logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.feature_selection import SelectKBest, RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
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       "      <th>fbs</th>\n",
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       "      <th>slope</th>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>141</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "<p>1025 rows × 14 columns</p>\n",
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      ],
      "text/plain": [
       "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak   \n",
       "0      52    1   0       125   212    0        1      168      0      1.0  \\\n",
       "1      53    1   0       140   203    1        0      155      1      3.1   \n",
       "2      70    1   0       145   174    0        1      125      1      2.6   \n",
       "3      61    1   0       148   203    0        1      161      0      0.0   \n",
       "4      62    0   0       138   294    1        1      106      0      1.9   \n",
       "...   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
       "1021   60    1   0       125   258    0        0      141      1      2.8   \n",
       "1022   47    1   0       110   275    0        0      118      1      1.0   \n",
       "1023   50    0   0       110   254    0        0      159      0      0.0   \n",
       "1024   54    1   0       120   188    0        1      113      0      1.4   \n",
       "\n",
       "      slope  ca  thal  target  \n",
       "0         2   2     3       0  \n",
       "1         0   0     3       0  \n",
       "2         0   0     3       0  \n",
       "3         2   1     3       0  \n",
       "4         1   3     2       0  \n",
       "...     ...  ..   ...     ...  \n",
       "1020      2   0     2       1  \n",
       "1021      1   1     3       0  \n",
       "1022      1   1     2       0  \n",
       "1023      2   0     2       1  \n",
       "1024      1   1     3       0  \n",
       "\n",
       "[1025 rows x 14 columns]"
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     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "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",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
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       "      <td>110</td>\n",
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       "      <td>0</td>\n",
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       "      <td>120</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "<p>1025 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak   \n",
       "0      52    1   0       125   212    0        1      168      0      1.0  \\\n",
       "1      53    1   0       140   203    1        0      155      1      3.1   \n",
       "2      70    1   0       145   174    0        1      125      1      2.6   \n",
       "3      61    1   0       148   203    0        1      161      0      0.0   \n",
       "4      62    0   0       138   294    1        1      106      0      1.9   \n",
       "...   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
       "1021   60    1   0       125   258    0        0      141      1      2.8   \n",
       "1022   47    1   0       110   275    0        0      118      1      1.0   \n",
       "1023   50    0   0       110   254    0        0      159      0      0.0   \n",
       "1024   54    1   0       120   188    0        1      113      0      1.4   \n",
       "\n",
       "      slope  ca  thal  \n",
       "0         2   2     3  \n",
       "1         0   0     3  \n",
       "2         0   0     3  \n",
       "3         2   1     3  \n",
       "4         1   3     2  \n",
       "...     ...  ..   ...  \n",
       "1020      2   0     2  \n",
       "1021      1   1     3  \n",
       "1022      1   1     2  \n",
       "1023      2   0     2  \n",
