Predicting Heart Disease.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e35c934",
   "metadata": {},
   "source": [
    "# Predicting Heart Disease\n",
    "\n",
    "​The project begins with an exploratory data analysis (EDA) to understand the descriptive statistics of the dataset. This includes examining both numerical and categorical features that describe the patient ​​\n",
    "\n",
    "'s profile, such as age, sex, type of chest pain, resting blood pressure, cholesterol levels, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, ST depression induced by exercise relative to rest, and the slope of the peak exercise ST segment.\n",
    "\n",
    "These factors are considered for their potential role in indicating the presence of heart disease, and the project will delve into these\n",
    "\n",
    "variables to identify patterns and insights. Through EDA, the project seeks to understand the data at hand, clean and prepare it for modeling. The comprehensive list of patient information, such as physiological and clinical parameters, suggests a thorough approach in considering relevant factors that could influence heart disease outcomes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bdd38c0-a675-456e-b8cf-9d22b06ae50f",
   "metadata": {},
   "source": [
    "In this project, we will use the [Kaggle dataset](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction) and build a K-Nearest Neighbors classifier to accurately predict the likelihood of a patient having a heart disease in the future. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7a6956c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6089c814",
   "metadata": {},
   "source": [
    "## EDA: Descriptive Statistics\n",
    "\n",
    "We will start with exploring our dataset. As per the source, each patient has the following information collected about them:\n",
    "\n",
    "\n",
    "1. `Age`: age of the patient [years]\n",
    "2. `Sex`: sex of the patient [M: Male, F: Female]\n",
    "3. `ChestPainType`: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]\n",
    "4. `RestingBP`: resting blood pressure [mm Hg]\n",
    "5. `Cholesterol`: serum cholesterol [mm/dl]\n",
    "6. `FastingBS`: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]\n",
    "7. `RestingECG`: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]\n",
    "8. `MaxHR`: maximum heart rate achieved [Numeric value between 60 and 202]\n",
    "9. `ExerciseAngina`: exercise-induced angina [Y: Yes, N: No]\n",
    "10. `Oldpeak`: oldpeak = ST [Numeric value measured in depression]\n",
    "11. `ST_Slope`: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]\n",
    "12. `HeartDisease`: output class [1: heart disease, 0: Normal]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d12aa5ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load dataset\n",
    "df = pd.read_csv(\"dataset/heart.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc74cce6",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  \n",
       "3              Y      1.5     Flat             1  \n",
       "4              N      0.0       Up             0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9a59e51",
   "metadata": {},
   "source": [
    "The dataset seems to contain both numerical and categorical features. Let's look at the datatype for each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "344a302b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age                 int64\n",
      "Sex                object\n",
      "ChestPainType      object\n",
      "RestingBP           int64\n",
      "Cholesterol         int64\n",
      "FastingBS           int64\n",
      "RestingECG         object\n",
      "MaxHR               int64\n",
      "ExerciseAngina     object\n",
      "Oldpeak           float64\n",
      "ST_Slope           object\n",
      "HeartDisease        int64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "int64      6\n",
       "object     5\n",
       "float64    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.dtypes)\n",
    "df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab3ff7b",
   "metadata": {},
   "source": [
    "`7` features in total are numerical while `5` are categorical. However, two of the numerical features, `FastingBS` and `HeartDisease` are categorical as well. \n",
    "\n",
    "We will focus on the numerical variables first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1bd0d2c0",
   "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>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.510893</td>\n",
       "      <td>132.396514</td>\n",
       "      <td>198.799564</td>\n",
       "      <td>0.233115</td>\n",
       "      <td>136.809368</td>\n",
       "      <td>0.887364</td>\n",
       "      <td>0.553377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.432617</td>\n",
