ML_Project.ipynb

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    "<h1 align=\"center\">Machine Learning Project</h1>\n",
    "<h3 align=\"center\">Predicting Heart Disease</h3>\n",
    "<h3 align=\"center\">Vladyslav Honcharuk</h3>"
   ]
  },
  {
   "cell_type": "markdown",
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    "The main goal of my project is to predict whether patient has low or high likelihood of heard disease occurance.\n",
    "As a dataset I will be using the dataset named \"Cleveland Heart Disease Dataset\", which comes from a study conducted in 1988 and originates from the UCI Machine Learning Repository. \n",
    "\n",
    "There are 14 attributes with instances (values in brackets)\n",
    "\n",
    "age. The age of the patient.\n",
    "\n",
    "sex. The gender of the patient. (1 = male, 0 = female).\n",
    "\n",
    "cp. Type of chest pain. (1 = typical angina, 2 = atypical angina, 3 = non — anginal pain, 4 = asymptotic).\n",
    "\n",
    "trestbps. Resting blood pressure in mmHg.\n",
    "chol. Serum Cholestero in mg/dl.\n",
    "\n",
    "fbs. Fasting Blood Sugar. (1 = fasting blood sugar is more than 120mg/dl, 0 = otherwise).\n",
    "\n",
    "restecg. Resting ElectroCardioGraphic results (0 = normal, 1 = ST-T wave abnormality, 2 = left ventricular hyperthrophy).\n",
    "\n",
    "thalach. Max heart rate achieved.\n",
    "\n",
    "exang. Exercise induced angina (1 = yes, 0 = no).\n",
    "\n",
    "oldpeak. ST depression induced by exercise relative to rest.\n",
    "\n",
    "slope. Peak exercise ST segment (1 = upsloping, 2 = flat, 3 = downsloping).\n",
    "\n",
    "ca. Number of major vessels (0–3) colored by flourosopy.\n",
    "\n",
    "thal. Thalassemia (3 = normal, 6 = fixed defect, 7 = reversible defect).\n",
    "\n",
    "num. Diagnosis of heart disease (0 = absence, 1, 2, 3, 4 = present).\n",
    "\n",
    "The last attribute num is the one I will try to predict. I am supposed to provide a probability between 0 and 4 describing how confident my model is in predicting whether or not a patient is likely to have a heart disease."
   ]
  },
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   "cell_type": "markdown",
   "id": "da072b48",
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   "source": [
    "The project is made in Python, because this high-level language is one of the best programming languages to conduct a Machine Learning study.\n",
    "I will start with importing all the necessary libraries for making plots, doing complex calculations and building ML models, because standart Python library doesn't contain all this features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f4c9e255",
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import svm\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
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   "cell_type": "markdown",
   "id": "66f84d85",
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   "source": [
    "I changed a little bit the original dataset, so that is easier to import it to the python workflow. I added the names of attributes in the first row of heart_disease_database.csv file, which contains the dataset I will be using. Also all the missing values in dataset were represented by question mark '?' and to make it more easier for me to process data I replaced question marks with NaN ( not a number) values, which are standart when working with missing values in python.\n",
    "\n",
    "Below I defined columns for future use, imported a dataset, which will be stored as a DataFrame object, which is used in pandas library for storing 2D data structures. And displayed what the dataset looks like by showing first 5 and last 5 instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37438e19",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0    63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
       "1    67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
       "2    67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
       "3    37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
       "4    41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
       "..    ...  ...  ...       ...    ...  ...      ...      ...    ...      ...   \n",
       "298  45.0  1.0  1.0     110.0  264.0  0.0      0.0    132.0    0.0      1.2   \n",
       "299  68.0  1.0  4.0     144.0  193.0  1.0      0.0    141.0    0.0      3.4   \n",
       "300  57.0  1.0  4.0     130.0  131.0  0.0      0.0    115.0    1.0      1.2   \n",
       "301  57.0  0.0  2.0     130.0  236.0  0.0      2.0    174.0    0.0      0.0   \n",
       "302  38.0  1.0  3.0     138.0  175.0  0.0      0.0    173.0    0.0      0.0   \n",
       "\n",
       "     slope   ca  thal  num  \n",
       "0      3.0  0.0   6.0    0  \n",
       "1      2.0  3.0   3.0    2  \n",
       "2      2.0  2.0   7.0    1  \n",
       "3      3.0  0.0   3.0    0  \n",
       "4      1.0  0.0   3.0    0  \n",
       "..     ...  ...   ...  ...  \n",
       "298    2.0  0.0   7.0    1  \n",
       "299    2.0  2.0   7.0    2  \n",
       "300    2.0  1.0   7.0    3  \n",
       "301    2.0  1.0   3.0    1  \n",
