Predicting_Breast_Cancer_Logistic_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a6b0d68d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import neccesary libraries\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "05ac99fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading breast-cancer-wisconsin-data.zip to C:\\Users\\HP\\Desktop\\Projects\\Breast_Cancer_Prediction\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0.00/48.6k [00:00<?, ?B/s]\n",
      "100%|##########| 48.6k/48.6k [00:00<00:00, 216kB/s]\n",
      "100%|##########| 48.6k/48.6k [00:00<00:00, 215kB/s]\n"
     ]
    }
   ],
   "source": [
    "#Download cancer dataset directly from kaggle\n",
    "# set the API keys and username\n",
    "\n",
    "os.environ['KAGGLE_USERNAME'] = \"usefulness121\"\n",
    "os.environ['KAGGLE_KEY'] = \"4f61b7e9ebe449d2e963fefbfa255b0f\"\n",
    "\n",
    "!kaggle datasets download -d uciml/breast-cancer-wisconsin-data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4bb86f43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  breast-cancer-wisconsin-data.zip\n",
      "  inflating: data.csv                \n"
     ]
    }
   ],
   "source": [
    "# Unzip dataset\n",
    "!unzip breast-cancer-wisconsin-data.zip\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "21d92da8",
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <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",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>842302</td>\n",
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       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
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       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
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       "    <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>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
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       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
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       "    <tr>\n",
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       "      <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>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</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>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
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       "    <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>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
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       "<p>5 rows × 33 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",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in the dataset as a dataframe\n",
    "df = pd.read_csv('data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "53a825c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 33)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the dimension of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f01a9401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                           0\n",
       "diagnosis                    0\n",
       "radius_mean                  0\n",
       "texture_mean                 0\n",
       "perimeter_mean               0\n",
       "area_mean                    0\n",
       "smoothness_mean              0\n",
       "compactness_mean             0\n",
       "concavity_mean               0\n",
       "concave points_mean          0\n",
       "symmetry_mean                0\n",
       "fractal_dimension_mean       0\n",
       "radius_se                    0\n",
       "texture_se                   0\n",
       "perimeter_se                 0\n",
       "area_se                      0\n",
       "smoothness_se                0\n",
       "compactness_se               0\n",
       "concavity_se                 0\n",
       "concave points_se            0\n",
       "symmetry_se                  0\n",
       "fractal_dimension_se         0\n",
       "radius_worst                 0\n",
       "texture_worst                0\n",
       "perimeter_worst              0\n",
       "area_worst                   0\n",
       "smoothness_worst             0\n",
       "compactness_worst            0\n",
       "concavity_worst              0\n",
       "concave points_worst         0\n",
       "symmetry_worst               0\n",
       "fractal_dimension_worst      0\n",
       "Unnamed: 32                569\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "926591cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the column with the null values\n",
    "df.dropna(axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bb64a348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the dimension again\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7892eeab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diagnosis\n",
       "B    357\n",
       "M    212\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the number of values in the diagnosis column\n",
    "df.diagnosis.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "79cfcb6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Encoding the data column to convert the text label to numeric label\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder = LabelEncoder()\n",
    "df.iloc[:,1] =labelencoder.fit_transform(df.iloc[:,1].values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "56ff0798",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
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       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
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       "      <td>842302</td>\n",
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       "      <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>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
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       "      <td>842517</td>\n",
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       "      <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>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
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       "      <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>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</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>1</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>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>1</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>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</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>5</th>\n",
       "      <td>843786</td>\n",
       "      <td>1</td>\n",
       "      <td>12.45</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.15780</td>\n",
       "      <td>0.08089</td>\n",
       "      <td>...</td>\n",
       "      <td>15.47</td>\n",
       "      <td>23.75</td>\n",
       "      <td>103.40</td>\n",
       "      <td>741.6</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.5249</td>\n",
       "      <td>0.5355</td>\n",
       "      <td>0.1741</td>\n",
       "      <td>0.3985</td>\n",
       "      <td>0.12440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>844359</td>\n",
       "      <td>1</td>\n",
       "      <td>18.25</td>\n",
       "      <td>19.98</td>\n",
       "      <td>119.60</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>0.09463</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>0.11270</td>\n",
       "      <td>0.07400</td>\n",
       "      <td>...</td>\n",
       "      <td>22.88</td>\n",
       "      <td>27.66</td>\n",
       "      <td>153.20</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>0.1442</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.3784</td>\n",
       "      <td>0.1932</td>\n",
       "      <td>0.3063</td>\n",
       "      <td>0.08368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>84458202</td>\n",
       "      <td>1</td>\n",
       "      <td>13.71</td>\n",
       "      <td>20.83</td>\n",
       "      <td>90.20</td>\n",
       "      <td>577.9</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.16450</td>\n",
       "      <td>0.09366</td>\n",
       "      <td>0.05985</td>\n",
       "      <td>...</td>\n",
       "      <td>17.06</td>\n",
       "      <td>28.14</td>\n",
       "      <td>110.60</td>\n",
       "      <td>897.0</td>\n",
       "      <td>0.1654</td>\n",
       "      <td>0.3682</td>\n",
       "      <td>0.2678</td>\n",
       "      <td>0.1556</td>\n",
       "      <td>0.3196</td>\n",
       "      <td>0.11510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>844981</td>\n",
       "      <td>1</td>\n",
       "      <td>13.00</td>\n",
       "      <td>21.82</td>\n",
       "      <td>87.50</td>\n",
