Python Project – Predicting Heart Disease Risk with K-Nearest Neighbors.ipynb

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   "source": [
    "# Predicting Heart Disease Risk Using the K-Nearest Neighbors"
   ]
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  {
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   "metadata": {},
   "source": [
    "## Table of Contents"
   ]
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   "source": [
    "- [1. Introduction](#1.-Introduction)\n",
    "- [2. EDA: Descriptive Statistics](#2.-EDA:-Descriptive-Statistics)\n",
    "- [3. EDA: Visualizations](#3.-EDA:-Visualizations)\n",
    "- [4. Data Cleaning](#4.-Data-Cleaning)\n",
    "- [5. Feature Selection](#5.-Feature-Selection)\n",
    "- [6. Building a Classifier with One Feature](#6.-Building-a-Classifier-with-One-Feature)\n",
    "- [7. Building a Classifier with Multiple Features](#7.-Building-a-Classifier-with-Multiple-Features)\n",
    "- [8. Hyperparameter Tuning with GridSearchCV](#8.-Hyperparameter-Tuning-with-GridSearchCV)\n",
    "- [9. Impact of Gender Imbalance on Performance](#9.-Impact-of-Gender-Imbalance-on-Performance)\n",
    "- [10. Conclusion](#10.-Conclusion)"
   ]
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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e539f2",
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   "source": [
    "The [World Health Organization](https://en.wikipedia.org/wiki/World_Health_Organization) (WHO) estimates that `17.9` million people die from [cardiovascular diseases](https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)) (CVDs) every year. Multiple risk factors can contribute to CVD in an individual, such as an unhealthy diet, lack of physical activity, and mental illness. Identifying these risk factors early can help prevent many premature deaths.\n",
    "\n",
    "In this project, we will assume to be working for an R&D company focused on providing healthcare solutions. The company has collected anonymized data from multiple hospitals regarding several patients. The [dataset](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction) includes relevant information for each patient, such as personal details and medical data, including whether or not they have had heart disease previously.\n",
    "\n",
    "Our task is to use the dataset to accurately predict the likelihood of a new patient developing heart disease in the future. The dataset has the following features:\n",
    "\n",
    "- `Age` – Age of the patient **[years]**.\n",
    "- `Sex` – Sex of the patient **[`M`: Male, `F`: Female]**.\n",
    "- `ChestPainType` – Type of chest pain **[`TA`: Typical Angina, `ATA`: Atypical Angina, `NAP`: Non-Anginal Pain, `ASY`: Asymptomatic]**.\n",
    "- `RestingBP` – Resting blood pressure **[mm Hg]**.\n",
    "- `Cholesterol` – Serum cholesterol **[mg/dL]**.\n",
    "- `FastingBS` – Fasting blood sugar **[`1`: > 120 mg/dL, `0`: otherwise]**.\n",
    "- `RestingECG` – Resting electrocardiogram results **[`Normal`: Normal, `ST`: ST-T wave abnormality, `LVH`: probable or definite left ventricular hypertrophy by Estes' criteria]**.\n",
    "- `MaxHR` – Maximum heart rate **[between `60` and `202`]**.\n",
    "- `ExerciseAngina` – Exercise-induced angina **[`Y`: Yes, `N`: No]**.\n",
    "- `Oldpeak` – ST depression value.\n",
    "- `ST_Slope` – Slope of the peak exercise ST segment **[`Up`: upsloping, `Flat`: flat, `Down`: downsloping]**.\n",
    "- `HeartDisease` – Output class **[`1`: heart disease, `0`: normal]**.\n",
    "\n",
    "Before we build our **K-Nearest Neighbors** classifier, we're going to import the relevant libraries and conduct exploratory data analysis on the dataset."
   ]
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       "  <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",
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       "    <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",
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       "      <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",
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       "    <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",
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       "    <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",
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      "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  "
      ]
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   "source": [
    "# Import the relevant libraries\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the heart disease prediction dataset and display the first few rows\n",
    "df = pd.read_csv(\"Datasets/heart_disease_prediction.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1117419",
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   "source": [
    "After examining the first few rows of the dataset, we observe that it contains both categorical and numerical features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67200023",
   "metadata": {},
   "source": [
    "## 2. EDA: Descriptive Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "600b4dad",
   "metadata": {},
   "source": [
    "Now, let's identify which columns are numerical and which are categorical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "858f14f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "int64      6\n",
       "object     5\n",
       "float64    1\n",
       "Name: count, dtype: int64"
      ]
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   "source": [
    "# Display the data types of the columns and the count of each data type\n",
    "display(df.dtypes)\n",
    "display(df.dtypes.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bacd0ca1",
   "metadata": {},
   "source": [
    "In total, `7` features are numerical, while `5` are categorical. However, two of the numerical features, `FastingBS` and `HeartDisease`, are actually categorical as well because they contain only the values `0` and `1`. Next, let's examine the numerical variables."
