Heart Disease Prediction.ipynb

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    "# Heart Disease Prediction using Machine Learning\n",
    "\n",
    "**Steps involved:**\n",
    "\n",
    "* **Exploratory data analysis (EDA)** - the process of going through a dataset and finding out more about it.\n",
    "* **Model training** - create model(s) to learn to predict a target variable based on other variables.\n",
    "* **Model evaluation** - evaluating a models predictions using problem-specific evaluation metrics.\n",
    "* **Model comparison** - comparing several different models to find the best one.\n",
    "* **Model fine-tuning** - once we've found a good model, how can we improve it?\n",
    "* **Feature importance** - since we're predicting the presence of heart disease, are there some things which are more important for prediction?\n",
    "* **Cross-validation** - if we do build a good model, can we be sure it will work on unseen data?\n",
    "* Reporting what we've found - if we had to present our work, what would we show someone?\n",
    "\n",
    "## 1. Problem Definition\n",
    "In our case, the problem we will be exploring is **binary classification** (a sample can only be one of two things).\n",
    "\n",
    "This is because we're going to be using a number of differnet features (pieces of information) about a person to predict whether they have heart disease or not.\n",
    "\n",
    "In a statement,\n",
    "\n",
    "*`Given clinical parameters about a patient, can we predict whether or not they have heart disease?`*\n",
    "\n",
    "## 2. Data\n",
    "What we'll want to do here is dive into the data our problem definition is based on. This may involve, sourcing, defining different parameters, talking to experts about it and finding out what we should expect.\n",
    "\n",
    "The original data came from the **Cleveland database from UCI Machine Learning Repository**.\n",
    "\n",
    "Howevever, we've downloaded it in a formatted way from **Kaggle**.\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",
    "***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",
    "However, due to the nature of experimentation, the evaluation metric may change over time.\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "### Heart Disease Data Dictionary\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",
    "\n",
    "2. **sex** - (1 = male; 0 = female)\n",
    "\n",
    "3. **cp** - chest pain type\n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "    \n",
    "4. **trestbps** - resting blood pressure (in mm Hg on admission to the hospital)\n",
    "    * anything above 130-140 is typically cause for concern\n",
    "    \n",
    "5. **chol** - serum cholestoral in mg/dl\n",
    "    * serum = LDL + HDL + .2 * triglycerides\n",
    "    * above 200 is cause for concern\n",
    "    \n",
    "6. **fbs** - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "    * '>126' mg/dL signals diabetes\n",
    "    \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",
    "    \n",
    "8. **thalach** - maximum heart rate achieved\n",
    "\n",
    "9. **exang** - exercise induced angina (1 = yes; 0 = no)\n",
    "\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",
    "    \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",
    "    \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",
    "    \n",
    "13. **thal** - thalium stress result\n",
    "    * 1,3: normal\n",
    "    * 6: fixed defect: used to be defect but ok now\n",
    "    * 7: reversable defect: no proper blood movement when excercising\n",
    "    \n",
    "14. **target** - have disease or not (1=yes, 0=no) (= the predicted attribute)\n",
    "\n",
    "**Note:** No personal identifiable information (PPI) can be found in the dataset.\n",
    "\n",
    "### Libraries Required\n",
    "\n",
    "* **pandas** for data analysis.\n",
    "* **NumPy** for numerical operations.\n",
    "* **Matplotlib/seaborn** for plotting or data visualization.\n",
    "* **Scikit-Learn** for machine learning modelling and evaluation."