       "      <td>18.514154</td>\n",
       "      <td>109.384145</td>\n",
       "      <td>0.423046</td>\n",
       "      <td>25.460334</td>\n",
       "      <td>1.066570</td>\n",
       "      <td>0.497414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-2.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age   RestingBP  Cholesterol   FastingBS       MaxHR  \\\n",
       "count  918.000000  918.000000   918.000000  918.000000  918.000000   \n",
       "mean    53.510893  132.396514   198.799564    0.233115  136.809368   \n",
       "std      9.432617   18.514154   109.384145    0.423046   25.460334   \n",
       "min     28.000000    0.000000     0.000000    0.000000   60.000000   \n",
       "25%     47.000000  120.000000   173.250000    0.000000  120.000000   \n",
       "50%     54.000000  130.000000   223.000000    0.000000  138.000000   \n",
       "75%     60.000000  140.000000   267.000000    0.000000  156.000000   \n",
       "max     77.000000  200.000000   603.000000    1.000000  202.000000   \n",
       "\n",
       "          Oldpeak  HeartDisease  \n",
       "count  918.000000    918.000000  \n",
       "mean     0.887364      0.553377  \n",
       "std      1.066570      0.497414  \n",
       "min     -2.600000      0.000000  \n",
       "25%      0.000000      0.000000  \n",
       "50%      0.600000      1.000000  \n",
       "75%      1.500000      1.000000  \n",
       "max      6.200000      1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc1f5d4",
   "metadata": {},
   "source": [
    "From the table above, we can observe that:\n",
    "\n",
    "- The average age of patients is ~`53` years.\n",
    "- The median for `Cholesterol` is higher than its mean by roughly `25` mm/dl, indicating that it could be a left-skewed distribution with a possibility of outliers skewing the distribution.\n",
    "- `RestingBP` and `Cholesterol` have a minimum value of zero.\n",
    "- There don't seem to be any missing values in these columns. But we will have to confirm it across the entire dataset as well.\n",
    "\n",
    "`RestingBP` can't be `0`. And, as per the [American Heart Association](https://www.heart.org/en/health-topics/cholesterol/about-cholesterol/what-your-cholesterol-levels-mean), serum cholesterol is a composite of different measurements. So, it is unlikely that `Cholesterol` would be `0` as well. We will have to clean both of these up later.\n",
    "\n",
    "Next, we will look at the categorical variables. It would also be beneficial to look at how the target feature, `HeartDisease`, is related to those categories. Before that, let's quickly check if there are any missing values in the dataset or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5c436572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age               0\n",
       "Sex               0\n",
       "ChestPainType     0\n",
       "RestingBP         0\n",
       "Cholesterol       0\n",
       "FastingBS         0\n",
       "RestingECG        0\n",
       "MaxHR             0\n",
       "ExerciseAngina    0\n",
       "Oldpeak           0\n",
       "ST_Slope          0\n",
       "HeartDisease      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "847a31a8",
   "metadata": {},
   "source": [
    "There are no missing values in this dataset!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75988364",
   "metadata": {},
   "source": [
    "## EDA: Categorical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47977c45",
   "metadata": {},
   "source": [
    "We identified that most of the categorical columns are all of dtype **object**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "304a72e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>ST_Slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>918</td>\n",
       "      <td>918</td>\n",
       "      <td>918</td>\n",
       "      <td>918</td>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>Normal</td>\n",
       "      <td>N</td>\n",
       "      <td>Flat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>725</td>\n",
       "      <td>496</td>\n",
       "      <td>552</td>\n",
       "      <td>547</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sex ChestPainType RestingECG ExerciseAngina ST_Slope\n",
       "count   918           918        918            918      918\n",
       "unique    2             4          3              2        3\n",
       "top       M           ASY     Normal              N     Flat\n",
       "freq    725           496        552            547      460"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe32c99",
   "metadata": {},
   "source": [
