Heart Disease Prediction.ipynb

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   "source": [
    "----------------------------------------------\n",
    "\n",
    "# 🫀🩺Heart Disease Prediction\n",
    "* *by Prarthana Singh*\n",
    "\n",
    "  ----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc323d0-3ec2-4e62-a972-b0f7e22b371d",
   "metadata": {},
   "source": [
    "# <p style=\"background-color: #6A5ACD; font-family:calibri; color:#FFFFFF; font-size:140%; font-family:Monospace; text-align:center; border-radius:15px 50px;\">Importing Necessary Libraries</p>"
   ]
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  {
   "cell_type": "markdown",
   "id": "d421c438-4f89-4c8e-ac20-7101b68c9f0c",
   "metadata": {},
   "source": [
    "# <p style=\"background-color:lightgray; font-family:verdana; font-size:250%; text-align:center; border-radius: 15px 20px;\">🟠Machine Learning Essentials🟠</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35036f7b-1e7b-496c-8414-fa0d8863f125",
   "metadata": {},
   "source": [
    "# <p style=\"border-radius:10px; border:#DEB887 solid; padding:25px; background-color: #FFFAF0; font-size:100%;color:#52017A;text-align:center;\">What is Machine learning?</p>"
   ]
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  {
   "cell_type": "markdown",
   "id": "1ad9b0ad-3ac0-4721-b9f5-7acf72750f3c",
   "metadata": {},
   "source": [
    "<div style=\"border-radius:10px; border:#D0C2F0 solid; padding: 15px; background-color: #FFF0F4; font-size:100%; text-align:left\">\n",
    "\n",
    "<h3 align=\"left\"><font color='#5E5273'> Variable Types</font></h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891dc022-ad61-4991-b0ea-789ca01926c5",
   "metadata": {},
   "source": [
    "# 1. **Import Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dfbf09e2-80c2-4a3f-bedb-df837e08f9a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings \n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9e2177-f625-4128-b393-53970a0bff14",
   "metadata": {},
   "source": [
    "# 2. **Load the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "877e028c-4c4c-481a-8082-440fd800756e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('heart.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b537970a-8e0d-4aa0-879e-2435b0f008b5",
   "metadata": {},
   "source": [
    "\n",
    "    \n",
    "# 3. **Display Top 5 Rows of The Dataset**\n",
    "     \n",
    "    "
   ]
  },
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   "execution_count": 12,
   "id": "88f434a9-b3f9-481f-8ad1-68e126641e1c",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>52</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <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>0</td>\n",
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       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
       "1   0     3       0  \n",
       "2   0     3       0  \n",
       "3   1     3       0  \n",
       "4   3     2       0  "
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     "execution_count": 12,
     "metadata": {},
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   "source": [
    "# Display Top 5 Rows\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5f0b75f-9ed2-4e16-870d-67fb7406dbe9",
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   "outputs": [],
   "source": [
    "# Let's understand data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b5cf326d-e03d-489d-83dc-318fdfd10d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1025, 14)"
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "# Shape of the data( number of rows and columns)\n",
    "data.shape"
   ]
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  {
   "cell_type": "markdown",
   "id": "1eb39976-4bde-45ee-ab48-96558c337ff3",
   "metadata": {},
   "source": [
    "# 4. **Get Information About Our Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "14676ef7-29ea-4ad8-a49b-55d17d98b508",
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   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1025 entries, 0 to 1024\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       1025 non-null   int64  \n",
      " 1   sex       1025 non-null   int64  \n",
      " 2   cp        1025 non-null   int64  \n",
      " 3   trestbps  1025 non-null   int64  \n",
      " 4   chol      1025 non-null   int64  \n",
      " 5   fbs       1025 non-null   int64  \n",
      " 6   restecg   1025 non-null   int64  \n",
      " 7   thalach   1025 non-null   int64  \n",
      " 8   exang     1025 non-null   int64  \n",
      " 9   oldpeak   1025 non-null   float64\n",
      " 10  slope     1025 non-null   int64  \n",
      " 11  ca        1025 non-null   int64  \n",
      " 12  thal      1025 non-null   int64  \n",
      " 13  target    1025 non-null   int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 112.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae7e614d-2d75-4500-9bb8-c89a319dd235",
   "metadata": {},
   "source": [
    "\n",
    "# 5. **Check For Duplicate Data and Drop Them**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2693e171-c938-4141-b283-0f4a5228b602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data.duplicated().any() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa9f097c-22dd-44fc-8319-9177c6d5824c",
   "metadata": {},
   "source": [
    "* **contain duplicates need to drop these duplicate values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0aac4eda-6f4b-4e0b-9fa6-1d5b6eea4605",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data.drop_duplicates(keep='first',inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c9ddc08a-cffa-4287-868f-a6241e788b98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.duplicated().any()\n"
   ]
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   "execution_count": 22,
   "id": "d0f6ea68-d78b-4375-b330-744aea90b73d",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
       "1   0     3       0  \n",
       "2   0     3       0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be222907-061a-4a87-984e-54c65ac4e15c",
   "metadata": {},
   "source": [
    "# 6. **Check The Null Values in The Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "31145fe8-b6f3-4c43-a469-ac0488439e32",
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f817573-16d8-40ce-be20-08ae7e6f1bd9",
   "metadata": {},
   "source": [
    "# 7. **Get Overall Statistics About The Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c31f7d7d-1ddb-4fa2-9fdd-ee874ea04004",
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   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>cp</th>\n",
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       "      <th>chol</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>302.00000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
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       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.42053</td>\n",
       "      <td>0.682119</td>\n",
       "      <td>0.963576</td>\n",
       "      <td>131.602649</td>\n",
       "      <td>246.500000</td>\n",
       "      <td>0.149007</td>\n",
       "      <td>0.526490</td>\n",
       "      <td>149.569536</td>\n",
       "      <td>0.327815</td>\n",
       "      <td>1.043046</td>\n",
       "      <td>1.397351</td>\n",
       "      <td>0.718543</td>\n",
       "      <td>2.314570</td>\n",
       "      <td>0.543046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.04797</td>\n",
       "      <td>0.466426</td>\n",
       "      <td>1.032044</td>\n",
       "      <td>17.563394</td>\n",
       "      <td>51.753489</td>\n",
       "      <td>0.356686</td>\n",
       "      <td>0.526027</td>\n",
       "      <td>22.903527</td>\n",
       "      <td>0.470196</td>\n",
       "      <td>1.161452</td>\n",
       "      <td>0.616274</td>\n",
       "      <td>1.006748</td>\n",
       "      <td>0.613026</td>\n",
       "      <td>0.498970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.50000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>152.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  302.00000  302.000000  302.000000  302.000000  302.000000  302.000000   \n",
       "mean    54.42053    0.682119    0.963576  131.602649  246.500000    0.149007   \n",
       "std      9.04797    0.466426    1.032044   17.563394   51.753489    0.356686   \n",
       "min     29.00000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     48.00000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.50000    1.000000    1.000000  130.000000  240.500000    0.000000   \n",
       "75%     61.00000    1.000000    2.000000  140.000000  274.750000    0.000000   \n",
       "max     77.00000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  302.000000  302.000000  302.000000  302.000000  302.000000  302.000000   \n",
       "mean     0.526490  149.569536    0.327815    1.043046    1.397351    0.718543   \n",
       "std      0.526027   22.903527    0.470196    1.161452    0.616274    1.006748   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.250000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  152.500000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  302.000000  302.000000  \n",
       "mean     2.314570    0.543046  \n",
       "std      0.613026    0.498970  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d639fb82-e0f2-4982-8057-9ef1914f4982",
   "metadata": {},
   "source": [
