K_MEANS_ENHANCING USER AUTHENTICATION EXPERIENCE THROUGH BIG DATA ANALYSIS AND SINGLE SIGN-ON INTEGRATION.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ENHANCING USER AUTHENTICATION EXPERIENCE THROUGH BIG DATA ANALYSIS AND SINGLE SIGN-ON INTEGRATION\n",
    "## K-MEANS ALGORITHM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../rba-dataset.csv', nrows=2000, index_col='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records: 2000\n",
      "Total number of columns: 15\n"
     ]
    }
   ],
   "source": [
    "# Calculate total records and columns\n",
    "total_records = df.shape[0]\n",
    "total_columns = df.shape[1]\n",
    "print(f'Total number of records: {total_records}')\n",
    "print(f'Total number of columns: {total_columns}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Login Timestamp              User ID  Round-Trip Time [ms]  \\\n",
      "index                                                                       \n",
      "0      2020-02-03 12:43:30.772 -4324475583306591935                   NaN   \n",
      "1      2020-02-03 12:43:43.549 -4324475583306591935                   NaN   \n",
      "2      2020-02-03 12:43:55.873 -3284137479262433373                   NaN   \n",
      "3      2020-02-03 12:43:56.180 -4324475583306591935                   NaN   \n",
      "4      2020-02-03 12:43:59.396 -4618854071942621186                   NaN   \n",
      "\n",
      "          IP Address Country    Region       City  \n",
      "index                                              \n",
      "0        10.0.65.171      NO         -          -  \n",
      "1       194.87.207.6      AU         -          -  \n",
      "2      81.167.144.58      NO  Vestland  Urangsvag  \n",
      "3      170.39.78.152      US         -          -  \n",
      "4          10.0.0.47      US  Virginia    Ashburn  \n"
     ]
    }
   ],
   "source": [
    "subset = df.iloc[:5, :7]\n",
    "print(subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 81 entries, 15 to 1990\n",
      "Data columns (total 15 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Login Timestamp           81 non-null     object \n",
      " 1   User ID                   81 non-null     int64  \n",
      " 2   Round-Trip Time [ms]      81 non-null     float64\n",
      " 3   IP Address                81 non-null     object \n",
      " 4   Country                   81 non-null     object \n",
      " 5   Region                    81 non-null     object \n",
      " 6   City                      81 non-null     object \n",
      " 7   ASN                       81 non-null     int64  \n",
      " 8   User Agent String         81 non-null     object \n",
      " 9   Browser Name and Version  81 non-null     object \n",
      " 10  OS Name and Version       81 non-null     object \n",
      " 11  Device Type               81 non-null     object \n",
      " 12  Login Successful          81 non-null     bool   \n",
      " 13  Is Attack IP              81 non-null     bool   \n",
      " 14  Is Account Takeover       81 non-null     bool   \n",
      "dtypes: bool(3), float64(1), int64(2), object(9)\n",
      "memory usage: 8.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_feature = ['Login Timestamp', 'IP Address', 'Browser Name and Version', 'OS Name and Version', \n",
    "              'Device Type', 'Login Successful', 'Is Attack IP', 'Is Account Takeover']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 81 entries, 15 to 1990\n",
      "Data columns (total 8 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Login Timestamp           81 non-null     object\n",
      " 1   IP Address                81 non-null     object\n",
      " 2   Browser Name and Version  81 non-null     object\n",
      " 3   OS Name and Version       81 non-null     object\n",
      " 4   Device Type               81 non-null     object\n",
      " 5   Login Successful          81 non-null     bool  \n",
      " 6   Is Attack IP              81 non-null     bool  \n",
      " 7   Is Account Takeover       81 non-null     bool  \n",
      "dtypes: bool(3), object(5)\n",
      "memory usage: 4.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data = df[df_feature]\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\workspace\\AppData\\Local\\Temp\\ipykernel_11992\\2108848982.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[bool_columns] = data[bool_columns].astype(int)\n",
