Heart Attack Data Exploration and Visualisations.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "union-market",
   "metadata": {},
   "source": [
    "## Heart Disease Data Exploration and Visualisations\n",
    "\n",
    "The data that we explore on this notebook was retrieved from Kaggle and can be found [here](https://www.kaggle.com/fedesoriano/heart-failure-prediction). \\\n",
    "The kaggle user who uploaded the data is: <em>fedesoriano</em>\n",
    "\n",
    "This dataset was created by combining different datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. \\\n",
    "The creators are:\n",
    "1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.\n",
    "2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.\n",
    "3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.\n",
    "4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.\n",
    "\n",
    "**Citation:** fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved 24/9 from https://www.kaggle.com/fedesoriano/heart-failure-prediction. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "convinced-tiffany",
   "metadata": {},
   "source": [
    "**In this notebook we explore, clean and visualize the data that is discussed above.**\n",
    "\n",
    "We use **pandas**, to display and explore the data's statistics and we use **matplotlib** to create visualizations from set data. \n",
    "\n",
    "Some data preprocessing is performed as some outliers are found. We export the clean data that we use for the classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "tested-moldova",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "declared-arizona",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>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",
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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",
       "    </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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets import the data and preview it \n",
    "\n",
    "df_heart = pd.read_csv(\"data/heart.csv\")\n",
    "df_heart.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "latter-bobby",
   "metadata": {},
   "source": [
    "### The Data \n",
    "\n",
    "**Age:** age of the patient (years) \\\n",
    "**Sex:** sex of the patient (M: Male, F: Female) \\\n",
    "**ChestPainType:** chest pain type (TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic) \\\n",
    "**RestingBP:** resting blood pressure (mm Hg) \\\n",
    "**Cholesterol:** serum cholesterol (mm/dl) \\\n",