       "1024      1   1     3  \n",
       "\n",
       "[1025 rows x 13 columns]"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.drop(columns='target')\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "1020    1\n",
       "1021    0\n",
       "1022    0\n",
       "1023    1\n",
       "1024    0\n",
       "Name: target, Length: 1025, dtype: int64"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['target']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x , y, test_size=0.3, random_state=21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <th>405</th>\n",
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       "      <th>726</th>\n",
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       "      <td>2</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>66</td>\n",
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       "      <td>2</td>\n",
       "      <td>146</td>\n",
       "      <td>278</td>\n",
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       "      <td>0</td>\n",
       "      <td>152</td>\n",
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       "    <tr>\n",
       "      <th>772</th>\n",
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       "      <td>150</td>\n",
       "      <td>244</td>\n",
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       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
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       "      <td>138</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>207</th>\n",
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       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>325</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>717 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak   \n",
       "935   56    1   1       120   240    0        1      169      0      0.0  \\\n",
       "736   59    1   0       110   239    0        0      142      1      1.2   \n",
       "405   56    1   2       130   256    1        0      142      1      0.6   \n",
       "726   48    1   0       124   274    0        0      166      0      0.5   \n",
       "204   66    0   2       146   278    0        0      152      0      0.0   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "48    66    0   2       146   278    0        0      152      0      0.0   \n",
       "772   62    0   0       150   244    0        1      154      1      1.4   \n",
       "824   61    1   0       138   166    0        0      125      1      3.6   \n",
       "207   41    1   2       112   250    0        1      179      0      0.0   \n",
       "969   52    1   1       120   325    0        1      172      0      0.2   \n",
       "\n",
       "     slope  ca  thal  \n",
       "935      0   0     2  \n",
       "736      1   1     3  \n",
       "405      1   1     1  \n",
       "726      1   0     3  \n",
       "204      1   1     2  \n",
       "..     ...  ..   ...  \n",
       "48       1   1     2  \n",
       "772      1   0     2  \n",
       "824      1   1     2  \n",
       "207      2   0     2  \n",
       "969      2   0     2  \n",
       "\n",
       "[717 rows x 13 columns]"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [],
   "source": [
    "scalar = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_scaled = scalar.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_scaled = scalar.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.1841217 ,  0.68063596,  0.06019045, ..., -2.24956757,\n",
       "        -0.72191138, -0.49611943],\n",
       "       [ 0.514987  ,  0.68063596, -0.92064027, ..., -0.62525745,\n",
       "         0.27158279,  1.10621223],\n",
       "       [ 0.1841217 ,  0.68063596,  1.04102116, ..., -0.62525745,\n",
       "         0.27158279, -2.09845108],\n",
       "       ...,\n",
       "       [ 0.73556387,  0.68063596, -0.92064027, ..., -0.62525745,\n",
       "         0.27158279, -0.49611943],\n",