      "C:\\Users\\workspace\\AppData\\Local\\Temp\\ipykernel_11992\\2108848982.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[column] = label_encoder.fit_transform(data[column])\n",
      "C:\\Users\\workspace\\AppData\\Local\\Temp\\ipykernel_11992\\2108848982.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[column] = label_encoder.fit_transform(data[column])\n",
      "C:\\Users\\workspace\\AppData\\Local\\Temp\\ipykernel_11992\\2108848982.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[column] = label_encoder.fit_transform(data[column])\n",
      "C:\\Users\\workspace\\AppData\\Local\\Temp\\ipykernel_11992\\2108848982.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[column] = label_encoder.fit_transform(data[column])\n",
      "C:\\Users\\workspace\\AppData\\Local\\Temp\\ipykernel_11992\\2108848982.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[column] = label_encoder.fit_transform(data[column])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Convert boolean columns to integers (True -> 1, False -> 0)\n",
    "bool_columns = ['Login Successful', 'Is Attack IP', 'Is Account Takeover']\n",
    "data[bool_columns] = data[bool_columns].astype(int)\n",
    "\n",
    "# Label encode object columns\n",
    "label_encoder = LabelEncoder()\n",
    "for column in data.select_dtypes(include='object'):\n",
    "    data[column] = label_encoder.fit_transform(data[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 81 entries, 15 to 1990\n",
      "Data columns (total 8 columns):\n",
      " #   Column                    Non-Null Count  Dtype\n",
      "---  ------                    --------------  -----\n",
      " 0   Login Timestamp           81 non-null     int32\n",
      " 1   IP Address                81 non-null     int32\n",
      " 2   Browser Name and Version  81 non-null     int32\n",
      " 3   OS Name and Version       81 non-null     int32\n",
      " 4   Device Type               81 non-null     int32\n",
      " 5   Login Successful          81 non-null     int32\n",
      " 6   Is Attack IP              81 non-null     int32\n",
      " 7   Is Account Takeover       81 non-null     int32\n",
      "dtypes: int32(8)\n",
      "memory usage: 3.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bool_columns = data.select_dtypes(include='bool').columns\n",
    "\n",
    "# Loop through each boolean column and convert boolean values to integers\n",
    "for column in bool_columns:\n",
    "    data[column] = data[column].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 81 entries, 15 to 1990\n",
      "Data columns (total 8 columns):\n",
      " #   Column                    Non-Null Count  Dtype\n",
      "---  ------                    --------------  -----\n",
      " 0   Login Timestamp           81 non-null     int32\n",
      " 1   IP Address                81 non-null     int32\n",
      " 2   Browser Name and Version  81 non-null     int32\n",
      " 3   OS Name and Version       81 non-null     int32\n",
      " 4   Device Type               81 non-null     int32\n",
      " 5   Login Successful          81 non-null     int32\n",
      " 6   Is Attack IP              81 non-null     int32\n",
      " 7   Is Account Takeover       81 non-null     int32\n",
      "dtypes: int32(8)\n",
      "memory usage: 3.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Login Timestamp</th>\n",
       "      <th>IP Address</th>\n",
       "      <th>Browser Name and Version</th>\n",
       "      <th>OS Name and Version</th>\n",
       "      <th>Device Type</th>\n",
       "      <th>Login Successful</th>\n",
       "      <th>Is Attack IP</th>\n",
       "      <th>Is Account Takeover</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744</th>\n",
       "      <td>76</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1748</th>\n",
       "      <td>77</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>78</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>79</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Login Timestamp  IP Address  Browser Name and Version  \\\n",
       "index                                                          \n",
       "15                   0          51                         1   \n",
       "41                   1          49                        11   \n",
       "51                   2          56                         1   \n",
       "76                   3          35                         3   \n",
       "108                  4           2                         2   \n",
