Capstone.ipynb

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  {
   "cell_type": "markdown",
   "id": "51940b27-d000-4dc7-907c-519610ee9fbe",
   "metadata": {},
   "source": [
    "**Capstone for the HarvardX Course \"Introduction to Data Science with Python\"**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccbe62d8-bec7-400e-9953-cb1a39acd6e3",
   "metadata": {},
   "source": [
    "**Task**: Compare scenarios from different countries' COVID-19 Response to emphasize the problem of relying on a single metric such as accuracy for prediction. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40026a42-35a5-4614-a9e4-33a5cd3efbd9",
   "metadata": {},
   "source": [
    "**Problem Setting**: At the peak of the COVID-19 pandemic, hospital authorities had to make a call about who to admit and who to send home given the limited available resources. Our problem is to have a classifier that suggests whether a patient should be immediately admitted to the hospital or sent home."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac671861-4426-4e13-ad83-7f270dac1730",
   "metadata": {},
   "source": [
    "**The Data**: The data consists of the following predictors:\n",
    "- Age\n",
    "- Sex\n",
    "- Cough\n",
    "- Fever\n",
    "- Chills\n",
    "- Sore Throat\n",
    "- Headache\n",
    "- Fatigue\n",
    "\n",
    "The outcome is a classification prediction to indicate the **urgency of admission**. \n",
    "- **Positive**: Indicates that a patient that was admitted within 1 day from the onset of symptoms.\n",
    "- **Negative**: Indicates everyone else.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eaacc0f-2dd9-4ce7-b20b-98996e825b11",
   "metadata": {},
   "source": [
    "**I. Data Preprocessing and Exploratory Data Analysis (EDA)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3ebaa2d3-3e73-4749-ad9f-e0ba330e8643",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "\n",
    "# Your code here\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import os \n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy import signal\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline"
   ]
  },
  {
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   "execution_count": 46,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cough</th>\n",
       "      <th>fever</th>\n",
       "      <th>chills</th>\n",
       "      <th>sore_throat</th>\n",
       "      <th>headache</th>\n",
       "      <th>fatigue</th>\n",
       "      <th>Urgency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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      "text/plain": [
       "    age  sex  cough  fever  chills  sore_throat  headache  fatigue  Urgency\n",
       "0  30.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "1  47.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "2  49.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "3  50.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "4  59.0  0.0    0.0    1.0     0.0          0.0       0.0      0.0        0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the datafile \"covid.csv\"\n",
    "df = pd.read_csv(\"covid.csv\")\n",
    "\n",
    "# Take a quick look at the dataframed\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5d64010e-b0a3-4e49-9b4d-07ecc1dfbb34",
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "0       0\n",
      "1       0\n",
      "2       0\n",
      "3       0\n",
      "4       0\n",
      "       ..\n",
      "996     2\n",
      "997     0\n",
      "998     0\n",
      "999     1\n",
      "1000    0\n",
      "Length: 1001, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check if there are any missing or Null values\n",
    "missingvalues = df.isnull().sum(axis=1)\n",
    "print(missingvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5391af75-3f6d-47e6-8c39-d71e833136fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows with null values: 37\n"
     ]
    }
   ],
   "source": [
    "# Find the number of rows with missing values\n",
    "numrows_missingvalues = (missingvalues > 0).sum()\n",
    "num_null = numrows_missingvalues\n",
    "print(\"Number of rows with null values:\", num_null)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cd1721c3-5922-47a0-b038-08ff4d69de59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# kNN impute the missing data\n",
    "# Use a k value of 5\n",
    "\n",
    "# Your code here\n",
    "df_drop = df.drop('Urgency', axis=1)\n",
    "imputer = KNNImputer(n_neighbors=5)\n",
    "df_imputed = imputer.fit_transform(df_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e3b50115-2e01-4abb-8e77-907cf603e029",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cough</th>\n",
       "      <th>fever</th>\n",
       "      <th>chills</th>\n",
       "      <th>sore_throat</th>\n",
       "      <th>headache</th>\n",
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      "text/plain": [
       "       age  sex  cough  fever  chills  sore_throat  headache  fatigue  Urgency\n",
       "0     30.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "1     47.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "2     49.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "3     50.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "4     59.0  0.0    0.0    1.0     0.0          0.0       0.0      0.0        0\n",
       "...    ...  ...    ...    ...     ...          ...       ...      ...      ...\n",
       "996   72.0  1.0    0.0    0.4     0.0          0.0       0.0      0.0        1\n",
       "997   56.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        1\n",
       "998   43.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0        1\n",
       "999   39.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0        1\n",
       "1000  50.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        1\n",
       "\n",
       "[1001 rows x 9 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace the original dataframe with the imputed data, continue to use df for the dataframe\n",
    "\n",
    "# Your code here\n",
    "df_imputed = pd.DataFrame(df_imputed, columns=df_drop.columns)\n",
    "df_imputed['Urgency']= df['Urgency']\n",
    "df = df_imputed\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ae03777-e045-4018-a513-03302c315f86",
   "metadata": {},
   "source": [