       "       [-1.47020482,  0.68063596,  1.04102116, ...,  0.99905267,\n",
       "        -0.72191138, -0.49611943],\n",
       "       [-0.25703204,  0.68063596,  0.06019045, ...,  0.99905267,\n",
       "        -0.72191138, -0.49611943]])"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.fit(x_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.predict(x_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8479776847977685"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(x_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8116883116883117"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(x_test_scaled,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg1 = LogisticRegression(random_state=0,\n",
    "                              C=1,\n",
    "                              fit_intercept= True\n",
    "                              ).fit(x_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8479776847977685"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg1.score(x_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8116883116883117"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg1.score(x_test_scaled,y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [],
   "source": [
    "disease = df.loc[df['target'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Totalt antal med hjärtsjukdom: 526\n"
     ]
    }
   ],
   "source": [
    "print(f\"Totalt antal med hjärtsjukdom: {disease.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [],
   "source": [
    "disease_under_40 = disease.loc[disease['age'] < 40]\n",
    "disease_over_40 = disease.loc[(disease['age'] >= 40) & (disease['age'] < 50)]\n",
    "disease_over_50 = disease.loc[disease['age'] >= 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Totalt antal under 40 år med hjärtsjukdom: 42\n",
      "Totalt antal mellan 40 och 50 år med hjärtsjukdom: 157\n",
      "Totalt antal över 50 år med hjärtsjukdom: 327\n"
     ]
    }
   ],
   "source": [
    "print(f\"Totalt antal under 40 år med hjärtsjukdom: {disease_under_40.shape[0]}\")\n",
    "print(f\"Totalt antal mellan 40 och 50 år med hjärtsjukdom: {disease_over_40.shape[0]}\")\n",
    "print(f\"Totalt antal över 50 år med hjärtsjukdom: {disease_over_50.shape[0]}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Som man ser ovan så av dom 526 som har hjärtsjukdom i vår data så är det 157 av dom som får det när dom är mellan 40 och 50 år och sedan 327 som får det när dom är 50 år eller mer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Totalt antal kvinnor med hjärtsjukdom: 226\n",
      "Totalt antal män med hjärtsjukdom: 300\n"
     ]
    }
   ],
   "source": [
    "print(f\"Totalt antal kvinnor med hjärtsjukdom: {disease['sex'].value_counts()[0]}\")\n",
    "print(f\"Totalt antal män med hjärtsjukdom: {disease['sex'].value_counts()[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age: 36.246291837594704\n",
      "sex: 71.88297850123784\n",
      "cp: 145.784585799761\n",
      "trestbps: 18.983926473616727\n",
      "thalach: 149.46244196149752\n",
      "exang: 155.93132623293232\n",
      "oldpeak: 177.9530691406036\n",
      "slope: 110.06773982386646\n",
      "ca: 166.69671508834278\n",
      "thal: 77.93133688469725\n"
     ]
    }
   ],
   "source": [
    "selector = SelectKBest()\n",
    "selector.fit(x_train_scaled,y_train)\n",
    "\n",
    "selected_features = x_train.columns[selector.get_support()]\n",
    "selected_coef = selector.scores_[selector.get_support()]\n",
    "\n",
    "for feature, coef in zip(selected_features, selected_coef):\n",
    "    print(f\"{feature}: {coef}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "#T_d447b_row8_col3 {\n",
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       "}\n",
       "#T_d447b_row8_col5, #T_d447b_row8_col11 {\n",
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       "}\n",
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       "  color: #000000;\n",