    "We can confirm that those columns are indeed categorical given the number of unique values in each of them. But, we can't gather much else. Also, `FastingBS` and `HeartDisease` are categorical as well since they only contain binary values. We can confirm that quickly as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99fc3fcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), array([0, 1]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"FastingBS\"].unique(), df[\"HeartDisease\"].unique() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79253815",
   "metadata": {},
   "source": [
    "Let's start looking at the categories in more detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5af0a32f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x1500 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_cols = [\"Sex\", \"ChestPainType\", \"FastingBS\", \"RestingECG\", \"ExerciseAngina\", \"ST_Slope\", \"HeartDisease\"]\n",
    "\n",
    "fig = plt.figure(figsize=(16,15))\n",
    "\n",
    "for idx, col in enumerate(categorical_cols):\n",
    "    ax = plt.subplot(4, 2, idx+1)\n",
    "    sns.countplot(x=df[col], ax=ax)\n",
    "    # add data labels to each bar\n",
    "    for container in ax.containers:\n",
    "        ax.bar_label(container, label_type=\"center\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d97efb3",
   "metadata": {},
   "source": [
    "- The dataset is highly skewed towards male patients. There are `725` male patients and `193` female patients. This could potentially induce a bias in our model.\n",
    "- `496` patients had `ASY` (asymptotic) chest pain type.\n",
    "- `552` patients had a normal restin ECG.\n",
    "- `704` patients had blood sugar lower than `120` mg/dl\n",
    "\n",
    "Grouping these by `HeartDisease` will give us a better idea about the data distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a5f28e00",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the categorical columns you wish to visualize, excluding 'HeartDisease'.\n",
    "categorical_cols = [\"Sex\", \"ChestPainType\", \"FastingBS\", \"RestingECG\", \"ExerciseAngina\", \"ST_Slope\"]\n",
    "\n",
    "# Initialize a large figure to accommodate the multiple subplots.\n",
    "fig = plt.figure(figsize=(16, 15))\n",
    "\n",
    "# Iterate through the categorical columns to create a subplot for each.\n",
    "for idx, col in enumerate(categorical_cols):\n",
    "    # Create a subplot in a 4x2 grid. The index+1 is the position of the subplot.\n",
    "    ax = plt.subplot(4, 2, idx+1)\n",
    "    \n",
    "    # Create a count plot for each category in the column, grouped by 'HeartDisease'.\n",
    "    # 'hue' parameter differentiates the data points by 'HeartDisease'.\n",
    "    sns.countplot(x=df[col], hue=df[\"HeartDisease\"], ax=ax)\n",
    "\n",
    "    # Loop through each bar container in the plot.\n",
    "    for container in ax.containers:\n",
    "        # Add a label to each bar, positioning it at the center.\n",
    "        # 'label_type=\"center\"' puts the label in the center of the bar.\n",
    "        ax.bar_label(container, label_type=\"center\")\n",
    "\n",
    "# Adjust the layout to ensure there is no overlap and everything is displayed properly.\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plot.\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b44da4c",
   "metadata": {},
   "source": [
    "- We can further notice how skewed the dataset is towards male patients. Only `50` female patients in the dataset have been diagnosed with heart disease.\n",
    "- A significant number of patients, `392`, diagnosed with heart disease have asymptomatic (ASY) chest pain. While chest pain could be a relevant feature for our model, asymptomatic implies that those patients who had a heart disease did not have chest pain as a symptom. \n",
    "- A high number (`170`) of patients with blood sugar greater than 120 mg/dl were diagnosed with heart disease in relation to those who were not diagnosed as such.\n",
    "- Out of all patients who had an exercise-induced angina, `316` were diagnosed with a heart disease.\n",
    "- Out of all patients with a flat ST slope, `381` were diagnosed with a heart disease.\n",
    "\n",
    "Looking at the data distribution from the above plots, we can start to identify some features that could be relevant to us. We will clean up the dataset a bit first before narrowing down on our features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb5c3c5",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "\n",
    "We identified that there are no missing values. However, as we noticed earlier, a couple of columns have 0 values which don't make sense.\n",
    "\n",
    "We will look at how many zero values `RestingBP` and `Cholesterol` contain and decide how to handle those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3ad28327",