    "**EDA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "58a5f925-8134-4b54-8b63-056b2fa35613",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       age  sex  cough  fever  chills  sore_throat  headache  fatigue  \\\n",
      "0     30.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0   \n",
      "1     47.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0   \n",
      "2     49.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0   \n",
      "3     50.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0   \n",
      "4     59.0  0.0    0.0    1.0     0.0          0.0       0.0      0.0   \n",
      "...    ...  ...    ...    ...     ...          ...       ...      ...   \n",
      "996   72.0  1.0    0.0    0.4     0.0          0.0       0.0      0.0   \n",
      "997   56.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0   \n",
      "998   43.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0   \n",
      "999   39.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0   \n",
      "1000  50.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0   \n",
      "\n",
      "      Urgency Age_Group  \n",
      "0           0     31-40  \n",
      "1           0     41-50  \n",
      "2           0     41-50  \n",
      "3           0     51-60  \n",
      "4           0     51-60  \n",
      "...       ...       ...  \n",
      "996         1     71-80  \n",
      "997         1     51-60  \n",
      "998         1     41-50  \n",
      "999         1     31-40  \n",
      "1000        1     51-60  \n",
      "\n",
      "[1001 rows x 10 columns]\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <th>cough</th>\n",
       "      <th>fever</th>\n",
       "      <th>chills</th>\n",
       "      <th>sore_throat</th>\n",
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       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>51-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>51-60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex  cough  fever  chills  sore_throat  headache  fatigue  Urgency  \\\n",
       "0  30.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0   \n",
       "1  47.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0   \n",
       "2  49.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0   \n",
       "3  50.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        0   \n",
       "4  59.0  0.0    0.0    1.0     0.0          0.0       0.0      0.0        0   \n",
       "\n",
       "  Age_Group  \n",
       "0     31-40  \n",
       "1     41-50  \n",
       "2     41-50  \n",
       "3     51-60  \n",
       "4     51-60  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the ages into bins or intervals \n",
    "age = df['age']\n",
    "\n",
    "bins = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
    "labels = ['0-10', '11-20', '21-30', '31-40', '41-50', '51-60', '61-70', '71-80', '81-90']\n",
    "df['Age_Group'] = pd.cut(df['age'], bins=bins, labels=labels, right=False)\n",
    "print(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "61936608-e1c6-4d38-b91a-44fd51c04d9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count the number of individuals needing hospital beds urgently in each age group\n",
    "urgency_counts = df.groupby('Age_Group')['Urgency'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3c70e7a2-8b73-4b5e-bc87-bb35ff0b1fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot an appropriate graph to answer the following question\n",
    "# Your code here\n",
    "plt.figure(figsize=(10, 6))\n",
    "urgency_counts.plot(kind='bar', color='green', edgecolor='black')\n",
    "plt.xlabel('Age Group', fontsize=10)\n",
    "plt.ylabel('Count of Individuals who Need Hospital Beds Urgently', fontsize=10)\n",
    "plt.title('Count of Individuals Needing Hospital Beds Urgently by Age Group', fontsize=12, weight='bold')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a38e87e-8ffd-472e-8953-40ab44132be8",
   "metadata": {},
   "source": [
    "### ⏸ Which age group has the most urgent need for a hospital bed?\n",
    "\n",
    "\n",
    "#### A. 60 - 70 \n",
    "\n",
    "#### B. 50 - 60 \n",
    "\n",
    "#### C. 20 - 30\n",
    "\n",
    "#### D. 40 - 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "78904079-f1fe-4a17-a527-4cdbb689f01c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submit an answer choice as a string below (eg. if you choose option A, put 'A')\n",
    "answer1 = 'D'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f308a907-5bdb-4852-ab41-0e04da059182",
   "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>cough</th>\n",
       "      <th>fever</th>\n",
       "      <th>chills</th>\n",
       "      <th>sore_throat</th>\n",
       "      <th>headache</th>\n",
       "      <th>fatigue</th>\n",
       "      <th>Urgency</th>\n",
       "      <th>Age_Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>31-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>61-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>72.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>71-80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>51-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>31-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>51-60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>492 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  sex  cough  fever  chills  sore_throat  headache  fatigue  \\\n",
       "6     38.0  0.0    1.0    0.0     0.0          0.0       0.0      0.0   \n",
       "7     45.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0   \n",
       "10    42.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0   \n",
       "12    65.0  1.0    0.0    0.4     0.0          0.0       0.0      0.0   \n",
       "13    44.0  0.0    0.0    1.0     0.0          0.0       0.0      1.0   \n",
       "...    ...  ...    ...    ...     ...          ...       ...      ...   \n",
       "996   72.0  1.0    0.0    0.4     0.0          0.0       0.0      0.0   \n",
       "997   56.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0   \n",
       "998   43.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0   \n",
       "999   39.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0   \n",
       "1000  50.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0   \n",
       "\n",
       "      Urgency Age_Group  \n",
       "6           1     31-40  \n",
       "7           1     41-50  \n",
       "10          1     41-50  \n",
       "12          1     61-70  \n",
