heart_disease_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<bound method DataFrame.info of      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
      "0     63    1   1       145   233    1        2      150      0      2.3   \n",
      "1     67    1   4       160   286    0        2      108      1      1.5   \n",
      "2     67    1   4       120   229    0        2      129      1      2.6   \n",
      "3     37    1   3       130   250    0        0      187      0      3.5   \n",
      "4     41    0   2       130   204    0        2      172      0      1.4   \n",
      "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
      "298   45    1   1       110   264    0        0      132      0      1.2   \n",
      "299   68    1   4       144   193    1        0      141      0      3.4   \n",
      "300   57    1   4       130   131    0        0      115      1      1.2   \n",
      "301   57    0   2       130   236    0        2      174      0      0.0   \n",
      "302   38    1   3       138   175    0        0      173      0      0.0   \n",
      "\n",
      "     slope   ca  thal  num  \n",
      "0        3  0.0   6.0    0  \n",
      "1        2  3.0   3.0    2  \n",
      "2        2  2.0   7.0    1  \n",
      "3        3  0.0   3.0    0  \n",
      "4        1  0.0   3.0    0  \n",
      "..     ...  ...   ...  ...  \n",
      "298      2  0.0   7.0    1  \n",
      "299      2  2.0   7.0    2  \n",
      "300      2  1.0   7.0    3  \n",
      "301      2  1.0   3.0    1  \n",
      "302      1  NaN   3.0    0  \n",
      "\n",
      "[303 rows x 14 columns]>\n",
      "First few rows of the dataset:\n"
     ]
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
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       "      <th>0</th>\n",
       "      <td>63</td>\n",
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       "      <td>145</td>\n",
       "      <td>233</td>\n",
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       "      <td>150</td>\n",
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       "      <th>1</th>\n",
       "      <td>67</td>\n",
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       "      <td>160</td>\n",
       "      <td>286</td>\n",
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       "      <td>108</td>\n",
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       "      <th>2</th>\n",
       "      <td>67</td>\n",
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       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>37</td>\n",
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       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</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   63    1   1       145   233    1        2      150      0      2.3      3   \n",
       "1   67    1   4       160   286    0        2      108      1      1.5      2   \n",
       "2   67    1   4       120   229    0        2      129      1      2.6      2   \n",
       "3   37    1   3       130   250    0        0      187      0      3.5      3   \n",
       "4   41    0   2       130   204    0        2      172      0      1.4      1   \n",
       "\n",
       "    ca  thal  num  \n",
       "0  0.0   6.0    0  \n",
       "1  3.0   3.0    2  \n",
       "2  2.0   7.0    1  \n",
       "3  0.0   3.0    0  \n",
       "4  0.0   3.0    0  "
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     "text": [
      "Summary statistics for the dataset:\n"
     ]
    },
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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",
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       "      <th>restecg</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.438944</td>\n",
       "      <td>0.679868</td>\n",
       "      <td>3.158416</td>\n",
       "      <td>131.689769</td>\n",
       "      <td>246.693069</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.990099</td>\n",
       "      <td>149.607261</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.600660</td>\n",
       "      <td>0.672241</td>\n",
       "      <td>4.734219</td>\n",
       "      <td>0.937294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.038662</td>\n",
       "      <td>0.467299</td>\n",
       "      <td>0.960126</td>\n",
       "      <td>17.599748</td>\n",
       "      <td>51.776918</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.994971</td>\n",
       "      <td>22.875003</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.937438</td>\n",
       "      <td>1.939706</td>\n",
       "      <td>1.228536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</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>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</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>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
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      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.438944    0.679868    3.158416  131.689769  246.693069    0.148515   \n",
