CAD_Phase2.ipynb

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   "metadata": {},
   "source": [
    "# Import the necessary library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa04d54f-70bb-4ba3-8952-5b6cd43cea2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "714d506b-8c75-44c7-8718-ce9a501cf235",
   "metadata": {},
   "source": [
    "# Import the data to the Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9db55f8-5653-4739-9238-11f4e5a56bf0",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
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       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('CAD_Phase2.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ebee32f3-14fe-4029-8aa6-a963a61b2016",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
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       "  </thead>\n",
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       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
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       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
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       "      <td>66</td>\n",
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       "      <td>28.1</td>\n",
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       "      <td>21</td>\n",
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       "    <tr>\n",
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       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  \n",
       "0                     0.627   50  \n",
       "1                     0.351   31  \n",
       "2                     0.672   32  \n",
       "3                     0.167   21  \n",
       "4                     2.288   33  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Input=data.drop('Outcome',axis='columns')\n",
    "Input.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f34fb14d-9e50-4265-af2f-066572e11b23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Output=data.Outcome\n",
    "Output.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "061590b5-4f68-460a-939e-d833c024dd2d",
   "metadata": {},
   "source": [
    "# Creating Classification Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e4aa5a50-078a-4ff1-9728-45a329a1884e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf=SVC()\n",
    "clf.fit(Input,Output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e15ab95-c7be-48e1-b408-085cca5ab71f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7578605604921396"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_scores=cross_val_score(SVC(),Input,Output,cv=10)\n",
    "clf_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a8aec747-047d-4ce9-89ec-4081fd7359a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict([[6,148,72,35,0,33.6,0.627,50]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be913fa-8f09-4011-857d-eaef9ae2e266",
   "metadata": {},
   "source": [
    "# Creating the emsemble Learing Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "684d62db-e435-4e8d-8a9b-b63d111ae52b",
   "metadata": {},
   "outputs": [
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      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(base_estimator=SVC(), n_estimators=20)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(base_estimator=SVC(), n_estimators=20)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "BaggingClassifier(base_estimator=SVC(), n_estimators=20)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ens= BaggingClassifier(base_estimator=clf,n_estimators=20)\n",
    "ens.fit(Input,Output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1fd67092-753f-4f74-9000-146164421a23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7578605604921396"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ens_scores=cross_val_score(SVC(),Input,Output,cv=10)\n",
    "ens_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d9e22665-354d-4146-9d9a-5d2282912fa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ens.predict([[6,148,72,35,0,33.6,0.627,50]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dcdd840-fc2f-4120-baba-984f30f5656c",
   "metadata": {},
   "source": [
    "# Comparing the Classification and Bagging Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ed89af4b-8b50-4743-a4a8-bb6db634fe6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['SupportVector Model', 'BaggingClassifier'], [clf_scores.mean(), ens_scores.mean()],width=0.5)\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Accuracy Comparison Between the two Model\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c27cc41d-dcdc-4ec4-b70d-a32ab64cff8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.91      0.84       500\n",
      "           1       0.76      0.50      0.60       268\n",
      "\n",
      "    accuracy                           0.77       768\n",
      "   macro avg       0.76      0.71      0.72       768\n",
      "weighted avg       0.77      0.77      0.75       768\n",
      "\n",
      "Support Vector Classifier Score: 0.7578605604921396\n"
     ]
    }
   ],
   "source": [
    "clf_report = classification_report(Output, clf.predict(Input))\n",
    "print(clf_report)\n",
    "print(\"Support Vector Classifier Score:\",clf_scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f013c743-7675-4699-84d6-2c9d448c3267",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.92      0.84       500\n",
      "           1       0.76      0.50      0.60       268\n",
      "\n",
      "    accuracy                           0.77       768\n",
      "   macro avg       0.77      0.71      0.72       768\n",
      "weighted avg       0.77      0.77      0.76       768\n",
      "\n",
      "Bagging Classifier Score: 0.7578605604921396\n"
     ]
    }
   ],
   "source": [
    "ens_report = classification_report(Output, ens.predict(Input))\n",
    "print(ens_report)\n",
    "print(\"Bagging Classifier Score:\",ens_scores.mean())"
   ]
  }
 ],
 "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",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}