zn0w3VecWzECzWtKjp2pfHTTxeLTCvYqRMbG0d4+LcVHancSr1T5/Tp04SHh5OQkMDcuXOxsrLi22+/ZciQIVhaWuq8HF9fX3x9fYmPjycmJob09HQURdEeh7S3ty9tNFWJSUrl8I83sK5mgXNjW478eKPImH7tXmTRQE/mbj/Fn9Z/yeAOzmRm5bDnbDQaUxOC3+hkhOTCmHQuZE5ODrNmzeLMmTNoNBry8/OZMGECsbGx/OUvf2Hfvn1s2bKF2rWLHnt7Fjs7O+zs7EodXO3OxSQCkP4km7fDvi52TL92L+Lt2ow1f+rJltM/s/boD1iaa2jfohGzfNtrTxYQVYeJouMuqHXr1vHJJ5/wzjvv0KVLF7p37862bdt45ZVX2L17NytXrmTUqFG89dZbhs6sk8cHVxk7wnOjoX+IsSM8V9LSfylxns4HuA4dOsSQIUPw8/Mr9NHUzMyMgIAAhg0bxqlTp8qXVIgqTudC3rlzB1dX1xLnOzs7c+9e5dqjJYTa6FzIBg0aEBMTU+L8CxcuUK9ePb2EEqKq0rmQ/fv3JzQ0lG+//d+u5ILDFTt37mT//v2V9kRwIdRC55062dnZTJ48me+//x5bW1uSk5NxcHAgNTWV1NRU3Nzc+Oyzz7CyUsdxM9mpoz+yU0e/nrVTR+fDHhYWFmzdupUDBw5w4sQJ4uPjycvLw8XFBW9vb/z8/Cr1rROEUAOd15CVjawh9UfWkPqllzVkgfDwcMLDw7l9+zYajQZ7e3t69uzJq6++Wq6QQohSFDI9PZ3AwECioqJQFIXatWuTn59PeHg4O3bsoG/fvqxatQqNpnJfsS2EMelcyL///e9ERUURGBjImDFjqFXr6Vn2ycnJbNmyhS1bttCsWTNmzpxpsLBCPO90Puxx7Ngx/Pz8mDFjhraMALa2tixYsICBAweyd+9eg4QUoqrQuZAZGRk4OzuXON/Dw4PU1FR9ZBKiytK5kJ07d+bYsWPk5+cXOz8iIoL27dvrLZgQVZHO99Tx9fVlyZIlBAQEMHbsWJo3b46JiQm3bt3iiy++4Mcff+TDDz80eGAhnmclHods1apVsVfyA8+crss9dSqCHIfUHzkOqV9lOg5pyHvqCCGKZ5R76gghilfqM3Wio6PJyMgodK/L3NxcMjIyiIyMZPHixXoNKERVonMhb9y4QWBgIHFxcSWOMTU1lUIKUQ46F3L16tUkJCQwadIkTExMCAkJYenSpTx8+JD9+/eTlJTEgQMHDBhViOefzschz58/z/Dhw5k7dy7Tpk1Do9Hg4ODA1KlT+eKLL7C1tWXr1q2GzCrEc69UZ+q0atUKgGrVqtG0aVMuXboEQM2aNRk6dCiRkZGGSSlEFaFzIevWrVvo1Dh7e3uuXbumfVyvXj3u3r2r13BCVDU6F7Jjx46EhoYSGxsLPP3KuO+++05b0rNnz2JjY2OIjEJUGToXMjAwkIcPH+Lr60tycjKjRo0iMzOTPn360K9fP7766iv69u1ryKxCPPd0LqS9vT1Hjx5l7ty52Nra0qBBA3bs2IGTkxNmZmZMnDiRoKAgQ2YV4rkn99QRf0jOZdUvvXyVwB/55JNP5HQ7IcpJb4VMTEzkypUr+lqcEFWS3gophCg/KaQQKiKFFEJFpJBCqEiJV3t8/PHHpVrQxYsXyx1Gn2r6rTV2hOfG44Tiv5Jd6J/eCglF77UjhCidEgu5ffv2iswhhOAZhZQvzxGi4slOHSFURAophIpIIYVQESmkECoihRRCRUp9o+SrV68SHh5OQkICY8aMwcrKimvXrtGtWzdD5BOiSilVIZcvX86uXbtQFAUTExP69OnDw4cPCQoKonv37qxduxZLS0tDZRXiuafzR9bt27ezc+dOJk+eTFhYmParBDw9PRk7dizh4eFs2rTJYEGFqAp0LuSePXvo06cPc+bMwc7OTju9Vq1aLFq0iNdff53Dhw8bJKQQVYXOhYyPj6djx44lzm/fvj2JiYl6CSVEVaVzIW1sbLhz506J869fv07t2rX1EkqIqkrnQvr4+LBr1y5u3LihnVZwdUdERAShoaF4eXnpP6EQVYjOt4F8+PAh/v7+3Lx5k5YtW3L58mU8PDzIyMjgypUrNGnShLCwMGxtbQ2dWSdmFk2MHeG5IddD6pf5Cy1KnKfzGrJWrVqEhYUxadIksrOzsbS05Oeff+bx48eMGzeOvXv3qqaMQlRWz+2NkmUNqT+yhtSvZ60hdT4xICEhQadxjRs31nWRQojf0bmQ3t7eOt2iIzo6ulyBhKjKdC5kYGBgkULm5eVx//59IiIiqFGjBjNnztR7QCGqEp0L+ayypaenM2LECG7evKmXUEJUVXq5/Mra2ho/Pz9CQ0P1sTghqiy9XQ+Zk5NDSkqKvhYnRJVU7r2s2dnZREdHs3XrVlq3bq23YEJURXrZy6ooCpaWlsybN09vwYSoinQu5IwZM4qdbmpqSr169ejRo4ecqSNEOelcyEaNGuHh4UGzZs0MGEeIqk3nnTorVqzgyJEjhswiRJWncyGrV68u98sRwsB0/si6bNkyFi9eTFZWFq+99hq2trZoNJoi4+RcViHKTuerPdzd3cnNzSU/P/+Z57Sq5VxWudpDf+RqD/3Sy9UekyZNku9/FMLASlxDBgcHM2LECF566aWKzqQXal5DenXvzLK35+Pu3oaHDx/xxd4jLH17JRkZmcaOViw1rSHPfn+OkE93c/nqDUxMTXjJpRUzJ43hJdf/nZTy488XWRvyGZeuXKdWTWu8u3oSOGE0NnXUcc+nMt0xYP/+/cTFxRkkUFXWvVsnjh/bjYWFOW/9eQU7d+1l8iR/jh7eKZ9A/sC/z19g6rwlPErPYNbkPzFtnD/xtxMZO+NN/nP5KgBRP15gQlAwv8bdYtKY4YwcMoATZ75hzPQFpD18ZOSf4I+V+qsERPmsXLmEuLjbePUYypMnTwCIi0vg449W0LtXd45/dcbICdVr5doQGtavx65Na6herRoAr/v24PVRk1kb8hmb167g3TUb0Zhq+L9PPsC+6dMdjD26dWLwmOn8Y/seFsyYZMwf4Q/Jl+1UIEtLS+7fe8CWrbu0ZQT419ffAeDmJucClyTt4SOu3viV3t5dtGUEeMHWhvZt3fj54mVuJyZxPSaWAX28tWUEaOFgR/fOHTh07KQxopfKM9eQP/zwA3l5eaVa4BtvvFGePM+1rKws+g0YXWT6Sy+5ABAXf7uiI1Ua1jWsOLx7U6EyFkhNfYhGoyHp3n0AWrZoVmSMfdNGnIw4S2LSPRo1qGfouGX2zEKGhYURFham04IKvoBHCqk7e/smdO/WmfdXLeU/F6M5cOC4sSOplkajwcGu6I66qzd+5fx/LtO5gwdW/y1rRubjIuNS055uP95PTq68hRw2bBgvv/xyBUWpWmxs6hBzIwqAjIxMZs9eQlZWlpFTVS6ZmY95a/lqACaM9sOxuT3WNaz4Z/hZJgYM0+4ky8rK5mzUOQCys3KMllcXzyxk+/btGTBggEFeOCkpqVTjGzRoYJAcxqIoCiP9p2FhYc6MwPF8dXwPo0ZPZ98+OV9YF4+fPGHGwne4eiOGiQHDeaWtOwBjRgxmw5b/Y+E7q5gYMIz8vHzWbdrO48dPt9k1ZkXPLlOTEo9DtmrVivfff99ghXR1dS3V9mlpzwBS83HI36tWrRo/nz+Fubk5LV581dhxilDTcUiAh4/SCXzzbc5fuMyg/r34y6LZ2rVhfn4+73+0iZ1fHCI/Px+A7p070Na9DWs2buPLXZto7tDUmPH1c6aOvn3++edMmTKF7Oxs5s2bh5lZ1T0C8+TJE44cPcmsmROpW9eGBw/kVigleZCSypQ5f+bK9Rj8BvqydMHMQsdvTU1NWRg0hQkBw4iLv03DBvVo3LABa0M+RaMxpXHD+kZM/8dKbMGgQYOwt7c32Au3bt2aTz/9FD8/P+7du8f06dMN9lpq4ezsyJEvd7L6g418EvJZoXk1a1qTn59PVla2kdKpX0ZGpraMY4YP4s1Zk4uMOfrPcF6oa8ur7dx5wdZGO/3cTxdp49wSS0uLioxcaiUeh3z33XcNftpcixYtmDt3Lps3byY5Odmgr6UGN27EUrt2TSZPHo25ubl2ur19EwYP6su//hVJenqGEROq218/3MCV6zGM9htYbBkBtofuZ8WHG8jN/d/mUMS3Ufx44RIjBvevqKhlZvTv9sjLy+PcuXO8+OKLer0FiFq3IUeNGsz2Tz8iMvIcO3ftpW5dG6ZPG4eFhTndvAZx6dJVY0csQg3bkL/ExjHQfwo1rWuwcNaUYnfODOjtzT/PfMOcxX+jcwcPenbrxO3Eu2wP3ccrbd3Z8P47xV4yWNGetQ1p9EIailoLCTB06AAWzJ+Oq4szGRmZnD5zliVLV3L9eoyxoxVLDYUM3X+E5as/fuaYi2ePAXD0ZDhbdnxO3K3b1LW1oZ+PFxPHDCv2pAJjkEKKclFDIZ8nevl+SCGE4UkhhVARKaQQKiKFFEJFpJBCqIgUUggVkUIKoSJSSCFURAophIpIIYVQESmkECoihRRCRaSQQqiIFFIIFZFCCqEiUkghVEQKKYSKSCGFUBEppBAqIoUUQkWkkEKoiBRSCBWRQgqhIlJIIVRECimEikghhVARKaQQKiKFFEJFpJBCqIgUUggVkUIKoSJSSCFURAophIo8t9+gLERlJGtIIVRECimEikghhVARKaQQKiKFFEJFpJBCqIgUUggVkUIKoSJSSCFURAophIpIIY3k8OHD9OvXD3d3d3x9fTlw4ICxI1V60dHRuLi4cOfOHWNHKTMppBEcO3aM+fPn07lzZ9avX8+rr77KwoULOX78uLGjVVoxMTFMmTKF3NxcY0cpFzm53Ah8fHxwdXVlzZo12mmzZ8/m6tWrHDt2zIjJKp/c3FxCQ0P54IMPMDc3JzU1lYiICBo2bGjsaGUia8gKFh8fT1xcHL169So0vXfv3sTExBAfH2+kZJXTuXPnWL16NePHj2f+/PnGjlNuUsgKFhMTA0Dz5s0LTXdwcADg119/rfBMlZmjoyMnT55kxowZaDQaY8cpNzNjB6hqHj16BIC1tXWh6TVq1AAgPT29wjNVZi+88IKxI+iVrCErWMEmu4mJSbHTTU3ln6Qqk3/9ClazZk2g6JowIyOj0HxRNUkhK1jBtmNcXFyh6Tdv3iw0X1RNUsgK5uDgQNOmTYscczxx4gTNmjWjcePGRkom1EB26hhBYGAgwcHB1K5dm+7du3P69GmOHTtW6LikqJqkkEYwePBgsrOz2bp1K59//jl2dnasXLmSvn37GjuaMDI5U0cIFZFtSCFURAophIpIIYVQESmkECoihRRCRaSQQqiIFLKUFi1ahLOzc6H/WrduTbt27fDz82P//v0VksPb25uAgADt44CAALy9vUu9nPT0dJKTk/WWq+D9eZZ9+/bh7OzMvn379PKaH330Ec7Ozty6dUuVyysNOTGgjIKDg7GxsQGeXqmRnp7OoUOHWLRoESkpKYwfP75C80ydOpXHjx+X6jkXL15k2rRprF69mg4dOhgomSgNKWQZ9ezZk6ZNmxaaNnToUPr27cv69esZPXo0FhYWFZanc+fOpX7OtWvXuHv3rgHSiLKSj6x6VK1aNby9vUlPT+f69evGjiMqISmknhVceJyXlwc83dZbvHgxb731Fm5ubnTt2lW7zXb+/HnGjRtH27Ztadu2LePHj+fChQtFlnn06FEGDhyIu7s7/fv3JzIyssiY4rYhf/nlF4KCgujQoQMeHh4EBATwww8/AE+3k4KDgwEYM2ZMoefeuXOHN998k44dO+Lm5sYbb7zBoUOHirzmxYsXGT9+PG3btqVLly5s3769LG/ZM126dImZM2fSqVMnXFxc8PT0ZN68ecXe6jEmJoYxY8bg7u5O9+7dWbt2LTk5OYXGpKWlsXz5crp06YKrqyu+vr589tlnqOUMUvnIqkf5+flERUVhYWGBo6OjdvqRI0do3rw5f/7zn7l//z62tracPXuWKVOm0KpVK4KCgsjOzmbfvn34+/uzbds22rdvDzzdARIcHEzbtm1ZsGABN2/eZOrUqeTn59OkSZMSs8TGxjJs2DDMzMwYPXo0tra27Nmzh3HjxrFz5058fHy4d+8eoaGhTJ06FTc3NwCSkpLw8/NDURQCAgKoXbs2p06dYsGCBdy9e5eJEycCcP36dQICAqhVqxbTp08nJyeH9evXa/8Q6cPVq1cZNWoUDg4OTJ48merVq/Pjjz9y8OBB7t69y44dOwqNL/jjs3DhQqKiotiwYQOJiYm89957AGRmZjJ69GgSExMZNWoUDRs2JDIykhUrVhAbG8vbb7+tt+xlpohSWbhwoeLk5KRcunRJefDggfLgwQPl7t27yvnz55WgoCDFyclJWbFihXa8l5eX0qpVK+XmzZvaaXl5eUqPHj2UESNGKLm5udrpGRkZio+PjzJw4EBFURQlNzdX8fT0VIYMGaJkZ2drx+3du1dxcnJSRo8erZ02evRoxcvLS/s4KChIcXd3V2JjY7XTkpOTFQ8PD2XWrFmFlhMZGVno53v11VeVpKSkQj/33LlzFVdXV+X+/fuKoijKzJkzlZdffllJSEjQjrlx44bi6uqqODk5PfM9LHjdvXv3PnPc0qVLlZdeeklJSUkpNH3OnDmKk5OTdvq6desUJycnJSgoqNC4RYsWKU5OTsqVK1e041xcXLSPC3zwwQeKk5OTEh0dXWh58fHxz8xnCPKRtYwGDRqEp6cnnp6evPbaawwfPpxTp04REBDAvHnzCo21t7fH3t5e+/jy5cvEx8fTs2dP0tLSSE5OJjk5mSdPnuDl5UV0dDR37tzh0qVLPHjwgMGDB2Nubq59/sCBA6ldu3aJ2fLz84mIiKBbt27au9kB2NjYsGvXLhYvXlzi806ePEn79u0xMzPT5kpOTqZXr15kZ2dz9uxZ8vPz+frrr+nWrRuNGjXSPt/R0ZHXXnut1O9lSZYtW8bp06epU6eOdlp6ejqWlpbA0zXeb02YMKHQ44LDQhEREcDTi8CdnJyoV69eoZ+tZ8+eAJw5c0Zv2ctKPrKW0fvvv6+945mpqSm1atXC0dFR+8vyW3Xr1i30uOD2HatWrWLVqlXFLj8xMVG7nfTbMgNoNJpCRfu91NRUMjMzix3j5ORU4vNSUlJ49OgRJ0+e5OTJkyXmKlj+73MBtGjRgtOnT5f4GqVhYmJCSkoKISEhXL16lbi4OBISErTbe/n5+UVe+7cK8hUcT4yLi+PJkyd4enoW+3qJiYl6yV0eUsgyateuXZHDHiX5/f1CC36RgoKCePnll4t9TosWLUhKSgIgKyuryPzf/zL+VsF2XGnvYFfwvN69ezNixIhix9jZ2Wn/v7S5Sis8PJzp06dTv359OnbsSNeuXXF1deWbb74hJCSkyPiS7uRX8P7n5eXh4eHBjBkzin29+vXr6y17WUkhjaBgZ4yVlRWdOnUqNO/ChQukpaVRrVo17S9/bGxsoTGKonD79m1atmxZ7PJtbGyoVq2a9sZZv7Vlyxbu37/PwoULi8yztbWlevXq5ObmFsmVkJDA5cuXqV69OjY2NlhbWxfJBej17Jbly5fj4ODA3r17sbKy0k7/8ssvix3/+/ek4KbTBWvKJk2akJGRUeRnS0tL47vvvnvmp46KItuQRuDq6kq9evXYsWOH9vaP8HT7aPbs2QQHB6PRaGjTpg1NmjRh9+7dhc7COXLkCCkpKSUu38zMjM6dOxMREVHoY1haWhpbtmzRfmQuWIMWrNXMzMzo2rUrERERXLlypdAy33vvPQIDA0lJScHExAQfHx++/vprrl27ph1z69YtwsPDy/7G/E5qaiqNGzcuVMbExEROnDgBUGSPblhYWKHH27Ztw8TERHtIx9vbmytXrhTJuHHjRoKCglRx7FjWkEZgbm7OkiVLmD17NoMHD2bo0KFYWlry+eefk5CQwOrVqzEze/pPs2TJEgIDAxk+fDhDhgwhKSmJnTt3FtrRUZx58+bh5+eHn58f/v7+WFtbExYWRmZmJrNnzwaerhEBdu/ezf379xkwYADz58/n+++/x9/fH39/fxo3bkx4eDhnzpxh+PDh2jVQUFAQ4eHhBAQEMHbsWDQaDTt27KBGjRpkZ2fr9D7s37+fn376qcj01q1bM3LkSLp27crRo0dZunQpbm5u3Lp1i7CwMO0fp9/+MYOna8709HTc3d2JiIjgzJkzTJw4UbvmmzJlCidOnGDGjBmMGDGCli1bcu7cOQ4ePEjXrl3p2rWrTrkNSQppJL1792br1q1s3LiRDRs2YGpqSsuWLdm4cSNeXl7acV5eXoSEhPDRRx/x4Ycf0qBBA/72t7+xc+fOZy7f0dGR0NBQPvzwQzZv3oypqSnu7u6sXLlSWypPT098fX05c+YMkZGR9OrVC3t7e8LCwli3bp22wHZ2dgQHBxc6mb1Ro0bs3r2bVatWsXnzZiwsLPDz8wModvuuOFFRUURFRRWZ3qNHD0aOHMmyZcuwsrLi9OnTHDx4kIYNG/LGG2/g4+PDyJEjiYyMpE2bNtrnbdq0ib/+9a8cPnyYBg0aEBwczNixY7Xz69SpQ2hoKOvWreP48eOEhobSuHFjpk+fzuTJk1Vx13i5yZUQKmL8PwlCCC0ppBAqIoUUQkWkkEKoiBRSCBWRQgqhIlJIIVRECimEikghhVCR/wce5A89N73cWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import Seaborn\n",
    "sns.set(font_scale=1.5) \n",
    "\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    plot conusion matrix using seaborn's heatmap().\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