       "13          1     41-50  \n",
       "...       ...       ...  \n",
       "996         1     71-80  \n",
       "997         1     51-60  \n",
       "998         1     41-50  \n",
       "999         1     31-40  \n",
       "1000        1     51-60  \n",
       "\n",
       "[492 rows x 10 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop rows where Urgency is equal to 0 (not urgent)\n",
    "df_symptoms = df[df['Urgency'] !=0]\n",
    "df_symptoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9b42ff7e-27b1-4df4-a8e0-1460645804eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot an appropriate graph to answer the following question    \n",
    "\n",
    "# Your code here\n",
    "urgent_patients = df_symptoms[df_symptoms['Urgency'] == 1]\n",
    "\n",
    "frequency_cough = urgent_patients['cough'].sum()\n",
    "frequency_fever = urgent_patients['fever'].sum()\n",
    "frequency_sorethroat = urgent_patients['sore_throat'].sum()\n",
    "frequency_fatigue = urgent_patients['fatigue'].sum()\n",
    "\n",
    "symptoms = ['Cough', 'Fever', 'Sore Throat', 'Fatigue']\n",
    "frequencies = [frequency_cough, frequency_fever, frequency_sorethroat, frequency_fatigue]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(symptoms, frequencies, color='pink', edgecolor='black')\n",
    "plt.xlabel('Symptoms', fontsize=10)\n",
    "plt.ylabel('Frequency', fontsize=10)\n",
    "plt.title('Frequency of Symptoms for Patients with Urgent Need for Hospitalization', fontsize=12, weight='bold')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d567fc47-2e9f-4034-977e-e431e49bb07d",
   "metadata": {},
   "source": [
    "### ⏸ Among the following symptoms, which is the most common one for patients with urgent need of hospitalization?\n",
    "\n",
    "#### A. Cough\n",
    "\n",
    "#### B. Fever\n",
    "\n",
    "#### C. Sore Throat\n",
    "\n",
    "#### D. Fatigue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "141c1ace-fbfb-4d76-9fd7-2d80dd7a4185",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submit an answer choice as a string below (eg. if you choose option A, put 'A')\n",
    "answer2 = 'B'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4c9a8389-dd30-4e6f-b307-0fa0b0e5ae63",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>sex</th>\n",
       "      <th>cough</th>\n",
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       "      <th>sore_throat</th>\n",
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       "      <th>fatigue</th>\n",
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       "<p>509 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex  cough  fever  chills  sore_throat  headache  fatigue  Urgency  \\\n",
       "0    30.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0   \n",
       "1    47.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0   \n",
       "2    49.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0   \n",
       "3    50.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        0   \n",
       "4    59.0  0.0    0.0    1.0     0.0          0.0       0.0      0.0        0   \n",
       "..    ...  ...    ...    ...     ...          ...       ...      ...      ...   \n",
       "667  40.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0   \n",
       "668  40.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0   \n",
       "669  25.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        0   \n",
       "670  22.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0   \n",
       "671  36.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        0   \n",
       "\n",
       "    Age_Group  \n",
       "0       31-40  \n",
       "1       41-50  \n",
       "2       41-50  \n",
       "3       51-60  \n",
       "4       51-60  \n",
       "..        ...  \n",
       "667     41-50  \n",
       "668     41-50  \n",
       "669     21-30  \n",
       "670     21-30  \n",
       "671     31-40  \n",
       "\n",
       "[509 rows x 10 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping the rows where the patients are 'Urgent' in preparation for plotting\n",
    "df_nonsymptoms = df[df['Urgency'] !=1]\n",
    "df_nonsymptoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7c662a1a-015c-49d1-931f-eaac8456dd2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot an appropriate graph to answer the following question    \n",
    "# Your code here\n",
    "nonurgent_patients = df_nonsymptoms[df_nonsymptoms['Urgency'] == 0]\n",
    "\n",
    "frequency_cough = nonurgent_patients['cough'].sum()\n",
    "frequency_fever = nonurgent_patients['fever'].sum()\n",
    "frequency_sorethroat = nonurgent_patients['sore_throat'].sum()\n",
    "frequency_fatigue = nonurgent_patients['fatigue'].sum()\n",
    "\n",
    "symptoms = ['Cough', 'Fever', 'Sore Throat', 'Fatigue']\n",
    "frequencies = [frequency_cough, frequency_fever, frequency_sorethroat, frequency_fatigue]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(symptoms, frequencies, color='pink', edgecolor='black')\n",
    "plt.xlabel('Symptoms', fontsize=10)\n",
    "plt.ylabel('Frequency', fontsize=10)\n",
    "plt.title('Frequency of Symptoms for Patients with No Need for Hospitalization', fontsize=12, weight='bold')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "456883ae-5170-4bee-b211-e502a5d71c40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting two side-by-side bar charts showing the difference in the frequency of symptoms with urgent patients and non-urgent patients.\n",
    "urgent_patients = df_symptoms[df_symptoms['Urgency'] == 1]\n",
    "\n",
    "frequency_cough = urgent_patients['cough'].sum()\n",
    "frequency_fever = urgent_patients['fever'].sum()\n",
    "frequency_sorethroat = urgent_patients['sore_throat'].sum()\n",
    "frequency_fatigue = urgent_patients['fatigue'].sum()\n",
    "\n",
    "symptoms = ['Cough', 'Fever', 'Sore Throat', 'Fatigue']\n",
    "frequencies = [frequency_cough, frequency_fever, frequency_sorethroat, frequency_fatigue]\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12,6))\n",
    "axs[0].bar(symptoms, frequencies, color='pink', edgecolor='black')\n",
    "axs[0].set_title('Frequency of Symptoms for Patients with Urgent Need for Hospitalization', fontsize=8, weight='bold')\n",
    "axs[0].set_xlabel('Symptom')\n",
    "axs[0].set_ylabel('Frequency')\n",
    "\n",
    "nonurgent_patients = df_nonsymptoms[df_nonsymptoms['Urgency'] == 0]\n",
    "\n",