    "# 8. **Draw Correlation Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fe9285df-13f7-4ad7-b7de-80ab6c82bc04",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(data.corr(),  annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc79d01-18ec-4296-b525-f666fd9a8486",
   "metadata": {},
   "source": [
    "# 9. **How Many People Have Heart Disease, And How Many Don't Have Heart Disease In This Dataset ?** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3fc70793-2863-4de9-ac8d-523f72d3d7c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    164\n",
       "0    138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "37dd6928-f752-4414-8640-76a4e727ae7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='target', data=data, hue='target')\n",
    "plt.xticks([0,1], ['No-Disease', 'Disease'])\n",
    "plt.legend(labels=['No-Disease', 'Disease'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8062289c-0db2-42c2-bc79-815a48d10401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data.columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efa6558b-f20d-4d5a-b182-b2b3cfb22aab",
   "metadata": {},
   "source": [
    "# **10. Check Age Distribution in The Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1269320b-e3ce-453a-a22f-35f77837291c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.distplot(data['age'],bins=20)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87242202-e12c-4475-b759-25b68e8ac064",
   "metadata": {},
   "source": [
    "* ***Most people have age 60***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b6a7cff-a8e9-4952-86a3-0e829286e33f",
   "metadata": {},
   "source": [
    "# 11. **Find Count of Male And Female in this Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "502ef90b-9d35-492a-b36a-ca52d39b82c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "1    206\n",
       "0     96\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data['sex'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f1167a46-1331-4e2d-b767-3c30efd43608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.countplot(x='sex', data=data,hue='sex')\n",
    "plt.xticks([0,1], ['Female', 'male'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0bf24ca8-ebb8-4c85-b0c8-3dc3fa3d03db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    164\n",
       "0    138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['target'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47a5a7e-22b4-4116-b734-5e2510f20ae0",
   "metadata": {},
   "source": [
    "# **12. Find Gender Distribution According to The Target Variable.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3b30d6e8-1744-4ff9-a3c4-28c2a1c1687a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='sex', data=data,hue='target')\n",
    "plt.xticks([0,1], ['Female', 'Male'])\n",
    "plt.legend(labels= ['No-Disease', 'Disease'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97d6e47b-3da8-4015-9b3c-c67c3600a25a",
   "metadata": {},
   "source": [
    "====================================================================================\n",
    "* For Females:\n",
    "\n",
    "The blue bar represents over 20 cases of no heart disease.\n",
    "The orange bar represents over 60 cases of heart disease.\n",
    "  \n",
    "====================================================================================\n",
    "* For Males:\n",
    "\n",
    "The blue bar represents over 100 cases of no heart disease.\n",
    "The orange bar represents over 80 cases of heart disease.\n",
    "  \n",
    "===================================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "575c91df-70e3-4fe2-9f4f-f6b9b05f1fcc",
   "metadata": {},
   "source": [
    "# **13. Check Chest Pain Type(cp)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4ea88360-0c24-4ae8-833f-8a9f5abd1973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cp\n",
       "0    143\n",
       "2     86\n",
       "1     50\n",
       "3     23\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['cp'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "29043d83-f930-444c-8022-8f0d0a96b85f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='cp', data=data, hue='cp')\n",
    "plt.xticks([0,1,2,3], ['Typical angina', 'Atypical angina', 'Non-anginal pain' ,'Asymptomatic'])\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(labels= ['Typical angina', 'Atypical angina', 'Non-anginal pain' ,'Asymptomatic'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d312a787-ef31-4f93-a8eb-8a357253736e",
   "metadata": {},
   "source": [
    "# **14. Chest Pain Distribution As Per Target Variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6164e2a6-80df-440c-9c33-e54e63ebfa61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28381097350>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='cp',data=data, hue='target')\n",
    "plt.xticks([0,1,2,3], ['Typical angina', 'Atypical angina', 'Non-anginal pain' ,'Asymptomatic'], rotation=35)\n",
    "plt.legend(labels= ['No-Disease', 'Disease'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4481a3c2-0f65-434f-b152-bf8736c39f45",
   "metadata": {},
   "source": [
    "==================================================================================\n",
    "\n",
    "**Typical angina:**\n",
    "\n",
    "Most individuals with typical angina do not have heart disease, as indicated by the higher blue bar.\n",
    "* This suggests that typical angina is more common among individuals without heart disease in this dataset.\n",
    "\n",
    "----------------------------------------------------------------------------------\n",
    "\n",
    "**Atypical angina:**\n",
    "\n",
    "* There is a relatively balanced distribution between those with and without heart disease, though there are slightly more cases with heart disease (orange bar).\n",
    "This means that atypical angina is somewhat equally associated with both groups.\n",
    "\n",
    "-------------------------------------------------------------\n",
    "\n",
    "**Non-anginal pain:**\n",
    "\n",
    "* A higher count of individuals with non-anginal pain have heart disease (orange bar), compared to those without (blue bar).\n",
    "This indicates that non-anginal pain may be more associated with heart disease.\n",
    "\n",
    "---------------------------------------------------------------\n",
    "\n",
    "**Asymptomatic:**\n",
    "\n",
    "* More individuals in the asymptomatic category have heart disease (orange bar) than do not (blue bar).\n",
    "This suggests that individuals who are asymptomatic, or do not report chest pain, have a higher likelihood of heart disease in this dataset.\n",
    "\n",
    "===================================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a0d69f-c8d8-41d7-ac1b-81b05adaa9d8",
   "metadata": {},
   "source": [
    "# **15. Fasting Blood Sugar Distribution According to Target Variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9ab53867-976a-4c81-8870-257b52f0b490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fbs\n",
       "0    257\n",
       "1     45\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['fbs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "46f3e846-7ad7-48ce-b25c-3269e5928251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    164\n",
       "0    138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0ed2198e-2537-4862-89e4-c817516a2fc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='fbs', ylabel='count'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='fbs', data=data, hue='target')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c05cea50-b379-4434-9c25-8b6a1004dbd1",
   "metadata": {},
   "source": [
    "===================================================================================\n",
    "\n",
    "* Blue Bars (target=0): These represent cases where the target outcome is 0, meaning individuals do not have heart disease.\n",
    "  \n",
    "   * For fbs = 0 (normal fasting blood sugar), there are over 100 cases of people without heart disease.\n",
    "   * For fbs = 1 (elevated fasting blood sugar), there are very few cases of people without heart disease.\n",
    "\n",
    "===================================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "056d1ef4-3290-43d9-98ad-3e2b0f3f43e2",
   "metadata": {},
   "source": [
    "# **16. restecg Distribution According to Target Variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bf865a9f-ede1-4ec5-8c2c-1c538c7cdb0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "restecg\n",
       "1    151\n",
       "0    147\n",
       "2      4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['restecg'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a916af33-be5c-41d8-a9bb-d3567ca27012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28380ad3740>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='restecg', data=data, hue ='target')\n",
    "plt.legend(labels=['No-Diseae', 'Disease'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c3c0035-d46e-4f32-9107-304a0fa3ec72",
   "metadata": {},
   "source": [
    "\n",
    "------------------------------------------------------------\n",
    "\n",
    "* For restecg = 0:\n",
    "\n",
    "The blue bar represents nearly 80 cases with no disease.\n",
    "The orange bar represents over 60 cases with disease.\n",
    "\n",
    "* For restecg = 1:\n",
    "\n",
    "The blue bar represents over 40 cases with no disease.\n",
    "The orange bar represents over 80 cases with disease.\n",
    "\n",
    "* For restecg = 2:\n",
    "\n",
    "The blue bar represents very few cases with no disease.\n",
    "The orange bar represents slightly fewer cases with disease.\n",