   "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>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>55</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>155</td>\n",
       "      <td>N</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "449   55   M           NAP          0            0          0     Normal   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "449    155              N      1.5     Flat             1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"RestingBP\"] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4efa8b0",
   "metadata": {},
   "outputs": [
    {
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       "      <th>515</th>\n",
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       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ST</td>\n",
       "      <td>160</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>0</td>\n",
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       "      <td>110</td>\n",
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       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>122</td>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>172 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "293   65   M           ASY        115            0          0     Normal   \n",
       "294   32   M            TA         95            0          1     Normal   \n",
       "295   61   M           ASY        105            0          1     Normal   \n",
       "296   50   M           ASY        145            0          1     Normal   \n",
       "297   57   M           ASY        110            0          1         ST   \n",
       "..   ...  ..           ...        ...          ...        ...        ...   \n",
       "514   43   M           ASY        122            0          0     Normal   \n",
       "515   63   M           NAP        130            0          1         ST   \n",
       "518   48   M           NAP        102            0          1         ST   \n",
       "535   56   M           ASY        130            0          0        LVH   \n",
       "536   62   M           NAP        133            0          1         ST   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "293     93              Y      0.0     Flat             1  \n",
       "294    127              N      0.7       Up             1  \n",
       "295    110              Y      1.5       Up             1  \n",
       "296    139              Y      0.7     Flat             1  \n",
       "297    131              Y      1.4       Up             1  \n",
       "..     ...            ...      ...      ...           ...  \n",
       "514    120              N      0.5       Up             1  \n",
       "515    160              N      3.0     Flat             0  \n",
       "518    110              Y      1.0     Down             1  \n",
       "535    122              Y      1.0     Flat             1  \n",
       "536    119              Y      1.2     Flat             1  \n",
       "\n",
       "[172 rows x 12 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Cholesterol\"] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dbe3eca",
   "metadata": {},
   "source": [
    "`RestingBP` has only one zero value. We can remove that row from consideration. There are `172` zero values for `Cholesterol`. That's a relatively high number. We can't remove them all and replacing those values with the median might not be an ideal approach, but that's what we will go for now.\n",
    "\n",
    "To be more accurate, we will replace the zero values in `Cholesterol` in relation to `HeartDisease`. So, the 0 values in `Cholesterol` for patients who were diagnosed with a heart disease will be replaced by the median of the non-zero values for patients who were diagnosed with a heart disase. And we'll do the same for those who were not diagnosed with a heart disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "61c9e24a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the DataFrame to preserve the original data.\n",
    "df_clean = df.copy()\n",
    "\n",
    "# Remove records where RestingBP is 0, as these might be data entry errors.\n",
    "df_clean = df_clean[df_clean[\"RestingBP\"] != 0]\n",
    "\n",
    "# Create a mask to identify records with no heart disease (HeartDisease is 0).\n",
    "heartdisease_mask = df_clean[\"HeartDisease\"] == 0\n",
    "\n",
    "# Select cholesterol levels for patients without heart disease.\n",
    "cholesterol_without_heartdisease = df_clean.loc[heartdisease_mask, \"Cholesterol\"]\n",
    "# Select cholesterol levels for patients with heart disease.\n",
    "cholesterol_with_heartdisease = df_clean.loc[~heartdisease_mask, \"Cholesterol\"]\n",
    "\n",
    "# Replace zero values in Cholesterol for patients without heart disease with the group's median.\n",
