final_model (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0                 tmsp  country  amount  success         PSP  \\\n",
      "0           0  2019-01-01 00:01:11  Germany      89        0     UK_Card   \n",
      "1           1  2019-01-01 00:01:17  Germany      89        1     UK_Card   \n",
      "2           2  2019-01-01 00:02:49  Germany     238        0     UK_Card   \n",
      "3           3  2019-01-01 00:03:13  Germany     238        1     UK_Card   \n",
      "4           4  2019-01-01 00:04:33  Austria     124        0  Simplecard   \n",
      "\n",
      "   3D_secured    card  \n",
      "0           0    Visa  \n",
      "1           0    Visa  \n",
      "2           1  Diners  \n",
      "3           1  Diners  \n",
      "4           0  Diners  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "data = pd.read_csv('https://raw.githubusercontent.com/nalie-linda/IU_project1/main/PSP_Jan_Feb_2019.csv')\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50410 entries, 0 to 50409\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Unnamed: 0  50410 non-null  int64 \n",
      " 1   tmsp        50410 non-null  object\n",
      " 2   country     50410 non-null  object\n",
      " 3   amount      50410 non-null  int64 \n",
      " 4   success     50410 non-null  int64 \n",
      " 5   PSP         50410 non-null  object\n",
      " 6   3D_secured  50410 non-null  int64 \n",
      " 7   card        50410 non-null  object\n",
      "dtypes: int64(4), object(4)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Quallity checks \n",
    "1. Missing values: There are no missing values.\n",
    "2. Data type conversions\n",
    "3. Handle any outliers or any values that are not valid\n",
    "4. Check for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50410 entries, 0 to 50409\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   Unnamed: 0  50410 non-null  int64         \n",
      " 1   tmsp        50410 non-null  datetime64[ns]\n",
      " 2   country     50410 non-null  object        \n",
      " 3   amount      50410 non-null  int64         \n",
      " 4   success     50410 non-null  int64         \n",
      " 5   PSP         50410 non-null  object        \n",
      " 6   3D_secured  50410 non-null  int64         \n",
      " 7   card        50410 non-null  object        \n",
      "dtypes: datetime64[ns](1), int64(4), object(3)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#convert tmsp to datetime\n",
    "data['tmsp'] = pd.to_datetime(data['tmsp'], format='%Y-%m-%d %H:%M:%S')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    50410.000000\n",
       "mean       202.395715\n",
       "std         96.274730\n",
       "min          6.000000\n",
       "25%        133.000000\n",
       "50%        201.000000\n",
       "75%        269.000000\n",
       "max        630.000000\n",
       "Name: amount, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['amount'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Visa', 'Diners', 'Master'], dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unique values for card variable\n",
    "data['card'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['UK_Card', 'Simplecard', 'Moneycard', 'Goldcard'], dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unique values for PSP variable\n",
    "data['PSP'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data analysis\n",
    "\n",
    "Explore Data: Perform exploratory data analysis (EDA) to visualize distributions, \n",
    "correlations, and relationships between features and the target variable.\n",
    "1. Distribution of success  among the different PSPs: Success rate is low among all PSPs as seen from the graph.\n",
    "2. Distribution of success among each card type\n",
    "3. Distribution of success rate and failure rate in each country for each PSP\n",
    "4. Distribution of success among 3D secured cards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "success\n",
       "0    40182\n",
       "1    10228\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['success'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success         0     1\n",
      "PSP                    \n",
      "Goldcard     1905  1303\n",
      "Moneycard    6482  1815\n",
      "Simplecard  10478  1968\n",
      "UK_Card     21317  5142\n"
     ]
    }
   ],
   "source": [
    "psp_counts = data.groupby(['PSP', 'success']).size().unstack().fillna(0)\n",
    "print(psp_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the distribution of success among each PSP\n",
    "plt.figure(figsize=(12, 6))\n",
    "psp_counts.plot(kind='bar', stacked=True, colormap='viridis')\n",
    "\n",
    "plt.title('Distribution of Success (0, 1) Among Each PSP')\n",
    "plt.xlabel('PSP')\n",
    "plt.ylabel('Count of Transactions')\n",