       "302    1.0  NaN   3.0    0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = [\"age\", \"sex\", \"cp\", \"trestbps\", \"chol\", \"fbs\", \"restecg\",\n",
    "           \"thalach\", \"exang\", \"oldpeak\", \"slope\", \"ca\", \"thal\", \"num\"]\n",
    "df = pd.read_csv(\"heart_disease_database.csv\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f26509",
   "metadata": {},
   "source": [
    "It is good to know some insights from your data, like datatypes, shape of the dataset, and statistical info about values of each attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5bc436fd",
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    float64\n",
      " 1   sex       303 non-null    float64\n",
      " 2   cp        303 non-null    float64\n",
      " 3   trestbps  303 non-null    float64\n",
      " 4   chol      303 non-null    float64\n",
      " 5   fbs       303 non-null    float64\n",
      " 6   restecg   303 non-null    float64\n",
      " 7   thalach   303 non-null    float64\n",
      " 8   exang     303 non-null    float64\n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    float64\n",
      " 11  ca        299 non-null    float64\n",
      " 12  thal      301 non-null    float64\n",
      " 13  num       303 non-null    int64  \n",
      "dtypes: float64(13), int64(1)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f44c26f1",
   "metadata": {},
   "outputs": [
    {
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       "      <td>54.438944</td>\n",
       "      <td>9.038662</td>\n",
       "      <td>29.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>77.0</td>\n",
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       "      <th>sex</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.679868</td>\n",
       "      <td>0.467299</td>\n",
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       "      <th>cp</th>\n",
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       "      <td>3.158416</td>\n",
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       "    <tr>\n",
       "      <th>trestbps</th>\n",
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       "      <td>131.689769</td>\n",
       "      <td>17.599748</td>\n",
       "      <td>94.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>200.0</td>\n",
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       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>303.0</td>\n",
       "      <td>246.693069</td>\n",
       "      <td>51.776918</td>\n",
       "      <td>126.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.990099</td>\n",
       "      <td>0.994971</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>303.0</td>\n",
       "      <td>149.607261</td>\n",
       "      <td>22.875003</td>\n",
       "      <td>71.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>153.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>303.0</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>303.0</td>\n",
       "      <td>1.600660</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>299.0</td>\n",
       "      <td>0.672241</td>\n",
       "      <td>0.937438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>301.0</td>\n",
       "      <td>4.734219</td>\n",
       "      <td>1.939706</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.937294</td>\n",
       "      <td>1.228536</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count        mean        std    min    25%    50%    75%    max\n",
       "age       303.0   54.438944   9.038662   29.0   48.0   56.0   61.0   77.0\n",
       "sex       303.0    0.679868   0.467299    0.0    0.0    1.0    1.0    1.0\n",
       "cp        303.0    3.158416   0.960126    1.0    3.0    3.0    4.0    4.0\n",
       "trestbps  303.0  131.689769  17.599748   94.0  120.0  130.0  140.0  200.0\n",
       "chol      303.0  246.693069  51.776918  126.0  211.0  241.0  275.0  564.0\n",
       "fbs       303.0    0.148515   0.356198    0.0    0.0    0.0    0.0    1.0\n",
       "restecg   303.0    0.990099   0.994971    0.0    0.0    1.0    2.0    2.0\n",
       "thalach   303.0  149.607261  22.875003   71.0  133.5  153.0  166.0  202.0\n",
       "exang     303.0    0.326733   0.469794    0.0    0.0    0.0    1.0    1.0\n",
       "oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2\n",
       "slope     303.0    1.600660   0.616226    1.0    1.0    2.0    2.0    3.0\n",
       "ca        299.0    0.672241   0.937438    0.0    0.0    0.0    1.0    3.0\n",
       "thal      301.0    4.734219   1.939706    3.0    3.0    3.0    7.0    7.0\n",
       "num       303.0    0.937294   1.228536    0.0    0.0    0.0    2.0    4.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50074b2d",
   "metadata": {},
   "source": [
    "Then I should check for the NaN values to decide what to do with them. As there is only 6 missing values in the whole dataset I decided to replace this values with a mean values of the attribute which contains this value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd0ef0c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          4\n",
       "thal        2\n",
       "num         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5efae64a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e84af92",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"ca\"].fillna(df[\"ca\"].mean(), inplace=True)\n",