       "std      9.038662    0.467299    0.960126   17.599748   51.776918    0.356198   \n",
       "min     29.000000    0.000000    1.000000   94.000000  126.000000    0.000000   \n",
       "25%     48.000000    0.000000    3.000000  120.000000  211.000000    0.000000   \n",
       "50%     56.000000    1.000000    3.000000  130.000000  241.000000    0.000000   \n",
       "75%     61.000000    1.000000    4.000000  140.000000  275.000000    0.000000   \n",
       "max     77.000000    1.000000    4.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  299.000000   \n",
       "mean     0.990099  149.607261    0.326733    1.039604    1.600660    0.672241   \n",
       "std      0.994971   22.875003    0.469794    1.161075    0.616226    0.937438   \n",
       "min      0.000000   71.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    2.000000    0.000000   \n",
       "75%      2.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    3.000000    3.000000   \n",
       "\n",
       "             thal         num  \n",
       "count  301.000000  303.000000  \n",
       "mean     4.734219    0.937294  \n",
       "std      1.939706    1.228536  \n",
       "min      3.000000    0.000000  \n",
       "25%      3.000000    0.000000  \n",
       "50%      3.000000    0.000000  \n",
       "75%      7.000000    2.000000  \n",
       "max      7.000000    4.000000  "
      ]
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     "text": [
      "Checking for missing values in each column:\n",
      "age         0\n",
      "sex         0\n",
      "cp          0\n",
      "trestbps    0\n",
      "chol        0\n",
      "fbs         0\n",
      "restecg     0\n",
      "thalach     0\n",
      "exang       0\n",
      "oldpeak     0\n",
      "slope       0\n",
      "ca          4\n",
      "thal        2\n",
      "num         0\n",
      "dtype: int64\n",
      "Missing values before imputation:\n",
      "age         0\n",
      "sex         0\n",
      "cp          0\n",
      "trestbps    0\n",
      "chol        0\n",
      "fbs         0\n",
      "restecg     0\n",
      "thalach     0\n",
      "exang       0\n",
      "oldpeak     0\n",
      "slope       0\n",
      "ca          4\n",
      "thal        2\n",
      "num         0\n",
      "dtype: int64\n",
      "Missing values after imputation:\n",
      "age         0\n",
      "sex         0\n",
      "cp          0\n",
      "trestbps    0\n",
      "chol        0\n",
      "fbs         0\n",
      "restecg     0\n",
      "thalach     0\n",
      "exang       0\n",
      "oldpeak     0\n",
      "slope       0\n",
      "ca          0\n",
      "thal        0\n",
      "num         0\n",
      "dtype: int64\n",
      "Cleaned dataset:\n"
     ]
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>129</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>187</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>172</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  trestbps  chol  thalach  oldpeak   ca  num  sex_1   cp_2   cp_3  \\\n",
       "0   63       145   233      150      2.3  0.0    0   True  False  False   \n",
       "1   67       160   286      108      1.5  3.0    2   True  False  False   \n",
       "2   67       120   229      129      2.6  2.0    1   True  False  False   \n",
       "3   37       130   250      187      3.5  0.0    0   True  False   True   \n",
       "4   41       130   204      172      1.4  0.0    0  False   True  False   \n",
       "\n",
       "    cp_4  fbs_1  restecg_1  restecg_2  exang_1  slope_2  slope_3  thal_6.0  \\\n",
       "0  False   True      False       True    False    False     True      True   \n",
       "1   True  False      False       True     True     True    False     False   \n",
       "2   True  False      False       True     True     True    False     False   \n",
       "3  False  False      False      False    False    False     True     False   \n",
       "4  False  False      False       True    False    False    False     False   \n",
       "\n",
       "   thal_7.0  \n",
       "0     False  \n",
       "1     False  \n",
       "2      True  \n",
       "3     False  \n",
       "4     False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First rows with the binary target variable:\n"
     ]
    },
    {
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      "text/plain": [
       "   num  disease_binary\n",
       "0    0               0\n",
       "1    2               1\n",
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       "3    0               0\n",
       "4    0               0"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary statistics for age, cholesterol, and thalach (based on target):\n"
     ]
    },
    {
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       "      <th colspan=\"8\" halign=\"left\">thalach</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
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       "      <td>44.75</td>\n",
       "      <td>52.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>76.0</td>\n",
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       "      <td>267.25</td>\n",