    "plt.legend(title='Success', labels=['Failed (0)', 'Successful (1)'])\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success      0     1\n",
      "card                \n",
      "Diners    7590  2178\n",
      "Master   23379  5623\n",
      "Visa      9213  2427\n"
     ]
    }
   ],
   "source": [
    "#Distribution of success rate by card\n",
    "card_counts = data.groupby(['card', 'success']).size().unstack().fillna(0)\n",
    "print(card_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distribution of success among each card\n",
    "plt.figure(figsize=(12, 6))\n",
    "card_counts.plot(kind='bar', stacked=True, colormap='viridis')\n",
    "\n",
    "plt.title('Distribution of Success (0, 1) Among Each card')\n",
    "plt.xlabel('card')\n",
    "plt.ylabel('Count of Transactions')\n",
    "plt.legend(title='Success', labels=['Failed (0)', 'Successful (1)'])\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success         0     1\n",
      "3D_secured             \n",
      "0           31120  7279\n",
      "1            9062  2949\n"
     ]
    }
   ],
   "source": [
    "cards_3D = data.groupby(['3D_secured', 'success']).size().unstack().fillna(0)\n",
    "print(cards_3D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the distribution of success among each PSP\n",
    "plt.figure(figsize=(12, 6))\n",
    "cards_3D.plot(kind='bar', stacked=True, colormap='plasma')\n",
    "\n",
    "plt.title('Distribution of Success (0, 1) 3D secured cards')\n",
    "plt.xlabel('3D_security')\n",
    "plt.ylabel('Count of Transactions')\n",
    "plt.legend(title='Success', labels=['Failed (0)', 'Successful (1)'])\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyzing Success(1) and failure(0) for each PSP in different countries**\n",
    "- Success rate is highest among transactions made through Goldcard PSP in Austria and Switzerland followed by Germany.\n",
    "- Simplecard PSP has the lowest success rate of transactions across all countries, followed by uk_card PSP.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           PSP      country  Failure (0)  Success (1)  Total  success%  \\\n",
      "0     Goldcard      Austria          335          248    583      42.5   \n",
      "1     Goldcard      Germany         1175          764   1939      39.4   \n",
      "2     Goldcard  Switzerland          395          291    686      42.4   \n",
      "3    Moneycard      Austria         1286          368   1654      22.2   \n",
      "4    Moneycard      Germany         3904         1056   4960      21.3   \n",
      "5    Moneycard  Switzerland         1292          391   1683      23.2   \n",
      "6   Simplecard      Austria         2031          383   2414      15.9   \n",
      "7   Simplecard      Germany         6247         1184   7431      15.9   \n",
      "8   Simplecard  Switzerland         2200          401   2601      15.4   \n",
      "9      UK_Card      Austria         4174         1014   5188      19.5   \n",
      "10     UK_Card      Germany        12819         3084  15903      19.4   \n",
      "11     UK_Card  Switzerland         4324         1044   5368      19.4   \n",
      "\n",
      "    Failure%  \n",
      "0       57.5  \n",
      "1       60.6  \n",
      "2       57.6  \n",
      "3       77.8  \n",
      "4       78.7  \n",
      "5       76.8  \n",
      "6       84.1  \n",
      "7       84.1  \n",
      "8       84.6  \n",
      "9       80.5  \n",
      "10      80.6  \n",
      "11      80.6  \n"
     ]
    }
   ],
   "source": [
    "# Calculate the success rate (1) and failure rate (0) for each country for each PSP\n",
    "country_psp = data.groupby(['PSP', 'country', 'success']).size().unstack(fill_value=0).reset_index()\n",
    "# Rename columns for better understanding\n",
    "country_psp.columns = ['PSP', 'country', 'Failure (0)', 'Success (1)']\n",
    "# Add a 'Total' column\n",
    "country_psp['Total'] = country_psp['Failure (0)'] + country_psp['Success (1)']\n",
    "\n",
    "grand_total = country_psp['Total'].sum()\n",
    "\n",
    "country_psp['success%'] = round((country_psp['Success (1)'] / country_psp['Total'])*100,1)\n",
    "country_psp['Failure%'] = round((country_psp['Failure (0)'] / country_psp['Total'])*100,1)\n",
    "\n",
    "print(country_psp)\n",
    "\n",
    "# print(grand_total)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PSP      country success  Goldcard  Moneycard  Simplecard  UK_Card  \\\n",
      "0        Austria       0       335       1286        2031     4174   \n",
      "1        Austria       1       248        368         383     1014   \n",
      "2        Germany       0      1175       3904        6247    12819   \n",
      "3        Germany       1       764       1056        1184     3084   \n",