    "frequency_cough = nonurgent_patients['cough'].sum()\n",
    "frequency_fever = nonurgent_patients['fever'].sum()\n",
    "frequency_sorethroat = nonurgent_patients['sore_throat'].sum()\n",
    "frequency_fatigue = nonurgent_patients['fatigue'].sum()\n",
    "\n",
    "symptoms = ['Cough', 'Fever', 'Sore Throat', 'Fatigue']\n",
    "frequencies = [frequency_cough, frequency_fever, frequency_sorethroat, frequency_fatigue]\n",
    "\n",
    "axs[1].bar(symptoms, frequencies, color='pink', edgecolor='black')\n",
    "axs[1].set_title('Frequency of Symptoms for Patients with No Need for Hospitalization', fontsize=8, weight='bold')\n",
    "axs[1].set_xlabel('Symptom')\n",
    "axs[1].set_ylabel('Frequency')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af15670-c394-43fa-9f9b-3c3d46d7d964",
   "metadata": {},
   "source": [
    "### ⏸ As compared to patients with urgent need of hospitalization, patients with no urgency have cough as a more common symptom?\n",
    "\n",
    "#### A. True\n",
    "\n",
    "#### B. False\n",
    "\n",
    "#### C. It is the same\n",
    "\n",
    "#### D. Cannot say"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "23860746-02c9-4f6f-853a-94ec5e635f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submit an answer choice as a string below (eg. if you choose option A, put 'A')\n",
    "answer3 = 'A'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7faa2119-305e-4a71-a4a9-e92d4f89e93f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age            0\n",
      "sex            0\n",
      "cough          0\n",
      "fever          0\n",
      "chills         0\n",
      "sore_throat    0\n",
      "headache       0\n",
      "fatigue        0\n",
      "Urgency        0\n",
      "Age_Group      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train and test sets with 70% for training\n",
    "# Use random state of 60 and set of data as the train split\n",
    "print(df.isnull().sum())\n",
    "# Your code here\n",
    "df = df.dropna()\n",
    "df_train, df_test = train_test_split(df, train_size=0.7, random_state=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "906e20e6-dc61-42dc-936d-593abb571782",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>cough</th>\n",
       "      <th>fever</th>\n",
       "      <th>chills</th>\n",
       "      <th>sore_throat</th>\n",
       "      <th>headache</th>\n",
       "      <th>fatigue</th>\n",
       "      <th>Urgency</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>72.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  sex  cough  fever  chills  sore_throat  headache  fatigue  Urgency\n",
       "0     30.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "1     47.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "2     49.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "3     50.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "4     59.0  0.0    0.0    1.0     0.0          0.0       0.0      0.0        0\n",
       "...    ...  ...    ...    ...     ...          ...       ...      ...      ...\n",
       "996   72.0  1.0    0.0    0.4     0.0          0.0       0.0      0.0        1\n",
       "997   56.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        1\n",
       "998   43.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0        1\n",
       "999   39.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0        1\n",
       "1000  50.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        1\n",
       "\n",
       "[1001 rows x 9 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fixing the table to help it look better and readable by placing the two charts side by side for comparison. \n",
    "df['Urgency'] = df_imputed['Urgency']\n",
    "df = df.drop(columns=['Age_Group'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1a381121-6155-49f4-921e-d8fda7039301",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age            0\n",
      "sex            0\n",
      "cough          0\n",
      "fever          0\n",
      "chills         0\n",
      "sore_throat    0\n",
      "headache       0\n",
      "fatigue        0\n",
      "Urgency        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train and test sets with 70% for training\n",
    "# Use random state of 60 and set of data as the train split\n",
    "print(df.isnull().sum())\n",
    "# Your code here\n",
    "df = df.dropna()\n",
    "df_train, df_test = train_test_split(df, train_size=0.7, random_state=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "743d1a98-8592-4560-bd09-40040bd030eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the train data into a csv called \"covid_train.csv\"\n",
    "# Remember to not include the default indices\n",
    "df_train.to_csv('covid_train.csv', index=False)\n",
    "\n",
    "# Save the test data into a csv called \"covid_test.csv\"\n",
    "# Remember to not include the default indices\n",
    "df_test.to_csv('covid_test.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4fb85cbe-7e97-4b88-85bd-d8a920962181",
   "metadata": {},
   "outputs": [
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       "    age  sex  cough  fever  chills  sore_throat  headache  fatigue  Urgency\n",
       "0  37.0  1.0    0.0    1.0     0.0          1.0       0.0      0.0        0\n",
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       "4  40.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0        0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the validity of covid_train as a dataset\n",
    "covid_train = pd.read_csv('covid_train.csv')\n",
    "covid_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "75dd42a0-4aab-400c-a379-f54420ad77e6",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "    age  sex  cough  fever  chills  sore_throat  headache  fatigue  Urgency\n",
       "0  40.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0        0\n",
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       "4  29.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the validity of covid test as a dataset\n",
    "covid_test = pd.read_csv('covid_test.csv')\n",
    "covid_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "036a5059-607b-4158-97b2-befb5b6a8ff0",
   "metadata": {},
   "source": [
    "**II. Prediction Accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b602cc07-745f-4b1e-8144-e53d86cbd13c",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "    age  sex  cough  fever  chills  sore_throat  headache  fatigue  Urgency\n",