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "id": "8b807a6e",
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   "outputs": [
    {
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       "  <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",
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       "      <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",
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       "    <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",
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      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display summary statistics for the numerical features\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec145120",
   "metadata": {},
   "source": [
    "From the table above, we can observe that:\n",
    "\n",
    "- The average age of patients is around `54` years.\n",
    "- The median cholesterol level is roughly `24` mm/dL higher than the mean, suggesting a left-skewed distribution with potential outliers.\n",
    "- `RestingBP` and `Cholesterol` have a minimum value of `0`.\n",
    "\n",
    "`RestingBP` represents resting blood pressure, and a value of `0` is unlikely. Similarly, according to the [American Heart Association](https://www.heart.org/en/health-topics/cholesterol/about-cholesterol/what-your-cholesterol-levels-mean), serum cholesterol is a composite measure, including `HDL`, `LDL`, and `20%` of triglyceride levels, making a value of `0` improbable for serum cholesterol.\n",
    "\n",
    "Next, let's quickly check for any missing values in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "39106cfa",
   "metadata": {
    "scrolled": true
   },
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values in each column of the dataset\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87665271",
   "metadata": {},
   "source": [
    "Fortunately, there are no missing values in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "388d9e56",
   "metadata": {},
   "source": [
    "## 3. EDA: Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19aeb237",
   "metadata": {},
   "source": [
    "Our observations so far have been limited to a subset of the columns. Additionally, we don't yet know how the dataset is distributed in relation to our target variable, `HeartDisease`. For instance, how many female patients had heart disease?\n",
    "\n",
    "Next, we’ll explore the categorical features and use visualizations to better understand the distribution of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "81180114",
   "metadata": {},
   "outputs": [
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       "      <td>918</td>\n",
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       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>M</td>\n",
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       "      <td>N</td>\n",
       "      <td>Flat</td>\n",
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       "      <th>freq</th>\n",
       "      <td>725</td>\n",
       "      <td>496</td>\n",
       "      <td>552</td>\n",
       "      <td>547</td>\n",
       "      <td>460</td>\n",
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       "        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"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(array([0, 1], dtype=int64), array([0, 1], dtype=int64))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display summary statistics for categorical features\n",
    "display(df.describe(include=['object']))\n",
    "\n",
    "# Display unique values for 'FastingBS' and 'HeartDisease' columns\n",
    "df[\"FastingBS\"].unique(), df[\"HeartDisease\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "749c1737",
   "metadata": {},
   "source": [
    "The `Sex` and `ExerciseAngina` columns have two unique values, with `M` and `N` being the most frequent, respectively. `ChestPainType` includes four categories, with `ASY` being the most common. Moreover, `RestingECG` and `ST_Slope` have three possible values, with `Normal` and `Flat` being the most frequent.\n",
    "\n",
    "The unique values for both `FastingBS` and `HeartDisease` are `0` and `1`, indicating they represent two-class categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb7779dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1400 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the categorical columns\n",
    "categorical_cols = [\"ST_Slope\", \"RestingECG\", \"Sex\", \"ExerciseAngina\",\n",
    "                    \"FastingBS\", \"HeartDisease\", \"ChestPainType\"]\n",
    "\n",
    "# Set the figure size and style\n",
    "fig = plt.figure(figsize=(14, 14))\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "# Iterate through each categorical column to create subplots\n",
    "for i, col in enumerate(categorical_cols, start=1):\n",
    "    ax = plt.subplot(4, 2, i)\n",
    "    sns.countplot(x=df[col], ax=ax)\n",
    "\n",