       "      <td>564.0</td>\n",
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       "      <td>19.199080</td>\n",
       "      <td>96.0</td>\n",
       "      <td>148.75</td>\n",
       "      <td>161.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>202.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>139.0</td>\n",
       "      <td>56.625899</td>\n",
       "      <td>7.938416</td>\n",
       "      <td>35.0</td>\n",
       "      <td>52.00</td>\n",
       "      <td>58.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>251.474820</td>\n",
       "      <td>...</td>\n",
       "      <td>283.50</td>\n",
       "      <td>409.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>139.258993</td>\n",
       "      <td>22.593233</td>\n",
       "      <td>71.0</td>\n",
       "      <td>125.00</td>\n",
       "      <td>142.0</td>\n",
       "      <td>156.5</td>\n",
       "      <td>195.0</td>\n",
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       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  age                                                      \\\n",
       "                count       mean       std   min    25%   50%   75%   max   \n",
       "disease_binary                                                              \n",
       "0               164.0  52.585366  9.511957  29.0  44.75  52.0  59.0  76.0   \n",
       "1               139.0  56.625899  7.938416  35.0  52.00  58.0  62.0  77.0   \n",
       "\n",
       "                 chol              ...                thalach              \\\n",
       "                count        mean  ...     75%    max   count        mean   \n",
       "disease_binary                     ...                                      \n",
       "0               164.0  242.640244  ...  267.25  564.0   164.0  158.378049   \n",
       "1               139.0  251.474820  ...  283.50  409.0   139.0  139.258993   \n",
       "\n",
       "                                                              \n",
       "                      std   min     25%    50%    75%    max  \n",
       "disease_binary                                                \n",
       "0               19.199080  96.0  148.75  161.0  172.0  202.0  \n",
       "1               22.593233  71.0  125.00  142.0  156.5  195.0  \n",
       "\n",
       "[2 rows x 24 columns]"
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "from ucimlrepo import fetch_ucirepo\n",
    "\n",
    "# Fetch dataset\n",
    "heart_disease = fetch_ucirepo(id=45)\n",
    "\n",
    "# Create DataFrame\n",
    "data = pd.DataFrame(heart_disease.data.features)\n",
    "data['num'] = heart_disease.data.targets['num']\n",
    "\n",
    "# Display the first few rows\n",
    "print(\"First few rows of the dataset:\")\n",
    "display(data.head())\n",
    "\n",
    "# Summary statistics\n",
    "print(\"Summary statistics for the dataset:\")\n",
    "display(data.describe())\n",
    "\n",
    "# Check for missing or irregular values\n",
    "print(\"Checking for missing values in each column:\")\n",
    "print(data.isnull().sum())\n",
    "\n",
    "# Replace '?' with NaN\n",
    "data.replace(\"?\", pd.NA, inplace=True)\n",
    "\n",
    "# Convert all columns to numeric (force non-numeric to NaN)\n",
    "data = data.apply(pd.to_numeric, errors='coerce')\n",
    "\n",
    "# Check for missing values again\n",
    "print(\"Missing values before imputation:\")\n",
    "print(data.isnull().sum())\n",
    "\n",
    "# Impute missing values with column median\n",
    "data.fillna(data.median(), inplace=True)\n",
    "\n",
    "# Verify missing values are resolved\n",
    "print(\"Missing values after imputation:\")\n",
    "print(data.isnull().sum())\n",
    "\n",
    "# Convert binary and categorical features to 'category' data type\n",
    "categorical_columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'thal']\n",
    "for col in categorical_columns:\n",
    "    data[col] = data[col].astype('category')\n",
    "\n",
    "# Perform one-hot encoding for categorical features\n",
    "data = pd.get_dummies(data, drop_first=True)\n",
    "\n",
    "# Display the cleaned dataset\n",
    "print(\"Cleaned dataset:\")\n",
    "display(data.head())\n",
    "\n",
    "# Create binary target variable\n",
    "data['disease_binary'] = (data['num'] > 0).astype(int)\n",
    "\n",
    "# Verify the new column\n",
    "print(\"First rows with the binary target variable:\")\n",
    "display(data[['num', 'disease_binary']].head())\n",
    "\n",
    "# Summary statistics for key features based on the binary target\n",
    "print(\"Summary statistics for age, cholesterol, and thalach (based on target):\")\n",
    "display(data.groupby('disease_binary')[['age', 'chol', 'thalach']].describe())\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Set style\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Histogram: Age distribution by disease_binary\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(data=data, x='age', hue='disease_binary', kde=True, element=\"step\", stat=\"density\")\n",