       "}\n",
       "#T_d447b_row10_col11, #T_d447b_row12_col11 {\n",
       "  background-color: #7396f5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d447b_row10_col12 {\n",
       "  background-color: #4358cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d447b_row11_col1 {\n",
       "  background-color: #8caffe;\n",
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       "}\n",
       "#T_d447b_row11_col3 {\n",
       "  background-color: #688aef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d447b_row11_col5 {\n",
       "  background-color: #6a8bef;\n",
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       "  background-color: #bcd2f7;\n",
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       "  background-color: #b7cff9;\n",
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       "}\n",
       "#T_d447b_row12_col3 {\n",
       "  background-color: #536edd;\n",
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       "}\n",
       "#T_d447b_row12_col7 {\n",
       "  background-color: #b6cefa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d447b_row12_col10 {\n",
       "  background-color: #a5c3fe;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d447b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d447b_level0_col0\" class=\"col_heading level0 col0\" >age</th>\n",
       "      <th id=\"T_d447b_level0_col1\" class=\"col_heading level0 col1\" >sex</th>\n",
       "      <th id=\"T_d447b_level0_col2\" class=\"col_heading level0 col2\" >cp</th>\n",
       "      <th id=\"T_d447b_level0_col3\" class=\"col_heading level0 col3\" >trestbps</th>\n",
       "      <th id=\"T_d447b_level0_col4\" class=\"col_heading level0 col4\" >chol</th>\n",
       "      <th id=\"T_d447b_level0_col5\" class=\"col_heading level0 col5\" >fbs</th>\n",
       "      <th id=\"T_d447b_level0_col6\" class=\"col_heading level0 col6\" >restecg</th>\n",
       "      <th id=\"T_d447b_level0_col7\" class=\"col_heading level0 col7\" >thalach</th>\n",
       "      <th id=\"T_d447b_level0_col8\" class=\"col_heading level0 col8\" >exang</th>\n",
       "      <th id=\"T_d447b_level0_col9\" class=\"col_heading level0 col9\" >oldpeak</th>\n",
       "      <th id=\"T_d447b_level0_col10\" class=\"col_heading level0 col10\" >slope</th>\n",
       "      <th id=\"T_d447b_level0_col11\" class=\"col_heading level0 col11\" >ca</th>\n",
       "      <th id=\"T_d447b_level0_col12\" class=\"col_heading level0 col12\" >thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d447b_level0_row0\" class=\"row_heading level0 row0\" >age</th>\n",
       "      <td id=\"T_d447b_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_d447b_row0_col1\" class=\"data row0 col1\" >-0.200202</td>\n",
       "      <td id=\"T_d447b_row0_col2\" class=\"data row0 col2\" >0.022091</td>\n",
       "      <td id=\"T_d447b_row0_col3\" class=\"data row0 col3\" >0.260809</td>\n",
       "      <td id=\"T_d447b_row0_col4\" class=\"data row0 col4\" >0.262770</td>\n",
       "      <td id=\"T_d447b_row0_col5\" class=\"data row0 col5\" >0.164194</td>\n",
       "      <td id=\"T_d447b_row0_col6\" class=\"data row0 col6\" >-0.100336</td>\n",
       "      <td id=\"T_d447b_row0_col7\" class=\"data row0 col7\" >-0.530747</td>\n",
       "      <td id=\"T_d447b_row0_col8\" class=\"data row0 col8\" >0.046243</td>\n",
       "      <td id=\"T_d447b_row0_col9\" class=\"data row0 col9\" >0.184544</td>\n",
       "      <td id=\"T_d447b_row0_col10\" class=\"data row0 col10\" >-0.118102</td>\n",
       "      <td id=\"T_d447b_row0_col11\" class=\"data row0 col11\" >0.095830</td>\n",
       "      <td id=\"T_d447b_row0_col12\" class=\"data row0 col12\" >0.085711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d447b_level0_row1\" class=\"row_heading level0 row1\" >sex</th>\n",
       "      <td id=\"T_d447b_row1_col0\" class=\"data row1 col0\" >-0.200202</td>\n",