       "0  37.0  1.0    0.0    1.0     0.0          1.0       0.0      0.0        0\n",
       "1  35.0  0.0    0.0    0.0     0.0          0.0       1.0      0.0        0\n",
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       "4  40.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0        0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the datafile \"covid_train.csv\"\n",
    "df_train = pd.read_csv('covid_train.csv')\n",
    "\n",
    "# Take a quick look at the dataframe\n",
    "df_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "100ea91f-fc82-4645-bf54-36d400512e2d",
   "metadata": {},
   "outputs": [
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       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex  cough  fever  chills  sore_throat  headache  fatigue  Urgency\n",
       "0  40.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0        0\n",
       "1  42.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        1\n",
       "2  53.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "3  20.0  0.0    0.0    1.0     0.0          0.0       0.0      0.0        0\n",
       "4  29.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the datafile \"covid_test.csv\"\n",
    "df_test = pd.read_csv('covid_test.csv')\n",
    "\n",
    "# Take a quick look at the dataframe\n",
    "df_test.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "29bf8d24-c34a-4221-a14b-66458af73b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the train predictors\n",
    "df_train_predictors = ['age', 'sex', 'cough', 'fever', 'chills', 'sore_throat', 'headache', 'fatigue']\n",
    "X_train = df_train[df_train_predictors]\n",
    "\n",
    "# Get the train response variable\n",
    "df_train_response = ['Urgency']\n",
    "y_train = df_train[df_train_response]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f9052fd3-56d1-4118-a01e-0929eda32894",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the test predictors\n",
    "df_test_predictors = ['age', 'sex', 'cough', 'fever', 'chills', 'sore_throat', 'headache', 'fatigue']\n",
    "X_test = df_test[df_test_predictors]\n",
    "\n",
    "# Get the test response variable\n",
    "df_test_response = ['Urgency']\n",
    "y_test = df_test[df_test_response]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d0b88b23-9d76-47ae-8f8c-81209dd5e276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### edTest(test_model) ###\n",
    "\n",
    "# Define your classification model\n",
    "model = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "# Fit the model on the train data\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f0ea1451-a83d-4605-82be-b53b63b98218",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy is 0.6378737541528239\n"
     ]
    }
   ],
   "source": [
    "### edTest(test_accuracy) ###\n",
    "\n",
    "# Predict and compute the accuracy on the test data\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "model_accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Model Accuracy is {model_accuracy}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "448edbb1-c6a7-4b85-b1aa-788f809b8fca",
   "metadata": {},
   "source": [
    "**III. Evaluating Predictions with Other Metrics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8257d9b9-cb18-4c57-ae02-e9b5c181394a",
   "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>cough</th>\n",
       "      <th>fever</th>\n",
       "      <th>chills</th>\n",
       "      <th>sore_throat</th>\n",
       "      <th>headache</th>\n",
       "      <th>fatigue</th>\n",
       "      <th>Urgency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex  cough  fever  chills  sore_throat  headache  fatigue  Urgency\n",
       "0  37.0  1.0    0.0    1.0     0.0          1.0       0.0      0.0        0\n",
       "1  35.0  0.0    0.0    0.0     0.0          0.0       1.0      0.0        0\n",
       "2  39.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        1\n",
       "3  48.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "4  40.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0        0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the datafile \"covid_train.csv\"\n",
    "df_train = pd.read_csv('covid_train.csv')\n",
    "\n",
    "# Take a quick look at the dataframe\n",
    "df_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6baae39d-e3c1-431d-af79-b99b782edd5c",
   "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>cough</th>\n",
       "      <th>fever</th>\n",
       "      <th>chills</th>\n",
       "      <th>sore_throat</th>\n",
       "      <th>headache</th>\n",
       "      <th>fatigue</th>\n",
       "      <th>Urgency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
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       "      <td>42.0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex  cough  fever  chills  sore_throat  headache  fatigue  Urgency\n",
       "0  40.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0        0\n",
       "1  42.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        1\n",
       "2  53.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "3  20.0  0.0    0.0    1.0     0.0          0.0       0.0      0.0        0\n",
       "4  29.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the datafile \"covid_test.csv\"\n",
    "df_test = pd.read_csv('covid_test.csv')\n",
    "\n",
    "# Take a quick look at the dataframe\n",
    "df_test.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3dc7fe14-7c77-4e3c-b070-f51dd0ce81ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the train predictors\n",
    "df_train_predictors = ['age', 'sex', 'cough', 'fever', 'chills', 'sore_throat', 'headache', 'fatigue']\n",
    "X_train = df_train[df_train_predictors]\n",
    "\n",
    "# Get the train response variable\n",
    "df_train_response = ['Urgency']\n",
    "y_train = df_train[df_train_response]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c4d9cbeb-f841-4008-a185-4dbb5af08720",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the test predictors\n",
    "df_test_predictors = ['age', 'sex', 'cough', 'fever', 'chills', 'sore_throat', 'headache', 'fatigue']\n",
    "X_test = df_test[df_test_predictors]\n",
    "\n",
    "# Get the test response variable\n",
    "df_test_response = ['Urgency']\n",