    "df_clean.loc[heartdisease_mask, \"Cholesterol\"] = cholesterol_without_heartdisease.replace(to_replace=0, value=cholesterol_without_heartdisease.median())\n",
    "# Replace zero values in Cholesterol for patients with heart disease with the group's median.\n",
    "df_clean.loc[~heartdisease_mask, \"Cholesterol\"] = cholesterol_with_heartdisease.replace(to_replace=0, value=cholesterol_with_heartdisease.median())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6408d9a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Cholesterol</th>\n",
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       "      <td>917.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>239.700109</td>\n",
       "      <td>132.540894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>54.352727</td>\n",
       "      <td>17.999749</td>\n",
       "    </tr>\n",
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       "      <th>min</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>214.000000</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>225.000000</td>\n",
       "      <td>130.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>267.000000</td>\n",
       "      <td>140.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>603.000000</td>\n",
       "      <td>200.000000</td>\n",
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      ],
      "text/plain": [
       "       Cholesterol   RestingBP\n",
       "count   917.000000  917.000000\n",
       "mean    239.700109  132.540894\n",
       "std      54.352727   17.999749\n",
       "min      85.000000   80.000000\n",
       "25%     214.000000  120.000000\n",
       "50%     225.000000  130.000000\n",
       "75%     267.000000  140.000000\n",
       "max     603.000000  200.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[[\"Cholesterol\", \"RestingBP\"]].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512c073a",
   "metadata": {},
   "source": [
    "The minimum values for both have changed! There are no more zero values in either of those."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c71332d",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "\n",
    "Thanks to our EDA and a general understanding of the features, we can identify some of the features that we could start with:\n",
    "\n",
    "- `Age`\n",
    "- `Sex`\n",
    "- `ChestPainType`\n",
    "- `Cholesterol`\n",
    "- `FastingBS`\n",
    "\n",
    "\n",
    "We will also identify how stronly the feature columns are correlated to the target colummn. That should help us narrow down on the features.\n",
    "\n",
    "In order to do that, we will first convert our categorical columns into dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "22829bb7",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   Age  RestingBP  Cholesterol  FastingBS  MaxHR  Oldpeak  HeartDisease  \\\n",
       "0   40        140          289          0    172      0.0             0   \n",
       "1   49        160          180          0    156      1.0             1   \n",
       "2   37        130          283          0     98      0.0             0   \n",
       "3   48        138          214          0    108      1.5             1   \n",
       "4   54        150          195          0    122      0.0             0   \n",
       "\n",
       "   Sex_M  ChestPainType_ATA  ChestPainType_NAP  ChestPainType_TA  \\\n",
       "0      1                  1                  0                 0   \n",
       "1      0                  0                  1                 0   \n",
       "2      1                  1                  0                 0   \n",
       "3      0                  0                  0                 0   \n",
       "4      1                  0                  1                 0   \n",
       "\n",
       "   RestingECG_Normal  RestingECG_ST  ExerciseAngina_Y  ST_Slope_Flat  \\\n",
       "0                  1              0                 0              0   \n",
       "1                  1              0                 0              1   \n",
       "2                  0              1                 0              0   \n",
       "3                  1              0                 1              1   \n",
       "4                  1              0                 0              0   \n",
       "\n",
       "   ST_Slope_Up  \n",
       "0            1  \n",
       "1            0  \n",
       "2            1  \n",
       "3            0  \n",
       "4            1  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean = pd.get_dummies(df_clean, drop_first=True)\n",
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ccf85c",
   "metadata": {},
   "source": [
    "Now, we can find how they are correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e48e3b51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Calculate the absolute value of correlation coefficients matrix.\n",