      "4    Switzerland       0       395       1292        2200     4324   \n",
      "5    Switzerland       1       291        391         401     1044   \n",
      "6    Grand Total              3208       8297       12446    26459   \n",
      "\n",
      "PSP  Grand Total  \n",
      "0           7826  \n",
      "1           2013  \n",
      "2          24145  \n",
      "3           6088  \n",
      "4           8211  \n",
      "5           2127  \n",
      "6          50410  \n"
     ]
    }
   ],
   "source": [
    "# Create the pivot table\n",
    "pivot_table = pd.pivot_table(data, \n",
    "                             values='amount', \n",
    "                             index=['country', 'success'], \n",
    "                             columns='PSP', \n",
    "                             aggfunc='count', \n",
    "                             fill_value=0, \n",
    "                             margins=True, \n",
    "                             margins_name='Grand Total')\n",
    "\n",
    "\n",
    "\n",
    "# Reset the index\n",
    "pivot_table = pivot_table.reset_index()\n",
    "\n",
    "# Display the pivot table\n",
    "print(pivot_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pivot table \n",
    "df_success = country_psp.pivot(index='PSP', columns='country', values='success%')\n",
    "df_failure = country_psp.pivot(index='PSP', columns='country', values='Failure%')\n",
    "\n",
    "# Set up the figure and axes\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(18, 8))\n",
    "\n",
    "# Plot success rates\n",
    "df_success.plot(kind='bar', ax=axes[0])\n",
    "axes[0].set_title('Success Rate (%) across Different Countries for Different PSPs')\n",
    "axes[0].set_ylabel('Success Rate (%)')\n",
    "axes[0].set_xlabel('PSP')\n",
    "axes[0].legend(title='Country')\n",
    "axes[0].grid(axis='y')\n",
    "\n",
    "# Plot failure rates\n",
    "df_failure.plot(kind='bar', ax=axes[1])\n",
    "axes[1].set_title('Failure Rate (%) across Different Countries for Different PSPs')\n",
    "axes[1].set_ylabel('Failure Rate (%)')\n",
    "axes[1].set_xlabel('PSP')\n",
    "axes[1].legend(title='Country')\n",
    "axes[1].grid(axis='y')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering\n",
    "\n",
    "1. Factoring in Fees for successful and Failed transactions.\n",
    "2. Computing avg fees of a transaction through each PSP in different countries.\n",
    "3. Calculating success and failure rates for different PSPs in different countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the fees for each PSP\n",
    "fees = {\n",
    "    'PSP': ['Moneycard', 'Goldcard', 'UK_Card', 'Simplecard'],\n",
    "    'fee_success': [5, 10, 3, 1],\n",
    "    'fee_failure': [2, 5, 1, 0.5]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          PSP  fee_success  fee_failure\n",
      "0   Moneycard            5          2.0\n",
      "1    Goldcard           10          5.0\n",
      "2     UK_Card            3          1.0\n",
      "3  Simplecard            1          0.5\n"
     ]
    }
   ],
   "source": [
    "df_fees = pd.DataFrame(fees)\n",
    "print(df_fees)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           PSP      country  Failure (0)  Success (1)  Total  success%  \\\n",
      "0     Goldcard      Austria          335          248    583      42.5   \n",
      "1     Goldcard      Germany         1175          764   1939      39.4   \n",
      "2     Goldcard  Switzerland          395          291    686      42.4   \n",
      "3    Moneycard      Austria         1286          368   1654      22.2   \n",
      "4    Moneycard      Germany         3904         1056   4960      21.3   \n",
      "5    Moneycard  Switzerland         1292          391   1683      23.2   \n",
      "6   Simplecard      Austria         2031          383   2414      15.9   \n",
      "7   Simplecard      Germany         6247         1184   7431      15.9   \n",
      "8   Simplecard  Switzerland         2200          401   2601      15.4   \n",
      "9      UK_Card      Austria         4174         1014   5188      19.5   \n",
      "10     UK_Card      Germany        12819         3084  15903      19.4   \n",
      "11     UK_Card  Switzerland         4324         1044   5368      19.4   \n",
      "\n",
      "    Failure%  fee_success  fee_failure  \n",
      "0       57.5           10          5.0  \n",
      "1       60.6           10          5.0  \n",
      "2       57.6           10          5.0  \n",
      "3       77.8            5          2.0  \n",
      "4       78.7            5          2.0  \n",
      "5       76.8            5          2.0  \n",
      "6       84.1            1          0.5  \n",
      "7       84.1            1          0.5  \n",
      "8       84.6            1          0.5  \n",