   ]
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   "cell_type": "code",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# We want our plots to appear in the notebook\n",
    "%matplotlib inline \n",
    "\n",
    "## Models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "## Model Evaluators\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 precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.metrics import plot_roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85673081",
   "metadata": {},
   "source": [
    "## Load Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7c5579dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "    }\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",
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       "      <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",
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       "      <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",
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       "    <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"heart-disease.csv\")\n",
    "\n",
    "df.head()"
   ]
  },
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    "## Data Exploration (exploratory data analysis or EDA)\n",
    "\n",
    "The goal is to become a subject matter expert on the dataset we're working with. So when we start building models, we 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 we might want to walk through:\n",
    "\n",
    "1. What question(s) are we trying to solve (or prove wrong)?\n",
    "2. What kind of data do we have and how do we treat different types?\n",
    "3. What’s missing from the data and how do we deal with it?\n",
    "4. Where are the outliers and why should we care about them?\n",
    "5. How can we add, change or remove features to get more out of our data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5113ebba",
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    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.value_counts()"
   ]
  },
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   "cell_type": "markdown",
   "id": "2f55b572",
   "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 our target classes have the same number of samples.\n",
    "\n",
    "If we'd prefer these values in percentages, value_counts() takes a parameter, normalize which can be set to true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c30e3f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.544554\n",
       "0    0.455446\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5257e75f",
   "metadata": {},
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD1CAYAAACrz7WZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAOH0lEQVR4nO3dXYxc912H8eeLTQJthXDktXH8glNwWpyqqGgbChWoNJQEUdW5ieSIIqtEskBpaRGoTehF4CJSBKiABEWyWhMjSiKrFGJVotQYSoSgSTd9jeOmtpo22drEG8I7klunPy72BIbpbHZ3zsxu/PfzuZk5/3POzk/V6vHJ6cxOqgpJUlu+bb0HkCRNnnGXpAYZd0lqkHGXpAYZd0lqkHGXpAZtXO8BADZv3ly7d+9e7zEk6ZLyyCOPPFNVM6P2vSjivnv3bubm5tZ7DEm6pCT56lL7vC0jSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUoBfFh5guGcl6T9AWvyhGmhqv3CWpQcZdkhq0bNyTHE5yPsmjQ+vvSPJ4kpNJfnNg/c4kZ7p9N05jaEnSC1vJPfd7gd8H/vj5hSQ/AewDXl1VF5Js6db3AvuB64Crgb9Ocm1VPTfpwSVJS1v2yr2qHgSeHVr+ReCeqrrQHXO+W98H3F9VF6rqCeAMcP0E55UkrcC499yvBX4syUNJ/i7Ja7v17cBTA8fNd2uSpDU07lshNwKbgNcBrwWOJnk5MOq9giPf75bkIHAQYNeuXWOOIUkaZdwr93ngI7XoYeCbwOZufefAcTuAs6N+QFUdqqrZqpqdmRn5RSKSpDGNG/e/AN4IkORa4ArgGeAYsD/JlUmuAfYAD09gTknSKix7WybJfcAbgM1J5oG7gMPA4e7tkV8HDlRVASeTHAUeAy4Ct/tOGUlae6kXwUfAZ2dn65L4DlX//MBkvQh+96RLWZJHqmp21D4/oSpJDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktSgZeOe5HCS8923Lg3v+9UklWTzwNqdSc4keTzJjZMeWJK0vJVcud8L3DS8mGQn8CbgyYG1vcB+4LrunPcn2TCRSSVJK7Zs3KvqQeDZEbt+B3g3MPhdafuA+6vqQlU9AZwBrp/EoJKklRvrnnuStwBfq6rPDe3aDjw1sD3frUmS1tDG1Z6Q5CXAe4GfGrV7xNrIb0FOchA4CLBr167VjiFJegHjXLl/H3AN8LkkXwF2AJ9O8j0sXqnvHDh2B3B21A+pqkNVNVtVszMzM2OMIUlayqqv3KvqC8CW57e7wM9W1TNJjgF/muR9wNXAHuDhCc0q6QXkN0b9h7PGUXeNvOFwSVnJWyHvA/4ReEWS+SS3LXVsVZ0EjgKPAR8Dbq+q5yY1rCRpZZa9cq+qW5fZv3to+27g7n5jSZL68BOqktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDVrJNzEdTnI+yaMDa7+V5ItJPp/kz5N898C+O5OcSfJ4khunNLck6QWs5Mr9XuCmobXjwKuq6tXAl4A7AZLsBfYD13XnvD/JholNK0lakWXjXlUPAs8OrX28qi52m58EdnTP9wH3V9WFqnoCOANcP8F5JUkrMIl77j8P/GX3fDvw1MC++W5NkrSGesU9yXuBi8CHnl8acVgtce7BJHNJ5hYWFvqMIUkaMnbckxwA3gz8bFU9H/B5YOfAYTuAs6POr6pDVTVbVbMzMzPjjiFJGmGsuCe5CXgP8Jaq+u+BXceA/UmuTHINsAd4uP+YkqTV2LjcAUnuA94AbE4yD9zF4rtjrgSOJwH4ZFX9QlWdTHIUeIzF2zW3V9Vz0xpekjTasnGvqltHLH/wBY6/G7i7z1CSpH78hKokNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDlo17ksNJzid5dGDtqiTHk5zuHjcN7LszyZkkjye5cVqDS5KWtpIr93uBm4bW7gBOVNUe4ES3TZK9wH7guu6c9yfZMLFpJUkrsmzcq+pB4Nmh5X3Ake75EeDmgfX7q+pCVT0BnAGun8yokqSVGvee+9aqOgfQPW7p1rcDTw0cN9+tSZLW0KT/D9WMWKuRByYHk8wlmVtYWJjwGJJ0eRs37k8n2QbQPZ7v1ueBnQPH7QDOjvoBVXWoqmaranZmZmbMMSRJo4wb92PAge75AeCBgfX9Sa5Mcg2wB3i434iSpNXauNwBSe4D3gBsTjIP3AXcAxxNchvwJHALQFWdTHIUeAy4CNxeVc9NaXZJ0hKWjXtV3brErhuWOP5u4O4+Q0mS+vETqpLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ3qFfckv5zkZJJHk9yX5DuSXJXkeJLT3eOmSQ0rSVqZseOeZDvwS8BsVb0K2ADsB+4ATlTVHuBEty1JWkN9b8tsBL4zyUbgJcBZYB9wpNt/BLi552tIklZp7LhX1deA32bxC7LPAf9WVR8HtlbVue6Yc8CWSQwqSVq5PrdlNrF4lX4NcDXw0iRvXcX5B5PMJZlbWFgYdwxJ0gh9bsv8JPBEVS1U1TeAjwA/CjydZBtA93h+1MlVdaiqZqtqdmZmpscYkqRhfeL+JPC6JC9JEuAG4BRwDDjQHXMAeKDfiJKk1do47olV9VCSDwOfBi4CnwEOAS8Djia5jcV/AG6ZxKCSpJUbO+4AVXUXcNfQ8gUWr+IlSevET6hKUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1qFfck3x3kg8n+WKSU0l+JMlVSY4nOd09bprUsJKklel75f57wMeq6pXAD7L4Hap3ACeqag9wotuWJK2hseOe5LuAHwc+CFBVX6+qfwX2AUe6w44AN/cbUZK0Wn2u3F8OLAB/lOQzST6Q5KXA1qo6B9A9bpnAnJKkVegT943ADwF/WFWvAf6LVdyCSXIwyVySuYWFhR5jSJKG9Yn7PDBfVQ912x9mMfZPJ9kG0D2eH3VyVR2qqtmqmp2ZmekxhiRp2Nhxr6p/Ap5K8opu6QbgMeAYcKBbOwA80GtCSdKqbex5/juADyW5Avgy8DYW/8E4muQ24Englp6vIUlapV5xr6rPArMjdt3Q5+dKkvrxE6qS1CDjLkkNMu6S1CDjLkkNMu6S1CDjLkkNMu6S1CDjLkkNMu6S1CDjLkkNMu6S1CDjLkkNMu6S1CDjLkkNMu6S1CDjLkkN6h33JBuSfCbJR7vtq5IcT3K6e9zUf0xJ0mpM4sr9ncCpge07gBNVtQc40W1LktZQr7gn2QH8DPCBgeV9wJHu+RHg5j6vIUlavb5X7r8LvBv45sDa1qo6B9A9bun5GpKkVRo77kneDJyvqkfGPP9gkrkkcwsLC+OOIUkaoc+V++uBtyT5CnA/8MYkfwI8nWQbQPd4ftTJVXWoqmaranZmZqbHGJKkYWPHvarurKodVbUb2A/8TVW9FTgGHOgOOwA80HtKSdKqTON97vcAb0pyGnhTty1JWkMbJ/FDquoTwCe65/8M3DCJnytJGo+fUJWkBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBo0d9yQ7k/xtklNJTiZ5Z7d+VZLjSU53j5smN64kaSX6XLlfBH6lqn4AeB1we5K9wB3AiaraA5zotiVJa2jsuFfVuar6dPf8P4BTwHZgH3CkO+wIcHPPGSVJqzSRe+5JdgOvAR4CtlbVOVj8BwDYssQ5B5PMJZlbWFiYxBiSpE7vuCd5GfBnwLuq6t9Xel5VHaqq2aqanZmZ6TuGJGlAr7gn+XYWw/6hqvpIt/x0km3d/m3A+X4jSpJWq8+7ZQJ8EDhVVe8b2HUMONA9PwA8MP54kqRxbOxx7uuBnwO+kOSz3dqvAfcAR5PcBjwJ3NJrQknSqo0d96r6eyBL7L5h3J8rSerPT6hKUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1aGpxT3JTkseTnElyx7ReR5L0raYS9yQbgD8AfhrYC9yaZO80XkuS9K2mdeV+PXCmqr5cVV8H7gf2Tem1JElD+nxB9gvZDjw1sD0P/PDgAUkOAge7zf9M8viUZrkcbQaeWe8hlpWlvoJXDbskfjfz65fM7+b3LrVjWnEf9b9M/b+NqkPAoSm9/mUtyVxVza73HNIwfzfXzrRuy8wDOwe2dwBnp/RakqQh04r7p4A9Sa5JcgWwHzg2pdeSJA2Zym2ZqrqY5O3AXwEbgMNVdXIar6WRvN2lFyt/N9dIqmr5oyRJlxQ/oSpJDTLuktQg4y5JDZrW+9wliSSvZPHT6dtZ/KzLWeBYVZ1a18EuA165NyzJ29Z7Bl2+kryHxT89EuBhFt8iHeA+/5jg9PlumYYlebKqdq33HLo8JfkScF1VfWNo/QrgZFXtWZ/JLg/elrnEJfn8UruArWs5izTkm8DVwFeH1rd1+zRFxv3StxW4EfiXofUA/7D240j/613AiSSn+b8/JLgL+H7g7es11OXCuF/6Pgq8rKo+O7wjySfWfBqpU1UfS3Iti38CfDuLFxzzwKeq6rl1He4y4D13SWqQ75aRpAYZd0lqkHGXpAYZd0lqkHGXpAb9D60CTCyMejYhAAAAAElFTkSuQmCC\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 = [\"Red\", \"Green\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fda94e0d",
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,\n",
       "           count        mean        std    min    25%    50%    75%    max\n",
       " age       303.0   54.366337   9.082101   29.0   47.5   55.0   61.0   77.0\n",
       " sex       303.0    0.683168   0.466011    0.0    0.0    1.0    1.0    1.0\n",
       " cp        303.0    0.966997   1.032052    0.0    0.0    1.0    2.0    3.0\n",
       " trestbps  303.0  131.623762  17.538143   94.0  120.0  130.0  140.0  200.0\n",
       " chol      303.0  246.264026  51.830751  126.0  211.0  240.0  274.5  564.0\n",
       " fbs       303.0    0.148515   0.356198    0.0    0.0    0.0    0.0    1.0\n",
       " restecg   303.0    0.528053   0.525860    0.0    0.0    1.0    1.0    2.0\n",
       " thalach   303.0  149.646865  22.905161   71.0  133.5  153.0  166.0  202.0\n",
       " exang     303.0    0.326733   0.469794    0.0    0.0    0.0    1.0    1.0\n",
       " oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2\n",
       " slope     303.0    1.399340   0.616226    0.0    1.0    1.0    2.0    2.0\n",
       " ca        303.0    0.729373   1.022606    0.0    0.0    0.0    1.0    4.0\n",
       " thal      303.0    2.313531   0.612277    0.0    2.0    2.0    3.0    3.0\n",
       " target    303.0    0.544554   0.498835    0.0    0.0    1.0    1.0    1.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info(), df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c00d1d05",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency according to Gender\n",
    "\n",
    "If you want to compare two columns to each other, you can use the function **pd.crosstab(column_1, column_2)**.\n",
    "\n",
    "This is helpful if you want to start gaining an intuition about how your independent variables interact with your dependent variables.\n",
    "\n",
    "Let's compare our target column with the sex column.\n",
    "\n",