       "...                ...         ...                       ...   \n",
       "1744                76          17                        10   \n",
       "1748                77          13                        10   \n",
       "1916                78          52                         1   \n",
       "1987                79          20                         1   \n",
       "1990                80          10                         1   \n",
       "\n",
       "       OS Name and Version  Device Type  Login Successful  Is Attack IP  \\\n",
       "index                                                                     \n",
       "15                      14            0                 1             0   \n",
       "41                      11            0                 1             0   \n",
       "51                      14            1                 0             0   \n",
       "76                       3            1                 1             0   \n",
       "108                      9            0                 1             0   \n",
       "...                    ...          ...               ...           ...   \n",
       "1744                     2            0                 1             1   \n",
       "1748                    11            0                 1             1   \n",
       "1916                    14            0                 1             0   \n",
       "1987                    14            0                 1             0   \n",
       "1990                    14            0                 1             0   \n",
       "\n",
       "       Is Account Takeover  \n",
       "index                       \n",
       "15                       0  \n",
       "41                       0  \n",
       "51                       0  \n",
       "76                       0  \n",
       "108                      0  \n",
       "...                    ...  \n",
       "1744                     0  \n",
       "1748                     0  \n",
       "1916                     0  \n",
       "1987                     0  \n",
       "1990                     0  \n",
       "\n",
       "[81 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Login Timestamp</th>\n",
       "      <th>IP Address</th>\n",
       "      <th>Browser Name and Version</th>\n",
       "      <th>OS Name and Version</th>\n",
       "      <th>Device Type</th>\n",
       "      <th>Login Successful</th>\n",
       "      <th>Is Attack IP</th>\n",
       "      <th>Is Account Takeover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>28.320988</td>\n",
       "      <td>7.382716</td>\n",
       "      <td>10.444444</td>\n",
       "      <td>0.049383</td>\n",
       "      <td>0.703704</td>\n",
       "      <td>0.086420</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.526581</td>\n",
       "      <td>16.604086</td>\n",
       "      <td>5.231080</td>\n",
       "      <td>3.990614</td>\n",
       "      <td>0.218016</td>\n",
       "      <td>0.459468</td>\n",
       "      <td>0.282734</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Login Timestamp  IP Address  Browser Name and Version  \\\n",
       "count        81.000000   81.000000                 81.000000   \n",
       "mean         40.000000   28.320988                  7.382716   \n",
       "std          23.526581   16.604086                  5.231080   \n",
       "min           0.000000    0.000000                  0.000000   \n",
       "25%          20.000000   17.000000                  1.000000   \n",
       "50%          40.000000   25.000000                 10.000000   \n",
       "75%          60.000000   42.000000                 11.000000   \n",
       "max          80.000000   59.000000                 18.000000   \n",
       "\n",
       "       OS Name and Version  Device Type  Login Successful  Is Attack IP  \\\n",
       "count            81.000000    81.000000         81.000000     81.000000   \n",
       "mean             10.444444     0.049383          0.703704      0.086420   \n",
       "std               3.990614     0.218016          0.459468      0.282734   \n",
       "min               0.000000     0.000000          0.000000      0.000000   \n",
       "25%              10.000000     0.000000          0.000000      0.000000   \n",
       "50%              11.000000     0.000000          1.000000      0.000000   \n",
       "75%              14.000000     0.000000          1.000000      0.000000   \n",