    "\n",
    "---------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5409a31f-fc7d-4dd6-abf7-5410783ecea4",
   "metadata": {},
   "source": [
    "# **17. exang Distribution According to Target Variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d272aeba-f3da-4578-b97d-5f661df91aa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "exang\n",
       "0    203\n",
       "1     99\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['exang'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a53df236-0762-4769-98ba-19b60b228f53",
   "metadata": {},
   "source": [
    "-------------------------------------\n",
    "* exang = 1: This means the person does experience angina when engaging in exercise or physical activity.\n",
    "* exang = 0: This means the person does not experience angina during exercise.\n",
    "-----------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "671d09e8-d3de-4f41-aef8-b7ffd0e23272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xAwBAHdq+fbs+++wzTZ069YLrLRaLRo0apVGjRmn48OFKSEhQXl6eunTpIpfLpaCgIK/zeP7b7t27NWbMGA0dOlTS2SM9X3/9tdc2QUFBcjgccjgcmj17tqxWq7Zv367+/fvLbDYrOzu7Xr9ldSHEDgAAtaS0tFQul0sVFRXKyclRWlqanE6nBg0apLvvvvu87RctWqTIyEh17txZAQEBWr16tex2u6xWqxwOh+Lj4zVkyBAtWLBAV199tU6ePKlNmzZp6NCh6tatm2JjY7V27VoNHjxYJpNJM2fOVGVlpWf/Gzdu1PHjx9W7d281adJEmzdvVmVlpdq2bavQ0FBNnz5dU6dOVWVlpXr16qWCggLt3r1bFotFiYmJdfmjq1HEDgAAtSQtLU2RkZEKCgpSkyZNFBcXp5SUFCUmJnouKvjfQkNDtWDBAh05ckSBgYHq3r27Nm/e7Nl28+bN+vOf/6yxY8fq+++/l91uV+/evWWz2SSdjaV77rlHPXv2VNOmTTVjxgwVFhZ69m+1WrV27VrNmTNHJSUlio2N1T/+8Q916NBB0tkTmps1ayan06njx4/LarV6PjJfn5ncbrfb10P4WmFhocLCwlRQUCCLxeLrcVAH6vrKq7i433pVWlxeSkpKlJWVpZiYGAUHB/t6HPjAL/03cKl/v7nODgAAMDRiBwAAGBqxAwAADI3YAQAAhkbsAAD8Hp+luXzVxL89sQMA8FsNGjSQJBUXF/t4EvjKuX/7c/8tVAXX2QEA+K3AwEBZrVbl5uZKkq644ooLfqcUjMftdqu4uFi5ubmyWq0KDAys8r6IHQCAX7Pb7ZLkCR5cXqxWq+e/gaoidgAAfs1kMikyMlLNmzdXeXm5r8dBHWrQoEG1juicQ+wAAOqFwMDAGvnDh8sPJygDAABDI3YAAIChETsAAMDQiB0AAGBoxA4AADA0YgcAABgasQMAAAyN2AEAAIZG7AAAAEMjdgAAgKEROwAAwNB8Gju7du3S4MGDFRUVJZPJpPXr13vWlZeXa8aMGerYsaMaNWqkqKgo3X333Tp58qTXPvLy8jR69GhZLBZZrVaNGzdORUVFdfxKAACAv/Jp7Jw+fVpxcXFasmTJeeuKi4u1f/9+zZw5U/v379fatWt1+PBh/c///I/XdqNHj9bnn3+urVu3auPGjdq1a5cmTJhQVy8BAAD4OZPb7Xb7eghJMplMWrdunYYMGXLRbfbt26frrrtO33zzjVq1aqVDhw7pmmuu0b59+9StWzdJUlpamgYOHKhvv/1WUVFRF9xPaWmpSktLPfcLCwsVHR2tgoICWSyWGn1d8E/Zczv6egT8R6tZn/l6BAD1VGFhocLCwn7173e9OmenoKBAJpNJVqtVkpSRkSGr1eoJHUlyOBwKCAjQnj17Lrofp9OpsLAwzy06Orq2RwcAAD5Sb2KnpKREM2bM0B133OGpN5fLpebNm3ttFxQUpPDwcLlcrovuKzk5WQUFBZ7biRMnanV2AADgO0G+HuBSlJeXa+TIkXK73Vq6dGm192c2m2U2m2tgMgAA4O/8PnbOhc4333yj7du3e70nZ7fblZub67X9mTNnlJeXJ7vdXtejAgAAP+TXb2OdC50jR45o27ZtioiI8FofHx+v/Px8ZWZmepZt375dlZWV6tGjR12PCwAA/JBPj+wUFRXp6NGjnvtZWVk6cOCAwsPDFRkZqeHDh2v//v3auHGjKioqPOfhhIeHq2HDhmrfvr0SEhI0fvx4paamqry8XJMmTdLtt99+0U9iAQCAy4tPY+fjjz9Wnz59PPeTkpIkSYmJiZozZ442bNggSerUqZPX49577z3deOONkqSVK1dq0qRJ6tevnwICAjRs2DClpKTUyfwAAMD/+TR2brzxRv3SZX4u5RJA4eHhWrVqVU2OBQAADMSvz9kBAACoLmIHAAAYGrEDAAAMjdgBAACGRuwAAABDI3YAAIChETsAAMDQiB0AAGBoxA4AADA0YgcAABgasQMAAAyN2AEAAIZG7AAAAEMjdgAAgKEROwAAwNCIHQAAYGjEDgAAMDRiBwAAGBqxAwAADI3YAQAAhkbsAAAAQyN2AACAoRE7AADA0IgdAABgaMQOAAAwNGIHAAAYGrEDAAAMjdgBAACGRuwAAABDI3YAAIChETsAAMDQiB0AAGBoxA4AADA0YgcAABgasQMAAAyN2AEAAIZG7AAAAEMjdgAAgKH5NHZ27dqlwYMHKyoqSiaTSevXr/da73a7NWvWLEVGRiokJEQOh0NHjhzx2iYvL0+jR4+WxWKR1WrVuHHjVFRUVIevAgAA+DOfxs7p06cVFxenJUuWXHD9ggULlJKSotTUVO3Zs0eNGjXSgAEDVFJS4tlm9OjR+vzzz7V161Zt3LhRu3bt0oQJE+rqJQAAAD9ncrvdbl8PIUkmk0nr1q3TkCFDJJ09qhMVFaVp06Zp+vTpkqSCggLZbDYtX75ct99+uw4dOqRrrrlG+/btU7du3SRJaWlpGjhwoL799ltFRUVd0nMXFhYqLCxMBQUFslgstfL64F+y53b09Qj4j1azPvP1CADqqUv9++235+xkZWXJ5XLJ4XB4loWFhalHjx7KyMiQJGVkZMhqtXpCR5IcDocCAgK0Z8+ei+67tLRUhYWFXjcAAGBMfhs7LpdLkmSz2byW22w2zzqXy6XmzZt7rQ8KClJ4eLhnmwtxOp0KCwvz3KKjo2t4egAA4C/8NnZqU3JysgoKCjy3EydO+HokAABQS/w2dux2uyQpJyfHa3lOTo5nnd1uV25urtf6M2fOKC8vz7PNhZjNZlksFq8bAAAwJr+NnZiYGNntdqWnp3uWFRYWas+ePYqPj5ckxcfHKz8/X5mZmZ5ttm/frsrKSvXo0aPOZwYAAP4nyJdPXlRUpKNHj3ruZ2Vl6cCBAwoPD1erVq00ZcoUPf7444qNjVVMTIxmzpypqKgozye22rdvr4SEBI0fP16pqakqLy/XpEmTdPvtt1/yJ7EAAICx+TR2Pv74Y/Xp08dzPykpSZKUmJio5cuX65FHHtHp06c1YcIE5efnq1evXkpLS1NwcLDnMStXrtSkSZPUr18/BQQEaNiwYUpJSanz1wIAAPyT31xnx5e4zs7lh+vs+A+uswOgqur9dXYAAABqArEDAAAMjdgBAACGRuwAAABDI3YAAIChETsAAMDQiB0AAGBoxA4AADA0YgcAABgasQMAAAyN2AEAAIZG7AAAAEMjdgAAgKEROwAAwNCIHQAAYGjEDgAAMDRiBwAAGBqxAwAADI3YAQAAhkbsAAAAQyN2AACAoRE7AADA0IgdAABgaMQOAAAwNGIHAAAYGrEDAAAMjdgBAACGRuwAAABDI3YAAIChETsAAMDQiB0AAGBoxA4AADA0YgcAABgasQMAAAyN2AEAAIZG7AAAAEMjdgAAgKEROwAAwNCIHQAAYGh+HTsVFRWaOXOmYmJiFBISot/97neaN2+e3G63Zxu3261Zs2YpMjJSISEhcjgcOnLkiA+nBgAA/sSvY2f+/PlaunSpnn/+eR06dEjz58/XggUL9Nxzz3m2WbBggVJSUpSamqo9e/aoUaNGGjBggEpKSnw4OQAA8BdVip2+ffsqPz//vOWFhYXq27dvdWfy+PDDD3XrrbfqlltuUZs2bTR8+HDddNNN2rt3r6SzR3UWL16sv/zlL7r11lt17bXXasWKFTp58qTWr19fY3MAAID6q0qxs2PHDpWVlZ23vKSkRO+//361hzqnZ8+eSk9P11dffSVJOnjwoD744APdfPPNkqSsrCy5XC45HA7PY8LCwtSjRw9lZGRcdL+lpaUqLCz0ugEAAGMK+i0bf/rpp57//cUXX8jlcnnuV1RUKC0tTS1atKix4R599FEVFhaqXbt2CgwMVEVFhZ544gmNHj1akjzPb7PZvB5ns9m8Zvu/nE6nHnvssRqbEwAA+K/fFDudOnWSyWSSyWS64NtVISEhXufTVNebb76plStXatWqVerQoYMOHDigKVOmKCoqSomJiVXeb3JyspKSkjz3CwsLFR0dXRMjAwAAP/ObYicrK0tut1tXXnml9u7dq2bNmnnWNWzYUM2bN1dgYGCNDffwww/r0Ucf1e233y5J6tixo7755hs5nU4lJibKbrdLknJychQZGel5XE5Ojjp16nTR/ZrNZpnN5hqbEwAA+K/fFDutW7eWJFVWVtbKMP9XcXGxAgK8TysKDAz0PH9MTIzsdrvS09M9cVNYWKg9e/bo/vvvr5MZAQCAf/tNsfPfjhw5ovfee0+5ubnnxc+sWbOqPZgkDR48WE888YRatWqlDh066JNPPtGiRYt0zz33SJJMJpOmTJmixx9/XLGxsYqJidHMmTMVFRWlIUOG1MgMAACgfqtS7Lz88su6//771bRpU9ntdplMJs86k8lUY7Hz3HPPaebMmXrggQeUm5urqKgo/fGPf/Ta/yOPPKLTp09rwoQJys/PV69evZSWlqbg4OAamQEAANRvJvd/X474ErVu3VoPPPCAZsyYURsz1bnCwkKFhYWpoKBAFovF1+OgDmTP7ejrEfAfrWZ95usRANRTl/r3u0rX2fnxxx81YsSIKg8HAABQV6oUOyNGjNC7775b07MAAADUuCqds3PVVVdp5syZ+uijj9SxY0c1aNDAa/3kyZNrZDgAAIDqqtI5OzExMRffocmk48ePV2uousY5O5cfztnxH0Y4Z6frwyt8PQL+S+bTd/t6BNSRS/37XaUjO1lZWVUeDAAAoC5V6ZwdAACA+qJKR3bOXdTvYpYtW1alYQAAAGpalWLnxx9/9LpfXl6uf/3rX8rPz7/gF4QCAAD4SpViZ926dectq6ys1P3336/f/e531R4KAACgptTYOTsBAQFKSkrSs88+W1O7BAAAqLYaPUH52LFjOnPmTE3uEgAAoFqq9DZWUlKS1323261Tp05p06ZNSkxMrJHBAAAAakKVYueTTz7xuh8QEKBmzZrpmWee+dVPagEAANSlKsXOe++9V9NzAAAA1Ioqxc4533//vQ4fPixJatu2rZo1a1YjQwEAANSUKp2gfPr0ad1zzz2KjIxU79691bt3b0VFRWncuHEqLi6u6RkBAACqrEqxk5SUpJ07d+rtt99Wfn6+8vPz9dZbb2nnzp2aNm1aTc8IAABQZVV6G+uf//yn1qxZoxtvvNGzbODAgQoJCdHIkSO1dOnSmpoPAACgWqp0ZKe4uFg2m+285c2bN+dtLAAA4FeqFDvx8fGaPXu2SkpKPMt+/vlnPfbYY4qPj6+x4QAAAKqrSm9jLV68WAkJCWrZsqXi4uIkSQcPHpTZbNa7775bowMCAABUR5Vip2PHjjpy5IhWrlypL7/8UpJ0xx13aPTo0QoJCanRAQEAAKqjSrHjdDpls9k0fvx4r+XLli3T999/rxkzZtTIcAAAANVVpXN2XnzxRbVr1+685R06dFBqamq1hwIAAKgpVYodl8ulyMjI85Y3a9ZMp06dqvZQAAAANaVKsRMdHa3du3eft3z37t2Kioqq9lAAAAA1pUrn7IwfP15TpkxReXm5+vbtK0lKT0/XI488whWUAQCAX6lS7Dz88MP697//rQceeEBlZWWSpODgYM2YMUPJyck1OiAAAEB1VCl2TCaT5s+fr5kzZ+rQoUMKCQlRbGyszGZzTc8HAABQLVWKnXMaN26s7t2719QsAAAANa5KJygDAADUF8QOAAAwNGIHAAAYGrEDAAAMjdgBAACGRuwAAABDI3YAAIChETsAAMDQqnVRQVy6rg+v8PUI+C/rQn09AQCgrvj9kZ3vvvtOd955pyIiIhQSEqKOHTvq448/9qx3u92aNWuWIiMjFRISIofDoSNHjvhwYgAA4E/8OnZ+/PFHXX/99WrQoIG2bNmiL774Qs8884yaNGni2WbBggVKSUlRamqq9uzZo0aNGmnAgAEqKSnx4eQAAMBf+PXbWPPnz1d0dLReffVVz7KYmBjP/3a73Vq8eLH+8pe/6NZbb5UkrVixQjabTevXr9ftt99e5zMDAAD/4tdHdjZs2KBu3bppxIgRat68uTp37qyXX37Zsz4rK0sul0sOh8OzLCwsTD169FBGRsZF91taWqrCwkKvGwAAMCa/jp3jx49r6dKlio2N1TvvvKP7779fkydP1muvvSZJcrlckiSbzeb1OJvN5ll3IU6nU2FhYZ5bdHR07b0IAADgU34dO5WVlerSpYuefPJJde7cWRMmTND48eOVmpparf0mJyeroKDAcztx4kQNTQwAAPyNX8dOZGSkrrnmGq9l7du3V3Z2tiTJbrdLknJycry2ycnJ8ay7ELPZLIvF4nUDAADG5Nexc/311+vw4cNey7766iu1bt1a0tmTle12u9LT0z3rCwsLtWfPHsXHx9fprAAAwD/59aexpk6dqp49e+rJJ5/UyJEjtXfvXr300kt66aWXJEkmk0lTpkzR448/rtjYWMXExGjmzJmKiorSkCFDfDs8AADwC34dO927d9e6deuUnJysuXPnKiYmRosXL9bo0aM92zzyyCM6ffq0JkyYoPz8fPXq1UtpaWkKDg724eQAAMBf+HXsSNKgQYM0aNCgi643mUyaO3eu5s6dW4dTAQCA+sKvz9kBAACoLmIHAAAYGrEDAAAMjdgBAACGRuwAAABDI3YAAIChETsAAMDQiB0AAGBoxA4AADA0YgcAABgasQMAAAyN2AEAAIZG7AAAAEMjdgAAgKEROwAAwNCIHQAAYGjEDgAAMDRiBwAAGBqxAwAADI3YAQAAhkbsAAAAQyN2AACAoRE7AADA0IgdAABgaMQOAAAwNGIHAAAYGrEDAAAMjdgBAACGRuwAAABDI3YAAIChETsAAMDQiB0AAGBoxA4AADA0YgcAABgasQMAAAyN2AEAAIZG7AAAAEMjdgAAgKEROwAAwNDqVew89dRTMplMmjJlimdZSUmJJk6cqIiICDVu3FjDhg1TTk6O74YEAAB+pd7Ezr59+/Tiiy/q2muv9Vo+depUvf3221q9erV27typkydP6rbbbvPRlAAAwN/Ui9gpKirS6NGj9fLLL6tJkyae5QUFBXrllVe0aNEi9e3bV127dtWrr76qDz/8UB999JEPJwYAAP6iXsTOxIkTdcstt8jhcHgtz8zMVHl5udfydu3aqVWrVsrIyLjo/kpLS1VYWOh1AwAAxhTk6wF+zeuvv679+/dr3759561zuVxq2LChrFar13KbzSaXy3XRfTqdTj322GM1PSoAAPBDfn1k58SJE3rooYe0cuVKBQcH19h+k5OTVVBQ4LmdOHGixvYNAAD8i1/HTmZmpnJzc9WlSxcFBQUpKChIO3fuVEpKioKCgmSz2VRWVqb8/Hyvx+Xk5Mhut190v2azWRaLxesGAACMya/fxurXr58+++wzr2Vjx45Vu3btNGPGDEVHR6tBgwZKT0/XsGHDJEmHDx9Wdna24uPjfTEyAADwM34dO6Ghofr973/vtaxRo0aKiIjwLB83bpySkpIUHh4ui8WiBx98UPHx8frDH/7gi5EBAICf8evYuRTPPvusAgICNGzYMJWWlmrAgAF64YUXfD0WAADwE/Uudnbs2OF1Pzg4WEuWLNGSJUt8MxAAAPBrfn2CMgAAQHUROwAAwNCIHQAAYGjEDgAAMDRiBwAAGBqxAwAADI3YAQAAhkbsAAAAQyN2AACAoRE7AADA0IgdAABgaMQOAAAwNGIHAAAYGrEDAAAMjdgBAACGRuwAAABDI3YAAIChETsAAMDQiB0AAGBoxA4AADA0YgcAABgasQMAAAyN2AEAAIZG7AAAAEMjdgAAgKEROwAAwNCIHQAAYGjEDgAAMDRiBwAAGBqxAwAADI3YAQAAhkbsAAAAQyN2AACAoRE7AADA0IgdAABgaEG+HgAAgJqUPbejr0fAf7Sa9ZmvR5DEkR0AAGBwxA4AADA0YgcAABiaX8eO0+lU9+7dFRoaqubNm2vIkCE6fPiw1zYlJSWaOHGiIiIi1LhxYw0bNkw5OTk+mhgAAPgbv46dnTt3auLEifroo4+0detWlZeX66abbtLp06c920ydOlVvv/22Vq9erZ07d+rkyZO67bbbfDg1AADwJ379aay0tDSv+8uXL1fz5s2VmZmp3r17q6CgQK+88opWrVqlvn37SpJeffVVtW/fXh999JH+8Ic/+GJsAADgR/z6yM7/VVBQIEkKDw+XJGVmZqq8vFwOh8OzTbt27dSqVStlZGRcdD+lpaUqLCz0ugEAAGOqN7FTWVmpKVOm6Prrr9fvf/97SZLL5VLDhg1ltVq9trXZbHK5XBfdl9PpVFhYmOcWHR1dm6MDAAAfqjexM3HiRP3rX//S66+/Xu19JScnq6CgwHM7ceJEDUwIAAD8kV+fs3POpEmTtHHjRu3atUstW7b0LLfb7SorK1N+fr7X0Z2cnBzZ7faL7s9sNstsNtfmyAAAwE/49ZEdt9utSZMmad26ddq+fbtiYmK81nft2lUNGjRQenq6Z9nhw4eVnZ2t+Pj4uh4XAAD4Ib8+sjNx4kStWrVKb731lkJDQz3n4YSFhSkkJERhYWEaN26ckpKSFB4eLovFogcffFDx8fF8EgsAAEjy89hZunSpJOnGG2/0Wv7qq69qzJgxkqRnn31WAQEBGjZsmEpLSzVgwAC98MILdTwpAADwV34dO263+1e3CQ4O1pIlS7RkyZI6mAgAANQ3fn3ODgAAQHUROwAAwNCIHQAAYGjEDgAAMDRiBwAAGBqxAwAADI3YAQAAhkbsAAAAQyN2AACAoRE7AADA0IgdAABgaMQOAAAwNGIHAAAYGrEDAAAMjdgBAACGRuwAAABDI3YAAIChETsAAMDQiB0AAGBoxA4AADA0YgcAABgasQMAAAyN2AEAAIZG7AAAAEMjdgAAgKEROwAAwNCIHQAAYGjEDgAAMDRiBwAAGBqxAwAADI3YAQAAhkbsAAAAQyN2AACAoRE7AADA0IgdAABgaMQOAAAwNGIHAAAYGrEDAAAMjdgBAACGZpjYWbJkidq0aaPg4GD16NFDe/fu9fVIAADADxgidt544w0lJSVp9uzZ2r9/v+Li4jRgwADl5ub6ejQAAOBjhoidRYsWafz48Ro7dqyuueYapaam6oorrtCyZct8PRoAAPCxIF8PUF1lZWXKzMxUcnKyZ1lAQIAcDocyMjIu+JjS0lKVlpZ67hcUFEiSCgsLa23OitKfa23f+O1+alDh6xHwH7X5e1dX+P32L/x++4/a/v0+t3+32/2L29X72Pnhhx9UUVEhm83mtdxms+nLL7+84GOcTqcee+yx85ZHR0fXyozwP7/39QD4/5xhvp4ABsPvtx+po9/vn376SWFhF3+ueh87VZGcnKykpCTP/crKSuXl5SkiIkImk8mHk6EuFBYWKjo6WidOnJDFYvH1OABqEL/flxe3262ffvpJUVFRv7hdvY+dpk2bKjAwUDk5OV7Lc3JyZLfbL/gYs9kss9nstcxqtdbWiPBTFouF/zMEDIrf78vHLx3ROafen6DcsGFDde3aVenp6Z5llZWVSk9PV3x8vA8nAwAA/qDeH9mRpKSkJCUmJqpbt2667rrrtHjxYp0+fVpjx4719WgAAMDHDBE7o0aN0vfff69Zs2bJ5XKpU6dOSktLO++kZUA6+zbm7Nmzz3srE0D9x+83LsTk/rXPawEAANRj9f6cHQAAgF9C7AAAAEMjdgAAgKEROwAAwNCIHVxWlixZojZt2ig4OFg9evTQ3r17fT0SgBqwa9cuDR48WFFRUTKZTFq/fr2vR4IfIXZw2XjjjTeUlJSk2bNna//+/YqLi9OAAQOUm5vr69EAVNPp06cVFxenJUuW+HoU+CE+eo7LRo8ePdS9e3c9//zzks5eaTs6OloPPvigHn30UR9PB6CmmEwmrVu3TkOGDPH1KPATHNnBZaGsrEyZmZlyOByeZQEBAXI4HMrIyPDhZACA2kbs4LLwww8/qKKi4ryrattsNrlcLh9NBQCoC8QOAAAwNGIHl4WmTZsqMDBQOTk5XstzcnJkt9t9NBUAoC4QO7gsNGzYUF27dlV6erpnWWVlpdLT0xUfH+/DyQAAtc0Q33oOXIqkpCQlJiaqW7duuu6667R48WKdPn1aY8eO9fVoAKqpqKhIR48e9dzPysrSgQMHFB4erlatWvlwMvgDPnqOy8rzzz+vp59+Wi6XS506dVJKSop69Ojh67EAVNOOHTvUp0+f85YnJiZq+fLldT8Q/AqxAwAADI1zdgAAgKEROwAAwNCIHQAAYGjEDgAAMDRiBwAAGBqxAwAADI3YAQAAhkbsAAAAQyN2AACAoRE7AADA0IgdAABgaMQOAL9TWVkpp9OpmJgYhYSEKC4uTmvWrJHb7ZbD4dCAAQN07mv98vLy1LJlS82aNUuSVFFRoXHjxnke27ZtW/31r3/12v+YMWM0ZMgQLVy4UJGRkYqIiNDEiRNVXl7u2ebUqVO65ZZbFBISopiYGK1atUpt2rTR4sWL6+znAKBmBPl6AAD4v5xOp/7+978rNTVVsbGx2rVrl+688041a9ZMr732mjp27KiUlBQ99NBDuu+++9SiRQtP7FRWVqply5ZavXq1IiIi9OGHH2rChAmKjIzUyJEjPc/x3nvvKTIyUu+9956OHj2qUaNGqVOnTho/frwk6e6779YPP/ygHTt2qEGDBkpKSlJubq5Pfh4AqodvPQfgV0pLSxUeHq5t27YpPj7es/zee+9VcXGxVq1apdWrV+vuu+/WlClT9Nxzz+mTTz5RbGzsRfc5adIkuVwurVmzRtLZIzs7duzQsWPHFBgYKEkaOXKkAgIC9Prrr+vLL79U+/bttW/fPnXr1k2SdPToUcXGxurZZ5/VlClTau8HAKDGcWQHgF85evSoiouL1b9/f6/lZWVl6ty5syRpxIgRWrdunZ566iktXbr0vNBZsmSJli1bpuzsbP38888qKytTp06dvLbp0KGDJ3QkKTIyUp999pkk6fDhwwoKClKXLl0866+66io1adKkJl8qgDpC7ADwK0VFRZKkTZs2qUWLFl7rzGazJKm4uFiZmZkKDAzUkSNHvLZ5/fXXNX36dD3zzDOKj49XaGionn76ae3Zs8druwYNGnjdN5lMqqysrOmXA8APEDsA/Mo111wjs9ms7Oxs3XDDDRfcZtq0aQoICNCWLVs0cOBA3XLLLerbt68kaffu3erZs6ceeOABz/bHjh37TTO0bdtWZ86c0SeffKKuXbtKOnvE6ccff6ziqwLgS8QOAL8SGhqq6dOna+rUqaqsrFSvXr1UUFCg3bt3y2KxqGnTplq2bJkyMjLUpUsXPfzww0pMTNSnn36qJk2aKDY2VitWrNA777yjmJgY/e1vf9O+ffsUExNzyTO0a9dODodDEyZM0NKlS9WgQQNNmzZNISEhMplMtfjqAdQGPnoOwO/MmzdPM2fOlNPpVPv27ZWQkKBNmzapTZs2GjdunObMmeM5n+axxx6TzWbTfffdJ0n64x//qNtuu02jRo1Sjx499O9//9vrKM+lWrFihWw2m3r37q2hQ4dq/PjxCg0NVXBwcI2+VgC1j09jAcAl+PbbbxUdHa1t27apX79+vh4HwG9A7ADABWzfvl1FRUXq2LGjTp06pUceeUTfffedvvrqq/NObgbg3zhnBwAuoLy8XH/60590/PhxhYaGqmfPnlq5ciWhA9RDHNkBAACGxgnKAADA0IgdAABgaMQOAAAwNGIHAAAYGrEDAAAMjdgBAACGRuwAAABDI3YAAICh/T+t/rQKwIMH7QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='exang', data=data, hue ='target')\n",
    "plt.legend(labels = ['No-Disease', 'Disease'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe6159b9-399e-47d8-bbd1-694cf07dafa8",
   "metadata": {},
   "source": [
    "====================================================================================\n",
    "\n",
    "* **For exang = 0:**\n",
    "\n",
    "  * *The blue bar represents when the person does not experience angina during exercise, then there are over 60 cases with no disease.*\n",
    "  * *The orange bar represents when the person does not experience angina during exercise, then there are nearly 140 cases with disease.*\n",
    "-----------------------------------------------\n",
    "* **For exang = 1:**\n",
    "\n",
    "  * *The blue bar represents when the person does experience angina during exercise, then there are nearly 80 cases with no disease.*\n",
    "  * *The orange bar represents when the person does experience angina during exercise, then there are over 20 cases with disease.*\n",
    "\n",
    "===================================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cf51f480-df82-4b0b-be94-3aee705075a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc9f025-3407-49a6-b173-29121da3cb08",
   "metadata": {},
   "source": [
    "# **18. Slope Distribution According to Target Variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "575f3dd4-37e3-4eed-af9e-f6c25c178c0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "slope\n",
       "2    141\n",
       "1    140\n",
       "0     21\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['slope'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e78b45a3-9146-4c60-8343-477b4fb22a95",
   "metadata": {},
   "source": [
    "\n",
    "---------------------------------------------\n",
    "\n",
    "* **slope = 0:**\n",
    " * *Upsloping – The ST segment goes upward; generally considered less concerning.*\n",
    "  \n",
    "* **slope = 1:**\n",
    " * *Flat – The ST segment stays level; often associated with a higher risk of heart disease.*\n",
    "  \n",
    "* **slope = 2:**\n",
    "* *Downsloping – The ST segment goes downward; also considered a sign that could indicate heart disease.*\n",
    "\n",
    "--------------------------------------------\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "092ee59c-1896-485f-a923-55474f91b4d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='slope', data=data, hue ='target')\n",
    "plt.legend(labels=['No Disease', 'Disease'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5596e113-e8de-4a7f-9dda-e1e9a08fe7c5",
   "metadata": {},
   "source": [
    "------------------------------\n",
    "\n",
    "* For slope = 0:\n",
    "    * Blue bar: Represents cases with no disease. There are a few cases (around 5-10).\n",
    "    * Orange bar: Represents cases with disease. There are also a few cases (around 5-10).\n",
    "\n",
    "* For slope = 1:\n",
    "  * Blue bar: Represents cases with no disease. There are over 90 cases here.\n",
    "  * Orange bar: Represents cases with disease. There are about 50 cases here.\n",
    "\n",
    "* For slope = 2:\n",
    "   * Blue bar: Represents cases with no disease. There are around 20 cases here.\n",
    "   * Orange bar: Represents cases with disease. There are over 100 cases here.\n",
    "\n",
    "-----------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1a9e706-4133-480d-bf79-e474f3e43154",
   "metadata": {},
   "source": [
    "# **19. ca Distribution According to Target Variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2561eafb-5756-4aea-9cd0-73b6a8853250",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ca\n",
       "0    175\n",
       "1     65\n",
       "2     38\n",
       "3     20\n",
       "4      4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ca'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f0050751-1cef-4eb2-8512-18cb01f86855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='ca', data=data, hue='target')\n",
    "plt.legend(labels=['No Disease', 'Disease'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e4b26eb-a29d-4c75-8247-46f63e615140",
   "metadata": {},
   "source": [
    "* For ca = 0:\n",
    "    * The blue bar represents over 40 cases with no disease.\n",
    "    * The Orange bar represents over 120 cases with disease.\n",
    "\n",
    "* For ca = 1:\n",
    "    * The blue bar represents over 40 cases with no disease.\n",
    "    * The Orange bar represents nearly 40 cases with disease.\n",
    "\n",
    "* For ca = 2:\n",
    "    * The blue bar represents over 20 cases with no disease.\n",
    "    * The Orange bar represents few cases(around 5-10) with disease.\n",
    "\n",
    "* For ca = 3:\n",
    "    * The blue bar represents nearly 20 cases with no disease.\n",
    "    * The Orange bar represents few cases(around 0-10) with disease.\n",
    "\n",
    "* For ca = 4:\n",
    "    * The blue bar represents few cases(around 0-5) with no disease.\n",
    "    * The Orange bar represents few cases(around 0-10) with disease."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6934081-e790-4bcc-b9fe-1012bee4bc84",
   "metadata": {},
   "source": [
    "# **20. thal Distribution According to Target Variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "90a358a9-8920-46f3-bfbb-58f36246f2cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "thal\n",
       "2    165\n",
       "3    117\n",
       "1     18\n",
       "0      2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['thal'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "93c68a60-57c8-4412-9120-dd7a087286fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='thal', data=data, hue='target')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9295dca8-81e0-43bb-b35e-ce658a927814",
   "metadata": {},
   "source": [
    "-----------------------------\n",
    "* For thal = 0:\n",
    "  * The blue bar represents around(0-5) cases with no disease.\n",
    "  * The Orange bar also represents around(0-5) cases with disease.\n",
    "* For ca = 1:\n",
    "  * The blue bar represents around(5-10) cases with no disease.\n",
    "  * The Orange bar represents around(0-10) cases with disease.\n",
    "* For ca = 2:\n",
    "  * The blue bar represents over 20 cases with no disease.\n",