    "df[\"thal\"].fillna(df[\"thal\"].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd79ef5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4509ab0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "num         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "887e0f58",
   "metadata": {},
   "source": [
    "I made all the replacing work, now I will check if there is any duplicate rows in a dataset, which could mess up the statistical models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c80b0eca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate rows :  0\n"
     ]
    }
   ],
   "source": [
    "duplicate_rows = df[df.duplicated()]\n",
    "print(\"Number of duplicate rows : \", duplicate_rows.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3e2e80",
   "metadata": {},
   "source": [
    "There is no duplicate rows, so next I will check for outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4bf93433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAJzUlEQVR4nO3dYYhld3nH8d+z2SRuqnVNdxW71q42WFOrARNtlSK2BWvii1AqSCsUrFoqGIqvFBEVpFTRF9aUVtIlBBUUoaVQqwaxVAsxlU2JcWOqjEo0o+BGBcWsMbvz98W9YFx32bvZc8+T63w+MDD33jPn/B/u8J3LmblnaowRAOa3p3sBALuVAAM0EWCAJgIM0ESAAZrsPZ+NDxw4MA4fPrympQD8crrjjjvuH2McPP3+8wrw4cOHc/To0elWBbALVNW9Z7rfKQiAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGanNf/hHukbrzxxmxtbc1xKIBJbW9vZ//+/Tly5Mjk+54lwFtbW7nz2D05ddnlcxwOYDIX/fC7OXHixFr2PUuAk+TUZZfnxDOvm+twAJN47P99cG37dg4YoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKDJ3jkOsr29nT0/fmCOQwFMa+dUHnzwwbXsepZXwCdOnEjtPDTHoQCmNUZ2dnbWsmunIACaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQJNzBriq/rqqjlbV0ePHj8+xJoBd4ZwBHmPcNMa4ZoxxzcGDB+dYE8Cu4BQEQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoMkuA9+3bl7Hn4jkOBTCtquzZs55UzhLgQ4cOZecxvzrHoQCmteeiXHrppevZ9Vr2CsA5CTBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQZO9cB7roge9l3/9/fK7DAUzj1Mkkl6xl17ME+IorrpjjMACT294+mf37969l37ME+IYbbpjjMAAbxTlggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQJMaY6y+cdXxJPc+wmMdSHL/I/zaTWXm3WG3zbzb5k0ufObfHGMcPP3O8wrwhaiqo2OMa2Y52KOEmXeH3Tbzbps3Wd/MTkEANBFggCZzBvimGY/1aGHm3WG3zbzb5k3WNPNs54AB+HlOQQA0EWCAJpMHuKpeWlVfrqqtqnrTGR6vqnrf8vG7quq5U69hTivM+8rlnHdV1W1VdVXHOqd0rpkftt3zqupUVb18zvWtwyozV9WLq+rOqrq7qj4z9xqntsL39uOr6j+q6gvLmV/Vsc6pVNXNVfWdqjp2lsenb9cYY7KPJBcl+WqSpye5JMkXkvzOadtcl+QTSSrJ7yf53ynXMOfHivO+MMkTlp9fu8nzrjrzw7b7ryQfT/Ly7nXP8DzvT/KlJE9d3n5i97pnmPnNSd61/Pxgku8luaR77Rcw84uSPDfJsbM8Pnm7pn4F/PwkW2OMr40xfpLkI0muP22b65N8YCzcnmR/VT154nXM5ZzzjjFuG2N8f3nz9iRPmXmNU1vlOU6SG5L8a5LvzLm4NVll5r9I8m9jjG8kyRhj0+deZeaR5HFVVUkem0WAT867zOmMMT6bxQxnM3m7pg7woSTffNjt+5b3ne82m+J8Z3l1Fj9BN9k5Z66qQ0n+NMn7Z1zXOq3yPD8jyROq6r+r6o6q+svZVrceq8z8j0muTPKtJF9M8rdjjJ15ltdi8nbtvaDl/KI6w32n/53bKttsipVnqao/zCLAf7DWFa3fKjO/N8kbxxinFi+ONt4qM+9NcnWSP06yL8nnqur2McZX1r24NVll5j9JcmeSP0ryW0k+VVX/M8b4wZrX1mXydk0d4PuS/MbDbj8li5+O57vNplhplqp6TpIjSa4dY3x3prWtyyozX5PkI8v4HkhyXVWdHGP8+ywrnN6q39f3jzF+lORHVfXZJFcl2dQArzLzq5K8cyxOkG5V1deTPDPJ5+dZ4uymb9fEJ7H3JvlakqflZyfun3XaNi/Lz5/I/nz3yfc1z/vUJFtJXti93rlmPm37W7L5v4Rb5Xm+Msmnl9teluRYkt/tXvuaZ/7nJG9ffv6kJNtJDnSv/QLnPpyz/xJu8nZN+gp4jHGyql6f5NYsfot68xjj7qr6m+Xj78/it+LXZRGlB7L4KbqRVpz3rUl+Lck/LV8RnhwbfCWpFWf+pbLKzGOMe6rqk0nuSrKT5MgY44x/zrQJVnye35Hklqr6YhZReuMYY2MvU1lVH07y4iQHquq+JG9LcnGyvnZ5KzJAE++EA2giwABNBBigiQADNBFggCYCDNBEgAGaCDAboap+par+c3nt2WNV9YqqurqqPrO8+M2tVfXk5TVqv1xVv738ug9X1Wu71w9nMvW1IGBdXprkW2OMlyWLi4Fn8bbQ68cYx6vqFUn+bozxV8t3cN1SVf+QxbWY/6Vv2XB23gnHRqiqZ2TxttiPJvlYku8nuS2L6xUki7fLfnuM8ZLl9jcl+bMkV40x7pt/xXBuXgGzEcYYX6mqq7N4L/7fJ/lUkrvHGC84fduq2pPFxXFOJLk8i6tYwaOOc8BshKr69SQPjDE+lOQ9SX4vycGqesHy8Yur6lnLzd+Q5J4kf57k5qq6uGPNcC5eAbMpnp3k3VW1k+ShJK/L4t/fvG95PnhvkvdW1UNJXpPk+WOMHy6vy/uWLK5sBY8qzgEDNHEKAqCJAAM0EWCAJgIM0ESAAZoIMEATAQZo8lNTNRseOcASHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def find_outliers():\n",
    "    for element in columns:\n",
    "        plt.figure()\n",
    "        sns.boxplot(x=df[f\"{element}\"])\n",
    "\n",
    "find_outliers()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bba6fe6d",
   "metadata": {},
   "source": [
    "It seems that there is a bunch of outliers which could mess up the performance of models. I decided to use a Z-score, which is a great technic for removing outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a9bfa54",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = np.abs(stats.zscore(df))\n",
    "df = df[(z<3).all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "12543bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAJvUlEQVR4nO3dbYild3nH8d+VHcSNNdU4NoRNZQmjSUVqtIsPBPqQBo1FhCKFChYpggg6ri+k6LvWUl8bprSQSkuh2KKW2CLiapUUFLRuYtRokvZg48NUk9WtMe2ulsa/L+YeXJYsu5vMnGtm7s8HDnPOPWfm/K+dOV/uc++cc2qMEQCW74ruBQDMlQADNBFggCYCDNBEgAGarFzOlVdXV8fRo0d3aSkAB8/q6mpOnDhxYoxx2/mfu6wAHz16NCdPnty5lQHMQFWtPtF2hyAAmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoclnvCce8bWxsZLFYdC9jR2xubiZJjhw50rySn1tbW8v6+nr3MlgiAeaSLRaL3Hvf/Xn8yqu7l/KUHTrzaJLkez/ZG3eBQ2dOdy+BBnvjt4994/Err87ZG3+nexlP2eEHPp4ke2aW7fUwL44BAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0WUqANzY2srGxsYybAthRu9mvlV35rudZLBbLuBmAHbeb/XIIAqCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigycoybmRzczNnz57N8ePHl3Fz7JLFYpEr/m