Classifying_Bank_Transactions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = pd.read_csv('bank_transaction_features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>bank_transaction_id</th>\n",
       "      <th>bank_transaction_description</th>\n",
       "      <th>bank_transaction_amount</th>\n",
       "      <th>bank_transaction_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21786195</td>\n",
       "      <td>citylink</td>\n",
       "      <td>-13.80</td>\n",
       "      <td>MPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21786196</td>\n",
       "      <td>citylink</td>\n",
       "      <td>-13.14</td>\n",
       "      <td>DEB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21786197</td>\n",
       "      <td>1Jul19 OYSTER</td>\n",
       "      <td>-36.98</td>\n",
       "      <td>DEB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21786198</td>\n",
       "      <td>travelodge</td>\n",
       "      <td>-75.73</td>\n",
       "      <td>MPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21786199</td>\n",
       "      <td>6Jul19 RINGGO</td>\n",
       "      <td>-37.86</td>\n",
       "      <td>CSH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12495</th>\n",
       "      <td>24140325</td>\n",
       "      <td>COVERSURE</td>\n",
       "      <td>-28.46</td>\n",
       "      <td>FPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12496</th>\n",
       "      <td>24140326</td>\n",
       "      <td>DIRECTLINE INS FASTER PAYMENTS OUT</td>\n",
       "      <td>-35.64</td>\n",
       "      <td>FPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12497</th>\n",
       "      <td>24140327</td>\n",
       "      <td>SUNDRY DEBIT Direct Debit coversure</td>\n",
       "      <td>-22.37</td>\n",
       "      <td>DD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12498</th>\n",
       "      <td>24140328</td>\n",
       "      <td>17Aug19 insurance Direct Debit</td>\n",
       "      <td>-23.11</td>\n",
       "      <td>DD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12499</th>\n",
       "      <td>24140329</td>\n",
       "      <td>enterprise car insurance</td>\n",
       "      <td>-24.15</td>\n",
       "      <td>DD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12500 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bank_transaction_id         bank_transaction_description  \\\n",
       "0                 21786195                             citylink   \n",
       "1                 21786196                             citylink   \n",
       "2                 21786197                        1Jul19 OYSTER   \n",
       "3                 21786198                           travelodge   \n",
       "4                 21786199                        6Jul19 RINGGO   \n",
       "...                    ...                                  ...   \n",
       "12495             24140325                            COVERSURE   \n",
       "12496             24140326   DIRECTLINE INS FASTER PAYMENTS OUT   \n",
       "12497             24140327  SUNDRY DEBIT Direct Debit coversure   \n",
       "12498             24140328       17Aug19 insurance Direct Debit   \n",
       "12499             24140329             enterprise car insurance   \n",
       "\n",
       "       bank_transaction_amount bank_transaction_type  \n",
       "0                       -13.80                   MPO  \n",
       "1                       -13.14                   DEB  \n",
       "2                       -36.98                   DEB  \n",
       "3                       -75.73                   MPO  \n",
       "4                       -37.86                   CSH  \n",
       "...                        ...                   ...  \n",
       "12495                   -28.46                   FPO  \n",
       "12496                   -35.64                   FPO  \n",
       "12497                   -22.37                    DD  \n",
       "12498                   -23.11                    DD  \n",
       "12499                   -24.15                    DD  \n",
       "\n",
       "[12500 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12500 entries, 0 to 12499\n",
      "Data columns (total 4 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   bank_transaction_id           12500 non-null  int64  \n",
      " 1   bank_transaction_description  12369 non-null  object \n",
      " 2   bank_transaction_amount       12500 non-null  float64\n",
      " 3   bank_transaction_type         12500 non-null  object \n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 390.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>bank_transaction_id</th>\n",
       "      <th>bank_transaction_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.250000e+04</td>\n",
       "      <td>12500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.226077e+07</td>\n",
       "      <td>-19.613017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.391952e+05</td>\n",
       "      <td>15.060147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.178620e+07</td>\n",
       "      <td>-102.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.178932e+07</td>\n",
       "      <td>-28.022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.179244e+07</td>\n",
       "      <td>-19.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.179557e+07</td>\n",
       "      <td>-4.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.414033e+07</td>\n",
       "      <td>-0.320000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bank_transaction_id  bank_transaction_amount\n",
       "count         1.250000e+04             12500.000000\n",
       "mean          2.226077e+07               -19.613017\n",
       "std           9.391952e+05                15.060147\n",
       "min           2.178620e+07              -102.590000\n",
       "25%           2.178932e+07               -28.022500\n",
       "50%           2.179244e+07               -19.040000\n",
       "75%           2.179557e+07                -4.687500\n",
       "max           2.414033e+07                -0.320000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bank_transaction_id               0\n",
       "bank_transaction_description    131\n",
       "bank_transaction_amount           0\n",
       "bank_transaction_type             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_new = df_features.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = df_features.groupby('bank_transaction_type').nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>bank_transaction_id</th>\n",
       "      <th>bank_transaction_description</th>\n",
       "      <th>bank_transaction_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bank_transaction_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHG</th>\n",
       "      <td>2510</td>\n",
       "      <td>1543</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSH</th>\n",
       "      <td>2041</td>\n",
       "      <td>1686</td>\n",
       "      <td>1619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DD</th>\n",
       "      <td>1108</td>\n",
       "      <td>926</td>\n",
       "      <td>827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEB</th>\n",
       "      <td>4678</td>\n",
       "      <td>3899</td>\n",
       "      <td>3013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FPO</th>\n",
       "      <td>679</td>\n",