    "plt.title('Age Distribution by Heart Disease Presence')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Density')\n",
    "plt.show()\n",
    "\n",
    "# Boxplot: Thalach distribution by disease_binary\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(x='disease_binary', y='thalach', data=data)\n",
    "plt.title('Thalach (Maximum Heart Rate) Distribution by Heart Disease Presence')\n",
    "plt.xlabel('Heart Disease (0 = No, 1 = Yes)')\n",
    "plt.ylabel('Maximum Heart Rate (thalach)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insights & Reflections\n",
    "\n",
    "### Implications of Missing Values:\n",
    "Handling missing data with imputation (using the median) ensures that models aren't biased due to incomplete rows.\n",
    "\n",
    "### Distributions Observed:\n",
    "- Patients with heart disease (`disease_binary = 1`) tend to have lower maximum heart rates (`thalach`).\n",
    "- Age distributions appear to shift slightly towards older populations for those with heart disease.\n",
    "\n",
    "### Impact on Predictive Modeling:\n",
    "- Cleaning and preprocessing ensure that all data is numeric and ready for modeling.\n",
    "- Binary target creation simplifies the binary classification scenario.\n",
    "- Patterns in `thalach` and age could be useful predictors for heart disease presence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.84, F1 Score: 0.85, ROC AUC: 0.90\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.79      0.82        29\n",
      "           1       0.82      0.88      0.85        32\n",
      "\n",
      "    accuracy                           0.84        61\n",
      "   macro avg       0.84      0.83      0.83        61\n",
      "weighted avg       0.84      0.84      0.84        61\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix, classification_report\n",
    "\n",
    "# Define features and target\n",
    "X = data.drop(columns=['num', 'disease_binary'])\n",
    "y = data['disease_binary']\n",
    "\n",
    "# Train/test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.20)\n",
    "\n",
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Train the logistic regression model\n",
    "model = LogisticRegression(max_iter=1000)\n",
    "model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predictions\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "y_pred_proba = model.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "# Evaluation metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "print(f\"Accuracy: {accuracy:.2f}, F1 Score: {f1:.2f}, ROC AUC: {roc_auc:.2f}\")\n",
    "\n",
    "# Classification report\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "# Confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Plotting the confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
    "            xticklabels=['No Disease', 'Disease'], \n",
    "            yticklabels=['No Disease', 'Disease'])\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "The model demonstrates strong performance across all metrics, indicating that it is effective in predicting heart disease.\n",
    "\n",
    "The confusion matrix visualization provides a clear understanding of the model's performance in terms of true positives, true negatives, false positives, and false negatives. \n",
    "\n",
    "While the accuracy is high, the F1 score and ROC AUC provide additional assurance that the model is not performing perfectly due to class imbalance but is genuinely effective in distinguishing between the two classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Multi-Class Results:\n",
      "Accuracy: 0.56\n",
      "Macro F1-Score: 0.23\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.97      0.86        33\n",
      "           1       0.08      0.09      0.08        11\n",
      "           2       0.00      0.00      0.00         7\n",
      "           3       0.25      0.14      0.18         7\n",
      "           4       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.56        61\n",
      "   macro avg       0.22      0.24      0.23        61\n",
      "weighted avg       0.46      0.56      0.50        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "\n",
    "# Define features (X) and target (y)\n",
    "X = data.drop(columns=['num', 'disease_binary'])\n",
    "y = data['num']  # Multi-class target\n",
    "\n",
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Initialize and train the Random Forest model\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = rf_model.predict(X_test)\n",
    "\n",
    "# Evaluate model performance\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average='macro')  # Macro-average F1 for multi-class\n",
    "\n",
    "# Display results\n",
    "print(\"Random Forest Multi-Class Results:\")\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "print(f\"Macro F1-Score: {f1:.2f}\")\n",
    "\n",
    "# Classification report\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpretation\n",
    "\n",
    "## Complexity\n",