    "Remember from our data dictionary, for the target column, 1 = heart disease present, 0 = no heart disease. And for sex, 1 = male, 0 = female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5090cda1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f6c06796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a276c084",
   "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": "075d627d",
   "metadata": {},
   "source": [
    "### Making our crosstab visual\n",
    "\n",
    "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=(length, width)** (how big you want it to be) and **color=[colour_1, colour_2]** (the different colours you'd like to use)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "63e4364f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.target, df.sex).plot(kind=\"bar\",\n",
    "                                    figsize=(15,8),\n",
    "                                    color=[\"red\",\"green\"]);\n",
    "\n",
    "# Add some attributes to it\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": "5c94bdf0",
   "metadata": {},
   "source": [
    "### Age vs Max Heart rate for Heart Disease\n",
    "Let's try combining a couple of independent variables, such as, age and thalach (maximum heart rate) and then comparing them to our target variable heart disease.\n",
    "\n",
    "Because there are so many different values for age and thalach, we'll use a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1c36d347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c228d97910>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure\n",
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "#plot the positve values\n",
    "plt.scatter(df.age[df.target==1],\n",
    "            df.thalach[df.target==1],\n",
    "            c=\"red\")\n",
    "\n",
    "#plot the negative values\n",
    "plt.scatter(df.age[df.target==0],\n",
    "            df.thalach[df.target==0],\n",
    "            c=\"green\")\n",
    "\n",
    "#add information to the plot\n",
    "plt.title(\"Age and Max Heart Rate relation with Heart Disease\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Max Heart Rate\")\n",
    "plt.legend([\"Heart disease\", \"No Heart Disease\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4afbfa",
   "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.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "32ab9948",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.age.plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b11bd6c",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency per Chest Pain Type\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": 40,
   "id": "bb36900a",
   "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>cp</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></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>9</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>41</td>\n",
       "      <td>69</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cp        0   1   2   3\n",
       "target                 \n",
       "0       104   9  18   7\n",
       "1        39  41  69  16"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.target, df.cp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ff2a954d",
   "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": [
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\",\n",
    "                                   color=[\"green\", \"red\"])\n",
    "\n",
    "plt.title(\"Heart Disease frequency per chest pain type\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.legend([\"No Heart Disease\", \"Heart Disease\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8742168",
   "metadata": {},
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a210b45a",
   "metadata": {},
   "source": [
    "### Correlation between independent variables\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** for us, in other words, a big table of numbers telling us how related each variable is the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9d004d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5ae53adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.heatmap(corr_matrix,\n",
    "            annot=True,\n",
    "            linewidths=0.15,\n",
    "            fmt=\".2f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105b26e9",
   "metadata": {},
   "source": [
    "## 5. Modeling\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c4978cc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Everything except target variable\n",
    "X = df.drop(\"target\", axis=1)\n",
    "\n",
    "# Target variable\n",
    "y = df.target.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5fd76b29",