    "  * The Orange bar represents over 120 cases with disease.\n",
    "* For ca = 3:\n",
    "  * The blue bar represents over 80 cases with no disease.\n",
    "  * The Orange bar represents over 20 cases with disease.\n",
    "----------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "bff4b410-0af8-4540-b08c-1434605d4212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    164\n",
       "0    138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['target'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13466caf-61f5-4a73-998f-cc8349db8923",
   "metadata": {},
   "source": [
    "# **21. Check Resting Blood Pressure Distribution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "13220b21-eb92-4f4c-955a-6633ed61fa79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.columns\n",
    "data['trestbps'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "901c84b7-ff18-4e80-ad46-91946d094cc2",
   "metadata": {},
   "source": [
    "# **22. Compare Resting Blood Pressure As Per Sex Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "aead71be-be17-4bba-8a2c-60bbeca95282",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28380a94740>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data, hue='sex', aspect=4)\n",
    "g.map(sns.kdeplot, 'trestbps', shade=True)\n",
    "plt.legend(labels=['Male', 'Female'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8878ce-4801-4f1e-85d4-4c2b9a2120c5",
   "metadata": {},
   "source": [
    "# **Show Distribution of Serum Cholestrol**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3ec2c4b4-f3ac-4f7a-b70c-47b94bbb7f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGdCAYAAAA44ojeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAfkUlEQVR4nO3df3ST9fn/8VdK07QFUn5J2krRzjERETmC1ojbnBQ6hh5QzpTzYecw9cimRS31zMGOIEVckbNpRRF0czDPZCrbgU2HpTllluMoWKps4BRx4oEzSJmyNtCOENv7+8dGvpaCUki4r+DzcQ5HcudO8k6uxD65m7Qex3EcAQAAGJLm9gIAAACOR6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAnHS3F3A6Ojo6tG/fPvXu3Vsej8ft5QAAgFPgOI4OHTqk/Px8paV9/jGSlAyUffv2qaCgwO1lAACA07B3714NGjToc/dJyUDp3bu3pP/eQb/f7/JqvtxisZhqamo0fvx4eb1et5eDz2A2djEbu5hNckUiERUUFMS/jn+elAyUY9/W8fv9BIrLYrGYsrOz5ff7eTEbw2zsYjZ2MZuz41TensGbZAEAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABz0t1eAL68Lpz9J7eX0G0fLZro9hIA4EuBIygAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwp9uBsnHjRt14443Kz8+Xx+PR2rVrO53vOI7mzZunvLw8ZWVlqbi4WLt27eq0z8GDBzVt2jT5/X716dNHd9xxhw4fPnxGdwQAAJw7uh0ora2tuvzyy7V06dITnr948WItWbJEy5cv15YtW9SzZ0+VlJToyJEj8X2mTZumd955R6FQSK+++qo2btyoGTNmnP69AAAA55T07l5gwoQJmjBhwgnPcxxHVVVVevDBBzVp0iRJ0vPPP69AIKC1a9dq6tSpevfdd1VdXa2GhgaNHj1akvTkk0/qO9/5jn72s58pPz//DO4OAAA4F3Q7UD7P7t27FQ6HVVxcHN+Wk5OjoqIi1dfXa+rUqaqvr1efPn3icSJJxcXFSktL05YtW3TTTTd1ud5oNKpoNBo/HYlEJEmxWEyxWCyRdwHddOzxP505+Ho4iV5O0qXS8+1MZoPkYjZ2MZvk6s7jmtBACYfDkqRAINBpeyAQiJ8XDoc1cODAzotIT1e/fv3i+xyvsrJSFRUVXbbX1NQoOzs7EUvHGQqFQt2+zOKrkrCQJFu3bp3bS+i205kNzg5mYxezSY62trZT3jehgZIsc+bMUXl5efx0JBJRQUGBxo8fL7/f7+LKEIvFFAqFNG7cOHm93m5ddvj89UlaVfLsmF/i9hJO2ZnMBsnFbOxiNsl17DsgpyKhgZKbmytJampqUl5eXnx7U1OTRo4cGd/nwIEDnS736aef6uDBg/HLH8/n88nn83XZ7vV6eQIZcTqziLZ7krSa5EnF5xuvE7uYjV3MJjm685gm9OegFBYWKjc3V7W1tfFtkUhEW7ZsUTAYlCQFg0E1NzersbExvs+GDRvU0dGhoqKiRC4HAACkqG4fQTl8+LA++OCD+Ondu3dr27Zt6tevnwYPHqyysjItXLhQQ4YMUWFhoebOnav8/HxNnjxZknTJJZfo29/+tu68804tX75csVhMM2fO1NSpU/kEDwAAkHQagbJ161Z961vfip8+9t6Q6dOna+XKlXrggQfU2tqqGTNmqLm5Wddee62qq6uVmZkZv8wLL7ygmTNnauzYsUpLS9OUKVO0ZMmSBNwdAABwLuh2oFx33XVynJN/PNTj8WjBggVasGDBSffp16+fVq1a1d2bBgAAXxL8Lh4AAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYk/BAaW9v19y5c1VYWKisrCxddNFFevjhh+U4Tnwfx3E0b9485eXlKSsrS8XFxdq1a1eilwIAAFJUwgPl0Ucf1bJly/TUU0/p3Xff1aOPPqrFixfrySefjO+zePFiLVmyRMuXL9eWLVvUs2dPlZSU6MiRI4leDgAASEHpib7CTZs2adKkSZo4caIk6cILL9Rvf/tbvfnmm5L+e/SkqqpKDz74oCZNmiRJev755xUIBLR27VpNnTo10UsCAAApJuFHUK655hrV1tbq/ffflyT99a9/1RtvvKEJEyZIknbv3q1wOKzi4uL4ZXJyclRUVKT6+vpELwcAAKSghB9BmT17tiKRiIYOHaoePXqovb1djzzyiKZNmyZJCofDkqRAINDpcoFAIH7e8aLRqKLRaPx0JBKRJMViMcVisUTfBXTDscf/dObg6+F88U7GpNLz7Uxmg+RiNnYxm+TqzuOa8EB5+eWX9cILL2jVqlW69NJLtW3bNpWVlSk/P1/Tp08/reusrKxURUVFl+01NTXKzs4+0yUjAUKhULcvs/iqJCwkydatW+f2ErrtdGaDs4PZ2MVskqOtre2U9/U4n/14TQIUFBRo9uzZKi0tjW9buHChfvOb3+i9997Thx9+qIsuukhvv/22Ro4cGd/nm9/8pkaOHKknnniiy3We6AhKQUGBPv74Y/n9/kQuH90Ui8UUCoU0btw4eb3ebl12+Pz1SVpV8uyYX+L2Ek7ZmcwGycVs7GI2yRWJRDRgwAC1tLR84dfvhB9BaWtrU1pa57e29OjRQx0dHZKkwsJC5ebmqra2Nh4okUhEW7Zs0V133XXC6/T5fPL5fF22e71enkBGnM4sou2eJK0meVLx+cbrxC5mYxezSY7uPKYJD5Qbb7xRjzzyiAYPHqxLL71Ub7/9th577DHdfvvtkiSPx6OysjItXLhQQ4YMUWFhoebOnav8/HxNnjw50csBAAApKOGB8uSTT2ru3Lm6++67deDAAeXn5+sHP/iB5s2bF9/ngQceUGtrq2bMmKHm5mZde+21qq6uVmZmZqKXAwAAUlDCA6V3796qqqpSVVXVSffxeDxasGCBFixYkOibBwAA5wB+Fw8AADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmJPu9gKQGBfO/pMrt+vr4WjxVdLw+esVbfe4sgYAwLmHIygAAMAcAgUAAJhDoAAAAHMIFAAAYA6BAgAAzCFQAACAOQQKAAAwh0ABAADmECgAAMAcfpIs0A1u/cTe03Hsp/wCQCriCAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADAnKYHyz3/+U9/73vfUv39/ZWVl6bLLLtPWrVvj5zuOo3nz5ikvL09ZWVkqLi7Wrl27krEUAACQghIeKP/+9781ZswYeb1evfbaa/r73/+un//85+rbt298n8WLF2vJkiVavny5tmzZop49e6qkpERHjhxJ9HIAAEAKSk/0FT766KMqKCjQihUr4tsKCwvjf3ccR1VVVXrwwQc1adIkSdLzzz+vQCCgtWvXaurUqYleEgAASDEJD5Q//vGPKikp0Xe/+13V1dXp/PPP1913360777xTkrR7926Fw2EVFxfHL5OTk6OioiLV19efMFCi0aii0Wj8dCQSkSTFYjHFYrFE34WU5OvhuHO7aU6n/8KOYzPhNWLPsZkwG3uYTXJ153FNeKB8+OGHWrZsmcrLy/WTn/xEDQ0Nuvfee5WRkaHp06crHA5LkgKBQKfLBQKB+HnHq6ysVEVFRZftNTU1ys7OTvRdSEmLr3L39h8e3eHuAnBSoVDI7SXgJJiNXcwmOdra2k55X4/jOAn9p29GRoZGjx6tTZs2xbfde++9amhoUH19vTZt2qQxY8Zo3759ysvLi+9zyy23yOPx6KWXXupynSc6glJQUKCPP/5Yfr8/kctPWcPnr3fldn1pjh4e3aG5W9MU7fC4sgac2LHZjBs3Tl6v1+3l4DNisZhCoRCzMYjZJFckEtGAAQPU0tLyhV+/E34EJS8vT8OGDeu07ZJLLtHvf/97SVJubq4kqampqVOgNDU1aeTIkSe8Tp/PJ5/P12W71+vlCfQ/0XZ34yDa4XF9DTgxXid2MRu7mE1ydOcxTfineMaMGaOdO3d22vb+++/rggsukPTfN8zm5uaqtrY2fn4kEtGWLVsUDAYTvRwAAJCCEn4EZdasWbrmmmv005/+VLfccovefPNNPfvss3r22WclSR6PR2VlZVq4cKGGDBmiwsJCzZ07V/n5+Zo8eXKilwMAAFJQwgPlyiuv1Jo1azRnzhwtWLBAhYWFqqqq0rRp0+L7PPDAA2ptbdWMGTPU3Nysa6+9VtXV1crMzEz0cgAAQApKeKBI0g033KAbbrjhpOd7PB4tWLBACxYsSMbNAwCAFMfv4gEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5SQ+URYsWyePxqKysLL7tyJEjKi0tVf/+/dWrVy9NmTJFTU1NyV4KAABIEUkNlIaGBj3zzDMaMWJEp+2zZs3SK6+8otWrV6uurk779u3TzTffnMylAACAFJK0QDl8+LCmTZumX/ziF+rbt298e0tLi5577jk99thjuv766zVq1CitWLFCmzZt0ubNm5O1HAAAkELSk3XFpaWlmjhxooqLi7Vw4cL49sbGRsViMRUXF8e3DR06VIMHD1Z9fb2uvvrqLtcVjUYVjUbjpyORiCQpFospFosl6y6kFF8Px53bTXM6/Rd2HJsJrxF7js2E2djDbJKrO49rUgLlxRdf1FtvvaWGhoYu54XDYWVkZKhPnz6dtgcCAYXD4RNeX2VlpSoqKrpsr6mpUXZ2dkLWnOoWX+Xu7T88usPdBeCkQqGQ20vASTAbu5hNcrS1tZ3yvgkPlL179+q+++5TKBRSZmZmQq5zzpw5Ki8vj5+ORCIqKCjQ+PHj5ff7E3IbqW74/PWu3K4vzdHDozs0d2uaoh0eV9aAEzs2m3Hjxsnr9bq9HHxGLBZTKBRiNgYxm+Q69h2QU5HwQGlsbNSBAwd0xRVXxLe1t7dr48aNeuqpp7R+/XodPXpUzc3NnY6iNDU1KTc394TX6fP55PP5umz3er08gf4n2u5uHEQ7PK6vASfG68QuZmMXs0mO7jymCQ+UsWPHavv27Z223XbbbRo6dKh+/OMfq6CgQF6vV7W1tZoyZYokaefOndqzZ4+CwWCilwMAAFJQwgOld+/eGj58eKdtPXv2VP/+/ePb77jjDpWXl6tfv37y+/265557FAwGT/gGWQAA8OWTtE/xfJ7HH39caWlpmjJliqLRqEpKSvT000+7sRQAAGDQWQmU119/vdPpzMxMLV26VEuXLj0bNw8AAFIMv4sHAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzXPldPADOnuHz1yva7nF7Gd3y0aKJbi8BgMs4ggIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAnIQHSmVlpa688kr17t1bAwcO1OTJk7Vz585O+xw5ckSlpaXq37+/evXqpSlTpqipqSnRSwEAACkq4YFSV1en0tJSbd68WaFQSLFYTOPHj1dra2t8n1mzZumVV17R6tWrVVdXp3379unmm29O9FIAAECKSk/0FVZXV3c6vXLlSg0cOFCNjY36xje+oZaWFj333HNatWqVrr/+eknSihUrdMkll2jz5s26+uqrE70kAACQYhIeKMdraWmRJPXr10+S1NjYqFgspuLi4vg+Q4cO1eDBg1VfX3/CQIlGo4pGo/HTkUhEkhSLxRSLxZK5/JTh6+G4c7tpTqf/wo5Uns25/ro+dv/O9fuZiphNcnXncU1qoHR0dKisrExjxozR8OHDJUnhcFgZGRnq06dPp30DgYDC4fAJr6eyslIVFRVdttfU1Cg7Ozvh605Fi69y9/YfHt3h7gJwUqk4m3Xr1rm9hLMiFAq5vQScBLNJjra2tlPeN6mBUlpaqh07duiNN944o+uZM2eOysvL46cjkYgKCgo0fvx4+f3+M13mOWH4/PWu3K4vzdHDozs0d2uaoh0eV9aAE0vl2eyYX+L2EpIqFospFApp3Lhx8nq9bi8Hn8FskuvYd0BORdICZebMmXr11Ve1ceNGDRo0KL49NzdXR48eVXNzc6ejKE1NTcrNzT3hdfl8Pvl8vi7bvV4vT6D/iba7+wUo2uFxfQ04sVSczZfldc3/w+xiNsnRncc04Z/icRxHM2fO1Jo1a7RhwwYVFhZ2On/UqFHyer2qra2Nb9u5c6f27NmjYDCY6OUAAIAUlPAjKKWlpVq1apX+8Ic/qHfv3vH3leTk5CgrK0s5OTm64447VF5ern79+snv9+uee+5RMBjkEzwAAEBSEgJl2bJlkqTrrruu0/YVK1bo+9//viTp8ccfV1pamqZMmaJoNKqSkhI9/fTTiV4KAABIUQkPFMf54o80ZmZmaunSpVq6dGmibx4AAJwD+F08AADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABz0t1egEUXzv6T20sAAOBLjSMoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYQ6AAAABzCBQAAGAOgQIAAMwhUAAAgDkECgAAMIdAAQAA5hAoAADAHAIFAACYk+72AgDgXHDh7D+d8r6+Ho4WXyUNn79e0XZPElf1+T5aNNG12wa+CEdQAACAOQQKAAAwh2/xADCnO98uAXBu4ggKAAAwh0ABAADmECgAAMAcAgUAAJhDoAAAAHNcDZSlS5fqwgsvVGZmpoqKivTmm2+6uRwAAGCEax8zfumll1ReXq7ly5erqKhIVVVVKikp0c6dOzVw4EC3lgUAQEKl6sfm3f5Jw64dQXnsscd055136rbbbtOwYcO0fPlyZWdn61e/+pVbSwIAAEa4cgTl6NGjamxs1Jw5c+Lb0tLSVFxcrPr6+i77R6NRRaPR+OmWlhZJ0sGDBxWLxRK+vvRPWxN+neeq9A5HbW0dSo+lqb3Dvd8pgq6YjV1WZvPJJ5+4dttWxWIxtbW16ZNPPpHX603Idabq15RkPD8OHTokSXIc5wv3dSVQPv74Y7W3tysQCHTaHggE9N5773XZv7KyUhUVFV22FxYWJm2NOHX/5/YCcFLMxi4Lsxnwc7dXAMuS+fw4dOiQcnJyPneflPhR93PmzFF5eXn8dEdHhw4ePKj+/fvL4+Ffhm6KRCIqKCjQ3r175ff73V4OPoPZ2MVs7GI2yeU4jg4dOqT8/Pwv3NeVQBkwYIB69OihpqamTtubmpqUm5vbZX+fzyefz9dpW58+fZK5RHST3+/nxWwUs7GL2djFbJLni46cHOPKm2QzMjI0atQo1dbWxrd1dHSotrZWwWDQjSUBAABDXPsWT3l5uaZPn67Ro0frqquuUlVVlVpbW3Xbbbe5tSQAAGCEa4Fy66236l//+pfmzZuncDiskSNHqrq6ussbZ2Gbz+fTQw891OVbcHAfs7GL2djFbOzwOKfyWR8AAICziN/FAwAAzCFQAACAOQQKAAAwh0ABAADmECjoorKyUldeeaV69+6tgQMHavLkydq5c2enfY4cOaLS0lL1799fvXr10pQpU7r84L09e/Zo4sSJys7O1sCBA/WjH/1In3766dm8K+ecZcuWacSIEfEfIhUMBvXaa6/Fz2cuNixatEgej0dlZWXxbczGPfPnz5fH4+n0Z+jQofHzmY1NBAq6qKurU2lpqTZv3qxQKKRYLKbx48ertfX//8KrWbNm6ZVXXtHq1atVV1enffv26eabb46f397erokTJ+ro0aPatGmTfv3rX2vlypWaN2+eG3fpnDFo0CAtWrRIjY2N2rp1q66//npNmjRJ77zzjiTmYkFDQ4OeeeYZjRgxotN2ZuOuSy+9VPv374//eeONN+LnMRujHOALHDhwwJHk1NXVOY7jOM3NzY7X63VWr14