90L+NAuuLHP8pi8Zj7yB60WCxy+PDhXfneF90Drqq3VNXJqjp56tSpXVkEwBxddA94jHFHkjuS5NixY09q9+fIkSNJkttvv/3JfDl7xPHjx3P3Nx7uXsaB9NOnX5W1669xH9mDdvNRiWPAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigycoybmRtbW0ZNwOw43azX0sJ8Pr6+jJuBmDH7Wa/HIIAaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQJOV7gWwvxw6czqHH/h49zKeskNnfpAke2aWQ2dOJ7mmexksmQBzydbW1rqXsGM2N/8/SXLkyF6J3jUH6t+XSyPAXLL19fXuJcCB4hgwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoEmNMS79ylWnknxzh9ewmuT7O/w995M5z2/2+ZrT/N9PkjHGbed/4rICvBuq6uQY41jrIhrNeX6zz3P2xPzbHIIAaCLAAE32QoDv6F5AsznPb/b5mvv8SfbAMWCAudoLe8AAsyTAAE2WGuCqenpV/VtVfbmqvlZVfzJtv7qqPlVV/zF9fPYy17VMVXWoqr5UVR+bLs9p9oeq6qtVdW9VnZy2zWL+qnpWVX2kqh6oqvur6pVzmL2qbph+3tunH1XVO+cw+6VY9h7wT5LcMsZ4cZKbktxWVa9I8u4knx5jPD/Jp6fLB9XxJPefc3lOsyfJb40xbjrnb0DnMv/tST4xxrgxyYuz9Ttw4GcfYzw4/bxvSvJrSc4kuTMzmP2SjDFaTkmuTHJPkpcneTDJtdP2a5M82LWuXZ75umz9st2S5GPTtlnMPs33UJLV87Yd+PmTXJXkPzP9p/ecZj9v3lcl+dwcZ7/QaenHgKeH4PcmeSTJp8YYX0hyzRjju0kyffylZa9rSd6f5I+S/PScbXOZPUlGkk9W1d1V9ZZp2xzmvz7JqSR/Mx1++kBVPSPzmP1cv5/k76fzc5v9CS09wGOMx8fWw5Hrkrysql607DV0qKrXJnlkjHF391oa3TzGeGmS1yR5W1X9eveClmQlyUuT/OUY4yVJ/jcze8hdVU9L8rokH+5ey17S9lcQY4wfJrkryW1JHq6qa5Nk+vhI17p20c1JXldVDyX5hyS3VNXfZR6zJ0nGGP81fXwkW8cBX5Z5zP+dJN+ZHu0lyUeyFeQ5zL7tNUnuGWM8PF2e0+wXtOy/gnhuVT1rOn84ya1JHkjyz0neNF3tTUn+aZnrWoYxxnvGGNeNMY5m66HYZ8YYb8wMZk+SqnpGVT1z+3y2jgfelxnMP8b4XpJvV9UN06bfTvL1zGD2c7whPz/8kMxr9gta6jPhqupXk/xtkkPZiv+HxhjvrarnJPlQkucl+VaS3xtjnF7awpasqn4zybvGGK+dy+xVdX229nqTrYfkHxxj/NmM5r8pyQeSPC3JN5L8Yab7QA7+7Fcm+XaS68cYj07bZvFzvxhPRQZo4plwAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAWZfqKqPTi/i87XtF/KpqjdX1b9X1V1V9VdV9efT9udW1T9W1Ren0829q4cn5okY7AtVdfUY4/T0FPYvJnl1ks9l6zUVHkvymSRfHmO8vao+mOQvxhifrarnJTkxxviVtsXDBax0LwAu0Tuq6nen87+c5A+S/Ov201er6sNJXjB9/tYkL6yq7a+9qqqeOcZ4bJkLhosRYPa86bUzbk3yyjHGmaq6K1sv6H2hvdorpuueXcoC4UlyDJj94BeT/PcU3xuTvCJb76jyG1X17KpaSfL6c67/ySRv374wvRAO7DkCzH7wiSQrVfWVJH+a5PNJNpO8L8kXkvxLtl7e8dHp+u9IcqyqvlJVX0/y1uUvGS7Of8Kxb1XVL4wx/mfaA74zyV+PMe682NfBXmEPmP3sj6f3F7wvW296+dHW1cBlsgcM0MQeMEATAQZoIsAATQQYoIkAAzT5GV584+68kjetAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_outliers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "15242428",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(294, 14)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e34b0cf0",
   "metadata": {},
   "source": [
    "After normalization 9 instances were deleted. I also replaced the values 2, 3, and 4 in predicted class for 1, because I had problems with multiple classes and simplifying the predicted class attribute eased the task for me."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "67827d45",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"num\"].replace(to_replace=[2, 3, 4], value = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b7d015",
   "metadata": {},
   "source": [
    "A also decided to find the correlation between variables to define the attributes which have low correlation with the predicted class, so I could improve my future ML models. I used Pearson Correlation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "89d3d8b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pearson Correlation')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pearsonCorr = df.corr(method='pearson')\n",
    "fig = plt.subplots(figsize=(14,8))\n",
    "sns.heatmap(pearsonCorr, vmin=-1,vmax=1, cmap = \"Greens\", annot=True, linewidth=0.1)\n",
    "plt.title(\"Pearson Correlation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f839525c",
   "metadata": {},
   "source": [
    "This is how the dataset looks like after the cleaning process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "20d8dbc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.672241</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0    63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
       "1    67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
       "2    67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
       "3    37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
       "4    41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
       "..    ...  ...  ...       ...    ...  ...      ...      ...    ...      ...   \n",
       "298  45.0  1.0  1.0     110.0  264.0  0.0      0.0    132.0    0.0      1.2   \n",
       "299  68.0  1.0  4.0     144.0  193.0  1.0      0.0    141.0    0.0      3.4   \n",
       "300  57.0  1.0  4.0     130.0  131.0  0.0      0.0    115.0    1.0      1.2   \n",
       "301  57.0  0.0  2.0     130.0  236.0  0.0      2.0    174.0    0.0      0.0   \n",
       "302  38.0  1.0  3.0     138.0  175.0  0.0      0.0    173.0    0.0      0.0   \n",
       "\n",
       "     slope        ca  thal  num  \n",
       "0      3.0  0.000000   6.0    0  \n",
       "1      2.0  3.000000   3.0    1  \n",
       "2      2.0  2.000000   7.0    1  \n",
       "3      3.0  0.000000   3.0    0  \n",
       "4      1.0  0.000000   3.0    0  \n",
       "..     ...       ...   ...  ...  \n",
       "298    2.0  0.000000   7.0    1  \n",
       "299    2.0  2.000000   7.0    1  \n",
       "300    2.0  1.000000   7.0    1  \n",
       "301    2.0  1.000000   3.0    1  \n",
       "302    1.0  0.672241   3.0    0  \n",
       "\n",