    "    ax =  sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                      annot = True, # Annotate the boxes\n",
    "                      cbar = False)\n",
    "    plt.xlabel('Predicted Label')\n",
    "    plt.ylabel('True Label')\n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unavailable-connection",
   "metadata": {},
   "source": [
    "Now we've got a ROC curve, an AUC metric and a confusion matrix, let's get a classification report as well as cross-validated precision, recall and f1-score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "declared-crystal",
   "metadata": {},
   "source": [
    "### Classification Report\n",
    "\n",
    "We can make a classification report using `classification_report()` 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": 68,
   "id": "corporate-compact",
   "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": "radio-communist",
   "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()` 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.\n",
    "\n",
    "### Check evaluation metrics using cross-validation\n",
    "\n",
    "We're going to calculate precision, recall and f1-score of our model using cross-validation and to do so, we'll be using `cross_val_score()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "discrete-petersburg",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "macro-wisconsin",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import cross_val_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Instantiate logisticRegression model with best hyperparameters (found with GridSearchCV)\n",
    "clf = LogisticRegression(C=0.23357214690901212,\n",
    "                         solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "similar-revision",
   "metadata": {},
   "source": [
    "Now we've got an instantiated classifier, let's find some cross-validated metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "japanese-holly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81967213, 0.90163934, 0.8852459 , 0.88333333, 0.75      ])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated accuracy score\n",
    "cv_acc = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv = 5, # 5 fold cross-validation\n",
    "                         scoring='accuracy')\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cognitive-compression",
   "metadata": {},
   "source": [
    "Since there are 5 metrics here, we'll take the average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "catholic-placement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8479781420765027"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "historic-grammar",
   "metadata": {},
   "source": [
    "Now we'll do the same for other classification metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "executed-breed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8215873015873015"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated precision score\n",
