2. breast-cancer-m-or-b-Logistic Regression.ipynb

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   "source": [
    "# Breast Cancer Prediction using Logistic Regression\n",
    "\n",
    "## Introduction\n",
    "This project aims to predict whether a tumor is malignant (**M**) or benign (**B**) based on clinical and histological data. The dataset used for this analysis is the **Breast Cancer Dataset**, which contains various features extracted from cell nuclei in digital images.\n",
    "\n",
    "## Workflow Overview\n",
    "1. **Data Preprocessing**:\n",
    "   - Handled missing values (if any).\n",
    "   - Encoded categorical variables (e.g., diagnosis as 0 and 1).\n",
    "   - Scaled numerical features to standardize the data.\n",
    "2. **Model Selection**:\n",
    "   - Logistic Regression was chosen for its simplicity and effectiveness in binary classification tasks.\n",
    "3. **Evaluation**:\n",
    "   - Performance was evaluated using metrics such as **Accuracy**,**Precision**,**Recall**, **Confusion Matrix**, and other classification metrics.\n"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/breast-cancer-dataset/breast-cancer.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, confusion_matrix \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['M' 'B']\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>926424</td>\n",
       "      <td>M</td>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>926682</td>\n",
       "      <td>M</td>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>926954</td>\n",
       "      <td>M</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>927241</td>\n",
       "      <td>M</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>92751</td>\n",
       "      <td>B</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 32 columns</p>\n",
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      ],
      "text/plain": [
       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
       "..        ...       ...          ...           ...             ...        ...   \n",
       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
       "566    926954         M        16.60         28.08          108.30      858.1   \n",
       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
       "568     92751         B         7.76         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0            0.11840           0.27760         0.30010              0.14710   \n",
       "1            0.08474           0.07864         0.08690              0.07017   \n",
       "2            0.10960           0.15990         0.19740              0.12790   \n",
       "3            0.14250           0.28390         0.24140              0.10520   \n",
       "4            0.10030           0.13280         0.19800              0.10430   \n",
       "..               ...               ...             ...                  ...   \n",
       "564          0.11100           0.11590         0.24390              0.13890   \n",
       "565          0.09780           0.10340         0.14400              0.09791   \n",
       "566          0.08455           0.10230         0.09251              0.05302   \n",
       "567          0.11780           0.27700         0.35140              0.15200   \n",
       "568          0.05263           0.04362         0.00000              0.00000   \n",
       "\n",
       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
       "0    ...        25.380          17.33           184.60      2019.0   \n",
       "1    ...        24.990          23.41           158.80      1956.0   \n",
       "2    ...        23.570          25.53           152.50      1709.0   \n",
       "3    ...        14.910          26.50            98.87       567.7   \n",
       "4    ...        22.540          16.67           152.20      1575.0   \n",
       "..   ...           ...            ...              ...         ...   \n",
       "564  ...        25.450          26.40           166.10      2027.0   \n",
       "565  ...        23.690          38.25           155.00      1731.0   \n",
       "566  ...        18.980          34.12           126.70      1124.0   \n",
       "567  ...        25.740          39.42           184.60      1821.0   \n",
       "568  ...         9.456          30.37            59.16       268.6   \n",
       "\n",
       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0             0.16220            0.66560           0.7119   \n",
       "1             0.12380            0.18660           0.2416   \n",
       "2             0.14440            0.42450           0.4504   \n",
       "3             0.20980            0.86630           0.6869   \n",
       "4             0.13740            0.20500           0.4000   \n",
       "..                ...                ...              ...   \n",
       "564           0.14100            0.21130           0.4107   \n",
       "565           0.11660            0.19220           0.3215   \n",
       "566           0.11390            0.30940           0.3403   \n",
       "567           0.16500            0.86810           0.9387   \n",
       "568           0.08996            0.06444           0.0000   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                  0.2654          0.4601                  0.11890  \n",
       "1                  0.1860          0.2750                  0.08902  \n",
       "2                  0.2430          0.3613                  0.08758  \n",
       "3                  0.2575          0.6638                  0.17300  \n",
       "4                  0.1625          0.2364                  0.07678  \n",
       "..                    ...             ...                      ...  \n",
       "564                0.2216          0.2060                  0.07115  \n",
       "565                0.1628          0.2572                  0.06637  \n",
       "566                0.1418          0.2218                  0.07820  \n",
       "567                0.2650          0.4087                  0.12400  \n",
       "568                0.0000          0.2871                  0.07039  \n",