    "# 'abs' is used to consider the strength regardless of whether the correlation is positive or negative.\n",
    "correlations = abs(df_clean.corr())\n",
    "\n",
    "# Initialize the figure with a specific size to make sure the heatmap is readable.\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Create a heatmap to visualize the correlation matrix.\n",
    "# 'annot=True' displays the correlation coefficient on the heatmap.\n",
    "# 'cmap=\"Blues\"' uses a blue color palette for the heatmap\n",
    "sns.heatmap(correlations, annot=True, cmap=\"Blues\")\n",
    "\n",
    "# Rotate the x-axis labels to 45 degrees for better readability.\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Display the plot.\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fbd10db2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "# Create a heatmap for the correlations greater than 0.3.\n",
    "# 'annot=True' enables the annotation of the correlation values in each cell of the heatmap.\n",
    "# 'cmap=\"Blues\"' sets the color palette of the heatmap to various shades of blue.\n",
    "# Non-significant correlations (<= 0.3) are masked and will appear as white squares on the heatmap.\n",
    "sns.heatmap(correlations[correlations > 0.3], annot=True, cmap=\"Blues\")\n",
    "\n",
    "# Rotate the x-axis labels to 45 degrees for better readability.\n",
    "plt.xticks(rotation=45)# Create a heatmap for the correlations greater than 0.3.\n",
    "# 'annot=True' enables the annotation of the correlation values in each cell of the heatmap.\n",
    "# 'cmap=\"Blues\"' sets the color palette of the heatmap to various shades of blue.\n",
    "# Non-significant correlations (<= 0.3) are masked and will appear as white squares on the heatmap.\n",
    "sns.heatmap(correlations[correlations > 0.3], annot=True, cmap=\"Blues\")\n",
    "\n",
    "# Rotate the x-axis labels to 45 degrees for better readability.\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Rotate the y-axis labels to 45 degrees for consistency and better readability.\n",
    "plt.yticks(rotation=45)\n",
    "\n",
    "# Adjust the layout to ensure that no content is clipped as a result of the label rotation.\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plot.\n",
    "plt.show()\n",
    "\n",
    "# Adjust the layout to ensure that no content is clipped as a result of the label rotation.\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b81078d",
   "metadata": {},
   "source": [
    "From our correlation heatmap, we can identify the following features to be positively correlated (correlation coefficient greater than 0.3) to `HeartDisease`:\n",
    "\n",
    "- `Oldpeak`\n",
    "- `MaxHR`\n",
    "- `ChestPainType_ATA` \n",
    "- `ExerciseAngina_Y`\n",
    "- `ST_Slope_Flat`\n",
    "- `ST_Slope_Up`\n",
    "\n",
    "The correlation coefficient threshold was chosen arbitrarily. Surprisingly, `Cholesterol` is not strongly correlated to `HeartDisease`. We can consider ignoring the feature for now.\n",
    "\n",
    "Given everything we have attempted so far, we can narrow down our features to the following:\n",
    "\n",
    "- `Oldpeak`\n",
    "- `Sex_M`\n",
    "    - It has a relatively low value for the coefficient, but given what we observed in our EDA, let's also take it into account.\n",
    "- `ExerciseAngina_Y`\n",
    "- `ST_Slope_Flat`\n",
    "- `ST_Slope_Up`\n",
    "\n",
    "Time to create our model using these features!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "143bf6f0",
   "metadata": {},
   "source": [
    "## Building a Classifier with One Feature\n",
    "\n",
    "We will split our dataset into a training and a test set first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6b14956b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the feature set X and the target variable y for modeling.\n",
    "X = df_clean.drop([\"HeartDisease\"], axis=1)\n",
    "y = df_clean[\"HeartDisease\"]\n",
    "\n",
    "# Split the dataset into a training set and a validation set.\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.15, random_state=417)\n",
    "\n",
    "# List of features deemed significant for further modeling.\n",
    "features = [\n",
    "    \"Oldpeak\",\n",
    "    \"Sex_M\",\n",
    "    \"ExerciseAngina_Y\",\n",
    "    \"ST_Slope_Flat\",\n",
    "    \"ST_Slope_Up\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2131229",
   "metadata": {},
   "source": [
    "We will start with creating a model for each of the features above and evaluate their performance using accuracy as a metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ddcfc26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The k-NN classifier trained on Oldpeak and with k = 3 has an accuracy of 76.81%\n",