       "      <td>618</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPO</th>\n",
       "      <td>1484</td>\n",
       "      <td>1370</td>\n",
       "      <td>1260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       bank_transaction_id  bank_transaction_description  \\\n",
       "bank_transaction_type                                                      \n",
       "CHG                                   2510                          1543   \n",
       "CSH                                   2041                          1686   \n",
       "DD                                    1108                           926   \n",
       "DEB                                   4678                          3899   \n",
       "FPO                                    679                           618   \n",
       "MPO                                   1484                          1370   \n",
       "\n",
       "                       bank_transaction_amount  \n",
       "bank_transaction_type                           \n",
       "CHG                                        211  \n",
       "CSH                                       1619  \n",
       "DD                                         827  \n",
       "DEB                                       3013  \n",
       "FPO                                        577  \n",
       "MPO                                       1260  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_features['bank_transaction_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_features['bank_transaction_amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bank_transaction_type', ylabel='Count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bank_transaction_amount', ylabel='Count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_labels = pd.read_csv('bank_transaction_labels.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>bank_transaction_id</th>\n",
       "      <th>bank_transaction_category</th>\n",
       "      <th>bank_transaction_dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21786195</td>\n",
       "      <td>TRAVEL</td>\n",
       "      <td>TRAIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21786196</td>\n",
       "      <td>TRAVEL</td>\n",
       "      <td>TRAIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21786197</td>\n",
       "      <td>TRAVEL</td>\n",
       "      <td>TRAIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21786198</td>\n",
       "      <td>TRAVEL</td>\n",
       "      <td>TRAIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21786199</td>\n",
       "      <td>TRAVEL</td>\n",
       "      <td>TRAIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12495</th>\n",
       "      <td>24140325</td>\n",
       "      <td>INSURANCE</td>\n",
       "      <td>VAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12496</th>\n",
       "      <td>24140326</td>\n",
       "      <td>INSURANCE</td>\n",
       "      <td>VAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12497</th>\n",
       "      <td>24140327</td>\n",
       "      <td>INSURANCE</td>\n",
       "      <td>VAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12498</th>\n",
       "      <td>24140328</td>\n",
       "      <td>INSURANCE</td>\n",
       "      <td>VAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12499</th>\n",
       "      <td>24140329</td>\n",
       "      <td>INSURANCE</td>\n",
       "      <td>VAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bank_transaction_id bank_transaction_category bank_transaction_dataset\n",
       "0                 21786195                    TRAVEL                    TRAIN\n",
       "1                 21786196                    TRAVEL                    TRAIN\n",
       "2                 21786197                    TRAVEL                    TRAIN\n",
       "3                 21786198                    TRAVEL                    TRAIN\n",
       "4                 21786199                    TRAVEL                    TRAIN\n",
       "...                    ...                       ...                      ...\n",
       "12495             24140325                 INSURANCE                      VAL\n",
       "12496             24140326                 INSURANCE                      VAL\n",
       "12497             24140327                 INSURANCE                      VAL\n",
       "12498             24140328                 INSURANCE                      VAL\n",
       "12499             24140329                 INSURANCE                      VAL\n",
       "\n",
       "[12500 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "category = df_labels['bank_transaction_category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "transact = df_labels['bank_transaction_dataset']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bank_transaction_dataset', ylabel='Count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = transact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.ticker as ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(data = category)\n",
    "plt.xticks(rotation = 90) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bank_transaction_id          0\n",
       "bank_transaction_category    0\n",
       "bank_transaction_dataset     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_unique = df_labels.groupby('bank_transaction_category').nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>bank_transaction_id</th>\n",
       "      <th>bank_transaction_dataset</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bank_transaction_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACCOMMODATION_AND_MEALS</th>\n",
       "      <td>3765</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BANK_OR_FINANCE_CHARGES</th>\n",
       "      <td>2790</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSURANCE</th>\n",
       "      <td>1170</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOTOR_EXPENSES</th>\n",
       "      <td>1609</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAVEL</th>\n",
       "      <td>3166</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           bank_transaction_id  bank_transaction_dataset\n",
       "bank_transaction_category                                               \n",
       "ACCOMMODATION_AND_MEALS                   3765                         2\n",
       "BANK_OR_FINANCE_CHARGES                   2790                         2\n",
       "INSURANCE                                 1170                         2\n",
       "MOTOR_EXPENSES                            1609                         2\n",
       "TRAVEL                                    3166                         2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_description = df_features['bank_transaction_description']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                   citylink\n",
       "1                                   citylink\n",
       "2                              1Jul19 OYSTER\n",
       "3                                 travelodge\n",
       "4                              6Jul19 RINGGO\n",
       "                        ...                 \n",
       "12495                              COVERSURE\n",
       "12496     DIRECTLINE INS FASTER PAYMENTS OUT\n",
       "12497    SUNDRY DEBIT Direct Debit coversure\n",
       "12498         17Aug19 insurance Direct Debit\n",
       "12499               enterprise car insurance\n",