d3+fdd991JDn19fWO4zjOunXrnLS0NCccDsf3WbZsmeP3+51oNHp278A5rm/fvs4vf/lL5mLAoUOHnCFDhjihUMj55je/6dx3332O4/CacdtDDz3kXH755Sc8j9nYxREUfKGWlhZJUr9+/SRJjY2NisViKi4uju8zdOhQDR48WPX19ZKk+vp6XXbZZZ1+8F5JSYkikUj8X/s4M+3t7XrxxRfV2tqqYDDIXAwoLS3VxIkTO81A4jVjwa5du5Sfn6+vfOUrmjZtmvbs2SOJ2ViWEr/NGO7p6OhQWVmZxowZo+HDh0uSwuGwMjIyuvzCxkAgoHA4HN/n+J8KfOz0sX1werZv365gMKgjR46oV69eWrNmjYYNG6Zt27YxFxe9+OKLeuutt9TQ0NDlPF4z7ioqKtLKlSt18cUXa//+/aqoqNDXv/517dixg9kYRqDgc5WWlmrHjh2dvl8Ld1188cXatm2bWlpa9Lvf/U7Tp09XXV2d28v6Utu7d6/uu+8+hUIhZWZmur0cHGfChAnxv48YMUJFRUW64IIL9PLLLysrK8vFleHz8C0enNTMmTP16quv6s9//rMGDRoU356bm6ujR4+qubm50/5NTU3Kzc2N73P8u+CPnT62D05PRkaGvvrVr2rUqFGqrKzU5ZdfrieeeIK5uKixsVEHDhzQFVdcofT0dKWnp6uurk5LlixRenq6AoEAszGkT58++trXvqYPPviA141hBAq6cBxHM2fO1Jo1a7RhwwYVFhZ2On/UqFHyer2qra2Nb9u5c6f27NmjYDAoSQoGg9q+fbsOHDgQ3ycUCsnv92vYsGFn5458SXR0dCgajTIXF40dO1bbt2/Xtm3b4n9Gjx6tadOmxf/ObOw4fPiw/vGPfygvL4/XjWVuv0sX9tx1111OTk6O8/rrrzv79++P/2lra4vv88Mf/tAZPHiws2HDBmfr1q1OMBh0gsFg/PxPP/3UGT58uDN+/Hhn27ZtTnV1tXPeeec5c+bMceMunTNmz57t1NXVObt373b+9re/ObNnz3Y8Ho9TU1PjOA5zseSzn+JxHGbjpvvvv995/fXXnd27dzt/+ctfnOLiYmfAgAHOgQMHHMdhNlYRKOhC0gn/rFixIr7Pf/7zH+fuu+92+vbt62RnZzs33XSTs3///k7X89FHHzkTJkxwsrKynAEDBjj333+/E4vFzvK9ObfcfvvtzgUXXOBkZGQ45513njN27Nh4nDgOc7Hk+EBhNu659dZbnby8PCcjI8M5//zznVtvvdX54IMP4uczG5s8juM4bh7BAQAAOB7vQQEAAOYQKAAAwBwCBQAAmEOgAAAAcwgUAABgDoECAADMIVAAAIA5BAoAADCHQAEAAOYQKAAAwBwCBQAAmEOgAAAAc/4f7r+NMqp7W4cAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['chol'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adcd20a8-9c86-48f8-8c0d-38302f087bee",
   "metadata": {},
   "source": [
    "# **Plot Continuous Variables & Categorical Variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "eeeab085-96ca-4e0b-80ba-c0f2a099cb02",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_val= []\n",
    "con_val= []\n",
    "\n",
    "for col in data.columns:\n",
    "    if data[col].nunique() <=10:\n",
    "        cat_val.append(col)\n",
    "\n",
    "    else :\n",
    "        con_val.append(col)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "77b441a7-ec7f-42c5-8f97-a4932381853d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal', 'target'],\n",
       " ['age', 'trestbps', 'chol', 'thalach', 'oldpeak'])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_val, con_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "866c5790-ac77-47d7-857b-08b1dba0f7ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(cat_val, figsize=(12,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "941a9aae-2d9a-40fb-82ef-fe1856408cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(con_val, figsize=(12,8))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ac1244f-b6c3-40a4-b227-833c6a39f43d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "addc6078-6ed7-44ff-b924-e0fdfe2b03c5",
   "metadata": {},
   "source": [
    "# **Data Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e72d28fb-2f25-4944-90f6-d381399501b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_val= []\n",
    "con_val= []\n",
    "\n",
    "for col in data.columns:\n",
    "    if data[col].nunique() <=10:\n",
    "        cat_val.append(col)\n",
    "\n",
    "    else :\n",
    "        con_val.append(col)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "458f8c5a-4848-415f-9600-24b2a0d64f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal', 'target']"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d439c6ba-f5ec-4d16-abbc-a5d741ce88ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'trestbps', 'chol', 'thalach', 'oldpeak']"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con_val"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c49a0ba9-7414-47c8-81af-ecc1ffa10f5b",
   "metadata": {},
   "source": [
    "# **Encoding Categorical Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "2b523189-f2bd-45d9-8b4c-63591132cdfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal', 'target']"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2b4265c3-abd5-48b9-a6dc-3fc6d4896b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['cp'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "504aa59b-00d1-43b8-8a12-8e79a0870551",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_val.remove('sex')\n",
    "cat_val.remove('target')\n",
    "cat_val.remove('fbs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "74c1f7fb-a43c-432e-a138-e7f60341b44f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cp', 'restecg', 'exang', 'slope', 'ca', 'thal']"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_val"
   ]
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  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "de607d5d-3174-40a2-9195-6d2ecf3ab740",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
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       "      <td>False</td>\n",
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       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
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      "text/plain": [
       "   age  sex  trestbps  chol  fbs  thalach  oldpeak  target   cp_1   cp_2  ...  \\\n",
       "0   52    1       125   212    0      168      1.0       0  False  False  ...   \n",
       "1   53    1       140   203    1      155      3.1       0  False  False  ...   \n",
       "2   70    1       145   174    0      125      2.6       0  False  False  ...   \n",
       "3   61    1       148   203    0      161      0.0       0  False  False  ...   \n",
       "4   62    0       138   294    1      106      1.9       0  False  False  ...   \n",
       "\n",
       "   exang_1  slope_1  slope_2   ca_1   ca_2   ca_3   ca_4  thal_1  thal_2  \\\n",
       "0    False    False     True  False   True  False  False   False   False   \n",
       "1     True    False    False  False  False  False  False   False   False   \n",
       "2     True    False    False  False  False  False  False   False   False   \n",
       "3    False    False     True   True  False  False  False   False   False   \n",
       "4    False     True    False  False  False   True  False   False    True   \n",
       "\n",
       "   thal_3  \n",
       "0    True  \n",
       "1    True  \n",
       "2    True  \n",
       "3    True  \n",
       "4   False  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.get_dummies(data, columns=cat_val, drop_first=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "937b9eb7-9e32-46e3-8331-2c27b606f349",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'trestbps', 'chol', 'fbs', 'thalach', 'oldpeak', 'target',\n",
       "       'cp_1', 'cp_2', 'cp_3', 'restecg_1', 'restecg_2', 'exang_1', 'slope_1',\n",
       "       'slope_2', 'ca_1', 'ca_2', 'ca_3', 'ca_4', 'thal_1', 'thal_2',\n",
       "       'thal_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "4d040ce9-a84d-4f91-bc0a-239766c3be68",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     age  sex  trestbps  chol  fbs  thalach  oldpeak  target  cp_1  cp_2  ...  \\\n",
       "0     52    1       125   212    0      168        1       0     0     0  ...   \n",
       "1     53    1       140   203    1      155        3       0     0     0  ...   \n",
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       "3     61    1       148   203    0      161        0       0     0     0  ...   \n",
       "4     62    0       138   294    1      106        1       0     0     0  ...   \n",
       "..   ...  ...       ...   ...  ...      ...      ...     ...   ...   ...  ...   \n",
       "723   68    0       120   211    0      115        1       1     0     1  ...   \n",
       "733   44    0       108   141    0      175        0       1     0     1  ...   \n",
       "739   52    1       128   255    0      161        0       0     0     0  ...   \n",
       "843   59    1       160   273    0      125        0       0     0     0  ...   \n",
       "878   54    1       120   188    0      113        1       0     0     0  ...   \n",
       "\n",
       "     exang_1  slope_1  slope_2  ca_1  ca_2  ca_3  ca_4  thal_1  thal_2  thal_3  \n",
       "0          0        0        1     0     1     0     0       0       0       1  \n",
       "1          1        0        0     0     0     0     0       0       0       1  \n",
       "2          1        0        0     0     0     0     0       0       0       1  \n",
       "3          0        0        1     1     0     0     0       0       0       1  \n",
       "4          0        1        0     0     0     1     0       0       1       0  \n",
       "..       ...      ...      ...   ...   ...   ...   ...     ...     ...     ...  \n",
       "723        0        1        0     0     0     0     0       0       1       0  \n",
       "733        0        1        0     0     0     0     0       0       1       0  \n",
       "739        1        0        1     1     0     0     0       0       0       1  \n",
       "843        0        0        1     0     0     0     0       0       1       0  \n",
       "878        0        1        0     1     0     0     0       0       0       1  \n",
       "\n",
       "[302 rows x 23 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.astype(int)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ba97e6f-677c-4a8d-ae4c-77a089b97fa4",
   "metadata": {},
   "source": [
    "# **Feature Scaling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a820f84e-f4bb-47a4-86c6-acba4b9f5763",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "86efa0a4-7ec6-48a2-a8fc-7354c22c048c",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "data[con_val] = scaler.fit_transform(data[con_val])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "8025692f-67ec-486c-a2d6-8177ec1b128d",