    "**FastingBS:** fasting blood sugar (1: if FastingBS > 120 mg/dl, 0: otherwise) \\\n",
    "**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",
    "**MaxHR:** maximum heart rate achieved (Numeric value between 60 and 202) \\\n",
    "**ExerciseAngina:** exercise-induced angina (Y: Yes, N: No) \\\n",
    "**Oldpeak:** oldpeak = ST (Numeric value measured in depression) \\\n",
    "**ST_Slope:** the slope of the peak exercise ST segment (Up: upsloping, Flat: flat, Down: downsloping) \\\n",
    "**HeartDisease:** output class (1: heart disease, 0: Normal) \\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "instrumental-butter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 918 entries, 0 to 917\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Age             918 non-null    int64  \n",
      " 1   Sex             918 non-null    object \n",
      " 2   ChestPainType   918 non-null    object \n",
      " 3   RestingBP       918 non-null    int64  \n",
      " 4   Cholesterol     918 non-null    int64  \n",
      " 5   FastingBS       918 non-null    int64  \n",
      " 6   RestingECG      918 non-null    object \n",
      " 7   MaxHR           918 non-null    int64  \n",
      " 8   ExerciseAngina  918 non-null    object \n",
      " 9   Oldpeak         918 non-null    float64\n",
      " 10  ST_Slope        918 non-null    object \n",
      " 11  HeartDisease    918 non-null    int64  \n",
      "dtypes: float64(1), int64(6), object(5)\n",
      "memory usage: 86.2+ KB\n",
      "None\n"
     ]
    },
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets print some additional information and summary statistics of the data \n",
    "print(df_heart.info())\n",
    "df_heart.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "robust-burden",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Below we will visualise the distribution of each numerical feature of the dataset\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(16, 4))\n",
    "fig2, axs2 = plt.subplots(1, 2, figsize=(10.5, 4))\n",
    "\n",
    "count = 0 \n",
    "for feature in ['Age', 'RestingBP', 'Cholesterol']:\n",
    "    axs[count].boxplot(df_heart[feature])\n",
    "    axs[count].set_title(('Distribution of ' + feature))\n",
    "    count += 1\n",
    "\n",
    "count = 0\n",
    "for feature in ['MaxHR', 'Oldpeak']:\n",
    "    axs2[count].boxplot(df_heart[feature])\n",
    "    axs2[count].set_title(('Distribution of ' +  feature))\n",
    "    count += 1\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "palestinian-stable",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size before cleaning:  918\n",