       "Name: bank_transaction_description, Length: 12500, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                   citylink\n",
       "1                                   citylink\n",
       "2                              1jul19 oyster\n",
       "3                                 travelodge\n",
       "4                              6jul19 ringgo\n",
       "                        ...                 \n",
       "12495                              coversure\n",
       "12496     directline ins faster payments out\n",
       "12497    sundry debit direct debit coversure\n",
       "12498         17aug19 insurance direct debit\n",
       "12499               enterprise car insurance\n",
       "Name: bank_transaction_description, Length: 12500, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_description.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stop = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern = '[0-9]'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_description = df_description.str.replace(r'[^a-zA-Z ]\\s?',r'',regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                   citylink\n",
       "1                                   citylink\n",
       "2                                  JulOYSTER\n",
       "3                                 travelodge\n",
       "4                                  JulRINGGO\n",
       "                        ...                 \n",
       "12495                              COVERSURE\n",
       "12496     DIRECTLINE INS FASTER PAYMENTS OUT\n",
       "12497    SUNDRY DEBIT Direct Debit coversure\n",
       "12498              Auginsurance Direct Debit\n",
       "12499               enterprise car insurance\n",
       "Name: bank_transaction_description, Length: 12500, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_description =df_description.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                   citylink\n",
       "1                                   citylink\n",
       "2                                  JulOYSTER\n",
       "3                                 travelodge\n",
       "4                                  JulRINGGO\n",
       "                        ...                 \n",
       "12495                              COVERSURE\n",
       "12496     DIRECTLINE INS FASTER PAYMENTS OUT\n",
       "12497    SUNDRY DEBIT Direct Debit coversure\n",
       "12498              Auginsurance Direct Debit\n",
       "12499               enterprise car insurance\n",
       "Name: bank_transaction_description, Length: 12500, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.fit(df_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = vectorizer.transform(df_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12500, 845)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = X[:10001]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = X[10002:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_category = df_labels['bank_transaction_category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           TRAVEL\n",
       "1           TRAVEL\n",
       "2           TRAVEL\n",
       "3           TRAVEL\n",
       "4           TRAVEL\n",
       "           ...    \n",
       "12495    INSURANCE\n",
       "12496    INSURANCE\n",
       "12497    INSURANCE\n",
       "12498    INSURANCE\n",
       "12499    INSURANCE\n",
       "Name: bank_transaction_category, Length: 12500, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = df_category[:10001]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = df_category[10002:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_T = le.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, ..., 2, 2, 4])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_T = le.fit_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, ..., 2, 2, 2])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,X_T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = model.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94.5005499450055\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(X_T,predict)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2887,    0,    0,    4,  126],\n",
       "       [   0, 2248,    0,    0,    2],\n",
       "       [   1,    0,  880,   21,   18],\n",
       "       [  82,   11,   41, 1043,   58],\n",
       "       [  64,   45,    0,   77, 2393]], dtype=int64)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CM_train = confusion_matrix(X_T, predict)\n",
    "CM_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_test = model.predict(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.79343474779824\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(Y_T,predict_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9154020895158649"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Precision= precision_score(Y_T, predict_test,  average=\"macro\")\n",
    "Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9128406736169836"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Recall = recall_score(Y_T, predict_test,  average=\"macro\")\n",
    "Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9137805337127063"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F1 = f1_score(Y_T, predict_test,  average=\"macro\")\n",
    "F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "CM_test = confusion_matrix(Y_T, predict_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[707,   1,   0,   0,  40],\n",
       "       [  1, 536,   0,   0,   3],\n",
       "       [  1,   2, 234,   3,  10],\n",
       "       [ 19,   1,  21, 308,  25],\n",
       "       [ 29,  15,   0,  34, 508]], dtype=int64)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CM_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\rohit\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "classifier = LogisticRegression(random_state = 0) \n",
    "classifier.fit(x_train, X_T) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_pred = classifier.predict(x_train) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93.84061593840616\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(X_T,y_pred)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_logistic = classifier.predict(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.67333867093676\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(Y_T,predict_logistic)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "Precision= precision_score(Y_T, predict_logistic,  average=\"macro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9131480319531837"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91173455197089"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Recall = recall_score(Y_T, predict_logistic,  average=\"macro\")\n",
    "Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "F1 = f1_score(Y_T, predict_logistic,  average=\"macro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9117713254664093"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "CM_logistic = confusion_matrix(Y_T, predict_logistic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[706,   4,   0,   0,  38],\n",
       "       [  0, 539,   0,   0,   1],\n",
       "       [  0,   6, 236,   5,   3],\n",
       "       [ 18,   3,  22, 302,  29],\n",
       "       [ 26,  22,   0,  31, 507]], dtype=int64)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CM_logistic"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}