      "9       80.5            3          1.0  \n",
      "10      80.6            3          1.0  \n",
      "11      80.6            3          1.0  \n"
     ]
    }
   ],
   "source": [
    "# Merge the DataFrames on 'PSP'\n",
    "df_merged = pd.merge(country_psp, df_fees, on='PSP')\n",
    "print(df_merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           PSP      country  success%  Failure%  average_fee\n",
      "0     Goldcard      Austria      42.5      57.5       7.1250\n",
      "1     Goldcard      Germany      39.4      60.6       6.9700\n",
      "2     Goldcard  Switzerland      42.4      57.6       7.1200\n",
      "3    Moneycard      Austria      22.2      77.8       2.6660\n",
      "4    Moneycard      Germany      21.3      78.7       2.6390\n",
      "5    Moneycard  Switzerland      23.2      76.8       2.6960\n",
      "6   Simplecard      Austria      15.9      84.1       0.5795\n",
      "7   Simplecard      Germany      15.9      84.1       0.5795\n",
      "8   Simplecard  Switzerland      15.4      84.6       0.5770\n",
      "9      UK_Card      Austria      19.5      80.5       1.3900\n",
      "10     UK_Card      Germany      19.4      80.6       1.3880\n",
      "11     UK_Card  Switzerland      19.4      80.6       1.3880\n"
     ]
    }
   ],
   "source": [
    "# Calculate the average transaction fee\n",
    "df_merged['average_fee'] = (df_merged['success%'] * df_merged['fee_success'] / 100) + \\\n",
    "                           (df_merged['Failure%'] * df_merged['fee_failure'] / 100)\n",
    "# Use this data for feature engineering\n",
    "print(df_merged[['PSP', 'country', 'success%', 'Failure%', 'average_fee']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>tmsp</th>\n",
       "      <th>country</th>\n",
       "      <th>amount</th>\n",
       "      <th>success</th>\n",
       "      <th>PSP</th>\n",
       "      <th>3D_secured</th>\n",
       "      <th>card</th>\n",
       "      <th>Failure (0)</th>\n",
       "      <th>Success (1)</th>\n",
       "      <th>Total</th>\n",
       "      <th>success%</th>\n",
       "      <th>Failure%</th>\n",
       "      <th>fee_success</th>\n",
       "      <th>fee_failure</th>\n",
       "      <th>average_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-01 00:01:11</td>\n",
       "      <td>Germany</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>0</td>\n",
       "      <td>Visa</td>\n",
       "      <td>12819</td>\n",
       "      <td>3084</td>\n",
       "      <td>15903</td>\n",
       "      <td>19.4</td>\n",
       "      <td>80.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 00:01:17</td>\n",
       "      <td>Germany</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>0</td>\n",
       "      <td>Visa</td>\n",
       "      <td>12819</td>\n",
       "      <td>3084</td>\n",
       "      <td>15903</td>\n",
       "      <td>19.4</td>\n",
       "      <td>80.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:02:49</td>\n",
       "      <td>Germany</td>\n",
       "      <td>238</td>\n",
       "      <td>0</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>1</td>\n",
       "      <td>Diners</td>\n",
       "      <td>12819</td>\n",
       "      <td>3084</td>\n",
       "      <td>15903</td>\n",
       "      <td>19.4</td>\n",
       "      <td>80.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-01-01 00:03:13</td>\n",
       "      <td>Germany</td>\n",
       "      <td>238</td>\n",
       "      <td>1</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>1</td>\n",
       "      <td>Diners</td>\n",
       "      <td>12819</td>\n",
       "      <td>3084</td>\n",
       "      <td>15903</td>\n",
       "      <td>19.4</td>\n",
       "      <td>80.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-01-01 00:04:33</td>\n",
       "      <td>Austria</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>0</td>\n",
       "      <td>Diners</td>\n",
       "      <td>2031</td>\n",
       "      <td>383</td>\n",
       "      <td>2414</td>\n",
       "      <td>15.9</td>\n",
       "      <td>84.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                tmsp  country  amount  success         PSP  \\\n",
       "0           0 2019-01-01 00:01:11  Germany      89        0     UK_Card   \n",
       "1           1 2019-01-01 00:01:17  Germany      89        1     UK_Card   \n",
       "2           2 2019-01-01 00:02:49  Germany     238        0     UK_Card   \n",
       "3           3 2019-01-01 00:03:13  Germany     238        1     UK_Card   \n",
       "4           4 2019-01-01 00:04:33  Austria     124        0  Simplecard   \n",
       "\n",
       "   3D_secured    card  Failure (0)  Success (1)  Total  success%  Failure%  \\\n",
       "0           0    Visa        12819         3084  15903      19.4      80.6   \n",