      "Data size after cleaning:  746\n"
     ]
    }
   ],
   "source": [
    "# Above we can observe that for some of our numeric features, odd values such as 0 cholesterol or 0 bloop pressure exist\n",
    "# which are very uncommon if not non-existent. Thus these values seem to be faulty measurements or outliers which we remove\n",
    "# More outliers could exist but I am not personally familiar with such measurements to be able recognise them \n",
    "\n",
    "print('Data size before cleaning: ', len(df_heart))\n",
    "df_heart_clean = df_heart[(df_heart['Cholesterol'] > 5) &\n",
    "                         (df_heart['RestingBP'] > 10)]\n",
    "print('Data size after cleaning: ', len(df_heart_clean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "tough-peter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The dataset seems well balanced in terms of heart disease and non-heart disease cases \n",
    "# The dataset is fairly limited in size (less than 1000 data points) - neural network implementations will need more data \n",
    "\n",
    "# Lets do histograms showing heart disease density at each feature group \n",
    "patients = df_heart_clean[df_heart_clean['HeartDisease'] == 1]\n",
    "\n",
    "# plt.hist(df_heart['Age'], bins=10)\n",
    "fig, axs = plt.subplots(1, 3, figsize=(16, 4))\n",
    "fig2, axs2 = plt.subplots(1, 2, figsize=(10.5, 4))\n",
    "\n",
    "count = 0 \n",
    "for feature in ['Age', 'RestingBP', 'Cholesterol']:\n",
    "    axs[count].hist(patients[feature], bins=25)\n",
    "    axs[count].set_title(('Cases on different ' +  feature + ' groups'))\n",
    "    count += 1\n",
    "\n",
    "count = 0\n",
    "for feature in ['MaxHR', 'Oldpeak']:\n",
    "    axs2[count].hist(patients[feature], bins=25)\n",
    "    axs2[count].set_title(('Cases on different ' +  feature + ' groups'))\n",
    "    count += 1\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "accompanied-ratio",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vy+kudvh8Vf0acB6wpk22Bji3DZ8HrE6yR5JDgBXAJdNeuSTNvHuAp1XV44GVwLFJnoQXhUmaZ3ble+jOAJ6e5Drg6e01VXUVsB74JvAZ4KSq2rGrhUrSbKvOne3l7u1ReFGYpHlmt6lMXFUXARe14VuAo8aYbi2wdhdrk6Q51/awXQo8EnhXVX01yU9cFJZk8KKwrwzMPuZFYcCJAAcffPBMli9pgfBOEZI0jqraUVUr6c4HPjzJY8eZ3IvCJM0JA50kTUJV3U53hOJYvChM0jxjoJOkMSRZnGTvNvwg4GjgGrwoTNI8M6Vz6CRpgVkCrGvn0T0AWF9V5yf5MrA+yQnADcDzobsoLMnIRWHb8aIwSbPEQCdJY6iqK4AnDGn3ojBJ84qHXCVJknrOQCdJktRzBjpJkqSeM9BJkiT1nIFOkiSp5wx0kiRJPWegkyRJ6jkDnSRJUs8Z6CRJknrOQCdJktRzBjpJkqSeM9BJkiT1nIFOkiSp5wx0kiRJPWegkyRJ6jkDnSRJUs8Z6CRJknrOQCdJktRzBjpJkqSeM9BJkiT1nIFOksaQZFmSLyS5OslVSV7Z2k9L8p0kl7fHswbmOTXJxiTXJjlm7qqXtJDsNtcFSNI8th14TVVdluQhwKVJLmjj3lFVbx2cOMmhwGrgMOBA4HNJHlVVO2a1akkLjnvoJGkMVXVTVV3Whu8ArgaWjjPLccA5VXVPVV0PbAQOn/lKJS10BjpJmoQky4EnAF9tTScnuSLJ2Un2aW1LgRsHZtvM+AFQkqaFgU6SJpBkL+DjwKuq6gfAu4GfAVYCNwFvG5l0yOw1ZHknJtmQZMO2bdtmpmhJC4qBTpLGkWR3ujD34ar6BEBV3VxVO6rqR8B7ufew6mZg2cDsBwFbRi+zqs6qqlVVtWrx4sUz+wNIWhAMdJI0hiQB3gdcXVVvH2hfMjDZ84Ar2/B5wOokeyQ5BFgBXDJb9UpauLzKVZLGdgTwEuAbSS5vba8DXpRkJd3h1E3AywGq6qok64Fv0l0he5JXuEqaDQY6SRpDVV3M8PPiPj3OPGuBtTNWlCQN4SFXSZKknjPQSZIk9dyEgS7JnkkuSfL1duub01v7vkkuSHJde95nYB5vfSNJkjRLJrOH7h7gaVX1eLrvXDo2yZOAU4ALq2oFcGF7PfrWN8cCZyZZNAO1S5IkiUkEuurc2V7u3h5Fd4ubda19HXB8G/bWN5IkSbNoUufQJVnULtnfClxQVV8FDqiqm6C73yGwf5t8Ure+8ZvSJUmSpsekAl37RvSVdN96fniSx44z+aRufeM3pUuSJE2PKV3lWlW3AxfRnRt388i3pbfnrW2ySd36RpIkSdNjMle5Lk6ydxt+EHA0cA3dLW7WtMnWAOe2YW99I0mSNIsmc6eIJcC6dqXqA4D1VXV+ki8D65OcANwAPB+89Y0kSdJsmzDQVdUVwBOGtN8CHDXGPN76RpIkaZZ4L1dJQy0/5VMzvo5NZzx7xtchSQuBt/6SJEnqOQOdJElSzxnoJEmSes5AJ0mS1HMGOkmSpJ4z0EmSJPWcgU6SJKnnDHSSJEk9Z6CTJEnqOQOdJI0hybIkX0hydZKrkryyte+b5IIk17XnfQbmOTXJxiTXJjlm7qqXtJAY6CRpbNuB11TVY4AnASclORQ4BbiwqlYAF7bXtHGrgcOAY4Ezkyyak8olLSgGOkkaQ1XdVFWXteE7gKuBpcBxwLo22Trg+DZ8HHBOVd1TVdcDG4HDZ7VoSQuSgU6SJiHJcuAJwFeBA6rqJuhCH7B/m2wpcOPAbJtb2+hlnZhkQ5IN27Ztm9G6JS0MBjpJmkCSvYCPA6+qqh+MN+mQtrpPQ9VZVbWqqlYtXrx4usqUtIAZ6CRpHEl2pwtzH66qT7Tmm5MsaeOXAFtb+2Zg2cDsBwFbZqtWSQuXgU6SxpAkwPuAq6vq7QOjzgPWtOE1wLkD7auT7JHkEGAFcMls1Stp4dptrguQpHnsCOAlwDeSXN7aXgecAaxPcgJwA/B8gKq6Ksl64Jt0V8ieVFU7Zr1qSQuOgU6SxlBVFzP8vDiAo8aYZy2wdsaKkqQhPOQqSZLUcwY6SZKknjPQSZIk9ZyBTpIkqecMdJIkST1noJMkSeo5A50kSVLPGegkSZJ6zkAnSZLUcwY6SZKknjPQSZIk9ZyBTpIkqecMdJIkST1noJMkSeo5A50kSVLPGegkSZJ6zkAnSZLUcxMGuiTLknwhydVJrkryyta+b5ILklzXnvcZmOfUJBuTXJvkmJn8ASRJkha6yeyh2w68pqoeAzwJOCnJocApwIVVtQK4sL2mjVsNHAYcC5yZZNFMFC9JkqRJBLqquqmqLmvDdwBXA0uB44B1bbJ1wPFt+DjgnKq6p6quBzYCh09z3ZIkSWqmdA5dkuXAE4CvAgdU1U3QhT5g/zbZUuDGgdk2tzZJ6pUkZyfZmuTKgbbTknwnyeXt8ayBcZ5uImlOTDrQJdkL+Djwqqr6wXiTDmmrIcs7McmGJBu2bds22TIkaTa9n+7UkdHeUVUr2+PT4OkmkubWpAJdkt3pwtyHq+oTrfnmJEva+CXA1ta+GVg2MPtBwJbRy6yqs6pqVVWtWrx48c7WL0kzpqq+CNw6yck93UTSnJnMVa4B3gdcXVVvHxh1HrCmDa8Bzh1oX51kjySHACuAS6avZEmacycnuaIdkh25wn/Sp5t4hELSdJvMHrojgJcATxt1zsgZwNOTXAc8vb2mqq4C1gPfBD4DnFRVO2akekmafe8GfgZYCdwEvK21T+p0E/AIhaTpt9tEE1TVxQzvqACOGmOetcDaXahLkualqrp5ZDjJe4Hz28tJnW4iSTPBO0VI0hSMnDvcPA8YuQLW000kzZkJ99BJ0kKV5CPAkcB+STYDbwCOTLKS7nDqJuDl0J1ukmTkdJPteLqJpFlkoJOkMVTVi4Y0v2+c6T3dRNKc8JCrJElSzxnoJEmSes5AJ0mS1HMGOkmSpJ4z0EmSJPWcgU6SJKnnDHSSJEk9Z6CTJEnqOQOdJElSzxnoJEmSes5AJ0mS1HMGOkmSpJ4z0EmSJPWcgU6SJKnnDHSSJEk9Z6CTJEnqOQOdJElSzxnoJEmSes5AJ0mS1HMGOkmSpJ4z0EnSGJKcnWRrkisH2vZNckGS69rzPgPjTk2yMcm1SY6Zm6olLUQGOkka2/uBY0e1nQJcWFUrgAvba5IcCqwGDmvznJlk0eyVKmkhM9BJ0hiq6ovAraOajwPWteF1wPED7edU1T1VdT2wETh8NuqUJAOdJE3NAVV1E0B73r+1LwVuHJhuc2u7jyQnJtmQZMO2bdtmtFhJC4OBTpKmR4a01bAJq+qsqlpVVasWL148w2VJWggMdJI0NTcnWQLQnre29s3AsoHpDgK2zHJtkhYoA50kTc15wJo2vAY4d6B9dZI9khwCrAAumYP6JC1Au811AZI0XyX5CHAksF+SzcAbgDOA9UlOAG4Ang9QVVclWQ98E9gOnFRVO+akcEkLjoFOksZQVS8aY9RRY0y/Flg7cxVJ0nAecpUkSeo5A50kSVLPGegkSZJ6zkAnSZLUcwY6SZKknpsw0CU5O8nWJFcOtO2b5IIk17XnfQbGnZpkY5JrkxwzU4VLkiSpM5k9dO8Hjh3VdgpwYVWtAC5sr0lyKLAaOKzNc2aSRdNWrSRJku5jwkBXVV8Ebh3VfBywrg2vA44faD+nqu6pquuBjcDh01OqJEmShtnZc+gOqKqbANrz/q19KXDjwHSbW9t9JDkxyYYkG7Zt27aTZUiSJGm6L4rIkLYaNmFVnVVVq6pq1eLFi6e5DEmSpIVjZwPdzUmWALTnra19M7BsYLqDgC07X54kSZImsrOB7jxgTRteA5w70L46yR5JDgFWAJfsWomSJEkaz24TTZDkI8CRwH5JNgNvAM4A1ic5AbgBeD5AVV2VZD3wTWA7cFJV7Zih2iVJksQkAl1VvWiMUUeNMf1aYO2uFCVJkqTJ804RkiRJPWegkyRJ6jkDnSRJUs8Z6CRJknrOQCdJktRzBjpJkqSem/BrSyRJ95VkE3AHsAPYXlWrkuwLfBRYDmwCXlBVt81VjZIWDvfQSdLOe2pVrayqVe31KcCFVbUCuLC9lqQZZ6CTpOlzHLCuDa8Djp+7UiQtJAY6Sdo5BXw2yaVJTmxtB1TVTQDtef85q07SguI5dJK0c46oqi1J9gcuSHLNZGdsAfBEgIMPPnim6pO0gLiHTpJ2QlVtac9bgU8ChwM3J1kC0J63jjHvWVW1qqpWLV68eLZKlnQ/ZqCTpClK8uAkDxkZBp4BXAmcB6xpk60Bzp2bCiUtNB5ylaSpOwD4ZBLo+tG/q6rPJPk3YH2SE4AbgOfPYY2SFhADnSRNUVV9C3j8kPZbgKNmvyJJ02n5KZ+a0eVvOuPZ075MD7lKkiT1nIFOkiSp5wx0kiRJPWegkyRJ6jkDnSRJUs8Z6CRJknrOQCdJktRzBjpJkqSeM9BJkiT1nIFOkiSp5wx0kiRJPWegkyRJ6jkDnSRJUs8Z6CRJknrOQCdJktRzBjpJkqSeM9BJkiT1nIFOkiSp5wx0kiRJPWegkyRJ6jkDnSRJUs/NWKBLcmySa5NsTHLKTK1HkuYT+z5Jc2FGAl2SRcC7gGcChwIvSnLoTKxLkuYL+z5Jc2Wm9tAdDmysqm9V1X8B5wDHzdC6JGm+sO+TNCd2m6HlLgVuHHi9GXji4ARJTgRObC/vTHLtFJa/H/C9XapwAnnzTC5dEkDePOW/5YfPVC3TZMK+D+Z3/2ffNy/4Gd/PzUTfN1OBLkPa6ideVJ0FnLVTC082VNWqnZlX0vxxP/xbnrDvA/s/jc/P+P5vJj7jmTrkuhlYNvD6IGDLDK1LkuYL+z5Jc2KmAt2/ASuSHJLkgcBq4LwZWpckzRf2fZLmxIwccq2q7UlOBv4JWAScXVVXTeMqdupQhaR55371tzwLfR/cz94zDeVnfP837Z9xqu5zeockSZJ6xDtFSJIk9ZyBTpIkqed6FeiSVJK3Dbx+bZLT5rAkSVOQzsVJnjnQ9oIkn5nLuuajJMuTXDmq7bQkr52rmjQ9kuxIcvnAY3mSI5OcP8F8K5M8a7bq1OQMfJ5XJfl6klcnmfV81atAB9wD/M8k+811IZKmrrqTdl8BvD3JnkkeDKwFTprbyqRZdXdVrRx4bJrkfCsBA938M/J5HgY8ne4zesNsF9G3QLed7sqQ35/rQiTtnKq6EvgH4I/oOr0PVNW/z21V/ZLkoiTvTPKvSa5Mcvhc16Tpk+Tw9tl+rT0/un0NzhuBF7a9QS+c6zp1X1W1le4uMCe3IxJ7JvnbJN9on+dTAZJ8OsnPteGvJfnTNvymJL/Z9thelOTvk1yT5MNJhn1x+Y/N1J0iZtK7gCuSvGWuC5G0004HLgP+C/Ab8XfOg6vqvyf5JeBs4LFzXZAm7UFJLm/D11fV80aNvwb4pfY1OEcDf15Vv9L+6a+qqpNns1hNTVV9qx1y3R/4tdb2uCQ/C3w2yaOALwJPSbKJbmfVEW32JwMfApYATwAOo/ty8n9p01w81np7F+iq6gdJPgD8HnD3XNcjaeqq6q4kHwXurKp75rqeeWqs75Qaaf8IQFV9MclDk+xdVbfPSmXaVXdX1cpxxj8MWJdkBd3nvfusVKXpNLI37cnAXwFU1TVJvg08CvgSXY65HvgU8PQkPwUsr6prkywBLqmqzQBtA2A54wS6vh1yHfFO4ATgwXNch6Sd96P20HC3APuMatuXe2/oPTrw+aWi9x9vAr5QVY8FngvsOcf1aAqSPALYAWxl+P2doburzCrgKXR7674G/BZw6cA0gxu7O5hgJ1wvA11V3Qqspwt1knS/U1V3AjclOQogyb7Asdy7hf7C1v5k4PtV9f05KVQz4WHAd9rwywba7wAeMuvVaNKSLAbeA/x1uwjsi8CL27hHAQcD11bVfwE3Ai8AvkK3x+617Xmn9DLQNW8DvNpV0v3ZS4HXt8MtnwdOH7iA5LYk/0r3z8ON2/uXtwB/keRf6G4hN+ILwKFeFDHvPGjka0uAzwGfpTtPGOBMYFGSbwAfBV42cJrJl4Cbq+o/2vBB7EKg89ZfktQzSS4CXltVG+a6FknzQ5/30EmSJAn30EmSJPWee+gkSZJ6zkAnSZLUcwY6SZKknjPQSZIk9ZyBTpIkqef+P7hFfBhwT0IqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 756x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Below we will visualise the distribution of each categorical feature of the dataset\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(16, 4))\n",
    "fig2, axs2 = plt.subplots(1, 2, figsize=(10.5, 4))\n",
    "\n",
    "count = 0 \n",
    "for feature in ['Sex', 'ChestPainType', 'RestingECG']:\n",
    "    axs[count].hist(df_heart_clean[feature])\n",
    "    axs[count].set_title(('Distribution of ' +  feature))\n",
    "    count += 1\n",
    "\n",
    "count = 0\n",
    "for feature in ['ExerciseAngina', 'ST_Slope']:\n",
    "    axs2[count].hist(df_heart_clean[feature])\n",
    "    axs2[count].set_title(('Distribution of ' +  feature))\n",
    "    count += 1\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "attractive-project",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Below we will visualise the heart disease cases for each categorical feature of the dataset\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(16, 4))\n",
    "fig2, axs2 = plt.subplots(1, 2, figsize=(10.5, 4))\n",
    "\n",
    "count = 0 \n",
    "for feature in ['Sex', 'ChestPainType', 'RestingECG']:\n",
    "    patients = df_heart_clean.groupby([feature]).sum()\n",
    "    \n",
    "    axs[count].bar(patients.index, height=patients['HeartDisease'])\n",
    "    axs[count].set_title(('Cases on different ' +  feature + ' groups'))\n",
    "    count += 1\n",
    "\n",
    "count = 0\n",
    "for feature in ['ExerciseAngina', 'ST_Slope']:\n",
    "    patients = df_heart_clean.groupby([feature]).sum()\n",
    "    \n",
    "    axs2[count].bar(patients.index, height=patients['HeartDisease'])\n",
    "    axs2[count].set_title(('Cases on different ' +  feature + ' groups'))\n",
    "    count += 1\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "diverse-domain",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now lets export our clean dataset so we can use it in our other notebooks\n",
    "df_heart_clean.to_csv('data/data_clean.csv', index=False);"
   ]
  }
 ],
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