       "max              15.000000     1.000000          1.000000      1.000000   \n",
       "\n",
       "       Is Account Takeover  \n",
       "count                 81.0  \n",
       "mean                   0.0  \n",
       "std                    0.0  \n",
       "min                    0.0  \n",
       "25%                    0.0  \n",
       "50%                    0.0  \n",
       "75%                    0.0  \n",
       "max                    0.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaled_df = ((data - data.min()) / (data.max() - data.min())) * 9 + 1\n",
    "\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\workspace\\AppData\\Local\\Temp\\ipykernel_11992\\295899092.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[df_feature] = scaler.fit_transform(data[df_feature])\n"
     ]
    }
   ],
   "source": [
    "# Scale the features\n",
    "data[df_feature] = scaler.fit_transform(data[df_feature])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Login Timestamp</th>\n",
       "      <th>IP Address</th>\n",
       "      <th>Browser Name and Version</th>\n",
       "      <th>OS Name and Version</th>\n",
       "      <th>Device Type</th>\n",
       "      <th>Login Successful</th>\n",
       "      <th>Is Attack IP</th>\n",
       "      <th>Is Account Takeover</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-1.710798</td>\n",
       "      <td>1.374380</td>\n",
       "      <td>-1.227755</td>\n",
       "      <td>0.896531</td>\n",
       "      <td>-0.227921</td>\n",
       "      <td>0.648886</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>-1.668028</td>\n",
       "      <td>1.253177</td>\n",
       "      <td>0.695807</td>\n",
       "      <td>0.140083</td>\n",
       "      <td>-0.227921</td>\n",
       "      <td>0.648886</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>-1.625258</td>\n",
       "      <td>1.677386</td>\n",
       "      <td>-1.227755</td>\n",
       "      <td>0.896531</td>\n",
       "      <td>4.387482</td>\n",
       "      <td>-1.541104</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>-1.582488</td>\n",
       "      <td>0.404757</td>\n",
       "      <td>-0.843043</td>\n",
       "      <td>-1.877112</td>\n",
       "      <td>4.387482</td>\n",
       "      <td>0.648886</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>-1.539718</td>\n",
       "      <td>-1.595088</td>\n",
       "      <td>-1.035399</td>\n",
       "      <td>-0.364216</td>\n",
       "      <td>-0.227921</td>\n",
       "      <td>0.648886</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744</th>\n",
       "      <td>1.539718</td>\n",
       "      <td>-0.686068</td>\n",
       "      <td>0.503451</td>\n",
       "      <td>-2.129261</td>\n",
       "      <td>-0.227921</td>\n",
       "      <td>0.648886</td>\n",
       "      <td>3.251373</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1748</th>\n",
       "      <td>1.582488</td>\n",
       "      <td>-0.928473</td>\n",
       "      <td>0.503451</td>\n",
       "      <td>0.140083</td>\n",
       "      <td>-0.227921</td>\n",
       "      <td>0.648886</td>\n",
       "      <td>3.251373</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>1.625258</td>\n",
       "      <td>1.434981</td>\n",
       "      <td>-1.227755</td>\n",
       "      <td>0.896531</td>\n",
       "      <td>-0.227921</td>\n",
       "      <td>0.648886</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>1.668028</td>\n",
       "      <td>-0.504263</td>\n",
       "      <td>-1.227755</td>\n",
       "      <td>0.896531</td>\n",
       "      <td>-0.227921</td>\n",
       "      <td>0.648886</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>1.710798</td>\n",
       "      <td>-1.110277</td>\n",
       "      <td>-1.227755</td>\n",
       "      <td>0.896531</td>\n",
       "      <td>-0.227921</td>\n",
       "      <td>0.648886</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Login Timestamp  IP Address  Browser Name and Version  \\\n",
       "index                                                          \n",
       "15           -1.710798    1.374380                 -1.227755   \n",
       "41           -1.668028    1.253177                  0.695807   \n",
       "51           -1.625258    1.677386                 -1.227755   \n",
       "76           -1.582488    0.404757                 -0.843043   \n",
       "108          -1.539718   -1.595088                 -1.035399   \n",