      "The k-NN classifier trained on Sex_M and with k = 3 has an accuracy of 44.93%\n",
      "The k-NN classifier trained on ExerciseAngina_Y and with k = 3 has an accuracy of 73.19%\n",
      "The k-NN classifier trained on ST_Slope_Flat and with k = 3 has an accuracy of 81.88%\n",
      "The k-NN classifier trained on ST_Slope_Up and with k = 3 has an accuracy of 84.06%\n"
     ]
    }
   ],
   "source": [
    "for feature in features:\n",
    "    knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "    knn.fit(X_train[[feature]], y_train)\n",
    "    accuracy = knn.score(X_val[[feature]], y_val)\n",
    "    print(f\"The k-NN classifier trained on {feature} and with k = 3 has an accuracy of {accuracy*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76171c65",
   "metadata": {},
   "source": [
    "Our best forming model, with an accuracy of ~`82%`, was trained on the `ST_Slope_Flat` feature with `ExerciseAngina_Y` being a close second. These make sense given the data distributions we saw previously.\n",
    "\n",
    "We will train a model using all of these features next."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cacc6c3",
   "metadata": {},
   "source": [
    "## Building a Classifier with Multiple Features\n",
    "\n",
    "Before training on all of the above features, we need to normalize the data first. We will use scikit-learn's MinMaxScaler to scale the values between 0 and 1, and then train the model again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cbbf5de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train[features])\n",
    "X_val_scaled = scaler.transform(X_val[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "96a81789",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 86.23\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "accuracy = knn.score(X_val_scaled, y_val)\n",
    "print(f\"Accuracy: {accuracy*100:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d51e1f",
   "metadata": {},
   "source": [
    "The model's accuracy jumped to ~`83%`! That's not a significant improvement, but still a good start. Using all these features results in an improved model, but, let's see what parameters/hyperparameters might be optimal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f4b42d",
   "metadata": {},
   "source": [
    "## Hyperparameter Optimization\n",
    "\n",
    "Let's prepare our data first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "531aaa84",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_clean.drop([\"HeartDisease\"], axis=1)\n",
    "y = df_clean[\"HeartDisease\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state = 417)\n",
    "\n",
    "features = [\n",
    "    \"Oldpeak\",\n",
    "    \"Sex_M\",\n",
    "    \"ExerciseAngina_Y\",\n",
    "    \"ST_Slope_Flat\",\n",
    "    \"ST_Slope_Up\"\n",
    "]\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train[features])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "364195b9",
   "metadata": {},
   "source": [
    "We will use grid search to explore the following range of values for a couple of hyperparameters:\n",
    "\n",
    "- For `k` (`n_neighbors` in scikit-learn) we will use values in the range of 1 to 20.\n",
    "- We will use two distance metrics - `minkwoski` and `manhattan`.\n",
    "\n",
    "`minkwoski` is the default metric for KNeighborsClassifier in sklearn, so we don't expect `manhattan` to do better. But, let's see what happens!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8e1a3cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-1 {\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-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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",
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       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 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-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;metric&#x27;: [&#x27;minkowski&#x27;, &#x27;manhattan&#x27;],\n",
       "                         &#x27;n_neighbors&#x27;: range(1, 20)},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;metric&#x27;: [&#x27;minkowski&#x27;, &#x27;manhattan&#x27;],\n",
       "                         &#x27;n_neighbors&#x27;: range(1, 20)},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div><div class=\"sk-serial\"><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\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&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></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={'metric': ['minkowski', 'manhattan'],\n",
       "                         'n_neighbors': range(1, 20)},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_params = {\"n_neighbors\": range(1, 20),\n",