   "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>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>thalach</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>target</th>\n",
       "      <th>cp_1</th>\n",
       "      <th>cp_2</th>\n",
       "      <th>...</th>\n",
       "      <th>exang_1</th>\n",
       "      <th>slope_1</th>\n",
       "      <th>slope_2</th>\n",
       "      <th>ca_1</th>\n",
       "      <th>ca_2</th>\n",
       "      <th>ca_3</th>\n",
       "      <th>ca_4</th>\n",
       "      <th>thal_1</th>\n",
       "      <th>thal_2</th>\n",
       "      <th>thal_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.267966</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.376556</td>\n",
       "      <td>-0.667728</td>\n",
       "      <td>0</td>\n",
       "      <td>0.806035</td>\n",
       "      <td>0.216620</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.157260</td>\n",
       "      <td>1</td>\n",
       "      <td>0.478910</td>\n",
       "      <td>-0.841918</td>\n",
       "      <td>1</td>\n",
       "      <td>0.237495</td>\n",
       "      <td>2.085738</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.724733</td>\n",
       "      <td>1</td>\n",
       "      <td>0.764066</td>\n",
       "      <td>-1.403197</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.074521</td>\n",
       "      <td>1.151179</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.728383</td>\n",
       "      <td>1</td>\n",
       "      <td>0.935159</td>\n",
       "      <td>-0.841918</td>\n",
       "      <td>0</td>\n",
       "      <td>0.499898</td>\n",
       "      <td>-0.717939</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.839089</td>\n",
       "      <td>0</td>\n",
       "      <td>0.364848</td>\n",
       "      <td>0.919336</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.905464</td>\n",
       "      <td>0.216620</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  sex  trestbps      chol  fbs   thalach   oldpeak  target  cp_1  \\\n",
       "0 -0.267966    1 -0.376556 -0.667728    0  0.806035  0.216620       0     0   \n",
       "1 -0.157260    1  0.478910 -0.841918    1  0.237495  2.085738       0     0   \n",
       "2  1.724733    1  0.764066 -1.403197    0 -1.074521  1.151179       0     0   \n",
       "3  0.728383    1  0.935159 -0.841918    0  0.499898 -0.717939       0     0   \n",
       "4  0.839089    0  0.364848  0.919336    1 -1.905464  0.216620       0     0   \n",
       "\n",
       "   cp_2  ...  exang_1  slope_1  slope_2  ca_1  ca_2  ca_3  ca_4  thal_1  \\\n",
       "0     0  ...        0        0        1     0     1     0     0       0   \n",
       "1     0  ...        1        0        0     0     0     0     0       0   \n",
       "2     0  ...        1        0        0     0     0     0     0       0   \n",
       "3     0  ...        0        0        1     1     0     0     0       0   \n",
       "4     0  ...        0        1        0     0     0     1     0       0   \n",
       "\n",
       "   thal_2  thal_3  \n",
       "0       0       1  \n",
       "1       0       1  \n",
       "2       0       1  \n",
       "3       0       1  \n",
       "4       1       0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28b5838-226b-4d77-baac-4ce602d2bfdf",
   "metadata": {},
   "source": [
    "# **Splitting The Dataset Into The Training Set And Test Set**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a0608709-9469-4940-acd3-23e908b2869f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('target', axis=1)\n",
    "y = data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "6c2a8d63-f25f-4635-bb59-d00081ed630f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "cb4d3337-2985-45fb-bd4c-ddf256017a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3168569-ba4c-4e3c-8e19-3eb892660565",
   "metadata": {},
   "source": [
    "# **Logistic Regression**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d3bdd8ae-cab4-41a2-bd02-d439daa6f2ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "a617ef6d-6225-4813-bf4e-1d010a0c7bdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8136ef34-b269-4128-8b18-7e9e2364e9ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "ecac8432-f7ed-4cb8-85bf-f75893c8a993",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7868852459016393\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "lr_accu = accuracy_score(y_test, lr_pred)\n",
    "print(lr_accu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d6e1043-ea18-4cb8-8bea-bfd49f313c43",
   "metadata": {},
   "source": [
    "# **SVC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "7c7803e7-bd39-4b69-8828-689ba4159155",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "93cb31e0-c900-45a0-9302-0daf4944c89f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "3bfa2971-d1e0-4468-a371-8f270761fcdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8032786885245902\n"
     ]
    }
   ],
   "source": [
    "svc_pred = svc.predict(X_test)\n",
    "svc_accu = accuracy_score(y_test, svc_pred)\n",
    "print(svc_accu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "201a4c11-b75e-464b-a33b-c82e25a665f7",
   "metadata": {},
   "source": [
    "# **KNeighbors Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "ee12588f-4a98-4893-8ccf-12ac78438c74",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "9ce648ea-4e81-41e2-8856-18857ffa52fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier() # By default n_neighbors =5\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "0b8f85ff-b08f-493c-a049-87d30bbb611d",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "af4e1d5a-eac2-4c95-9fc8-f323e4fff19f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7377049180327869\n"
     ]
    }
   ],
   "source": [
    "knn_accu = accuracy_score(y_test, knn_pred)\n",
    "print(knn_accu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "b9a45ba9-0956-4f8b-ad4e-b87727b38f01",
   "metadata": {},
   "outputs": [],
   "source": [
    "score = []\n",
    "\n",
    "for i in range(1,50):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i) \n",
    "    knn.fit(X_train, y_train)\n",
    "    knn_pred = knn.predict(X_test)\n",
    "    score.append((i,  accuracy_score(y_test, knn_pred)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "03170cf2-a73c-4f54-ab5f-83d3c2bdb128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 0.7377049180327869),\n",
       " (2, 0.7868852459016393),\n",
       " (3, 0.7377049180327869),\n",
       " (4, 0.7540983606557377),\n",
       " (5, 0.7377049180327869),\n",
       " (6, 0.7868852459016393),\n",
       " (7, 0.7377049180327869),\n",
       " (8, 0.7868852459016393),\n",
       " (9, 0.7704918032786885),\n",
       " (10, 0.7540983606557377),\n",
       " (11, 0.7540983606557377),\n",
       " (12, 0.7540983606557377),\n",
       " (13, 0.7540983606557377),\n",
       " (14, 0.7704918032786885),\n",
       " (15, 0.7377049180327869),\n",
       " (16, 0.8032786885245902),\n",
       " (17, 0.7704918032786885),\n",
       " (18, 0.7704918032786885),\n",
       " (19, 0.7704918032786885),\n",
       " (20, 0.7704918032786885),\n",
       " (21, 0.7213114754098361),\n",
       " (22, 0.7540983606557377),\n",
       " (23, 0.7377049180327869),\n",
       " (24, 0.7540983606557377),\n",
       " (25, 0.7049180327868853),\n",
       " (26, 0.7377049180327869),\n",
       " (27, 0.7213114754098361),\n",
       " (28, 0.7377049180327869),\n",
       " (29, 0.7213114754098361),\n",
       " (30, 0.7213114754098361),\n",
       " (31, 0.7213114754098361),\n",
       " (32, 0.7377049180327869),\n",
       " (33, 0.7213114754098361),\n",
       " (34, 0.7377049180327869),\n",
       " (35, 0.7213114754098361),\n",
       " (36, 0.7377049180327869),\n",
       " (37, 0.7213114754098361),\n",
       " (38, 0.7377049180327869),\n",
       " (39, 0.7213114754098361),\n",
       " (40, 0.7213114754098361),\n",
       " (41, 0.7213114754098361),\n",
       " (42, 0.7213114754098361),\n",
       " (43, 0.7213114754098361),\n",
       " (44, 0.7213114754098361),\n",
       " (45, 0.7213114754098361),\n",
       " (46, 0.7213114754098361),\n",
       " (47, 0.7213114754098361),\n",
       " (48, 0.7213114754098361),\n",
       " (49, 0.7213114754098361)]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbee3b5e-0419-4797-9d16-850f58bbbce4",
   "metadata": {},
   "source": [
    "* **See at k=16 accuracy is high(80)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "27e8fbcb-3f75-450d-a86e-c64ca27aa704",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8032786885245902\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=16) \n",
    "knn.fit(X_train, y_train)\n",
    "knn_pred = knn.predict(X_test)\n",
    "knn_accu = accuracy_score(y_test, knn_pred)\n",
    "print(knn_accu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3df6a064-87db-4077-9b30-392b74905315",
   "metadata": {},
   "source": [
    "# **Non-Linear ML Algorithms**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "c8fa335c-84fb-436a-b58b-36cd771a5646",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data again because for Tree Based Algorithms Feature scalling is not require\n",
    "data = pd.read_csv('heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "7f6a127d-7f2b-49f3-90f2-423303d55cd2",
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <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",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