   "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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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0   0     1  \n",
       "1   0     2  \n",
       "2   0     2  \n",
       "3   0     2  \n",
       "4   0     2  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head() #Feature Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcc4e7ea",
   "metadata": {},
   "source": [
    "### Training and test split\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "61d05f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42) \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f86da6cb",
   "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>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
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       "      <td>120</td>\n",
       "      <td>295</td>\n",
       "      <td>0</td>\n",
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       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>231</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>135</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "     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  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "442a6b8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1,\n",
       "        1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
       "        1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
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       "        1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,\n",
       "        1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0,\n",
       "        0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1,\n",
       "        1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "        1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1],\n",
       "       dtype=int64),\n",
       " 242)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train, len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3ba6f264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
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       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
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       "      <td>150</td>\n",
       "      <td>276</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>170</td>\n",
       "      <td>288</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>126</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>409</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>110</td>\n",
       "      <td>265</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "     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  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c72d308c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "        0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64),\n",
       " 61)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test, len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "805b0683",
   "metadata": {},
   "source": [
    "## Model choices\n",
    "Now we've got our data prepared, we can start to fit models. We'll be using the following and comparing their results.\n",
    "\n",
    "* Logistic Regression - `LogisticRegression()`\n",
    "* K-Nearest Neighbors - `KNeighboursClassifier()`\n",
    "* RandomForest - `RandomForestClassifier()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "41db4cb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Scores:\n",
      "\n",
      "KNeighborClassifier\t:\t0.6885245901639344\n",
      "Logistic Regression\t:\t0.8524590163934426\n",
      "RandomForestClassifier\t:\t0.8524590163934426\n"
     ]
    }
   ],
   "source": [
    "#put all the models in a dictionary\n",
    "models = {\"KNeighborClassifier\": KNeighborsClassifier(),\n",
    "          \"Logistic Regression\": LogisticRegression(max_iter=1000), \n",
    "          \"RandomForestClassifier\": RandomForestClassifier()}\n",
    "\n",
    "#create a function to fit and score model\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluates given machine learning models.\n",
    "    models : a dict of different Scikit-Learn machine learning models\n",
    "    X_train : training data\n",
    "    X_test : testing data\n",
    "    y_train : labels assosciated with training data\n",
    "    y_test : labels assosciated with test data\n",
    "    \"\"\"\n",
    "    np.random.seed(80)\n",
    "    model_scores = {}\n",
    "    for name, model in models.items():\n",
    "        model.fit(X_train, y_train)\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores\n",
    "        \n",
    "model_scores = fit_and_score(models, X_train, X_test, y_train, y_test)\n",
    "\n",