       "[294 rows x 14 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "855865cf",
   "metadata": {},
   "source": [
    "I created a Model() function to decrease the amount of redandant code, this function could be used for simple ML models which don't take any attributes. I will outline that the division of the whole data into training and testing sets is made with the balanced predicted class for real life application. After evaluation of each classifying ML model I will show the accuracy, confusion matrix and some other characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "81ca192b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model(classifier, name_of_classifier, features, target, training_size=0.75, random_state=0):\n",
    "    training_features, testing_features, training_target, testing_target = train_test_split(features, target, train_size=training_size, stratify=target, random_state=random_state)\n",
    "    \n",
    "    print(f\"{name_of_classifier} classifier:\\n\")\n",
    "    print(f\"Shape of training features: {training_features.shape}\")\n",
    "    print(f\"Shape of training target: {training_target.shape}\")\n",
    "    print(f\"Shape of testing features: {testing_features.shape}\")\n",
    "    print(f\"Shape of testing target: {testing_target.shape}\\n\")\n",
    "    \n",
    "    classifier.fit(training_features, training_target)\n",
    "    predicted_target = classifier.predict(testing_features)\n",
    "    score = accuracy_score(testing_target, predicted_target)\n",
    "    print(f\"Accuracy = {score}\\n\")\n",
    "    print(classification_report(predicted_target, testing_target))\n",
    "    \n",
    "    print(\"Confusion matrix:\")\n",
    "    \n",
    "    conmat = confusion_matrix(testing_target, predicted_target)\n",
    "\n",
    "    val = np.mat(conmat) \n",
    "\n",
    "    df_cm = pd.DataFrame(val, index=[0, 1])\n",
    "\n",
    "    plt.figure()\n",
    "\n",
    "    heatmap = sns.heatmap(df_cm, annot=True, cmap=\"Blues\")\n",
    "\n",
    "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha=\"right\")\n",
    "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha=\"right\")\n",
    "\n",
    "    plt.ylabel(\"True label\")\n",
    "    plt.xlabel(\"Predicted label\")\n",
    "\n",
    "    plt.title(f\"{name_of_classifier} Model Results\")\n",
    "\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8588814c",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_labels = [\"age\", \"sex\", \"cp\", \"trestbps\", \"chol\", \"fbs\", \"restecg\",\n",
    "           \"thalach\", \"exang\", \"oldpeak\", \"slope\", \"ca\", \"thal\"]\n",
    "features = df.loc[:, features_labels]\n",
    "target = df.loc[:, [\"num\"]]\n",
    "\n",
    "training_size = 0.66\n",
    "random_state = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7cf1d4",
   "metadata": {},
   "source": [
    "I have chosen a size of training set = 66%, which I found the best while making ML models.\n",
    "\n",
    "The first classifier I implemented is ZeroR which stands for Zero Rule, it looks for the most frequent predicted class. It is useful because it's accuracy is a baseline for every other classifier and I decided to model it first to see how much more complex classifiers are better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "655dd157",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZeroR classifier:\n",
      "\n",
      "Shape of training features: (194, 13)\n",
      "Shape of training target: (194, 1)\n",
      "Shape of testing features: (100, 13)\n",
      "Shape of testing target: (100, 1)\n",
      "\n",
      "Accuracy = 0.55\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.55      0.71       100\n",
      "           1       0.00      0.00      0.00         0\n",
      "\n",
      "    accuracy                           0.55       100\n",
      "   macro avg       0.50      0.28      0.35       100\n",
      "weighted avg       1.00      0.55      0.71       100\n",
      "\n",
      "Confusion matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "zeror = DummyClassifier(strategy=\"most_frequent\")\n",
    "name_of_classifier = \"ZeroR\"\n",
    "\n",
    "Model(zeror, name_of_classifier, features, target, training_size=training_size, random_state=random_state)    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef23cd69",
   "metadata": {},
   "source": [
    "Then goes k-Nearest Neighbors classifier with 4 neighbors, which is not a complex one, but has a much better accuracy than ZeroR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fad1f495",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k-Nearest Neighbors classifier:\n",
      "\n",
      "Shape of training features: (194, 13)\n",
      "Shape of training target: (194, 1)\n",
      "Shape of testing features: (100, 13)\n",
      "Shape of testing target: (100, 1)\n",
      "\n",
      "Accuracy = 0.7\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.68      0.76        69\n",
      "           1       0.51      0.74      0.61        31\n",
      "\n",
      "    accuracy                           0.70       100\n",
      "   macro avg       0.68      0.71      0.68       100\n",
      "weighted avg       0.75      0.70      0.71       100\n",