    "Multi-class classification is inherently more challenging than binary classification due to the larger number of classes. That is why the accuracy and the f1-score are not that high.\n",
    "\n",
    "## Next Steps\n",
    "1. Fine-tune hyperparameters for better performance.\n",
    "2. Investigate class misclassifications to understand which classes are harder to predict."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n",
      "Best Parameters Found: {'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "Optimized Random Forest Results:\n",
      "Accuracy: 0.54\n",
      "Macro F1-Score: 0.20\n",
      "Cross-Validation Results:\n",
      "Mean Accuracy: 0.60\n",
      "Accuracy Standard Deviation: 0.03\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define parameter grid for Random Forest\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_depth': [None, 10, 20],\n",
    "    'min_samples_split': [2, 5],\n",
    "    'min_samples_leaf': [1, 2]\n",
    "}\n",
    "\n",
    "# Initialize GridSearchCV\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=RandomForestClassifier(random_state=42),\n",
    "    param_grid=param_grid,\n",
    "    cv=5,\n",
    "    scoring='accuracy',\n",
    "    verbose=1,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "# Fit GridSearchCV\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Best model\n",
    "best_rf = grid_search.best_estimator_\n",
    "print(\"Best Parameters Found:\", grid_search.best_params_)\n",
    "\n",
    "# Evaluate on test data\n",
    "y_pred = best_rf.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average='macro')\n",
    "\n",
    "print(\"Optimized Random Forest Results:\")\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "print(f\"Macro F1-Score: {f1:.2f}\")\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Cross-validation\n",
    "cv_scores = cross_val_score(best_rf, X, y, cv=5, scoring='accuracy')\n",
    "\n",
    "print(\"Cross-Validation Results:\")\n",
    "print(f\"Mean Accuracy: {cv_scores.mean():.2f}\")\n",
    "print(f\"Accuracy Standard Deviation: {cv_scores.std():.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Insights on Random Forest Multi-Class Classification Results\n",
    "\n",
    "## 1. Initial Performance\n",
    "- **Accuracy:** 56%\n",
    "- **Macro F1-Score:** 0.23\n",
    "\n",
    "The initial Random Forest model achieves moderate accuracy but a low Macro F1-Score. \n",
    "\n",
    "- The accuracy suggests the model is correct slightly more than half of the time.\n",
    "- The low Macro F1-Score indicates that the model struggles to predict all classes effectively, likely favoring the majority class while performing poorly on the minority classes.\n",
    "\n",
    "## 2. Hyperparameter Tuning\n",
    "After tuning the Random Forest hyperparameters (`max_depth`, `min_samples_leaf`, `min_samples_split`, `n_estimators`), the following results were observed:\n",
    "\n",
    "- **Best Parameters:**\n",
    "  - `max_depth=10`\n",
    "  - `min_samples_leaf=2`\n",
    "  - `min_samples_split=5`\n",
    "  - `n_estimators=200`\n",
    "\n",
    "- **Optimized Results:**\n",
    "  - **Accuracy:** 54% (a slight drop)\n",
    "  - **Macro F1-Score:** 0.20 (further decline)\n",
    "\n",
    "While the optimized model uses more specific hyperparameters and increases the number of estimators (improving stability), the performance slightly worsens. This may indicate overfitting on the training data or that the chosen hyperparameters did not generalize well to the test set.\n",
    "\n",
    "## 3. Cross-Validation Performance\n",
    "- **Mean Accuracy:** 60%\n",
    "- **Standard Deviation:** ±0.03\n",
    "\n",
    "The 5-fold cross-validation results suggest that the model performs more consistently across multiple splits of the dataset.\n",
    "\n",
    "- A mean accuracy of 60% indicates that, on average, the model performs better during cross-validation than on the hold-out test set.\n",
    "- The low standard deviation (±0.03) shows stability, meaning the model's performance does not ⬤"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature Importance\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "importances = best_rf.feature_importances_\n",
    "features = X.columns\n",
    "\n",
    "# Create a DataFrame for feature importances\n",
    "importance_df = pd.DataFrame({'Feature': features, 'Importance': importances})\n",
    "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Plot feature importances\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Importance', y='Feature', data=importance_df)\n",
    "plt.title(\"Feature Importance for Heart Disease Prediction\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most Important Features in Heart Disease Analysis\n",
    "\n",
    "## 1. Thalach (Maximum Heart Rate)\n",
    "- **Medical Context**: Thalach measures the highest heart rate achieved during exercise. A lower maximum heart rate is often associated with reduced cardiovascular efficiency, a common indicator of heart disease. Healthy hearts can maintain higher rates during exertion, while diseased hearts are less efficient.\n",