    "    # Remove all spines as well as x and y-axis ticks\n",
    "    ax.spines[:].set_visible(False)\n",
    "    ax.tick_params(axis='both', bottom=False, left=False)\n",
    "    \n",
    "    # Set title and remove x and y-axis labels\n",
    "    ax.set_title(col, weight='bold', color='#333333', size=18, pad=8)\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "\n",
    "    # Set y-axis limits and add horizontal grid lines\n",
    "    ax.set_ylim(0, 800)\n",
    "    ax.yaxis.grid(True, alpha=0.3)\n",
    "\n",
    "    # Adjust x and y-axis labels' color\n",
    "    ax.xaxis.set_tick_params(labelcolor='#333333')\n",
    "    ax.tick_params(axis='y', labelcolor='grey')\n",
    "    \n",
    "    # Add bar labels to each container\n",
    "    for container in ax.containers:\n",
    "        ax.bar_label(container, label_type=\"center\", color='white', weight='bold')\n",
    "\n",
    "# Adjust layout to add vertical space between subplots\n",
    "plt.subplots_adjust(hspace=0.4)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af85f969",
   "metadata": {},
   "source": [
    "From the subplots above, here are some insights:\n",
    "- The dataset is highly skewed toward male patients, with `725` males and `193` females. This could potentially induce bias in our models.\n",
    "- `496` patients had `ASY` (asymptomatic) chest pain type.\n",
    "- `552` patients had a `normal` resting ECG.\n",
    "- `704` patients had fasting blood sugar lower than `120` mg/dL.\n",
    "\n",
    "Next, grouping each category by `HeartDisease` will give us a better idea about the data distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b860b0b2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size\n",
    "fig = plt.figure(figsize=(14, 14))\n",
    "\n",
    "# Plot countplots for each categorical column, with hue based on 'HeartDisease'\n",
    "for i, col in enumerate([\"ST_Slope\", \"RestingECG\", \"Sex\",\n",
    "                         \"ExerciseAngina\", \"FastingBS\", \"ChestPainType\"], start=1):\n",
    "    ax = plt.subplot(4, 2, i)\n",
    "    sns.countplot(x=df[col], hue=df[\"HeartDisease\"], ax=ax)\n",
    "\n",
    "    # Remove all spines as well as x and y-axis ticks\n",
    "    ax.spines[:].set_visible(False)\n",
    "    ax.tick_params(axis='both', bottom=False, left=False)\n",
    "    \n",
    "    # Set title and remove x and y-axis labels\n",
    "    ax.set_title(col, weight='bold', color='#333333', size=18, pad=8)\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('')\n",
    "\n",
    "    # Set y-axis limits and add horizontal grid lines\n",
    "    ax.set_ylim(0, 500)\n",
    "    ax.yaxis.grid(True, alpha=0.3)\n",
    "\n",
    "    # Adjust x and y-axis labels' color\n",
    "    ax.xaxis.set_tick_params(labelcolor='#333333')\n",
    "    ax.tick_params(axis='y', labelcolor='grey')\n",
    "    \n",
    "    # Add bar labels to each container\n",
    "    for container in ax.containers:\n",
    "        ax.bar_label(container, color='#333333', weight='bold')\n",
    "        \n",
    "# Remove all legends and add one legend in the top right\n",
    "for ax in fig.axes:\n",
    "    ax.get_legend().remove()\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, fontsize='medium', title='HeartDisease', bbox_to_anchor=(0.95, 0.89))\n",
    "\n",
    "# Adjust layout to add vertical space between subplots\n",
    "plt.subplots_adjust(hspace=0.4)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c986b55",
   "metadata": {},
   "source": [
    "From the subplots above, we observe the following:\n",
    "\n",
    "- Only `50` female patients have been diagnosed with heart disease compared to `143` males.\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 models, asymptomatic implies that patients who had heart disease did not have chest pain as a symptom.\n",
    "- A high number of patients (`170`) with blood sugar greater than `120` mg/dL were diagnosed with heart disease compared to those who were not.\n",
    "- Out of all patients who had exercise-induced angina, `316` were diagnosed with heart disease.\n",
    "- Out of all patients with a flat ST slope, `381` were diagnosed with heart disease.\n",
    "\n",
    "Looking at the data distribution, we can start to identify some relevant features. We will clean up the dataset before narrowing down our features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4f2f008",
   "metadata": {},
   "source": [
    "## 4. Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce05922",
   "metadata": {},
   "source": [
    "Previously, we found that there are no missing values in our entire dataset. However, a few columns have `0` values that don't make sense. There are a few ways we can handle these values:\n",
    "\n",
    "1. Remove those rows entirely if their frequency of occurrence is low.\n",
    "2. Impute those `0s` with either the median value of the entire column, or the median value based on the `HeartDisease` status.\n",