      "\n",
      "Confusion matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "KNN = KNeighborsClassifier(n_neighbors=4)\n",
    "name_of_classifier = \"k-Nearest Neighbors\"\n",
    "\n",
    "Model(KNN, name_of_classifier, features, target, training_size=training_size, random_state=random_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df627703",
   "metadata": {},
   "source": [
    "Because there is a need to specify the number of neighbors to run the k-Nearest Neighbors classifier I decided to check which number of nearest neighbors will be the best for this exact event."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "915df5e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum error: 0.27 at K = 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_features, testing_features, training_target, testing_target = train_test_split(features, target, train_size=training_size, stratify=target, random_state=random_state)\n",
    "t = testing_target.squeeze()\n",
    "\n",
    "error_rate = []\n",
    "for i in range(1,40):\n",
    "    KNN = KNeighborsClassifier(n_neighbors=i)\n",
    "    KNN.fit(training_features,training_target)\n",
    "    predicted_target = KNN.predict(testing_features)\n",
    "    error_rate.append(np.mean(predicted_target != t))\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),error_rate,color='blue', linestyle='dashed', \n",
    "         marker='o',markerfacecolor='red', markersize=10)\n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')\n",
    "print(\"Minimum error:\",min(error_rate),\"at K =\",error_rate.index(min(error_rate)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e2307610",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum accuracy: 0.73 at K = 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = []\n",
    "\n",
    "for i in range(1,40):\n",
    "    KNN = KNeighborsClassifier(n_neighbors = i).fit(training_features,training_target)\n",
    "    predicted_target = KNN.predict(testing_features)\n",
    "    accuracy.append(accuracy_score(testing_target, predicted_target))\n",
    "    \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),accuracy,color = 'blue',linestyle='dashed', \n",
    "         marker='o',markerfacecolor='red', markersize=10)\n",
    "plt.title('accuracy vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Accuracy')\n",
    "print(\"Maximum accuracy:\",max(accuracy),\"at K =\",accuracy.index(max(accuracy)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1540ca8d",
   "metadata": {},
   "source": [
    "I found out that with k=2 the model shows the best result and accuracy = 73%.\n",
    "\n",
    "Then I decided to run the Support Vector Classifier, which is frequently implemented along the kNN. I foud out that the accuracy of SVC is equals the accuracy of a baseline, so I can't say that the version of SVC classifier is useful in this situation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aed8d978",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector classifier:\n",
      "\n",
      "Accuracy: 0.55\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.55      0.71       100\n",
      "           1       0.00      0.00      0.00         0\n",
      "\n",
      "    accuracy                           0.55       100\n",
      "   macro avg       0.50      0.28      0.35       100\n",
      "weighted avg       1.00      0.55      0.71       100\n",
      "\n",
      "Confusion matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name_of_classifier = \"Support Vector\"\n",
    "print(f\"{name_of_classifier} classifier:\\n\")\n",
    "rbf = svm.SVC(kernel='rbf', gamma=1, C=1, decision_function_shape='ovo').fit(training_features,training_target)\n",
    "\n",
    "rbf_pred = rbf.predict(testing_features)\n",
    "\n",
    "rbf_accuracy = accuracy_score(testing_target, rbf_pred)\n",
    "print(f\"Accuracy: {rbf_accuracy}\")\n",
    "\n",
    "print(classification_report(rbf_pred, testing_target))\n",
    "\n",
    "print(\"Confusion matrix:\")\n",
    "\n",
    "conmat = confusion_matrix(testing_target, rbf_pred)\n",
    "\n",
    "val = np.mat(conmat) \n",
    "\n",
    "df_cm = pd.DataFrame(val, index=[0, 1])\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "heatmap = sns.heatmap(df_cm, annot=True, cmap=\"Blues\")\n",
    "\n",
    "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha=\"right\")\n",
    "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha=\"right\")\n",
    "\n",
    "plt.ylabel(\"True label\")\n",
    "plt.xlabel(\"Predicted label\")\n",
    "\n",
    "plt.title(f\"{name_of_classifier} Model Results\")\n",
    "\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd25614",
   "metadata": {},
   "source": [
    "I created two versions of Logistic Regression Models. One with all the attributes and one with attributes with high correlation. I removed this attributes: \"trestbps\", \"chol\", \"fbs\", \"restecg\", which have low correlation with the predicted class. And I got a much better performance in the form of 6% - 84% compared to 90%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4a2e3c79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression classifier:\n",
      "\n",
      "Shape of training features: (194, 13)\n",