       "      <td id=\"T_d447b_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_d447b_row1_col2\" class=\"data row1 col2\" >0.050809</td>\n",
       "      <td id=\"T_d447b_row1_col3\" class=\"data row1 col3\" >0.022051</td>\n",
       "      <td id=\"T_d447b_row1_col4\" class=\"data row1 col4\" >-0.240300</td>\n",
       "      <td id=\"T_d447b_row1_col5\" class=\"data row1 col5\" >0.129315</td>\n",
       "      <td id=\"T_d447b_row1_col6\" class=\"data row1 col6\" >0.017932</td>\n",
       "      <td id=\"T_d447b_row1_col7\" class=\"data row1 col7\" >0.205993</td>\n",
       "      <td id=\"T_d447b_row1_col8\" class=\"data row1 col8\" >0.073119</td>\n",
       "      <td id=\"T_d447b_row1_col9\" class=\"data row1 col9\" >0.014016</td>\n",
       "      <td id=\"T_d447b_row1_col10\" class=\"data row1 col10\" >0.019883</td>\n",
       "      <td id=\"T_d447b_row1_col11\" class=\"data row1 col11\" >0.069622</td>\n",
       "      <td id=\"T_d447b_row1_col12\" class=\"data row1 col12\" >0.221889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d447b_level0_row2\" class=\"row_heading level0 row2\" >cp</th>\n",
       "      <td id=\"T_d447b_row2_col0\" class=\"data row2 col0\" >0.022091</td>\n",
       "      <td id=\"T_d447b_row2_col1\" class=\"data row2 col1\" >0.050809</td>\n",
       "      <td id=\"T_d447b_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_d447b_row2_col3\" class=\"data row2 col3\" >0.181374</td>\n",
       "      <td id=\"T_d447b_row2_col4\" class=\"data row2 col4\" >-0.037944</td>\n",
       "      <td id=\"T_d447b_row2_col5\" class=\"data row2 col5\" >0.224548</td>\n",
       "      <td id=\"T_d447b_row2_col6\" class=\"data row2 col6\" >-0.080905</td>\n",
       "      <td id=\"T_d447b_row2_col7\" class=\"data row2 col7\" >0.070912</td>\n",
       "      <td id=\"T_d447b_row2_col8\" class=\"data row2 col8\" >-0.093385</td>\n",
       "      <td id=\"T_d447b_row2_col9\" class=\"data row2 col9\" >0.182231</td>\n",
       "      <td id=\"T_d447b_row2_col10\" class=\"data row2 col10\" >-0.122941</td>\n",
       "      <td id=\"T_d447b_row2_col11\" class=\"data row2 col11\" >0.104507</td>\n",
       "      <td id=\"T_d447b_row2_col12\" class=\"data row2 col12\" >0.052240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d447b_level0_row3\" class=\"row_heading level0 row3\" >trestbps</th>\n",
       "      <td id=\"T_d447b_row3_col0\" class=\"data row3 col0\" >0.260809</td>\n",
       "      <td id=\"T_d447b_row3_col1\" class=\"data row3 col1\" >0.022051</td>\n",
       "      <td id=\"T_d447b_row3_col2\" class=\"data row3 col2\" >0.181374</td>\n",
       "      <td id=\"T_d447b_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_d447b_row3_col4\" class=\"data row3 col4\" >0.089431</td>\n",
       "      <td id=\"T_d447b_row3_col5\" class=\"data row3 col5\" >0.129127</td>\n",
       "      <td id=\"T_d447b_row3_col6\" class=\"data row3 col6\" >-0.114486</td>\n",
       "      <td id=\"T_d447b_row3_col7\" class=\"data row3 col7\" >0.030844</td>\n",
       "      <td id=\"T_d447b_row3_col8\" class=\"data row3 col8\" >-0.064432</td>\n",
       "      <td id=\"T_d447b_row3_col9\" class=\"data row3 col9\" >0.184926</td>\n",
       "      <td id=\"T_d447b_row3_col10\" class=\"data row3 col10\" >-0.006712</td>\n",
       "      <td id=\"T_d447b_row3_col11\" class=\"data row3 col11\" >0.050217</td>\n",
       "      <td id=\"T_d447b_row3_col12\" class=\"data row3 col12\" >-0.025575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d447b_level0_row4\" class=\"row_heading level0 row4\" >chol</th>\n",
       "      <td id=\"T_d447b_row4_col0\" class=\"data row4 col0\" >0.262770</td>\n",
       "      <td id=\"T_d447b_row4_col1\" class=\"data row4 col1\" >-0.240300</td>\n",
       "      <td id=\"T_d447b_row4_col2\" class=\"data row4 col2\" >-0.037944</td>\n",
       "      <td id=\"T_d447b_row4_col3\" class=\"data row4 col3\" >0.089431</td>\n",
       "      <td id=\"T_d447b_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_d447b_row4_col5\" class=\"data row4 col5\" >-0.008669</td>\n",