    "- **Statistical Influence**: Thalach has a strong relationship with the target variable (num), showing clear differences across severity levels during Exploratory Data Analysis (EDA).\n",
    "\n",
    "## 2. Oldpeak (ST Depression Induced by Exercise)\n",
    "- **Medical Context**: Oldpeak measures the depression in the ST segment of an electrocardiogram (ECG) induced by exercise. ST depression is a critical marker of myocardial ischemia (reduced blood flow to the heart). Higher oldpeak values often indicate the presence and severity of heart disease.\n",
    "- **Statistical Influence**: Oldpeak displayed a significant correlation with heart disease presence during EDA, with clear separations in boxplots and histograms for patients with and without heart disease.\n",
    "\n",
    "## 3. CA (Number of Major Vessels Colored by Fluoroscopy)\n",
    "- **Medical Context**: CA represents the number of major coronary vessels (0–3) that are obstructed, as observed through fluoroscopy. A higher CA value indicates more severe arterial blockage, which is directly associated with heart disease severity. It serves as a clear clinical indicator for diagnosing coronary artery disease.\n",
    "- **Statistical Influence**: CA was one of the most influential features in model-based importance methods (e.g., Random Forest). Its strong predictive power aligns with its role as a diagnostic tool in cardiology."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Domain-Based Explanation\n",
    "\n",
    "From the feature importance analysis, we have identified key features such as **thalach** (maximum heart rate), **oldpeak** (ST depression), and **ca** (number of major vessels colored by fluoroscopy) and explained their relevance below.\n",
    "\n",
    "## Explanation:\n",
    "\n",
    "### Thalach (Maximum Heart Rate):\n",
    "A lower maximum heart rate is often associated with heart disease because it indicates reduced cardiovascular efficiency, which is common in unhealthy hearts. This feature helps in assessing the heart's ability to respond to stress and physical activity.\n",
    "\n",
    "### Oldpeak (ST Depression Induced by Exercise):\n",
    "Elevated ST depression suggests ischemia (reduced blood flow), which is a critical indicator of heart disease. This feature is particularly important during exercise testing, as it reflects the heart's performance under stress.\n",
    "\n",
    "### CA (Number of Major Vessels):\n",
    "A higher number of blocked major vessels (detected via fluoroscopy) is a strong predictor of heart disease severity. This feature provides insight into the extent of coronary artery disease, which is crucial for determining treatment options.\n",
    "\n",
    "---\n",
    "\n",
    "# Communication to Non-Technical Stakeholders\n",
    "\n",
    "## Plain-Language Summary:\n",
    "\n",
    "Our model identifies key predictors of heart disease, including maximum heart rate (thalach), ST depression levels (oldpeak), and the number of blocked major vessels (ca). Patients with a low maximum heart rate or significant ST depression are at higher risk for heart disease. This model is reliable in identifying patterns, though it should complement other diagnostic tools. \n",
    "\n",
    "### Limitations:\n",
    "- The model relies on specific features and dataset biases, which might not generalize across all populations.\n",
    "\n",
    "---\n",
    "\n",
    "# Ethical and Practical Considerations\n",
    "\n",
    "## Biases in the Dataset:\n",
    "If the dataset doesn’t include diverse populations, the model may underperform for underrepresented groups, leading to inequitable healthcare outcomes.\n",
    "\n",
    "## Missing Data:\n",
    "Imputation assumptions (using median) may not perfectly reflect real-world patterns, potentially skewing results.\n",
    "\n",
    "## Model Assumptions:\n",
    "Simplifying heart disease prediction to key features may overlook important, subtle clinical indicators that could be critical for accurate diagnosis.\n",
    "\n",
    "## Practical Use:\n",
    "The model should assist doctors in identifying high-risk patients early, but it’s not a replacement for clinical judgment or further testing. It is essential that healthcare professionals use this model as a tool in conjunction with their expertise and additional diagnostic methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of components explaining 90% variance: 13\n",
      "Logistic Regression with PCA - Accuracy: 0.59\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Apply PCA\n",
    "pca = PCA()\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "# Calculate explained variance\n",
    "explained_variance = pca.explained_variance_ratio_\n",
    "\n",
    "# Plot cumulative explained variance\n",
    "import numpy as np\n",
    "\n",
    "cumulative_variance = np.cumsum(explained_variance)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(1, len(cumulative_variance)+1), cumulative_variance, marker='o', linestyle='--')\n",
    "plt.xlabel('Number of Principal Components')\n",
    "plt.ylabel('Cumulative Explained Variance')\n",