    "                \"metric\": [\"minkowski\", \"manhattan\"]\n",
    "              }\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn_grid = GridSearchCV(knn, grid_params, scoring='accuracy')\n",
    "knn_grid.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2a22a9e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83.56327543424318, {'metric': 'minkowski', 'n_neighbors': 19})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_grid.best_score_*100, knn_grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e4334d3",
   "metadata": {},
   "source": [
    "Our best model had an accuracy of ~`83%` with `19` `n_neighbors` and `minkowski` as the distance metric. \n",
    "\n",
    "The accuracy of this model is only slightly better. Since `GridSearchCV` employs a cross-validation approach, it is reasonable to assume that this is a better estimate of how the model performs compared to our prior attempt.\n",
    "\n",
    "We will evaluate our model on our test set now."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52c42a3c",
   "metadata": {},
   "source": [
    "## Model Evaluation on Test Set\n",
    "\n",
    "We need to first normalize our test set similar to how we scaled our training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2086c66d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Model Accuracy on test set: 86.23\n"
     ]
    }
   ],
   "source": [
    "X_test_scaled = scaler.transform(X_test[features])\n",
    "predictions = knn_grid.best_estimator_.predict(X_test_scaled)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(f\" Model Accuracy on test set: {accuracy*100:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf618cba",
   "metadata": {},
   "source": [
    "Our model got an accuracy of ~`87`%. That's really good! This means that our model is likely to correctly predict whether a patient is at risk for a heart disease ~`87`% of the time.\n",
    "\n",
    "However, the accuracy being higher than the one before raises some flags.\n",
    "\n",
    "One explanation that could explain is to look at how the data is distributed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d05278ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of patients by their sex in the entire dataset\n",
      "1    724\n",
      "0    193\n",
      "Name: Sex_M, dtype: int64\n",
      "\n",
      "Distribution of patients by their sex in the training dataset\n",
      "1    615\n",
      "0    164\n",
      "Name: Sex_M, dtype: int64\n",
      "\n",
      "Distribution of patients by their sex in the test dataset\n",
      "1    109\n",
      "0     29\n",
      "Name: Sex_M, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Distribution of patients by their sex in the entire dataset\")\n",
    "print(X.Sex_M.value_counts())\n",
    "\n",
    "print(\"\\nDistribution of patients by their sex in the training dataset\")\n",
    "print(X_train.Sex_M.value_counts())\n",
    "\n",
    "print(\"\\nDistribution of patients by their sex in the test dataset\")\n",
    "print(X_test.Sex_M.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4818a3b",
   "metadata": {},
   "source": [
    "We used `Sex` as one of our features for training the model.\n",
    "\n",
    "- `X` had `724` males and `193` females.\n",
    "- `X_train` had `615` males and `164` females.\n",
    "- `X_test` had `109` males and `29` females.\n",
    "\n",
    "\n",
    "We can see that the above datasets have a significantly higher number of male patients than female ones. We briefly mentioned previously that this could present a bias because of this imbalance in our dataset and we can see it potentially impacts our model. If the test dataset doesn't have that many female patients and the model was trained on a dataset with more male patients, then it is understandable it has better accuracy on the test set. Of course, there could be other factors contributing to this discrepancy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08f92bf4",
   "metadata": {},
   "source": [
    "## Summary and Next Steps\n",
    "\n",
    "Our final model was trained using the following features:\n",
    "\n",
    "- `Oldpeak`\n",
    "- `Sex_M`\n",
    "- `ExerciseAngina_Y`\n",
    "- `ST_Slope_Flat`\n",
    "- `ST_Slope_Up`\n",
    "\n",
    "and had a test set accuracy of `86.96`%. However, given the limitations of our data this accuracy might not be indicative of a well performing model.\n",
    "\n",
    "There are quite a few things we could try next to get better results:\n",
    "\n",
    "- Try out different features.\n",
    "- Expand the grid search parameters to identify more optimal hyperparameters.\n",
    "- Explore other algorithms that might perform better than k-NN.\n",
    "- Try and collect more data."
   ]
  }
 ],
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