       "      <td id=\"T_d447b_row4_col6\" class=\"data row4 col6\" >-0.207774</td>\n",
       "      <td id=\"T_d447b_row4_col7\" class=\"data row4 col7\" >0.006791</td>\n",
       "      <td id=\"T_d447b_row4_col8\" class=\"data row4 col8\" >-0.005832</td>\n",
       "      <td id=\"T_d447b_row4_col9\" class=\"data row4 col9\" >0.062777</td>\n",
       "      <td id=\"T_d447b_row4_col10\" class=\"data row4 col10\" >0.042885</td>\n",
       "      <td id=\"T_d447b_row4_col11\" class=\"data row4 col11\" >-0.040816</td>\n",
       "      <td id=\"T_d447b_row4_col12\" class=\"data row4 col12\" >0.092201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d447b_level0_row5\" class=\"row_heading level0 row5\" >fbs</th>\n",
       "      <td id=\"T_d447b_row5_col0\" class=\"data row5 col0\" >0.164194</td>\n",
       "      <td id=\"T_d447b_row5_col1\" class=\"data row5 col1\" >0.129315</td>\n",
       "      <td id=\"T_d447b_row5_col2\" class=\"data row5 col2\" >0.224548</td>\n",
       "      <td id=\"T_d447b_row5_col3\" class=\"data row5 col3\" >0.129127</td>\n",
       "      <td id=\"T_d447b_row5_col4\" class=\"data row5 col4\" >-0.008669</td>\n",
       "      <td id=\"T_d447b_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_d447b_row5_col6\" class=\"data row5 col6\" >-0.072304</td>\n",
       "      <td id=\"T_d447b_row5_col7\" class=\"data row5 col7\" >-0.035745</td>\n",
       "      <td id=\"T_d447b_row5_col8\" class=\"data row5 col8\" >-0.058350</td>\n",
       "      <td id=\"T_d447b_row5_col9\" class=\"data row5 col9\" >-0.006264</td>\n",
       "      <td id=\"T_d447b_row5_col10\" class=\"data row5 col10\" >-0.010510</td>\n",
       "      <td id=\"T_d447b_row5_col11\" class=\"data row5 col11\" >0.087413</td>\n",
       "      <td id=\"T_d447b_row5_col12\" class=\"data row5 col12\" >0.077146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d447b_level0_row6\" class=\"row_heading level0 row6\" >restecg</th>\n",
       "      <td id=\"T_d447b_row6_col0\" class=\"data row6 col0\" >-0.100336</td>\n",
       "      <td id=\"T_d447b_row6_col1\" class=\"data row6 col1\" >0.017932</td>\n",
       "      <td id=\"T_d447b_row6_col2\" class=\"data row6 col2\" >-0.080905</td>\n",
       "      <td id=\"T_d447b_row6_col3\" class=\"data row6 col3\" >-0.114486</td>\n",
       "      <td id=\"T_d447b_row6_col4\" class=\"data row6 col4\" >-0.207774</td>\n",
       "      <td id=\"T_d447b_row6_col5\" class=\"data row6 col5\" >-0.072304</td>\n",
       "      <td id=\"T_d447b_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_d447b_row6_col7\" class=\"data row6 col7\" >0.027920</td>\n",
       "      <td id=\"T_d447b_row6_col8\" class=\"data row6 col8\" >-0.083395</td>\n",
       "      <td id=\"T_d447b_row6_col9\" class=\"data row6 col9\" >-0.087267</td>\n",
       "      <td id=\"T_d447b_row6_col10\" class=\"data row6 col10\" >0.135958</td>\n",
       "      <td id=\"T_d447b_row6_col11\" class=\"data row6 col11\" >0.135913</td>\n",
       "      <td id=\"T_d447b_row6_col12\" class=\"data row6 col12\" >0.099272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d447b_level0_row7\" class=\"row_heading level0 row7\" >thalach</th>\n",
       "      <td id=\"T_d447b_row7_col0\" class=\"data row7 col0\" >-0.530747</td>\n",
       "      <td id=\"T_d447b_row7_col1\" class=\"data row7 col1\" >0.205993</td>\n",
       "      <td id=\"T_d447b_row7_col2\" class=\"data row7 col2\" >0.070912</td>\n",
       "      <td id=\"T_d447b_row7_col3\" class=\"data row7 col3\" >0.030844</td>\n",
       "      <td id=\"T_d447b_row7_col4\" class=\"data row7 col4\" >0.006791</td>\n",
       "      <td id=\"T_d447b_row7_col5\" class=\"data row7 col5\" >-0.035745</td>\n",
       "      <td id=\"T_d447b_row7_col6\" class=\"data row7 col6\" >0.027920</td>\n",
       "      <td id=\"T_d447b_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_d447b_row7_col8\" class=\"data row7 col8\" >-0.174264</td>\n",