    "3. Use more complex approaches, such as looking at the median of the values grouped by age, sex, and/or resting ECG of the patient. For example, the resting blood pressure of a female in her `30s` with a normal ECG might be lower than the median of the entire `RestingBP` column.\n",
    "\n",
    "The third option might give us a slightly more accurate result, but given the small size of our dataset, we can ignore it for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f704588e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of rows where \"RestingBP\" is 0: 1\n",
      "The number of rows where \"Cholesterol\" is 0: 172\n"
     ]
    }
   ],
   "source": [
    "# Print the number of rows where 'RestingBP' or 'Cholesterol' is 0\n",
    "print(f'The number of rows where \"RestingBP\" is 0: {df[df[\"RestingBP\"] == 0].shape[0]}')\n",
    "print(f'The number of rows where \"Cholesterol\" is 0: {df[df[\"Cholesterol\"] == 0].shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece5c32d",
   "metadata": {},
   "source": [
    "`RestingBP` has only `1` zero value, which we can just remove. On the other hand, there are `172` zero values for `Cholesterol`, which is a relatively high number. Hence, removing all these rows isn't practical.\n",
    "\n",
    "Although it might not be ideal, we will replace the zero values in `Cholesterol` based on the `HeartDisease` status. Specifically, the zero values in `Cholesterol` for patients diagnosed with heart disease will be replaced by the median of non-zero `Cholesterol` values for patients with heart disease. Similarly, zero values for patients without heart disease will be replaced by the median of non-zero `Cholesterol` values for patients without heart disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "76706163",
   "metadata": {
    "scrolled": true
   },
   "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>Cholesterol</th>\n",
       "      <th>RestingBP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>917.000000</td>\n",
       "      <td>917.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a copy of the DataFrame and remove rows where 'RestingBP' is 0\n",
    "df_clean = df.copy()\n",
    "df_clean = df_clean[df_clean[\"RestingBP\"] != 0]\n",
    "\n",
    "# Create a mask for rows where 'HeartDisease' is 0\n",
    "hd_mask = df_clean[\"HeartDisease\"] == 0\n",
    "\n",
    "# Extract cholesterol values based on heart disease status\n",
    "chol_without_hd = df_clean.loc[hd_mask, \"Cholesterol\"]\n",
    "chol_with_hd = df_clean.loc[~hd_mask, \"Cholesterol\"]\n",
    "\n",
    "# Impute zero values in 'Cholesterol' with the median of respective groups\n",
    "df_clean.loc[hd_mask, \"Cholesterol\"] = chol_without_hd.replace(0, chol_without_hd.median())\n",
    "df_clean.loc[~hd_mask, \"Cholesterol\"] = chol_with_hd.replace(0, chol_with_hd.median())\n",
    "\n",
    "# Display summary statistics for 'Cholesterol' and 'RestingBP'\n",
    "df_clean[[\"Cholesterol\", \"RestingBP\"]].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d4d07e2",
   "metadata": {},
   "source": [
    "As expected, the minimum values for both columns have changed. There are no more zero values in both `Cholesterol` and `RestingBP`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9323592f",
   "metadata": {},
   "source": [
    "## 5. Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc3b04b0",
   "metadata": {},
   "source": [
    "Now that we've cleaned our data, we can explore how the columns correlate with one another. Before that, we'll convert categorical columns into dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "690011e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert categorical columns to dummy variables\n",
    "df_clean = pd.get_dummies(df_clean, drop_first=True)\n",
    "\n",
    "# Compute absolute correlations and reset plot style\n",
    "correlations = abs(df_clean.corr())\n",
    "plt.style.use('default')\n",
    "\n",
    "# Create a mask to hide the upper triangle of the heatmap\n",
    "mask = np.zeros_like(correlations.iloc[1:, :-1])\n",
    "mask[np.triu_indices_from(mask, k=1)] = True\n",
    "\n",
    "# Create a heatmap showing correlations between features with annotations\n",
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "ax = sns.heatmap(correlations.iloc[1:, :-1], annot=True, annot_kws={\"size\": 10},\n",
    "                 vmin=0, vmax=1, linewidths=0.5, cmap=\"Blues\", fmt=\".2f\", mask=mask)\n",
    "\n",
    "# Set title and subtitle\n",
    "ax.set_title(\"Features like 'ExerciseAngina_Y', 'ST_Slope_Flat', and 'ST_Slope_Up'\\n\\\n",
    "Have a High Absolute Correlation with 'HeartDisease'\", \n",
    "             x=0.49, y=1.07, fontsize=16, ha='center', weight='bold')\n",
    "ax.text(s=\"Surprisingly, 'Cholesterol' Is Not Strongly Correlated with 'HeartDisease'\",\n",
    "        x=7.33, y=-0.72, fontsize=13.5, ha='center')\n",
    "\n",
    "# Customize tick parameters and labels, and rotate x-tick labels\n",
