Scikit-learn LIBRARY for heart disease prediction using classification by sklearn ML map #example.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4ec04661",
   "metadata": {},
   "outputs": [],
   "source": [
    "# standart imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "14528c23",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
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       "<p>303 rows × 14 columns</p>\n",
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      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease = pd.read_csv(\"data/heart-disease.csv\")\n",
    "heart_disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1418a876",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = heart_disease.drop(\"target\", axis=1)\n",
    "y = heart_disease[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2c074033",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2b59b771",
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply model \"Support Vector Machines\"\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "04ab7296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6229508196721312"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "629c97b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled points out of a total 61 points : 23\n"
     ]
    }
   ],
   "source": [
    "y_predsvc = model.fit(X_train, y_train).predict(X_test)\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\"% (X_test.shape[0], (y_test != y_predsvc).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dd47a155",
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply model \"Naive Bayes\"\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0ae605a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7868852459016393"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelgnb = GaussianNB()\n",
    "modelgnb.fit(X_train, y_train)\n",
    "\n",
    "modelgnb.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4ed2cf1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled points out of a total 61 points : 13\n"
     ]
    }
   ],
   "source": [
    "y_pred = modelgnb.fit(X_train, y_train).predict(X_test)\n",
    "print(\"Number of mislabeled points out of a total %d points : %d\"% (X_test.shape[0], (y_test != y_pred).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0cf275b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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