       "\n",
       "[569 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = '/kaggle/input/breast-cancer-dataset/breast-cancer.csv'  \n",
    "p = pd.read_csv(file_path)\n",
    "print(p['diagnosis'].unique())\n",
    "p"
   ]
  },
  {
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     "exception": false,
     "start_time": "2024-12-31T11:43:54.332474",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "p['diagnosis'] = p['diagnosis'].map({'M': 1, 'B': 0})\n",
    "features = p.drop('diagnosis', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7236eb3c",
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    "execution": {
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     "status": "completed"
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   "source": [
    "scaler = StandardScaler()\n",
    "scaled_features = scaler.fit_transform(features)\n",
    "\n",
    "scaled_df = pd.DataFrame(scaled_features, columns=features.columns)\n",
    "scaled_df['diagnosis'] = p['diagnosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3bdb322",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diagnosis                  1.000000\n",
      "concave points_worst       0.793566\n",
      "perimeter_worst            0.782914\n",
      "concave points_mean        0.776614\n",
      "radius_worst               0.776454\n",
      "perimeter_mean             0.742636\n",
      "area_worst                 0.733825\n",
      "radius_mean                0.730029\n",
      "area_mean                  0.708984\n",
      "concavity_mean             0.696360\n",
      "concavity_worst            0.659610\n",
      "compactness_mean           0.596534\n",
      "compactness_worst          0.590998\n",
      "radius_se                  0.567134\n",
      "perimeter_se               0.556141\n",
      "area_se                    0.548236\n",
      "texture_worst              0.456903\n",
      "smoothness_worst           0.421465\n",
      "symmetry_worst             0.416294\n",
      "texture_mean               0.415185\n",
      "concave points_se          0.408042\n",
      "smoothness_mean            0.358560\n",
      "symmetry_mean              0.330499\n",
      "fractal_dimension_worst    0.323872\n",
      "compactness_se             0.292999\n",
      "concavity_se               0.253730\n",
      "fractal_dimension_se       0.077972\n",
      "id                         0.039769\n",
      "symmetry_se               -0.006522\n",
      "texture_se                -0.008303\n",
      "fractal_dimension_mean    -0.012838\n",
      "smoothness_se             -0.067016\n",
      "Name: diagnosis, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "correlation_matrix = scaled_df.corr()\n",
    "print(correlation_matrix['diagnosis'].sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d160292",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "new_features=p[[\"concave points_worst\",\"perimeter_worst\",\"concave points_mean\",\"radius_worst\",\"perimeter_mean\",\"area_worst\",\"radius_mean\",\"area_mean\",\"concavity_mean\",\"concavity_worst\"]]\n",
    "new_target=p[\"diagnosis\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9be29f75",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-31T11:43:54.402669Z",
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     "status": "completed"
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   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(new_features.values,new_target,test_size=0.33 ,random_state=42)\n"
   ]
  },
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   "execution_count": 8,
   "id": "624cf195",
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   "outputs": [
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      ],
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       "LogisticRegression(max_iter=10000, random_state=42)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg=LogisticRegression(max_iter=10000, random_state=42 )\n",
    "log_reg.fit(xtrain,ytrain)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
   "id": "1b7ee92d",
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   "source": [
    "ypred=log_reg.predict(xtest)\n",
    "accuracy=accuracy_score(ytest,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "070710ac",
   "metadata": {
    "execution": {
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.973404255319149\n",
      "Precision: 0.9558823529411765\n",
      "Recall: 0.9701492537313433\n"
     ]
    }
   ],
   "source": [
    "#  Precision & Recall\n",
    "precision = precision_score(ytest, ypred)\n",
    "recall = recall_score(ytest, ypred)\n",
    "\n",
    "#  Accuracy\n",
    "accuracy = accuracy_score(ytest, ypred)\n",
    "\n",
    "print(f'Accuracy: {accuracy}')\n",
    "print(f'Precision: {precision}')\n",
    "print(f'Recall: {recall}')\n"
   ]
  },
  {
   "cell_type": "code",
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[118   3]\n",
      " [  2  65]]\n"
     ]
    }
   ],
   "source": [
    "conf_matrix = confusion_matrix(ytest, ypred)\n",
    "print('Confusion Matrix:')\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8703e5d1",
   "metadata": {
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   "source": [
    "## Metrics\n",
    "\n",
    "| Metric      | Value  |\n",
    "|-------------|--------|\n",
    "| **Accuracy** | 97.34% |\n",
    "| **Precision** | 95.55% |\n",
    "| **Recall**   | 97.01% |\n",
    "\n",
    "### Confusion Matrix\n",
    "\n",
    "\n",
    "- **True Positives (TP)**: 118\n",
    "- **True Negatives (TN)**: 2\n",
    "- **False Positives (FP)**: 3\n",
    "- **False Negatives (FN)**: 65\n",
    "\n",
    "\n",
    "\n",
    "### Analysis of Precision Importance\n",
    "\n",
    "Given the nature of the dataset, it is critical to minimize False Negatives (FN), where a malignant tumor is mistakenly classified as benign. Such an error could lead to improper treatment, potentially endangering a patient's life. \n",
    "\n",
    "Therefore, achieving a high `precision_score` is of utmost importance in this context.\n"
   ]
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