    "ax.tick_params(left=False, bottom=False)\n",
    "ax.tick_params(axis='both', labelsize=10)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
    "\n",
    "# Adjust annotations and hide values lower than 0.3\n",
    "for text in ax.texts:\n",
    "    t = text.get_text()\n",
    "    # Check if the text can be converted to float\n",
    "    try:\n",
    "        t = float(t)\n",
    "        if t < 0.3:\n",
    "            text.set_text('')  # Hide annotations for values lower than 0.3\n",
    "        else:\n",
    "            text.set_text(t)   # Keep annotations for values greater than or equal to 0.3\n",
    "    except:\n",
    "        pass  # Skip non-numeric annotations\n",
    "\n",
    "# Adjust the size and color of the colorbar values, and remove tick marks\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.ax.tick_params(labelcolor='#333333', labelsize=10, right=False)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59b51c4f",
   "metadata": {},
   "source": [
    "From our correlation heatmap, we can identify the following features as being correlated (absolute correlation coefficient greater than `0.3`) with `HeartDisease`: `ChestPainType_ATA`, `ExerciseAngina_Y`, `ST_Slope_Flat`, `ST_Slope_Up`, `Oldpeak`, `Sex_M`, and `MaxHR`.\n",
    "\n",
    "We chose the correlation coefficient threshold arbitrarily. Surprisingly, `Cholesterol` is not strongly correlated with `HeartDisease`, so we can consider ignoring this feature for now. Based on our findings so far, we will narrow down our features to `ExerciseAngina_Y`, `ST_Slope_Flat`, `ST_Slope_Up`, `Oldpeak`, and `Sex_M`.\n",
    "\n",
    "Although `Sex_M` has an absolute correlation coefficient of only `0.31`, we'll include it based on our EDA observations. Next, we'll create our models using these features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5f98336",
   "metadata": {},
   "source": [
    "## 6. Building a Classifier with One Feature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07e3e9d6",
   "metadata": {},
   "source": [
    "To build a classifier using only a single feature, we'll split the dataset into training and test sets. We'll then create a model for each selected feature and evaluate their performance using accuracy as the metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e6c5bf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The classifier trained on \"ExerciseAngina_Y\" achieved 53.42% train accuracy and 60.87% test accuracy\n",
      "The classifier trained on \"ST_Slope_Flat\" achieved 78.02% train accuracy and 76.09% test accuracy\n",
      "The classifier trained on \"ST_Slope_Up\" achieved 82.10% train accuracy and 79.13% test accuracy\n",
      "The classifier trained on \"Oldpeak\" achieved 54.29% train accuracy and 50.43% test accuracy\n",
      "The classifier trained on \"Sex_M\" achieved 64.63% train accuracy and 67.83% test accuracy\n"
     ]
    }
   ],
   "source": [
    "# Set up the features (X) and target (y) variables\n",
    "X = df_clean.drop([\"HeartDisease\"], axis=1)\n",
    "y = df_clean[\"HeartDisease\"]\n",
    "\n",
    "# Split the dataset into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
    "\n",
    "# Select specific features to evaluate\n",
    "features = [\"ExerciseAngina_Y\", \"ST_Slope_Flat\", \"ST_Slope_Up\", \"Oldpeak\", \"Sex_M\"]\n",
    "\n",
    "# Loop through each feature for k-NN classification\n",
    "for feature in features:\n",
    "    \n",
    "    # Initialize k-NN classifier with k=3 and train on the selected feature\n",
    "    knn = KNeighborsClassifier(n_neighbors=3)\n",
    "    knn.fit(X_train[[feature]], y_train)\n",
    "    \n",
    "    # Calculate training and test accuracy\n",
    "    train_accuracy = knn.score(X_train[[feature]], y_train)\n",
    "    test_accuracy = knn.score(X_test[[feature]], y_test)\n",
    "    \n",
    "    # Print the training and testing accuracy for the current feature\n",
    "    print(f'The classifier trained on \"{feature}\" achieved {train_accuracy*100:.2f}% train accuracy \\\n",
    "and {test_accuracy*100:.2f}% test accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "702c15c4",
   "metadata": {},
   "source": [
    "The classifiers trained on `ST_Slope_Flat` and `ST_Slope_Up` performed best overall, with test accuracies of `76.09%` and `79.13%`, respectively. `ExerciseAngina_Y` and `Oldpeak` had the lowest test accuracies, with a noticeable gap between train and test performance for `ExerciseAngina_Y`, indicating that the model performed better on the test set.\n",
    "\n",
    "As a result, we're going to train a classifier by omitting `Oldpeak` to optimize performance and minimize overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e947b913",
   "metadata": {},
   "source": [
    "## 7. Building a Classifier with Multiple Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8745d92b",
   "metadata": {},
   "source": [