       "...                ...         ...                       ...   \n",
       "1744          1.539718   -0.686068                  0.503451   \n",
       "1748          1.582488   -0.928473                  0.503451   \n",
       "1916          1.625258    1.434981                 -1.227755   \n",
       "1987          1.668028   -0.504263                 -1.227755   \n",
       "1990          1.710798   -1.110277                 -1.227755   \n",
       "\n",
       "       OS Name and Version  Device Type  Login Successful  Is Attack IP  \\\n",
       "index                                                                     \n",
       "15                0.896531    -0.227921          0.648886     -0.307562   \n",
       "41                0.140083    -0.227921          0.648886     -0.307562   \n",
       "51                0.896531     4.387482         -1.541104     -0.307562   \n",
       "76               -1.877112     4.387482          0.648886     -0.307562   \n",
       "108              -0.364216    -0.227921          0.648886     -0.307562   \n",
       "...                    ...          ...               ...           ...   \n",
       "1744             -2.129261    -0.227921          0.648886      3.251373   \n",
       "1748              0.140083    -0.227921          0.648886      3.251373   \n",
       "1916              0.896531    -0.227921          0.648886     -0.307562   \n",
       "1987              0.896531    -0.227921          0.648886     -0.307562   \n",
       "1990              0.896531    -0.227921          0.648886     -0.307562   \n",
       "\n",
       "       Is Account Takeover  \n",
       "index                       \n",
       "15                     0.0  \n",
       "41                     0.0  \n",
       "51                     0.0  \n",
       "76                     0.0  \n",
       "108                    0.0  \n",
       "...                    ...  \n",
       "1744                   0.0  \n",
       "1748                   0.0  \n",
       "1916                   0.0  \n",
       "1987                   0.0  \n",
       "1990                   0.0  \n",
       "\n",
       "[81 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def elbow_k_means_plot(dataset, max_k):\n",
    "    # Define range of clusters (K) to try\n",
    "    k_range = range(1, max_k) \n",
    "\n",
    "    # Initialize list to store inertia values\n",
    "    inertia_values = []\n",
    "\n",
    "    # Calculate inertia for each value of K\n",
    "    for k in k_range:\n",
    "        # Fit K-means clustering to the scaled data\n",
    "        kmeans = KMeans(n_clusters=k)\n",
    "        kmeans.fit(dataset)\n",
    "        \n",
    "        # Append inertia to list\n",
    "        inertia_values.append(kmeans.inertia_)\n",
    "\n",
    "    # Plot elbow curve\n",
    "    plt.plot(k_range, inertia_values, marker='o')\n",
    "    plt.xlabel('Number of Clusters (K)')\n",
    "    plt.ylabel('Inertia')\n",
    "    plt.title('Elbow Method for Optimal K')\n",
    "    plt.xticks(k_range)\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elbow_k_means_plot(data, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>KMeans(n_clusters=5)</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;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=5)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=5)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means = KMeans(n_clusters=5)\n",
    "k_means.fit(data[df_feature])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\workspace\\AppData\\Local\\Temp\\ipykernel_11992\\1826464156.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['k_means'] = k_means.labels_\n"
     ]
    }
   ],
   "source": [
    "data['k_means'] = k_means.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Login Timestamp</th>\n",
       "      <th>IP Address</th>\n",
       "      <th>Browser Name and Version</th>\n",
       "      <th>OS Name and Version</th>\n",
       "      <th>Device Type</th>\n",
       "      <th>Login Successful</th>\n",
       "      <th>Is Attack IP</th>\n",
       "      <th>Is Account Takeover</th>\n",
       "      <th>k_means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.741291e-17</td>\n",
       "      <td>1.644775e-17</td>\n",
       "      <td>2.193033e-17</td>\n",
       "      <td>-3.426614e-17</td>\n",
       "      <td>1.082810e-16</td>\n",
       "      <td>-8.223874e-17</td>\n",