    "y_test = df_test[df_test_response]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "14122561-bc05-44a1-91cd-de17d2025e4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.1, max_iter=10000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.1, max_iter=10000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.1, max_iter=10000)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a kNN classification model with k = 7\n",
    "knn_model = KNeighborsClassifier(n_neighbors=7)\n",
    "\n",
    "# Fit the above model on the train data\n",
    "knn_model.fit(X_train, y_train)\n",
    "\n",
    "# Define a Logistic Regression model with max_iter as 10000 and C as 0.1 (leave all other parameters at default values)\n",
    "log_model = LogisticRegression(max_iter=10000, C=0.1)\n",
    "\n",
    "# Fit the Logistic Regression model on the train data\n",
    "log_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a5a304-30cb-4c72-b515-3f91e8a313b9",
   "metadata": {},
   "source": [
    "Now it is time to evaluate the models.\n",
    "\n",
    "Create a dictionary called `metric_scores` which has keys `'Accuracy'`, `'Recall'`, `'Specificity'`, `'Precision'`, and `'F1-score'`. The value associated with each key should a list with a length 2, the first element being knn_model corresponding metric score and the second element being log_model corresponding metric score. All scores should be made on the **test** data.\n",
    "\n",
    "Ex: metric_scores[\"F1-score\"] = [knn_f1,log_f1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "27e416f2-ce8c-4e0a-8438-1fc8353e3cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict both models on the test data \n",
    "y_pred_knn = knn_model.predict(X_test)\n",
    "y_pred_log = log_model.predict(X_test)\n",
    "\n",
    "# Confusion matrix for both models\n",
    "knn_conf_matrix = confusion_matrix(y_test, y_pred_knn)\n",
    "log_conf_matrix = confusion_matrix(y_test, y_pred_log)\n",
    "\n",
    "# Function to compute specificity\n",
    "def specificity(conf_matrix):\n",
    "    true_negatives = conf_matrix[0, 0]\n",
    "    false_positives = conf_matrix[0, 1]\n",
    "    return true_negatives / (true_negatives + false_positives)\n",
    "\n",
    "# Function to compute precision_score\n",
    "def precision (conf_matrix): \n",
    "    true_positives = conf_matrix[1, 1]\n",
    "    false_positives = conf_matrix[0, 1]\n",
    "    return true_positives / (true_positives + false_positives) \n",
    "\n",
    "# Accuracy score for both models\n",
    "knn_accuracy = accuracy_score(y_test, y_pred_knn)\n",
    "log_accuracy = accuracy_score(y_test, y_pred_log)\n",
    "\n",
    "# Recall score for both models\n",
    "knn_recall = recall_score(y_test, y_pred_knn)\n",
    "log_recall = recall_score(y_test, y_pred_log)\n",
    "\n",
    "# F-1 score for both models\n",
    "knn_f1 = f1_score(y_test, y_pred_knn)\n",
    "log_f1 = f1_score(y_test, y_pred_log)\n",
    "\n",
    "# Specificity for both models \n",
    "knn_specificity = specificity(knn_conf_matrix)\n",
    "log_specificity = specificity(log_conf_matrix)\n",
    "\n",
    "# Precision for both models\n",
    "knn_precision = precision(knn_conf_matrix)\n",
    "log_precision = precision(log_conf_matrix)\n",
    "\n",
    "# For dictionary\n",
    "\n",
    "metric_scores = {\n",
    "    'Accuracy': [knn_accuracy, log_accuracy],\n",
    "    'Recall': [knn_recall, log_recall],\n",
    "    'Specificity': [knn_specificity, log_specificity],\n",
    "    'Precision': [knn_precision, log_precision],\n",
    "    'F1-score': [knn_f1, log_f1]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "fbf91499-261a-4dd3-8779-d1236aade060",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: [0.6677740863787376, 0.6079734219269103]\n",
      "Recall: [0.7163120567375887, 0.7163120567375887]\n",
      "Specificity: [0.625, 0.5125]\n",
      "Precision: [0.6273291925465838, 0.5642458100558659]\n",
      "F1-score: [0.6688741721854304, 0.63125]\n"
     ]
    }
   ],
   "source": [
    "# Display your results\n",
    "for metric, scores in metric_scores.items(): \n",
    "    print (f\"{metric}: {scores}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65b7698-3560-4590-a6d4-d2d991b71871",
   "metadata": {},
   "source": [
    "**IV. Plotting the ROC Curve**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "fb02b874-6b82-4ea4-a588-2afb30e3fdf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cough</th>\n",
       "      <th>fever</th>\n",
       "      <th>chills</th>\n",
       "      <th>sore_throat</th>\n",
       "      <th>headache</th>\n",
       "      <th>fatigue</th>\n",
       "      <th>Urgency</th>\n",
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      "text/plain": [
       "    age  sex  cough  fever  chills  sore_throat  headache  fatigue  Urgency\n",
       "0  37.0  1.0    0.0    1.0     0.0          1.0       0.0      0.0        0\n",
       "1  35.0  0.0    0.0    0.0     0.0          0.0       1.0      0.0        0\n",
       "2  39.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        1\n",
       "3  48.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "4  40.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0        0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the datafile \"covid_train.csv\"\n",
    "df_train = pd.read_csv('covid_train.csv')\n",
    "# Take a quick look at the dataframe\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b02c5b67-f594-419b-a599-f523d3d9f972",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>cough</th>\n",
       "      <th>fever</th>\n",
       "      <th>chills</th>\n",
       "      <th>sore_throat</th>\n",
       "      <th>headache</th>\n",
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       "      <th>2</th>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex  cough  fever  chills  sore_throat  headache  fatigue  Urgency\n",
       "0  40.0  1.0    0.0    1.0     0.0          0.0       0.0      0.0        0\n",
       "1  42.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        1\n",
       "2  53.0  0.0    0.0    0.0     0.0          0.0       0.0      0.0        0\n",
       "3  20.0  0.0    0.0    1.0     0.0          0.0       0.0      0.0        0\n",
       "4  29.0  1.0    0.0    0.0     0.0          0.0       0.0      0.0        0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the datafile \"covid_test.csv\"\n",
    "df_test = pd.read_csv('covid_test.csv')\n",