    "Before training on the selected features, it's important to note that they aren't yet normalized. Since the values are not all within the same range, we will first scale our selected features to the range `(0,1)` before training our baseline k-NN model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0fe25be3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline k-NN train accuracy: 84.86% \t Baseline k-NN test accuracy: 82.61%\n"
     ]
    }
   ],
   "source": [
    "# Initialize MinMaxScaler and define the selected features\n",
    "scaler = MinMaxScaler()\n",
    "features = [\"ExerciseAngina_Y\", \"ST_Slope_Flat\", \"ST_Slope_Up\", \"Sex_M\"]\n",
    "\n",
    "# Scale the training and test data for the selected features\n",
    "X_train_scaled = scaler.fit_transform(X_train[features])\n",
    "X_test_scaled = scaler.transform(X_test[features])\n",
    "\n",
    "# Train k-NN classifier with k=3 on the scaled data\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate and print training and testing accuracy\n",
    "train_accuracy = knn.score(X_train_scaled, y_train)\n",
    "test_accuracy = knn.score(X_test_scaled, y_test)\n",
    "print(f\"Baseline k-NN train accuracy: {train_accuracy*100:.2f}%\", \"\\t\", \\\n",
    "      f\"Baseline k-NN test accuracy: {test_accuracy*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17161be9",
   "metadata": {},
   "source": [
    "The baseline k-NN model's performance on the combined features surpasses the previous results from using single features, with train and test accuracies of `84.86%` and `82.61%`. This suggests that combining features improved the model's accuracy. Next, we'll explore which hyperparameters might be optimal for further improvement using the same four features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6bb9d5f",
   "metadata": {},
   "source": [
    "## 8. Hyperparameter Tuning with GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3712a667",
   "metadata": {},
   "source": [
    "`GridSearchCV` enables us to explore various hyperparameters for our model. For instance, with the `KNeighborsClassifier`, we can experiment with parameters such as `n_neighbors`, `weights`, and `metric`.\n",
    "\n",
    "Since `GridSearchCV` employs a cross-validation approach, it provides a more reliable estimate of the model's performance compared to a regular train-test split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1dda867d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned k-NN best cross-validation score: 84.70%\n",
      "Best parameters: {'leaf_size': 20, 'metric': 'minkowski', 'n_neighbors': 1, 'weights': 'uniform'}\n"
     ]
    }
   ],
   "source": [
    "# Define hyperparameter grid for KNeighborsClassifier\n",
    "grid_params = {\"n_neighbors\": range(1, 20),\n",
    "               \"metric\": [\"minkowski\", \"manhattan\"],\n",
    "               \"weights\": [\"uniform\", \"distance\"],\n",
    "               \"leaf_size\": range(10, 50, 10)}\n",
    "\n",
    "# Initialize KNeighborsClassifier and perform grid search for hyperparameter tuning\n",
    "knn = KNeighborsClassifier()\n",
    "knn_grid = GridSearchCV(knn, grid_params, scoring='accuracy')\n",
    "knn_grid.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Print the best cross-validation score and corresponding hyperparameters for the tuned k-NN model\n",
    "print(f'Tuned k-NN best cross-validation score: {knn_grid.best_score_ * 100:.2f}%')\n",
    "print(f'Best parameters: {knn_grid.best_params_}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab46838",
   "metadata": {},
   "source": [
    "The best cross-validation score from the grid search is `84.70%`, indicating improved performance compared to the baseline k-NN test accuracy of `82.61%`. The optimal hyperparameters are `leaf_size: 20`, `metric: minkowski`, `n_neighbors: 1`, and `weights: uniform`, suggesting effective tuning for the classifier.\n",
    "\n",
    "Next, let's evaluate our best model from the grid search on the train and test datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e406474c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned model train accuracy: 84.86% \t Tuned model test accuracy: 82.61%\n"
     ]
    }
   ],
   "source": [
    "# Generate training and testing set predictions using the best estimator from grid search\n",
    "train_predictions = knn_grid.best_estimator_.predict(X_train_scaled)\n",
    "test_predictions = knn_grid.best_estimator_.predict(X_test_scaled)\n",
    "\n",
    "# Calculate and print the training and test accuracy\n",
    "train_accuracy = accuracy_score(y_train, train_predictions)\n",
    "test_accuracy = accuracy_score(y_test, test_predictions)\n",
    "print(f\"Tuned model train accuracy: {train_accuracy*100:.2f}%\", \"\\t\", \\\n",
    "      f\"Tuned model test accuracy: {test_accuracy*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffa38949",
   "metadata": {},
   "source": [