    "print(\"Model Scores:\\n\")\n",
    "for name, score in model_scores.items():\n",
    "    print(\"{}\\t:\\t{}\".format(name,score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff05a1d4",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9250ed9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNeighborClassifier</th>\n",
       "      <td>0.688525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.852459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.852459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        accuracy\n",
       "KNeighborClassifier     0.688525\n",
       "Logistic Regression     0.852459\n",
       "RandomForestClassifier  0.852459"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=[\"accuracy\"])\n",
    "model_compare.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2c2273f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare.T.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c3373c",
   "metadata": {},
   "source": [
    "### Further Steps\n",
    "* **Hyperparameter tuning** - Each model you use has a series of dials you can turn to dictate how they perform. Changing these values may increase or decrease model performance.\n",
    "* **Feature importance** - If there are a large amount of features we're using to make predictions, do some have more importance than others? For example, for predicting heart disease, which is more important, sex or age?\n",
    "* **Confusion matrix** - Compares the predicted values with the true values in a tabular way, if 100% correct, all values in the matrix will be top left to bottom right (diagnol line).\n",
    "* **Cross-validation** - Splits your dataset into multiple parts and train and tests your model on each part and evaluates performance as an average.\n",
    "* **Precision** - Proportion of true positives over total number of samples. Higher precision leads to less false positives.\n",
    "* **Recall** - Proportion of true positives over total number of true positives and false negatives. Higher recall leads to less false negatives.\n",
    "* **F1 score** - Combines precision and recall into one metric. 1 is best, 0 is worst.\n",
    "* **Classification report** - Sklearn has a built-in function called classification_report() which returns some of the main classification metrics such as precision, recall and f1-score.\n",
    "* **ROC Curve** - Receiver Operating Characterisitc is a plot of true positive rate versus false positive rate.\n",
    "* **Area Under Curve (AUC)** - The area underneath the ROC curve. A perfect model achieves a score of 1.0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f38a9226",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning and cross-validation\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": "9aed9f24",
   "metadata": {},
   "source": [
    "### Tune KNeighborsClassifier (K-Nearest Neighbors or KNN) by hand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "75b4250b",
   "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": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create an array to store the score\n",
    "score = []\n",
    "\n",
    "#create the neighbors to be used for tuning\n",
    "neighbors = np.arange(1,21)\n",
    "\n",
    "for i in neighbors:\n",
    "    #initiate the algorithm\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    \n",
    "    #fit the model\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    #score the model and add the score to list\n",
    "    score.append(knn.score(X_test, y_test))\n",
    "    \n",
    "#display the scores\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "27f510db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 75.41%\n"
     ]
    }
   ],
   "source": [
    "#print the best acheived accuracy\n",
    "print(f\"Maximum KNN score on the test data: {max(score)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "0d666f41",
   "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": [
    " plt.plot(neighbors, score, label=\"Test score\")\n",
    "plt.xticks(np.arange(1, 21, 1))\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f41c4f6d",
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bac82cb",
   "metadata": {},
   "source": [
    "### Tuning models with `RandomizedSearchCV`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "feab1c4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create param grids\n",
    "\n",
    "# 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": "code",
   "execution_count": 129,
   "id": "5a87f93c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    }
   ],
   "source": [
    "# Setup random seed\n",