    "plt.title('Explained Variance by Principal Components')\n",
    "plt.show()\n",
    "\n",
    "# Determine number of components to explain 90% variance\n",
    "n_components = np.argmax(cumulative_variance >= 0.90) + 1\n",
    "print(f\"Number of components explaining 90% variance: {n_components}\")\n",
    "\n",
    "# Reduce data using selected components\n",
    "pca = PCA(n_components=n_components)\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "# Train-test split\n",
    "X_train_pca, X_test_pca, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "# Train a logistic regression model\n",
    "log_reg_pca = LogisticRegression(max_iter=1000)\n",
    "log_reg_pca.fit(X_train_pca, y_train)\n",
    "\n",
    "# Evaluate performance\n",
    "y_pred_pca = log_reg_pca.predict(X_test_pca)\n",
    "accuracy = accuracy_score(y_test, y_pred_pca)\n",
    "print(f\"Logistic Regression with PCA - Accuracy: {accuracy:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Insights on PCA and Its Impact\n",
    "\n",
    "## 1. Dimensionality Reduction\n",
    "The graph shows that **13 principal components** are sufficient to explain **90% of the variance** in the dataset. This reduction significantly lowers the number of features from the original feature count, simplifying the model while retaining most of the data's variability.\n",
    "\n",
    "## 2. Impact on Accuracy\n",
    "The Logistic Regression model with PCA achieves an accuracy of **0.59**, which is comparable to or slightly better than earlier results with the full feature set. This indicates that dimensionality reduction did not degrade performance, suggesting that many original features contained redundant or less informative signals.\n",
    "\n",
    "## 3. Computational Efficiency\n",
    "- **Improved Speed**: Reducing the feature space from the original size to **13 components** improves computational efficiency, especially for algorithms sensitive to feature dimensionality. \n",
    "- **Training and Prediction Times**: These are reduced, making PCA beneficial for real-world scenarios with large datasets or iterative model training processes.\n",
    "\n",
    "## 4. Trade-Off\n",
    "While PCA improves speed and simplifies the model, the interpretability of features is reduced because the principal components are linear combinations of the original features. However, for tasks prioritizing performance and efficiency, PCA proves valuable without compromising accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "# Use selected features for clustering\n",
    "clustering_features = ['age', 'chol', 'thalach', 'oldpeak']\n",
    "X_clustering = data[clustering_features]\n",
    "\n",
    "# Standardize features\n",
    "X_scaled = scaler.fit_transform(X_clustering)\n",
    "\n",
    "# Elbow method\n",
    "inertia = []\n",
    "silhouette_scores = []\n",
    "\n",
    "for k in range(2, 11):\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(X_scaled)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "    silhouette_scores.append(silhouette_score(X_scaled, kmeans.labels_))\n",
    "\n",
    "# Plot Elbow Method\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(range(2, 11), inertia, marker='o')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('Elbow Method for Optimal Clusters')\n",
    "plt.show()\n",
    "\n",
    "# Plot Silhouette Scores\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(range(2, 11), silhouette_scores, marker='o', color='r')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Silhouette Score')\n",
    "plt.title('Silhouette Scores for Optimal Clusters')\n",
    "plt.show()\n",
    "\n",
    "# Apply K-Means with optimal clusters\n",
    "kmeans = KMeans(n_clusters=3, random_state=42)\n",
    "clusters = kmeans.fit_predict(X_scaled)\n",
    "\n",
    "# Add cluster labels to data\n",
    "data['Cluster'] = clusters\n",
    "\n",
    "# Visualize clusters using PCA\n",
    "pca_2d = PCA(n_components=2)\n",
    "X_pca_2d = pca_2d.fit_transform(X_scaled)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x=X_pca_2d[:, 0], y=X_pca_2d[:, 1], hue=data['Cluster'], palette='viridis')\n",
    "plt.title('K-Means Clustering Results (PCA Reduced)')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Insights on Clustering Analysis\n",
    "\n",
    "## 1. Elbow Method for Optimal Clusters\n",
    "### What it shows:\n",
    "The elbow plot displays the \"inertia\" (within-cluster sum of squared distances) for different numbers of clusters (k = 2 to 10).\n",
    "\n",
    "### Key Observations:\n",
    "- The curve steadily decreases as the number of clusters increases.\n",
    "- There is no clear elbow point, but the rate of decrease slows after k = 3 or k = 4.\n",
    "\n",
    "### Conclusion:\n",
    "Based on the elbow method, a reasonable choice for the number of clusters is **3**, where the reduction in inertia starts to diminish.\n",
    "\n",
    "---\n",
    "\n",
    "## 2. Silhouette Scores for Optimal Clusters\n",
    "### What it shows:\n",
    "The silhouette score measures the quality of clustering, with higher scores indicating better-defined clusters.\n",