    "The tuned model's performance mirrors the baseline k-NN, with identical train (`84.86%`) and test (`82.61%`) accuracy, despite the cross-validation score of `84.70%`. This suggests that the hyperparameter tuning didn't improve generalization over the baseline model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b072de1a",
   "metadata": {},
   "source": [
    "## 9. Impact of Gender Imbalance on Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa153d27",
   "metadata": {},
   "source": [
    "To explain the potential reason for the slightly lower test accuracies of the baseline and optimized models compared to their training accuracies, let's first check the distribution of patients by sex in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8d3c2342",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of patients by sex in the training dataset:\n",
      "Sex_M\n",
      "True     538\n",
      "False    149\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Distribution of patients by sex in the test dataset:\n",
      "Sex_M\n",
      "True     186\n",
      "False     44\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Print the distribution of patients by sex in the training and test datasets\n",
    "print(\"Distribution of patients by sex in the training dataset:\")\n",
    "print(X_train.Sex_M.value_counts())\n",
    "\n",
    "print(\"\\nDistribution of patients by sex in the test dataset:\")\n",
    "print(X_test.Sex_M.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4792372e",
   "metadata": {},
   "source": [
    "We used sex as one of our features for training our models, with the training dataset (`X_train`) consisting of `538` males and `149` females, and the test dataset (`X_test`) containing `186` males and `44` females.\n",
    "\n",
    "The gender imbalance in both datasets likely impacted the train and test accuracies. With a larger proportion of males, a model may become biased toward predicting outcomes for this majority group, leading to inflated accuracy for males but poorer performance for females. Furthermore, this imbalance can cause a model to overfit, relying more on male-specific patterns while underrepresenting female characteristics.\n",
    "\n",
    "Based on the tuned model, let's create DataFrames to compare the actual and predicted values for both the training and test sets, calculate the percentage of correct and incorrect predictions by gender, and analyze the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "330f6d7b",
   "metadata": {
    "scrolled": true
   },
   "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>Correct (%)</th>\n",
       "      <th>Incorrect (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>85.234899</td>\n",
       "      <td>14.765101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>84.758364</td>\n",
       "      <td>15.241636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Correct (%)  Incorrect (%)\n",
       "Gender                            \n",
       "Female    85.234899      14.765101\n",
       "Male      84.758364      15.241636"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>Correct (%)</th>\n",
       "      <th>Incorrect (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>79.545455</td>\n",
       "      <td>20.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>83.333333</td>\n",
       "      <td>16.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Correct (%)  Incorrect (%)\n",
       "Gender                            \n",
       "Female    79.545455      20.454545\n",
       "Male      83.333333      16.666667"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create DataFrames to compare actual and predicted values for train and test sets\n",
    "train_comparison = pd.DataFrame({'Actual': y_train, 'Predicted': train_predictions})\n",
    "test_comparison = pd.DataFrame({'Actual': y_test, 'Predicted': test_predictions})\n",
    "\n",
    "# Add gender column to the comparison DataFrames and map True/False to 'Male'/'Female'\n",
    "train_comparison['Gender'] = X_train['Sex_M'].copy()\n",
    "train_comparison['Gender'] = train_comparison['Gender'].map({True:'Male', False:'Female'})\n",
    "test_comparison['Gender'] = X_test['Sex_M'].copy()\n",
    "test_comparison['Gender'] = test_comparison['Gender'].map({True:'Male', False:'Female'})\n",
    "\n",
    "# Calculate the percentage of correct and incorrect predictions by gender for train and test sets\n",
    "train_gender_comparison = train_comparison.groupby('Gender').apply(\n",
    "    lambda df: pd.Series({\n",
    "        'Correct (%)': (df['Actual'] == df['Predicted']).mean() * 100,\n",
    "        'Incorrect (%)': (df['Actual'] != df['Predicted']).mean() * 100}))\n",
    "test_gender_comparison = test_comparison.groupby('Gender').apply(\n",
    "    lambda df: pd.Series({\n",
    "        'Correct (%)': (df['Actual'] == df['Predicted']).mean() * 100,\n",
    "        'Incorrect (%)': (df['Actual'] != df['Predicted']).mean() * 100}))\n",
    "\n",
