Breast Cancer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ea1c84dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from lazypredict.Supervised import LazyClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20c8b25f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the dataset\n",
    "data = load_breast_cancer()\n",
    "X = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "y = data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9539bdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.08</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.06</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.06</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.10</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.10</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.70</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.06</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80    1001.00             0.12   \n",
       "1        20.57         17.77          132.90    1326.00             0.08   \n",
       "2        19.69         21.25          130.00    1203.00             0.11   \n",
       "3        11.42         20.38           77.58     386.10             0.14   \n",
       "4        20.29         14.34          135.10    1297.00             0.10   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0              0.28            0.30                 0.15           0.24   \n",
       "1              0.08            0.09                 0.07           0.18   \n",
       "2              0.16            0.20                 0.13           0.21   \n",
       "3              0.28            0.24                 0.11           0.26   \n",
       "4              0.13            0.20                 0.10           0.18   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                    0.08  ...         25.38          17.33           184.60   \n",
       "1                    0.06  ...         24.99          23.41           158.80   \n",
       "2                    0.06  ...         23.57          25.53           152.50   \n",
       "3                    0.10  ...         14.91          26.50            98.87   \n",
       "4                    0.06  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0     2019.00              0.16               0.67             0.71   \n",
       "1     1956.00              0.12               0.19             0.24   \n",
       "2     1709.00              0.14               0.42             0.45   \n",
       "3      567.70              0.21               0.87             0.69   \n",
       "4     1575.00              0.14               0.20             0.40   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                  0.27            0.46                     0.12  \n",
       "1                  0.19            0.28                     0.09  \n",
       "2                  0.24            0.36                     0.09  \n",
       "3                  0.26            0.66                     0.17  \n",
       "4                  0.16            0.24                     0.08  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(X)\n",
    "df.columns = data.feature_names\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5c369aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "37d4b211",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the LazyClassifier object\n",
    "clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b2ad4dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 16.65it/s]\n"
     ]
    }
   ],
   "source": [
    "# Fitting the model on the training data\n",
    "models, predictions = clf.fit(X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "16cb698c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
      "Model                                                                           \n",
      "LogisticRegression                 0.98               0.98     0.98      0.98   \n",
      "Perceptron                         0.98               0.98     0.98      0.98   \n",
      "XGBClassifier                      0.98               0.98     0.98      0.98   \n",
      "LinearSVC                          0.98               0.98     0.98      0.98   \n",
      "SVC                                0.98               0.97     0.97      0.98   \n",
      "PassiveAggressiveClassifier        0.98               0.97     0.97      0.98   \n",
      "AdaBoostClassifier                 0.98               0.97     0.97      0.98   \n",
      "ExtraTreesClassifier               0.98               0.97     0.97      0.98   \n",
      "CalibratedClassifierCV             0.98               0.97     0.97      0.98   \n",
      "SGDClassifier                      0.96               0.97     0.97      0.97   \n",
      "RandomForestClassifier             0.97               0.96     0.96      0.97   \n",
      "QuadraticDiscriminantAnalysis      0.96               0.96     0.96      0.96   \n",
      "BernoulliNB                        0.96               0.96     0.96      0.96   \n",
      "LGBMClassifier                     0.96               0.96     0.96      0.96   \n",
      "KNeighborsClassifier               0.96               0.95     0.95      0.96   \n",
      "BaggingClassifier                  0.95               0.95     0.95      0.95   \n",
      "DecisionTreeClassifier             0.94               0.94     0.94      0.94   \n",
      "LinearDiscriminantAnalysis         0.95               0.94     0.94      0.95   \n",
      "RidgeClassifier                    0.95               0.94     0.94      0.95   \n",
      "RidgeClassifierCV                  0.95               0.94     0.94      0.95   \n",
      "LabelSpreading                     0.94               0.94     0.94      0.94   \n",
      "LabelPropagation                   0.94               0.94     0.94      0.94   \n",
      "NuSVC                              0.94               0.93     0.93      0.94   \n",
      "GaussianNB                         0.94               0.93     0.93      0.94   \n",
      "NearestCentroid                    0.94               0.93     0.93      0.94   \n",
      "ExtraTreeClassifier                0.89               0.90     0.90      0.90   \n",
      "DummyClassifier                    0.63               0.50     0.50      0.49   \n",
      "\n",
      "                               Time Taken  \n",
      "Model                                      \n",
      "LogisticRegression                   0.02  \n",
      "Perceptron                           0.02  \n",
      "XGBClassifier                        0.12  \n",
      "LinearSVC                            0.02  \n",
      "SVC                                  0.04  \n",
      "PassiveAggressiveClassifier          0.02  \n",
      "AdaBoostClassifier                   0.22  \n",
      "ExtraTreesClassifier                 0.19  \n",
      "CalibratedClassifierCV               0.06  \n",
      "SGDClassifier                        0.02  \n",
      "RandomForestClassifier               0.30  \n",
      "QuadraticDiscriminantAnalysis        0.02  \n",
      "BernoulliNB                          0.03  \n",
      "LGBMClassifier                       0.24  \n",
      "KNeighborsClassifier                 0.04  \n",
      "BaggingClassifier                    0.07  \n",
      "DecisionTreeClassifier               0.03  \n",
      "LinearDiscriminantAnalysis           0.04  \n",
      "RidgeClassifier                      0.03  \n",
      "RidgeClassifierCV                    0.02  \n",
      "LabelSpreading                       0.03  \n",
      "LabelPropagation                     0.02  \n",
      "NuSVC                                0.04  \n",
      "GaussianNB                           0.02  \n",
      "NearestCentroid                      0.02  \n",
      "ExtraTreeClassifier                  0.02  \n",
      "DummyClassifier                      0.03  \n"
     ]
    }
   ],
   "source": [
    "# Printing the performance metrics of the models\n",
    "print(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "844bac4d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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