    "np.random.seed(80)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                                param_distributions=log_reg_grid,\n",
    "                                cv=5,\n",
    "                                n_iter=20,\n",
    "                                verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model\n",
    "rs_log_reg.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "e43d9f06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "4171ba68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "ed6145fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    }
   ],
   "source": [
    "# Setup random seed\n",
    "np.random.seed(80)\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\n",
    "rs_rf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "3a570c5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 410,\n",
       " 'min_samples_split': 10,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 5}"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "2b64dea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c482b83",
   "metadata": {},
   "source": [
    "### Tuning a model with GridSearchCV\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "ebb611b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    }
   ],
   "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",
    "                          log_reg_grid,\n",
    "                          cv=5,\n",
    "                          verbose=True)\n",
    "\n",
    "# Fit grid hyperparameter search model\n",
    "gs_log_reg.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "ecd59287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "3d7cae21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d90f977d",
   "metadata": {},
   "source": [
    "### Evaluating a classification model, beyond accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd57f2f",
   "metadata": {},
   "source": [
    "* ROC curve and AUC score - plot_roc_curve()\n",
    "* Confusion matrix - confusion_matrix()\n",
    "* Classification report - classification_report()\n",
    "* Precision - precision_score()\n",
    "* Recall - recall_score()\n",
    "* F1-score - f1_score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "2c86c78e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make preidctions on test data\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "194d9860",
   "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": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "43d49204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb5f6fa",
   "metadata": {},
   "source": [
    "### ROC Curve and AUC Scores\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "9b045c9f",
   "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": [
    "# Plot ROC curve and calculate AUC metric\n",
    "plot_roc_curve(gs_log_reg, X_test, y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0195453b",
   "metadata": {},
   "source": [
    "### Confusion matrix\n",
    "A confusion matrix is a visual way to show where your model made the right predictions and where it made the wrong predictions (or in other words, got confused).\n",
    "\n",
    "Scikit-Learn allows us to create a confusion matrix using confusion_matrix() and passing it the true labels and predicted labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "a896502f",
   "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": "code",
   "execution_count": 143,
   "id": "274942d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5) # Increase font size\n",
    "\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a confusion matrix using Seaborn's heatmap().\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot=True, # Annotate the boxes\n",
    "                     cbar=False)\n",
    "    plt.xlabel(\"true label\")\n",
    "    plt.ylabel(\"predicted label\")\n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "737582b5",
   "metadata": {},
   "source": [
    "### Classification report\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": 144,
   "id": "92a53b33",
   "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": "b23c1d35",
   "metadata": {},
   "source": [
    "* **Precision** - Indicates the proportion of positive identifications (model predicted class 1) which were actually correct. A model which produces no false positives has a precision of 1.0.\n",
    "* **Recall** - Indicates the proportion of actual positives which were correctly classified. A model which produces no false negatives has a recall of 1.0.\n",
    "* **F1 score** - A combination of precision and recall. A perfect model achieves an F1 score of 1.0.\n",
    "* **Support** - The number of samples each metric was calculated on.\n",