    "# Take a quick look at the dataframe\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a635a1b9-3e1a-42a0-8312-60ca61d696c0",
   "metadata": {},
   "source": [
    "Separate the predictors from the response variables in both train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1c068d65-4c56-409d-8694-6fb0721962da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the train predictors\n",
    "X_train = df_train[['age', 'sex', 'cough', 'fever', 'chills', 'sore_throat', 'headache', 'fatigue']]\n",
    "\n",
    "# Get the train response variable\n",
    "y_train = df_train['Urgency']\n",
    "\n",
    "# Get the test predictors\n",
    "X_test = df_test[['age', 'sex', 'cough', 'fever', 'chills', 'sore_throat', 'headache', 'fatigue']]\n",
    "\n",
    "# Get the test response variable\n",
    "y_test = df_test['Urgency']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1961a2fa-e044-4b11-ae9f-384c5f3be460",
   "metadata": {},
   "source": [
    "We want to compare two different classifiers: kNN and logistic regression.\n",
    "\n",
    "First, let's create a fit a kNN model and get's its predicted probabilities for the positive class on the test data.\n",
    "\n",
    "**Hint:** Remember that the `predict_proba` method returns probabilities for all classes. We only want the probabilities for the positive class (i.e., $y = 1$). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ac5a34db-42f9-4d18-9e4e-1e3190371764",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a kNN classification model with k = 7\n",
    "knn = KNeighborsClassifier(n_neighbors=7)\n",
    "\n",
    "# Fit the above model on the train data\n",
    "y_train_1d = np.ravel(y_train)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# Predict probabilities for the positive class on the test data using the kNN model\n",
    "y_pred_knn = knn.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66b88a15-2cce-4002-bba3-c4673c64c4c4",
   "metadata": {},
   "source": [
    "Now let's do the same for a logistic regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "44b45e15-8b41-4388-8629-ab4d53ce048b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a Logistic Regression model with max_iter as 10000, C as 0.1, and a random_state of 42\n",
    "logreg = LogisticRegression(max_iter=10000, C=0.1, random_state=42)\n",
    "\n",
    "# Fit the Logistic Regression model on the train data\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "# Predict probabilities for the positive class on the test data using the logistic regression model\n",
    "y_pred_logreg = logreg.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d16fc5-0488-423c-a616-9a9cc44bdaeb",
   "metadata": {},
   "source": [
    "#### ROC Curve Review\n",
    "\n",
    "The Bayes threshold of a binary classifier is the value for which all predicted probabilities greater than or equal to that value are labeled as the positive class. For example, a classifier with a Bayes threshold of 0.6 will classify all observations with a predicted probability, $p \\geq 0.6$, as the positive class (1) and all observations with a predicted probability, $p < 0.6$, as the negative class (0).\n",
    "\n",
    "The ROC curve shows us a model's false positive  and true positive rates across different settings of the Bayes Threshold. \n",
    "\n",
    "Recall that:\n",
    "$$\\text{False Positive Rate} = \\frac{FP}{TN + FP}$$\n",
    "$$\\text{True Positive Rate} = \\frac{TP}{TP + FN}$$\n",
    "\n",
    "We will compute the false positve rate (FPR) and true positive rate (TPR) for a range of thresholds and use these values to plot ROC curves for both the kNN and the logistic regression models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d32520d0-89ca-4f2f-8c6e-371b050f6131",
   "metadata": {},
   "source": [
    "#### Thresholds\n",
    "\n",
    "First, we need to generate a range of thresholds to use for turning our models' probability predictions into class labels. One naive approach might be to simply create a list of evenly spaced values between 0 and 1 and use these for both models. But this is not ideal. Consider the fact that some threshold changes will not actually affect the model's classification labels.\n",
    "\n",
    "Example: if a model predicts probabilities `[0.54, 0.77, 0.79]` for 3 observations, all thresholds in the half-open interval (0.54, 0.77] will produce the exact same class labels: `[0, 1, 1]`\n",
    "\n",
    "Another issue is that we may fail to see all changes in the class labels if our set of thresholds is not sufficiently granular. Consider the example of the 3 predicted probabilities above once more. If we use thresholds in steps of 0.05 (0, 0.05, 0.1, ..., 0.95, 1) we would only ever see class label predictions of [0, 1, 1] and [0, 0, 0]. We would miss an important threshold like 0.78 which would produce the class lables [0, 0, 1], potentially giving us a different TPR and FPR.\n",
    "\n",
    "Our approach will be to evaluate just those thresholds that result in different class label predictions. And these precise thresholds will depend on our model.\n",
    "\n",
    "Complete the `get_thresholds` function which takes a model's predicted probabilities on the test set and returns an array of thresholds to be considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "03c0411f-67b6-4eb0-8fda-b3de976c6c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_thresholds(y_pred_proba):\n",
    "    # We only need to consider unique predicted probabilities\n",
    "    unique_probas = np.unique(y_pred_proba)\n",
    "    # Sort unique probabilities in descending order\n",
    "    unique_probas_sorted = np.sort(unique_probas)[::-1]\n",
    "   \n",
    "    # We'll also add some additional thresholds to our set\n",
    "    # This ensures our ROC curves reach the corners of the plot, (0,0) and (1,1)\n",
    "    \n",
    "    # Insert 1.1 at the beginning of the threshold array\n",
    "    # 1.1 may seem like an odd threshold, but a value greater than 1\n",
    "    # is required if we want the ROC curve to reach the lower left corner\n",
    "    # (0 fpr, 0 tpr) considering one of our models produces probability predictions of 1\n",
    "    thresholds = np.insert(unique_probas_sorted, 0, 1.1)\n",
    "    # Append 0 to the end of the thresholds\n",
    "    thresholds = np.append(thresholds, 0)\n",
    "    return thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d6967c27-5aba-411f-b1b5-128c26f3ef1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_thresholds = get_thresholds(y_pred_knn)\n",