       "      <td id=\"T_d447b_row7_col9\" class=\"data row7 col9\" >-0.189018</td>\n",
       "      <td id=\"T_d447b_row7_col10\" class=\"data row7 col10\" >0.228668</td>\n",
       "      <td id=\"T_d447b_row7_col11\" class=\"data row7 col11\" >-0.039278</td>\n",
       "      <td id=\"T_d447b_row7_col12\" class=\"data row7 col12\" >0.032353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d447b_level0_row8\" class=\"row_heading level0 row8\" >exang</th>\n",
       "      <td id=\"T_d447b_row8_col0\" class=\"data row8 col0\" >0.046243</td>\n",
       "      <td id=\"T_d447b_row8_col1\" class=\"data row8 col1\" >0.073119</td>\n",
       "      <td id=\"T_d447b_row8_col2\" class=\"data row8 col2\" >-0.093385</td>\n",
       "      <td id=\"T_d447b_row8_col3\" class=\"data row8 col3\" >-0.064432</td>\n",
       "      <td id=\"T_d447b_row8_col4\" class=\"data row8 col4\" >-0.005832</td>\n",
       "      <td id=\"T_d447b_row8_col5\" class=\"data row8 col5\" >-0.058350</td>\n",
       "      <td id=\"T_d447b_row8_col6\" class=\"data row8 col6\" >-0.083395</td>\n",
       "      <td id=\"T_d447b_row8_col7\" class=\"data row8 col7\" >-0.174264</td>\n",
       "      <td id=\"T_d447b_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "      <td id=\"T_d447b_row8_col9\" class=\"data row8 col9\" >0.039237</td>\n",
       "      <td id=\"T_d447b_row8_col10\" class=\"data row8 col10\" >-0.048248</td>\n",
       "      <td id=\"T_d447b_row8_col11\" class=\"data row8 col11\" >-0.065957</td>\n",
       "      <td id=\"T_d447b_row8_col12\" class=\"data row8 col12\" >0.089021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d447b_level0_row9\" class=\"row_heading level0 row9\" >oldpeak</th>\n",
       "      <td id=\"T_d447b_row9_col0\" class=\"data row9 col0\" >0.184544</td>\n",
       "      <td id=\"T_d447b_row9_col1\" class=\"data row9 col1\" >0.014016</td>\n",
       "      <td id=\"T_d447b_row9_col2\" class=\"data row9 col2\" >0.182231</td>\n",
       "      <td id=\"T_d447b_row9_col3\" class=\"data row9 col3\" >0.184926</td>\n",
       "      <td id=\"T_d447b_row9_col4\" class=\"data row9 col4\" >0.062777</td>\n",
       "      <td id=\"T_d447b_row9_col5\" class=\"data row9 col5\" >-0.006264</td>\n",
       "      <td id=\"T_d447b_row9_col6\" class=\"data row9 col6\" >-0.087267</td>\n",
       "      <td id=\"T_d447b_row9_col7\" class=\"data row9 col7\" >-0.189018</td>\n",
       "      <td id=\"T_d447b_row9_col8\" class=\"data row9 col8\" >0.039237</td>\n",
       "      <td id=\"T_d447b_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "      <td id=\"T_d447b_row9_col10\" class=\"data row9 col10\" >-0.457514</td>\n",
       "      <td id=\"T_d447b_row9_col11\" class=\"data row9 col11\" >-0.082608</td>\n",
       "      <td id=\"T_d447b_row9_col12\" class=\"data row9 col12\" >0.013129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d447b_level0_row10\" class=\"row_heading level0 row10\" >slope</th>\n",
       "      <td id=\"T_d447b_row10_col0\" class=\"data row10 col0\" >-0.118102</td>\n",
       "      <td id=\"T_d447b_row10_col1\" class=\"data row10 col1\" >0.019883</td>\n",
       "      <td id=\"T_d447b_row10_col2\" class=\"data row10 col2\" >-0.122941</td>\n",
       "      <td id=\"T_d447b_row10_col3\" class=\"data row10 col3\" >-0.006712</td>\n",
       "      <td id=\"T_d447b_row10_col4\" class=\"data row10 col4\" >0.042885</td>\n",
       "      <td id=\"T_d447b_row10_col5\" class=\"data row10 col5\" >-0.010510</td>\n",
       "      <td id=\"T_d447b_row10_col6\" class=\"data row10 col6\" >0.135958</td>\n",
       "      <td id=\"T_d447b_row10_col7\" class=\"data row10 col7\" >0.228668</td>\n",
       "      <td id=\"T_d447b_row10_col8\" class=\"data row10 col8\" >-0.048248</td>\n",
       "      <td id=\"T_d447b_row10_col9\" class=\"data row10 col9\" >-0.457514</td>\n",