    "# Display the comparison results\n",
    "display(train_gender_comparison)\n",
    "display(test_gender_comparison)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f1a52a",
   "metadata": {},
   "source": [
    "The training results indicate that the model correctly predicts that `85.23%` of females and `84.76%` of males are at risk for heart disease, showing slightly better accuracy for the minority class (`females`). In contrast, the test results reveal a significant drop for females, with only `79.54%` accuracy compared to `83.33%` for males. This disparity highlights the model's bias toward the majority class (`males`), leading to lower performance on the minority class in the test set.\n",
    "\n",
    "However, here are steps that may help a model better capture the characteristics of the minority class and improve overall accuracy:\n",
    "* Integrate additional data sources, such as demographic or health-related features, that could provide more context about the patients.\n",
    "* Use techniques like oversampling the minority class (`females`) or undersampling the majority class (`males`) to create a more balanced dataset. However, since our dataset contains only `917` rows, with `724` male patients, this may not be an ideal solution.\n",
    "* Identify and create additional features that may help better capture the characteristics of the minority class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dffa0886",
   "metadata": {},
   "source": [
    "## 10. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79b1a471",
   "metadata": {},
   "source": [
    "In this project, we pretended to be working for an R&D company focused on healthcare solutions. We worked with a [dataset](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction) containing personal and medical data for each patient, including whether they had heart disease. Our goal was to predict the likelihood of heart disease in new patients. When reviewing the dataset, we noted it includes both categorical and numerical features, inspected the numerical variables using summary statistics, and found no missing values.\n",
    "\n",
    "Next, we explored the categorical features by reviewing summary statistics and unique values, and used visualizations for each category. We also used visualizations to group the categories by `HeartDisease`, helping us better understand the data distribution and identify relevant features. To address erroneous zero values, we removed the single zero in `RestingBP` and replaced the values in `Cholesterol` based on heart disease status. After cleaning the data, we converted categorical columns into dummy variables and analyzed correlations. Based on the results, we selected `ExerciseAngina_Y`, `ST_Slope_Flat`, `ST_Slope_Up`, `Oldpeak`, and `Sex_M` as features for model building.\n",
    "\n",
    "We split the dataset into training and test sets and created classifiers for each selected feature, evaluating performance using accuracy. The classifiers trained on `ST_Slope_Flat` and `ST_Slope_Up` performed best, with test accuracies of `76.09%` and `79.13%`, respectively. `ExerciseAngina_Y` and `Oldpeak` had the lowest test accuracies, with `ExerciseAngina_Y` showing better test performance. Consequently, we trained subsequent models by omitting `Oldpeak` to optimize performance and reduce overfitting.\n",
    "\n",
    "We scaled the combined features to a `(0,1)` range and trained a baseline k-NN model on them, achieving train and test accuracies of `84.86%` and `82.61%`, surpassing single-feature models. Next, we used `GridSearchCV` to fine-tune hyperparameters. The best cross-validation score was `84.70%`, improving on the baseline test accuracy. In addition, the optimal hyperparameters were `leaf_size: 20`, `metric: minkowski`, `n_neighbors: 1`, and `weights: uniform`. However, when evaluated, the tuned model's train and test accuracies matched the baseline at `84.86%` and `82.61%`, showing no improvement in generalization.\n",
    "\n",
    "To explain the slightly lower test accuracies of the baseline and optimized models compared to their training accuracies, we first checked the distribution of patients by sex. The training set had `538` males and `149` females, while the test set had `186` males and `44` females, indicating a gender imbalance in both sets. Using the tuned model, we compared actual and predicted values for both training and test sets, calculating the percentage of correct and incorrect predictions by gender. The training results show `85.23%` accuracy for females and `84.76%` for males. However, the test results show a drop for females (`79.54%` accuracy) compared to males (`83.33%`). This disparity highlights the model's bias toward the majority class (`males`), affecting female predictions. As a result, we suggested steps to help the model better capture minority class characteristics and improve accuracy."
   ]
  }
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