    "cv_precision = np.mean(cross_val_score(clf,\n",
    "                                       X,\n",
    "                                       y,\n",
    "                                       cv = 5,\n",
    "                                       scoring = 'precision'))\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "needed-tractor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9272727272727274"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated Recall score\n",
    "cv_recall = np.mean(cross_val_score(clf,\n",
    "                                     X,\n",
    "                                     y,\n",
    "                                     cv=5,\n",
    "                                     scoring='recall'))\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "working-transportation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8705403543192143"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross-validated f1 score\n",
    "cv_f1 =  np.mean(cross_val_score(clf,\n",
    "                                  X,\n",
    "                                  y,\n",
    "                                  cv=5,\n",
    "                                  scoring='f1'))\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "victorian-effectiveness",
   "metadata": {},
   "source": [
    "Let's visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "independent-emergency",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({'Accuracy': cv_acc,\n",
    "                          'Precision': cv_precision,\n",
    "                          'Recall': cv_recall,\n",
    "                          'F1': cv_f1},\n",
    "                          index=[0])\n",
    "cv_metrics.T.plot.bar(title='Cross Validated Classification Metrics', legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ongoing-carry",
   "metadata": {},
   "source": [
    "## Feature importance\n",
    "\n",
    "Feature importance is another way of asking, \"which features contributing most to the outcomes of the model and how did they contribute?\"\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? Finding feature importance is different for each machine learning model.\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](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) for LogisticRegression, 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": 90,
   "id": "sitting-continuity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "rental-cheese",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.23357214690901212, solver='liblinear')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit an instance of LogisticRegression (taken from above)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "amazing-humanitarian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00369922, -0.90424085,  0.67472828, -0.0116134 , -0.00170364,\n",
       "         0.04787689,  0.33490184,  0.02472939, -0.63120401, -0.57590906,\n",