       "      <td id=\"T_d447b_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "      <td id=\"T_d447b_row10_col11\" class=\"data row10 col11\" >0.108618</td>\n",
       "      <td id=\"T_d447b_row10_col12\" class=\"data row10 col12\" >0.004343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d447b_level0_row11\" class=\"row_heading level0 row11\" >ca</th>\n",
       "      <td id=\"T_d447b_row11_col0\" class=\"data row11 col0\" >0.095830</td>\n",
       "      <td id=\"T_d447b_row11_col1\" class=\"data row11 col1\" >0.069622</td>\n",
       "      <td id=\"T_d447b_row11_col2\" class=\"data row11 col2\" >0.104507</td>\n",
       "      <td id=\"T_d447b_row11_col3\" class=\"data row11 col3\" >0.050217</td>\n",
       "      <td id=\"T_d447b_row11_col4\" class=\"data row11 col4\" >-0.040816</td>\n",
       "      <td id=\"T_d447b_row11_col5\" class=\"data row11 col5\" >0.087413</td>\n",
       "      <td id=\"T_d447b_row11_col6\" class=\"data row11 col6\" >0.135913</td>\n",
       "      <td id=\"T_d447b_row11_col7\" class=\"data row11 col7\" >-0.039278</td>\n",
       "      <td id=\"T_d447b_row11_col8\" class=\"data row11 col8\" >-0.065957</td>\n",
       "      <td id=\"T_d447b_row11_col9\" class=\"data row11 col9\" >-0.082608</td>\n",
       "      <td id=\"T_d447b_row11_col10\" class=\"data row11 col10\" >0.108618</td>\n",
       "      <td id=\"T_d447b_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "      <td id=\"T_d447b_row11_col12\" class=\"data row11 col12\" >0.110204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d447b_level0_row12\" class=\"row_heading level0 row12\" >thal</th>\n",
       "      <td id=\"T_d447b_row12_col0\" class=\"data row12 col0\" >0.085711</td>\n",
       "      <td id=\"T_d447b_row12_col1\" class=\"data row12 col1\" >0.221889</td>\n",
       "      <td id=\"T_d447b_row12_col2\" class=\"data row12 col2\" >0.052240</td>\n",
       "      <td id=\"T_d447b_row12_col3\" class=\"data row12 col3\" >-0.025575</td>\n",
       "      <td id=\"T_d447b_row12_col4\" class=\"data row12 col4\" >0.092201</td>\n",
       "      <td id=\"T_d447b_row12_col5\" class=\"data row12 col5\" >0.077146</td>\n",
       "      <td id=\"T_d447b_row12_col6\" class=\"data row12 col6\" >0.099272</td>\n",
       "      <td id=\"T_d447b_row12_col7\" class=\"data row12 col7\" >0.032353</td>\n",
       "      <td id=\"T_d447b_row12_col8\" class=\"data row12 col8\" >0.089021</td>\n",
       "      <td id=\"T_d447b_row12_col9\" class=\"data row12 col9\" >0.013129</td>\n",
       "      <td id=\"T_d447b_row12_col10\" class=\"data row12 col10\" >0.004343</td>\n",
       "      <td id=\"T_d447b_row12_col11\" class=\"data row12 col11\" >0.110204</td>\n",
       "      <td id=\"T_d447b_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17b6e11c2b0>"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = disease.drop(columns='target').corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dom attribut som påverkar mest är "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = [1 if prob > 0.35 else 0 for prob in log_reg.predict_proba(x_test_scaled)[:,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x17b6e4c0a30>"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay(cm).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = accuracy_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8181818181818182"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### När man ändrar över vilken % som ska bli klassad som potentiellt hjärtsjuk så ändras ju såklart värdet på vår accuracy, då det ändå handlar om sjukdom så vill man ju kanske ha den så låg som möjligt men ändå ge ett bra resultat, jag beslutar mig för att mellan 30-40% så sätter den mest rätta på dom som faktiskt är sjuka. Men den får ju då ganska mycket fel i dom som inte är det, det bästa resultatet som jag fann överlag på accuracy är vid 50-60% men då är det många sjuka som faller mellan raderna."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}