    "* **Accuracy** - The accuracy of the model in decimal form. Perfect accuracy is equal to 1.0.\n",
    "* **Macro avg** - Short for macro average, the average precision, recall and F1 score between classes. Macro avg doesn’t class imbalance into effort, so if you do have class imbalances, pay attention to this metric.\n",
    "* **Weighted avg** - Short for weighted average, the weighted average precision, recall and F1 score between classes. Weighted means each metric is calculated with respect to how many samples there are in each class. This metric will favour the majority class (e.g. will give a high value when one class out performs another due to having more samples)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75416b39",
   "metadata": {},
   "source": [
    "### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "afe01e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8479781420765027"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate best model with best hyperparameters (found with GridSearchCV)\n",
    "clf = LogisticRegression(C=0.23357214690901212,\n",
    "                         solver=\"liblinear\")\n",
    "\n",
    "# Cross-validated accuracy score\n",
    "cv_acc = np.mean(cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5, # 5-fold cross-validation\n",
    "                         scoring=\"accuracy\")) # accuracy as scoring\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "376a7c69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8215873015873015"
      ]
     },
     "execution_count": 148,
     "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, # 5-fold cross-validation\n",
    "                                       scoring=\"precision\")) # precision as scoring\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "950e1be5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9272727272727274"
      ]
     },
     "execution_count": 149,
     "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, # 5-fold cross-validation\n",
    "                                    scoring=\"recall\")) # recall as scoring\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "f3f8553a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8705403543192143"
      ]
     },
     "execution_count": 150,
     "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, # 5-fold cross-validation\n",
    "                                scoring=\"f1\")) # f1 as scoring\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "c59c35cd",
   "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 Metrics\", legend=False);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7022ee07",
   "metadata": {},
   "source": [
    "### Feature importance\n",
    "Feature importance is another way of asking, \"which features contributing most to the outcomes of the model?\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "e81ecfd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit an instance of LogisticRegression (taken from above)\n",
    "clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "73f9ba8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00369922, -0.90424087,  0.67472828, -0.0116134 , -0.00170364,\n",
       "         0.04787689,  0.33490186,  0.02472938, -0.63120403, -0.57590919,\n",
       "         0.4709512 , -0.6516535 , -0.69984202]])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "6c1fd764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.003699219011760782,\n",
       " 'sex': -0.9042408714480176,\n",
       " 'cp': 0.6747282766540338,\n",
       " 'trestbps': -0.01161340334265323,\n",
       " 'chol': -0.0017036446360052262,\n",
       " 'fbs': 0.04787688669240361,\n",
       " 'restecg': 0.3349018562558094,\n",
       " 'thalach': 0.024729384743360134,\n",
       " 'exang': -0.631204028843173,\n",
       " 'oldpeak': -0.575909185434028,\n",
       " 'slope': 0.47095119664446533,\n",
       " 'ca': -0.6516535002884537,\n",
       " 'thal': -0.6998420233395882}"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match features to columns\n",
    "features_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "features_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "061130c5",
   "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": "eeb91f83",
   "metadata": {},
   "source": [
    "We were able to achieve an **accuracy** of **84.79% recall** of **92.72%** **precision** of **82.15%** and **f1 score** of **87.05%**  on our dataset. The score could be improved by collecting more data. Due to the project not being able to acheive the desired score of **95% accuracy** it will be dropped for now. Further improvements can be made to the machine learning model if more data is acquired."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}