      "Shape of training target: (194, 1)\n",
      "Shape of testing features: (100, 13)\n",
      "Shape of testing target: (100, 1)\n",
      "\n",
      "Accuracy = 0.84\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.84      0.86        57\n",
      "           1       0.80      0.84      0.82        43\n",
      "\n",
      "    accuracy                           0.84       100\n",
      "   macro avg       0.84      0.84      0.84       100\n",
      "weighted avg       0.84      0.84      0.84       100\n",
      "\n",
      "Confusion matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LogR = LogisticRegression(max_iter=10000)\n",
    "name_of_classifier = \"Logistic Regression\"\n",
    "\n",
    "Model(LogR, name_of_classifier, features, target, training_size=training_size, random_state=random_state)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "df0bd9f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression classifier:\n",
      "\n",
      "Shape of training features: (194, 9)\n",
      "Shape of training target: (194, 1)\n",
      "Shape of testing features: (100, 9)\n",
      "Shape of testing target: (100, 1)\n",
      "\n",
      "Accuracy = 0.9\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.89      0.91        57\n",
      "           1       0.87      0.91      0.89        43\n",
      "\n",
      "    accuracy                           0.90       100\n",
      "   macro avg       0.90      0.90      0.90       100\n",
      "weighted avg       0.90      0.90      0.90       100\n",
      "\n",
      "Confusion matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_labels_filtered = [\"age\", \"sex\", \"cp\", \"thalach\", \"exang\", \"oldpeak\", \"slope\", \"ca\", \"thal\"]\n",
    "LogR_f = LogisticRegression(max_iter=10000)\n",
    "name_of_classifier = \"Logistic Regression\"\n",
    "features_filtered = df.loc[:, features_labels_filtered]\n",
    "\n",
    "Model(LogR_f, name_of_classifier, features_filtered, target, training_size=training_size, random_state=random_state)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0acc1cf",
   "metadata": {},
   "source": [
    "Though Logistic Regression is better in this situation, I also decided to implement the Linear Regression, and I got 85% accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0a926e7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression classifier:\n",
      "\n",
      "Shape of training features: (194, 13)\n",
      "Shape of training target: (194, 1)\n",
      "Shape of testing features: (100, 13)\n",
      "Shape of testing target: (100, 1)\n",
      "\n",
      "Accuracy = 0.85\n",
      "\n",
      "Mean squared error: 0.1388706674635264\n"
     ]
    }
   ],
   "source": [
    "LR = LinearRegression()\n",
    "name_of_classifier = \"Linear Regression\"\n",
    "\n",
    "training_features, testing_features, training_target, testing_target = train_test_split(features, target, train_size=training_size, stratify=target, random_state=random_state)\n",
    "    \n",
    "print(f\"{name_of_classifier} classifier:\\n\")\n",
    "print(f\"Shape of training features: {training_features.shape}\")\n",
    "print(f\"Shape of training target: {training_target.shape}\")\n",
    "print(f\"Shape of testing features: {testing_features.shape}\")\n",
    "print(f\"Shape of testing target: {testing_target.shape}\\n\")\n",
    "\n",
    "LR.fit(training_features, training_target)\n",
    "predicted_target = LR.predict(testing_features)\n",
    "score = accuracy_score(testing_target, predicted_target.round())\n",
    "print(f\"Accuracy = {score}\\n\")\n",
    "print(f\"Mean squared error: {mean_squared_error(testing_target, predicted_target)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "225ba634",
   "metadata": {},
   "source": [
    "I was surprised when I saw such a low accuracy from a Decision Tree Classifier, which has 70% accuracy with all the attributes and 77% with attributes with high correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4eaefe67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree classifier:\n",
      "\n",
      "Shape of training features: (194, 13)\n",
      "Shape of training target: (194, 1)\n",
      "Shape of testing features: (100, 13)\n",
      "Shape of testing target: (100, 1)\n",
      "\n",
      "Accuracy = 0.7\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.78      0.70        45\n",
      "           1       0.78      0.64      0.70        55\n",
      "\n",
      "    accuracy                           0.70       100\n",
      "   macro avg       0.71      0.71      0.70       100\n",
      "weighted avg       0.71      0.70      0.70       100\n",
      "\n",
      "Confusion matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DT = tree.DecisionTreeClassifier(random_state=0)\n",
    "name_of_classifier = \"Decision Tree\"\n",
    "\n",
    "Model(DT, name_of_classifier, features, target, training_size=training_size, random_state=random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ff7043c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree classifier:\n",
      "\n",
      "Shape of training features: (194, 9)\n",
      "Shape of training target: (194, 1)\n",