    "\n",
    "### Key Observations:\n",
    "- The silhouette score is highest at k = 2 (around 0.26).\n",
    "- The score drops significantly as the number of clusters increases, with k = 3 having a reasonable silhouette score.\n",
    "\n",
    "### Conclusion:\n",
    "The silhouette scores confirm that k = 2 or k = 3 produces the best clustering quality, but k = 3 is likely chosen for a balance between simplicity and quality.\n",
    "\n",
    "---\n",
    "\n",
    "## 3. K-Means Clustering Results with PCA\n",
    "### What it shows:\n",
    "The scatter plot visualizes the clustering results (with k = 3) after reducing the data to two principal components using PCA.\n",
    "\n",
    "### Key Observations:\n",
    "- The points are colored based on their cluster labels (0, 1, and 2).\n",
    "- The clusters are somewhat distinct, but there is overlap between the teal and purple clusters (Cluster 1 and Cluster 0).\n",
    "- The yellow cluster (Cluster 2) is better separated on the right-hand side.\n",
    "\n",
    "### Conclusion:\n",
    "K-Means with k = 3 provides a decent clustering result after PCA reduction. While the clusters are not perfectly separated, they align reasonably well in two-dimensional space.\n",
    "\n",
    "---\n",
    "\n",
    "## Overall Conclusions:\n",
    "### Optimal Clusters:\n",
    "Both the elbow method and silhouette scores suggest that **3 clusters** is a reasonable choice for this dataset.\n",
    "\n",
    "### Cluster Visualization:\n",
    "The K-Means algorithm has identified three clusters, with one (yellow cluster) being better separated than the others.\n",
    "\n",
    "### Data Characteristics:\n",
    "The overlap between clusters suggests that the data may not have perfectly distinct groupings, and K-Means might be limited by its assumptions of spherical clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index (ARI): 0.13\n",
      "Normalized Mutual Information (NMI): 0.12\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import adjusted_rand_score, normalized_mutual_info_score\n",
    "\n",
    "# Compare clustering labels to ground truth (num)\n",
    "ari = adjusted_rand_score(data['num'], data['Cluster'])\n",
    "nmi = normalized_mutual_info_score(data['num'], data['Cluster'])\n",
    "\n",
    "print(f\"Adjusted Rand Index (ARI): {ari:.2f}\")\n",
    "print(f\"Normalized Mutual Information (NMI): {nmi:.2f}\")\n",
    "\n",
    "# Visualize clusters vs actual targets in PCA-reduced space\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Clustering results\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.scatterplot(x=X_pca_2d[:, 0], y=X_pca_2d[:, 1], hue=data['Cluster'], palette='viridis')\n",
    "plt.title(\"Clustering Results (K-Means)\")\n",
    "\n",
    "# Actual target labels\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.scatterplot(x=X_pca_2d[:, 0], y=X_pca_2d[:, 1], hue=data['num'], palette='tab10')\n",
    "plt.title(\"Actual Target Labels\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering Analysis Report\n",
    "\n",
    "## 1. Metrics\n",
    "- **Adjusted Rand Index (ARI):** 0.13  \n",
    "  The ARI measures the similarity between two clustering results, correcting for chance. A score of 0.13 is quite low, suggesting that the clustering (K-Means) does not align well with the actual target labels.\n",
    "\n",
    "- **Normalized Mutual Information (NMI):** 0.12  \n",
    "  NMI measures the mutual dependence between two sets of clusters. A score of 0.12 is also low, indicating poor agreement between the clustering output and the ground truth labels.\n",
    "\n",
    "### Interpretation\n",
    "Both ARI and NMI are close to zero, which means the K-Means algorithm did not recover the structure of the true target labels very well.\n",
    "\n",
    "## 2. Clustering Results (Left Plot)\n",
    "The K-Means clustering algorithm assigned points to 3 clusters. The clustering structure is somewhat discernible, with:\n",
    "- A clear \"yellow\" cluster on the right.\n",
    "- A \"purple\" cluster on the bottom left.\n",
    "\n",
    "However, there is significant overlap between clusters.\n",
    "\n",
    "## 3. Actual Target Labels (Right Plot)\n",
    "The ground truth contains 5 classes. The distribution of points in the actual target labels does not appear to align well with the clustering results. The points seem more evenly spread out, with no distinct separable clusters.\n",
    "\n",
    "## 4. Overall Comparison\n",
    "The mismatch between K-Means clusters (3 groups) and the actual labels (5 groups) likely contributed to the low ARI and NMI scores. K-Means assumes spherical clusters and does not account for class imbalance or complex data structures. The actual target labels appear more complex and less well-separated than what K-Means could handle.\n",
    "\n",
    "## 5. Conclusions\n",
    "K-Means struggled to find meaningful clusters in this dataset. The clustering does not align well with the actual target labels, as indicated by low ARI and NMI scores."
   ]
  }
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