       "      <td>519.8</td>\n",
       "      <td>0.12730</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.18590</td>\n",
       "      <td>0.09353</td>\n",
       "      <td>...</td>\n",
       "      <td>15.49</td>\n",
       "      <td>30.73</td>\n",
       "      <td>106.20</td>\n",
       "      <td>739.3</td>\n",
       "      <td>0.1703</td>\n",
       "      <td>0.5401</td>\n",
       "      <td>0.5390</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.4378</td>\n",
       "      <td>0.10720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>84501001</td>\n",
       "      <td>1</td>\n",
       "      <td>12.46</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.23960</td>\n",
       "      <td>0.22730</td>\n",
       "      <td>0.08543</td>\n",
       "      <td>...</td>\n",
       "      <td>15.09</td>\n",
       "      <td>40.68</td>\n",
       "      <td>97.65</td>\n",
       "      <td>711.4</td>\n",
       "      <td>0.1853</td>\n",
       "      <td>1.0580</td>\n",
       "      <td>1.1050</td>\n",
       "      <td>0.2210</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>0.20750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         1        17.99         10.38          122.80     1001.0   \n",
       "1    842517         1        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         1        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         1        11.42         20.38           77.58      386.1   \n",
       "4  84358402         1        20.29         14.34          135.10     1297.0   \n",
       "5    843786         1        12.45         15.70           82.57      477.1   \n",
       "6    844359         1        18.25         19.98          119.60     1040.0   \n",
       "7  84458202         1        13.71         20.83           90.20      577.9   \n",
       "8    844981         1        13.00         21.82           87.50      519.8   \n",
       "9  84501001         1        12.46         24.04           83.97      475.9   \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",
       "5          0.12780           0.17000         0.15780              0.08089   \n",
       "6          0.09463           0.10900         0.11270              0.07400   \n",
       "7          0.11890           0.16450         0.09366              0.05985   \n",
       "8          0.12730           0.19320         0.18590              0.09353   \n",
       "9          0.11860           0.23960         0.22730              0.08543   \n",
       "\n",
       "   ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
       "0  ...         25.38          17.33           184.60      2019.0   \n",
       "1  ...         24.99          23.41           158.80      1956.0   \n",
       "2  ...         23.57          25.53           152.50      1709.0   \n",
       "3  ...         14.91          26.50            98.87       567.7   \n",
       "4  ...         22.54          16.67           152.20      1575.0   \n",
       "5  ...         15.47          23.75           103.40       741.6   \n",
       "6  ...         22.88          27.66           153.20      1606.0   \n",
       "7  ...         17.06          28.14           110.60       897.0   \n",
       "8  ...         15.49          30.73           106.20       739.3   \n",
       "9  ...         15.09          40.68            97.65       711.4   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "5            0.1791             0.5249           0.5355                0.1741   \n",
       "6            0.1442             0.2576           0.3784                0.1932   \n",
       "7            0.1654             0.3682           0.2678                0.1556   \n",
       "8            0.1703             0.5401           0.5390                0.2060   \n",
       "9            0.1853             1.0580           1.1050                0.2210   \n",
       "\n",
       "   symmetry_worst  fractal_dimension_worst  \n",
       "0          0.4601                  0.11890  \n",
       "1          0.2750                  0.08902  \n",
       "2          0.3613                  0.08758  \n",
       "3          0.6638                  0.17300  \n",
       "4          0.2364                  0.07678  \n",
       "5          0.3985                  0.12440  \n",
       "6          0.3063                  0.08368  \n",
       "7          0.3196                  0.11510  \n",
       "8          0.4378                  0.10720  \n",
       "9          0.4366                  0.20750  \n",
       "\n",
       "[10 rows x 32 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "807e8d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the dataset into independent and dependent datasets\n",
    "\n",
    "x = df.iloc[:,2:].values\n",
    "y = df.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a3ac941f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting datasets into training(75%) and testing (25%)\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "256012a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(426, 30)\n",
      "(143, 30)\n",
      "(426,)\n",
      "(143,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "92218d62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard Scaler Nomarlization \n",
    "\n",
    "sc=StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.fit_transform(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8dd36a79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert y_train to interger to work well with x_train\n",
    "y_train = y_train.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "de9c7d21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build a logistic classification classifier\n",
    "\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(x_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "906f515c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Perform a classification prediction based on the test data\n",
    "predictions = classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6f467063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[95  2]\n",
      " [ 2 44]]\n"
     ]
    }
   ],
   "source": [
    "# convert y_test from an object to an int data types \n",
    "y_test = y_test.astype(int)\n",
    "\n",
    "# calculate the confusion matrix\n",
    "cm = confusion_matrix(y_test,predictions)\n",
    "print(cm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f9866a18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise the result\n",
    "\n",
    "sns.heatmap(cm, annot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2ce99a0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.972027972027972\n"
     ]
    }
   ],
   "source": [
    "#Calculate accuracy score for model\n",
    "print(accuracy_score(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "06fb6bc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 1 0 1 1 1 1 1\n",
      " 1 0 1 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0\n",
      " 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1\n",
      " 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0 1 0 0 1 1 1 0 0 0 0 0 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "# compare result for both cases manually\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "57a2ff3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 1 0 1 0 1 0 1 0 1 1 1\n",
      " 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0\n",
      " 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1\n",
      " 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0 1 0 0 1 1 1 0 0 1 0 0 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8b2b716b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9565217391304348\n"
     ]
    }
   ],
   "source": [
    "# calculate model precision\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import precision_score\n",
    "print(precision_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "121efaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.32167832 0.95652174 1.        ]\n",
      "[1.         0.95652174 0.        ]\n",
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "# Compute precision and recall\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, predictions)\n",
    "\n",
    "print(precision)\n",
    "print(recall)\n",
    "print(thresholds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "de4f90fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\users\\hp\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot precision-recall curve \n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "sns.lineplot(x=recall, y=precision, marker='o')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8294efc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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