    "\n",
    "logreg_thresholds = get_thresholds(y_pred_logreg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc7165b-3160-48a3-96b4-70fa17eb8322",
   "metadata": {},
   "source": [
    "#### FPR & TPR\n",
    "\n",
    "Now we can use the true $y$ class label and the predicted probabilities to determine the the fpr and tpr on the test data for a specific threshold. Complete the `get_fpr` and `get_tpr` functions below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "15f7ed37-4b24-4890-9e92-81f2ab68e7bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fpr(y_test, y_pred_proba, threshold):\n",
    "    # your code here\n",
    "    y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "\n",
    "    # True Positive (TP) and False Positive (FP)\n",
    "    FP = np.sum((y_pred == 1) & (y_test == 0))\n",
    "    TN = np.sum(y_test == 0) - FP\n",
    "\n",
    "    # False Positive Rate (FPR)\n",
    "    fpr = FP / (FP + TN)\n",
    "\n",
    "    return fpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1d52f5de-69a5-4ab2-8612-a043697c7904",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tpr(y_test, y_pred_proba, threshold):\n",
    "    # your code here\n",
    "    y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "\n",
    "    # True Positive (TP) and False Negative (FN)\n",
    "    TP = np.sum((y_pred == 1) & (y_test == 1))\n",
    "    FN = np.sum(y_test == 1) - TP\n",
    "\n",
    "    # True Positive Rate (TPR)\n",
    "    tpr = TP / (TP + FN)\n",
    "\n",
    "    return tpr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1649d333-249d-4f2c-b7b0-c66d81cbca54",
   "metadata": {},
   "source": [
    "Use these functions to get the FPR and TPR for both models using each threshold for that model. These calculations can be done in a loop, but you should also try doing them with Python list comprehensions as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a81e3977-3c20-4827-92ca-250adf34a41b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize empty list \n",
    "knn_fpr = []\n",
    "knn_tpr = []\n",
    "logreg_fpr = []\n",
    "logreg_tpr = []\n",
    "\n",
    "# Convert y_test to a one-dimensional array\n",
    "y_test_array = y_test.values.ravel() if isinstance(y_test, pd.DataFrame) else y_test.ravel()\n",
    "\n",
    "# For kNN Model \n",
    "for threshold in knn_thresholds:\n",
    "    fpr = get_fpr(y_test_array, y_pred_knn, threshold)\n",
    "    tpr = get_tpr(y_test_array, y_pred_knn, threshold)\n",
    "    knn_fpr.append(fpr)\n",
    "    knn_tpr.append(tpr)\n",
    "\n",
    "# For Logistic Regression Model \n",
    "for threshold in logreg_thresholds:\n",
    "    fpr = get_fpr(y_test_array, y_pred_logreg, threshold)\n",
    "    tpr = get_tpr(y_test_array, y_pred_logreg, threshold)\n",
    "    logreg_fpr.append(fpr)\n",
    "    logreg_tpr.append(tpr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfb91193-0491-4dad-bb1f-9dd3346c2dba",
   "metadata": {},
   "source": [
    "#### Area Under the Curve\n",
    "\n",
    "The AUC gives us an idea as to how well our model does across *all* thresholds.\n",
    "\n",
    "For our final calculations, use each model's predicted probabilities to compute its test AUC with the help of SKLearn's `roc_auc_score` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "0850c29b-e5d3-453f-b486-59a15793acd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the ROC AUC score of the Logistic model\n",
    "knn_auc = roc_auc_score(y_test, y_pred_knn)\n",
    "\n",
    "# Compute the ROC AUC score of the kNN model\n",
    "logreg_auc = roc_auc_score(y_test, y_pred_logreg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1ba024a-9bca-4ec0-a56e-7c41144f5459",
   "metadata": {},
   "source": [
    "You're ready to visualize your results!\n",
    "\n",
    "Most of the code is provided. You just need to fill in the values to be plotted for the two models' ROC curves.\n",
    "\n",
    "Remember that the convention is that false positive rate is on the $x$-axis and true positive rate is on the $y$-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "92895444-6c3d-4550-8bef-a72392343b83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Area under curve - Logistic Regression & kNN\n",
    "fig, ax = plt.subplots(figsize = (14,8))\n",
    "\n",
    "# Plot KNN Regression ROC Curve\n",
    "ax.plot(knn_fpr,\n",
    "        knn_tpr,\n",
    "        label=f'KNN (area = {knn_auc:.2f})',\n",
    "        color='g',\n",
    "        lw=3)\n",
    "\n",
    "# Plot Logistic Regression ROC Curve\n",
    "ax.plot(logreg_fpr,\n",
    "        logreg_tpr,\n",
    "        label=f'Logistic Regression (area = {logreg_auc:.2f})',\n",
    "        color = 'purple',\n",
    "        lw=3)\n",
    "\n",
    "# Threshold annotations\n",
    "label_kwargs = {}\n",
    "label_kwargs['bbox'] = dict(\n",
    "    boxstyle='round, pad=0.3', color='lightgray', alpha=0.6\n",
    ")\n",
    "eps = 0.02 # offset\n",
    "for i in range(0, len(logreg_fpr),15):\n",
    "    threshold = str(np.round(logreg_thresholds[i], 2))\n",
    "    ax.annotate(threshold, (logreg_fpr[i], logreg_tpr[i]-eps), fontsize=12, color='purple', **label_kwargs)\n",
    "\n",
    "for i in range(0, len(knn_fpr)-1):\n",
    "    threshold = str(np.round(knn_thresholds[i], 2))\n",
    "    ax.annotate(threshold, (knn_fpr[i], knn_tpr[i]+eps), fontsize=12, color='green', **label_kwargs)\n",
    "\n",
    "# Plot diagonal line representing a random classifier\n",
    "ax.plot([0, 1], [0, 1], 'k--', label='Random Classifier')\n",
    "\n",
    "# Scenario 1 - Brazil\n",
    "ax.fill_between([0,0.5],[0.5,0], color = 'red', alpha = 0.4, label='Scenario 1 - Brazil');\n",
    "\n",
    "# Scenario 2 - Germany\n",
    "ax.axhspan(0.8, 0.9, facecolor='y', alpha=0.4, label = 'Scenario 2 - Germany');\n",
    "\n",
    "# Scenario 3 - India\n",
    "ax.fill_between([0,1],[1,0],[0.5,-0.5], alpha = 0.4, color = 'blue', label = 'Scenario 3 - India');\n",
    "\n",
    "ax.set_xlim([0.0, 1.0]);\n",
    "ax.set_ylim([0.0, 1.05]);\n",
    "ax.set_xlabel('False Positive Rate', fontsize=20)\n",
    "ax.set_ylabel('True Positive Rate', fontsize=20)\n",
    "ax.set_title('Receiver Operating Characteristic', fontsize=20)\n",
    "ax.legend(loc=\"lower right\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7003d11-0588-4b49-8f7a-8530d3b2c879",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}