      "Shape of testing features: (100, 9)\n",
      "Shape of testing target: (100, 1)\n",
      "\n",
      "Accuracy = 0.77\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.78      0.80        58\n",
      "           1       0.71      0.76      0.74        42\n",
      "\n",
      "    accuracy                           0.77       100\n",
      "   macro avg       0.76      0.77      0.77       100\n",
      "weighted avg       0.77      0.77      0.77       100\n",
      "\n",
      "Confusion matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DT = tree.DecisionTreeClassifier(random_state=0)\n",
    "Model(DT, name_of_classifier, features_filtered, target, training_size=training_size, random_state=random_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d201217f",
   "metadata": {},
   "source": [
    "Then goes a Random Forest Classifier, which is the most complex classifier of all the classifiers I implemented. And It has a great accuracy of 90%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "12eec48f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest classifier:\n",
      "\n",
      "Shape of training features: (194, 13)\n",
      "Shape of training target: (194, 1)\n",
      "Shape of testing features: (100, 13)\n",
      "Shape of testing target: (100, 1)\n",
      "\n",
      "Accuracy = 0.9\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.88      0.91        59\n",
      "           1       0.84      0.93      0.88        41\n",
      "\n",
      "    accuracy                           0.90       100\n",
      "   macro avg       0.89      0.90      0.90       100\n",
      "weighted avg       0.90      0.90      0.90       100\n",
      "\n",
      "Confusion matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RF = RandomForestClassifier(max_depth=2, random_state=0, n_estimators=500)\n",
    "name_of_classifier = \"Random Forest\"\n",
    "\n",
    "Model(RF, name_of_classifier, features, target, training_size=training_size, random_state=random_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f87f9d",
   "metadata": {},
   "source": [
    "And the last but not the least classifier is Naive Bayes Classifier which should have the best accuracy based on the information that I have read throughout the Internet. And it shows also the 90% accuracy and its accuracy does not depend on the amount of attributes. So the accuracy stays the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8d6f3248",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes classifier:\n",
      "\n",
      "Shape of training features: (194, 13)\n",
      "Shape of training target: (194, 1)\n",
      "Shape of testing features: (100, 13)\n",
      "Shape of testing target: (100, 1)\n",
      "\n",
      "Accuracy = 0.9\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.89      0.91        57\n",
      "           1       0.87      0.91      0.89        43\n",
      "\n",
      "    accuracy                           0.90       100\n",
      "   macro avg       0.90      0.90      0.90       100\n",
      "weighted avg       0.90      0.90      0.90       100\n",
      "\n",
      "Confusion matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "NB = GaussianNB()\n",
    "name_of_classifier = \"Naive Bayes\"\n",
    "\n",
    "Model(NB, name_of_classifier, features, target, training_size=training_size, random_state=random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9035cd2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes classifier:\n",
      "\n",
      "Shape of training features: (194, 9)\n",
      "Shape of training target: (194, 1)\n",
      "Shape of testing features: (100, 9)\n",
      "Shape of testing target: (100, 1)\n",
      "\n",
      "Accuracy = 0.9\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.89      0.91        57\n",
      "           1       0.87      0.91      0.89        43\n",
      "\n",
      "    accuracy                           0.90       100\n",
      "   macro avg       0.90      0.90      0.90       100\n",
      "weighted avg       0.90      0.90      0.90       100\n",
      "\n",
      "Confusion matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Model(NB, name_of_classifier, features_filtered, target, training_size=training_size, random_state=random_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104c7caa",
   "metadata": {},
   "source": [
    "A measured accuracies of all the models and there is no one winner in this contest.\n",
    "The first place take Decision Tree, Naive Bayes and Logistic Regression without low correlation attributes Classifiers\n",
    "which have accuarcy of 90%.\n",
    "\n",
    "In my project I cleaned the heart disease dataset and implemented different classification Machine Learning models to predict whether a certain patient is likely to have a heart disease. Best of the classifiers show the accuracy of 90%, which is quite good nowadays, but it is not a 100% accurate models, and they can't be solely used to predict a heart disease of a person because it has 10% error, and 10 people out of 100 will get their prediction wrong. So I would recommend using this classifiers only with the supervisors recheck."
   ]
  }
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