       "      <td>1.370646e-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.543210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.006231e+00</td>\n",
       "      <td>1.006231e+00</td>\n",
       "      <td>1.006231e+00</td>\n",
       "      <td>1.006231e+00</td>\n",
       "      <td>1.006231e+00</td>\n",
       "      <td>1.006231e+00</td>\n",
       "      <td>1.006231e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.140717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.710798e+00</td>\n",
       "      <td>-1.716291e+00</td>\n",
       "      <td>-1.420111e+00</td>\n",
       "      <td>-2.633560e+00</td>\n",
       "      <td>-2.279212e-01</td>\n",
       "      <td>-1.541104e+00</td>\n",
       "      <td>-3.075623e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.553989e-01</td>\n",
       "      <td>-6.860675e-01</td>\n",
       "      <td>-1.227755e+00</td>\n",
       "      <td>-1.120664e-01</td>\n",
       "      <td>-2.279212e-01</td>\n",
       "      <td>-1.541104e+00</td>\n",
       "      <td>-3.075623e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.012564e-01</td>\n",
       "      <td>5.034508e-01</td>\n",
       "      <td>1.400830e-01</td>\n",
       "      <td>-2.279212e-01</td>\n",
       "      <td>6.488857e-01</td>\n",
       "      <td>-3.075623e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.553989e-01</td>\n",
       "      <td>8.289671e-01</td>\n",
       "      <td>6.958069e-01</td>\n",
       "      <td>8.965309e-01</td>\n",
       "      <td>-2.279212e-01</td>\n",
       "      <td>6.488857e-01</td>\n",
       "      <td>-3.075623e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.710798e+00</td>\n",
       "      <td>1.859191e+00</td>\n",
       "      <td>2.042300e+00</td>\n",
       "      <td>1.148680e+00</td>\n",
       "      <td>4.387482e+00</td>\n",
       "      <td>6.488857e-01</td>\n",
       "      <td>3.251373e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Login Timestamp    IP Address  Browser Name and Version  \\\n",
       "count     8.100000e+01  8.100000e+01              8.100000e+01   \n",
       "mean      2.741291e-17  1.644775e-17              2.193033e-17   \n",
       "std       1.006231e+00  1.006231e+00              1.006231e+00   \n",
       "min      -1.710798e+00 -1.716291e+00             -1.420111e+00   \n",
       "25%      -8.553989e-01 -6.860675e-01             -1.227755e+00   \n",
       "50%       0.000000e+00 -2.012564e-01              5.034508e-01   \n",
       "75%       8.553989e-01  8.289671e-01              6.958069e-01   \n",
       "max       1.710798e+00  1.859191e+00              2.042300e+00   \n",
       "\n",
       "       OS Name and Version   Device Type  Login Successful  Is Attack IP  \\\n",
       "count         8.100000e+01  8.100000e+01      8.100000e+01  8.100000e+01   \n",
       "mean         -3.426614e-17  1.082810e-16     -8.223874e-17  1.370646e-18   \n",
       "std           1.006231e+00  1.006231e+00      1.006231e+00  1.006231e+00   \n",
       "min          -2.633560e+00 -2.279212e-01     -1.541104e+00 -3.075623e-01   \n",
       "25%          -1.120664e-01 -2.279212e-01     -1.541104e+00 -3.075623e-01   \n",
       "50%           1.400830e-01 -2.279212e-01      6.488857e-01 -3.075623e-01   \n",
       "75%           8.965309e-01 -2.279212e-01      6.488857e-01 -3.075623e-01   \n",
       "max           1.148680e+00  4.387482e+00      6.488857e-01  3.251373e+00   \n",
       "\n",
       "       Is Account Takeover    k_means  \n",
       "count                 81.0  81.000000  \n",
       "mean                   0.0   2.543210  \n",
       "std                    0.0   1.140717  \n",
       "min                    0.0   0.000000  \n",
       "25%                    0.0   2.000000  \n",
       "50%                    0.0   3.000000  \n",
       "75%                    0.0   3.000000  \n",
       "max                    0.0   4.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2250x2250 with 90 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Assuming df is your DataFrame containing all features\n",
    "sns.pairplot(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=data['IP Address'], y=data['Login Successful'], c=data['k_means'], cmap='viridis')\n",
    "plt.xlabel('IP Address')\n",
    "plt.ylabel('Login Successful')\n",
    "plt.title('Cluster Distribution')\n",
    "plt.colorbar(label='Cluster')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "auth-insight-env",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}