       "1   0     3       0  \n",
       "2   0     3       0  \n",
       "3   1     3       0  \n",
       "4   3     2       0  "
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "c20f5b9e-87fd-46a7-b193-2de3dcdbe443",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop_duplicates(keep='first',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "9881e088-7e9c-43ec-be86-18398f3ac65e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(302, 14)"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "c409b3c9-0667-49b9-8bc1-e95a9a7baa91",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= data.drop('target', axis=1)\n",
    "y= data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "06839b10-4127-42ac-af13-3361134d5dc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2156ef86-c05f-4b60-bb79-0005094151ae",
   "metadata": {},
   "source": [
    "# **Decision Tree Classifier**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "5601bf54-3d1d-475b-b3c8-8a507a6420bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7704918032786885\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)\n",
    "dt_pred = dt.predict(X_test)\n",
    "dt_accu = accuracy_score(y_test, dt_pred)\n",
    "print(dt_accu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a8ea1f-03c3-453b-b26f-086ed1b469cf",
   "metadata": {},
   "source": [
    "# **Random Forest Classifier**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "6cb9f036-3205-4d89-901a-e8dbd49cbd06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8524590163934426\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "rf_pred = rf.predict(X_test)\n",
    "rf_accu = accuracy_score(y_test, rf_pred)\n",
    "print(rf_accu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdc5ce22-f912-4007-9d0d-3fb6c6498325",
   "metadata": {},
   "source": [
    "# **Gradient Boosting Classifier**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "bb5f3044-5fb7-4573-8e97-4b87ab2a723e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8032786885245902\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "gbc = GradientBoostingClassifier()\n",
    "gbc.fit(X_train, y_train)\n",
    "gbc_pred = gbc.predict(X_test)\n",
    "gbc_accu = accuracy_score(y_test, gbc_pred)\n",
    "print(gbc_accu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "b02745bf-a702-4fe0-b51b-840e9fa768f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = pd.DataFrame({'Model':['LR', 'SVM', 'KNN', 'DT', 'RF', 'GBC'],'Accuracy':[lr_accu, svc_accu, knn_accu, dt_accu, rf_accu, gbc_accu]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "057f26d6-f77d-492f-91dd-400c4506d7d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Model  Accuracy\n",
      "0    LR  0.786885\n",
      "1   SVM  0.803279\n",
      "2   KNN  0.803279\n",
      "3    DT  0.770492\n",
      "4    RF  0.852459\n",
      "5   GBC  0.803279\n"
     ]
    }
   ],
   "source": [
    "print(final_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "e679c3ab-a4be-4e40-87a0-332c0743e694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Model', ylabel='Accuracy'>"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Model', y ='Accuracy', data=final_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df1b52be-d218-400d-bfe1-c074f1aada79",
   "metadata": {},
   "source": [
    "* ***We select RandomForest as a best model because it's accuracy is High***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a818ad0c-bcd7-406f-92d9-7f697301fce2",
   "metadata": {},
   "source": [
    "* ***Earlier, we split the data into training and test sets to select the model with the best accuracy. Now that we've chosen Random Forest as the best model, we'll train it with a larger dataset.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "6b284eac-acef-4cb0-acf8-0fa00c9acfb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('target', axis=1)\n",
    "y = data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "3302360d-dfdd-4049-9d3c-8ce17aeacaff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14596da1-010d-4633-b423-41c423c7dd8d",
   "metadata": {},
   "source": [
    "# **Prediction on New Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "4e6787b3-0def-4053-9e52-0a3f10643d87",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = pd.DataFrame({\n",
    "    'age':52,\n",
    "    'sex':1,\n",
    "    'cp':0,\n",
    "    'trestbps':125,\n",
    "    'chol':212,\n",
    "    'fbs':0,\n",
    "    'restecg':1,\n",
    "    'thalach':168,\n",
    "    'exang':0,\n",
    "    'oldpeak':1.0,\n",
    "    'slope':2,\n",
    "    'ca':2,\n",
    "    'thal':3\n",
    "}, index=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "c9205702-0a79-40d7-9d17-dc7a148e4655",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "\n",
       "   ca  thal  \n",
       "0   2     3  "
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "0369cc74-ab64-4df8-b546-8da3d16ff701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Disease\n"
     ]
    }
   ],
   "source": [
    "p = rf.predict(new_data)\n",
    "if p[0] == 0:\n",
    "    print('No Disease')\n",
    "else:\n",
    "    print('Disease')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ca43798-c53a-4b1b-b817-9058235f9310",
   "metadata": {},
   "source": [
    "# **Save Model Usign Joblib**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "792b2992-4954-48c5-a099-c34c499b231b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model_joblib_heart']"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(rf, 'model_joblib_heart')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "7ae84c48-f1f6-4288-944d-54b718bd8896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = joblib.load('model_joblib_heart')\n",
    "model.predict(new_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8723410-2c97-4ea0-9865-772283043e60",
   "metadata": {},
   "source": [
    "# **Creating GUI**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "6af9bf03-af49-40cc-b67a-047234226806",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "02481f7b-d5a5-4fa2-8d1d-1da645f447ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "d1ed73a0-8fa0-4d6c-8a67-99899a953658",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_entry():\n",
    "    p1 = float(e1.get())\n",
    "    p2 = float(e2.get())\n",
    "    p3 = float(e3.get())\n",
    "    p4 = float(e4.get())\n",
    "    p5 = float(e5.get())\n",
    "    p6 = float(e6.get())\n",
    "    p7 = float(e7.get())\n",
    "    p8 = float(e8.get())\n",
    "    p9 = float(e9.get())\n",
    "    p10 = float(e10.get())\n",
    "    p11 = float(e11.get())\n",
    "    p12 = float(e12.get())\n",
    "    p13 = float(e13.get())\n",
    "\n",
    "\n",
    "\n",
    "    model = joblib.load('model_joblib_heart')\n",
    "    result = model.predict([[p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12,p13]])\n",
    "\n",
    "    if result == 0:\n",
    "      Label(root, text=\" No Heart Disease\",pady=12).grid(row=31,columnspan=2)\n",
    "    else:\n",
    "      Label(root, text=\"Possibility of Heart Disease\").grid(row=31)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "f139b1ed-7e73-4355-bf3e-ecda39dc5520",
   "metadata": {},
   "outputs": [],
   "source": [
    "root = Tk()\n",
    "root.title(\"Heart Disease Prediction\")\n",
    "\n",
    "label = Label(root, text=\"Heart Disease Prediction\", bg=\"black\", fg=\"white\").grid(row=0,columnspan=2)\n",
    "\n",
    "Label(root, text=\"Enter your age\",pady=6).grid(row=1)\n",
    "Label(root, text=\"Male Or Female [1/0]\",pady=6).grid(row=2)\n",
    "Label(root, text=\"Enter Value of CP\",pady=6).grid(row=3)\n",
    "Label(root, text=\"Enter Value of trestbps\",pady=6).grid(row=4)\n",
    "Label(root, text=\"Enter Value of Chol\",pady=6).grid(row=5)\n",
    "Label(root, text=\"Enter Value of fbs\",pady=6).grid(row=6)\n",
    "Label(root, text=\"Enter Value of restecg\",pady=6).grid(row=7)\n",
    "Label(root, text=\"Enter Value of thalach\",pady=6).grid(row=8)\n",
    "Label(root, text=\"Enter Value of exang\",pady=6).grid(row=9)\n",
    "Label(root, text=\"Enter Value of oldpeak\",pady=6).grid(row=10)\n",
    "Label(root, text=\"Enter Value of slope\",pady=6).grid(row=11)\n",
    "Label(root, text=\"Enter Value of ca\",pady=6).grid(row=12)\n",
    "Label(root, text=\"Enter Value of thal\",pady=6).grid(row=13)\n",
    "\n",
    "\n",
    "\n",
    "e1 = Entry(root)\n",
    "e2 = Entry(root)\n",
    "e3 = Entry(root)\n",
    "e4 = Entry(root)\n",
    "e5 = Entry(root)\n",
    "e6 = Entry(root)\n",
    "e7 = Entry(root)\n",
    "e8 = Entry(root)\n",
    "e9 = Entry(root)\n",
    "e10 = Entry(root)\n",
    "e11 = Entry(root)\n",
    "e12 = Entry(root)\n",
    "e13 = Entry(root)\n",
    "\n",
    "e1.grid(row=1, column=1)\n",
    "e2.grid(row=2, column=1)\n",
    "e3.grid(row=3, column=1)\n",
    "e4.grid(row=4, column=1)\n",
    "e5.grid(row=5, column=1)\n",
    "e6.grid(row=6, column=1)\n",
    "e7.grid(row=7, column=1)\n",
    "e8.grid(row=8, column=1)\n",
    "e9.grid(row=9, column=1)\n",
    "e10.grid(row=10, column=1)\n",
    "e11.grid(row=11, column=1)\n",
    "e12.grid(row=12, column=1)\n",
    "e13.grid(row=13, column=1)\n",
    "\n",
    "Button(root, text=\"Predict\",bg='white',activebackground='green',command= show_entry).grid()\n",
    "root.geometry(\"600x500\")\n",
    "mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdfd7998-a37f-454b-978c-034f382e9cb3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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