       "         0.47095113, -0.65165351, -0.699842  ]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "scheduled-dylan",
   "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": 84,
   "id": "homeless-confirmation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.003699218314634231,\n",
       " 'sex': -0.9042408481315317,\n",
       " 'cp': 0.6747282826858118,\n",
       " 'trestbps': -0.011613404239724763,\n",
       " 'chol': -0.0017036448048050072,\n",
       " 'fbs': 0.04787689050301493,\n",
       " 'restecg': 0.33490183888358416,\n",
       " 'thalach': 0.024729385199310817,\n",
       " 'exang': -0.6312040056168838,\n",
       " 'oldpeak': -0.5759090611742839,\n",
       " 'slope': 0.47095113183576787,\n",
       " 'ca': -0.6516535125690047,\n",
       " 'thal': -0.6998419969792693}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match coef of features to columns\n",
    "features_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "features_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mechanical-albania",
   "metadata": {},
   "source": [
    "Now we've match the feature coefficients to different features, let's visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "single-gateway",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "features_df = pd.DataFrame(features_dict, index=[0])\n",
    "features_df.T.plot.bar(title='Feature Importance', legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electrical-closure",
   "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": 88,
   "id": "alternate-biology",
   "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": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['sex'], df['target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "filled-adjustment",
   "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": 89,
   "id": "substantial-albany",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target   0    1\n",
       "slope          \n",
       "0       12    9\n",
       "1       91   49\n",
       "2       35  107"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Contrast slope (positive coefficient) with target\n",
    "pd.crosstab(df['slope'], df['target'])"
   ]
  },
  {
   "cell_type": "markdown",
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    " Looking back the data dictionary, we see slope is the \"slope of the peak exercise ST segment\" where:\n",
    "\n",
    "* 0: Upsloping: better heart rate with exercise (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",
    "\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",
    "\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",
    "\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",
    "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",
    "\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",
    "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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