       "1           0    Visa        12819         3084  15903      19.4      80.6   \n",
       "2           1  Diners        12819         3084  15903      19.4      80.6   \n",
       "3           1  Diners        12819         3084  15903      19.4      80.6   \n",
       "4           0  Diners         2031          383   2414      15.9      84.1   \n",
       "\n",
       "   fee_success  fee_failure  average_fee  \n",
       "0            3          1.0       1.3880  \n",
       "1            3          1.0       1.3880  \n",
       "2            3          1.0       1.3880  \n",
       "3            3          1.0       1.3880  \n",
       "4            1          0.5       0.5795  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the main dataset with the additional data on 'PSP' and 'country'\n",
    "data2 = pd.merge(data, df_merged, on=['PSP', 'country'], how='left')\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Several payment attempts of the same purchase**\n",
    "\n",
    "In this we consider payments made from the same country with the same amount, within one minute of each other.\n",
    "These are grouped together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of the original data\n",
    "data3 = data.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort by country, amount, and timestamp\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "data3.sort_values(by=['country', 'amount', 'tmsp'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27337 entries, 0 to 27336\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   first_psp  27337 non-null  object        \n",
      " 1   last_psp   27337 non-null  object        \n",
      " 2   country    27337 non-null  object        \n",
      " 3   amount     27337 non-null  int64         \n",
      " 4   tmsp       27337 non-null  datetime64[ns]\n",
      " 5   success    27337 non-null  bool          \n",
      "dtypes: bool(1), datetime64[ns](1), int64(1), object(3)\n",
      "memory usage: 1.1+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Initialize an empty list to store the grouped transactions\n",
    "grouped_transactions = []\n",
    "\n",
    "# Initialize variables to keep track of the current group\n",
    "group = []\n",
    "current_country = None\n",
    "current_amount = None\n",
    "\n",
    "# Loop through the dataframe and group transactions\n",
    "for idx, row in data3.iterrows():\n",
    "    if group:\n",
    "        prev_row = group[-1]\n",
    "        # Check if the current transaction is within one minute of the previous one\n",
    "        if (row['amount'] == prev_row['amount'] and\n",
    "            row['country'] == prev_row['country'] and\n",
    "            (row['tmsp'] - prev_row['tmsp']) <= timedelta(minutes=1)):\n",
    "            group.append(row)\n",
    "        else:\n",
    "            # Process the current group\n",
    "            grouped_transactions.append({\n",
    "                'first_psp': group[0]['PSP'],\n",
    "                'last_psp': group[-1]['PSP'],\n",
    "                'country': group[0]['country'],\n",
    "                'amount': group[0]['amount'],\n",
    "                'tmsp': group[0]['tmsp'],\n",
    "                'success': any(g['success'] == 1 for g in group)  # If any transaction in the group is successful\n",
    "            })\n",
    "            # Start a new group\n",
    "            group = [row]\n",
    "    else:\n",
    "        group = [row]\n",
    "\n",
    "# Add the last group\n",
    "if group:\n",
    "    grouped_transactions.append({\n",
    "        'first_psp': group[0]['PSP'],\n",
    "        'last_psp': group[-1]['PSP'],\n",
    "        'country': group[0]['country'],\n",
    "        'amount': group[0]['amount'],\n",
    "        'tmsp': group[0]['tmsp'],\n",
    "        'success': any(g['success'] == 1 for g in group)  # If any transaction in the group is successful\n",
    "    })\n",
    "\n",
    "# Convert the grouped transactions to a DataFrame\n",
    "df_grouped = pd.DataFrame(grouped_transactions)\n",
    "\n",
    "print(df_grouped.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27337 entries, 0 to 27336\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   first_psp  27337 non-null  object        \n",
      " 1   last_psp   27337 non-null  object        \n",
      " 2   country    27337 non-null  object        \n",
      " 3   amount     27337 non-null  int64         \n",
      " 4   tmsp       27337 non-null  datetime64[ns]\n",
      " 5   success    27337 non-null  bool          \n",
      "dtypes: bool(1), datetime64[ns](1), int64(1), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_grouped.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "success\n",
       "False    17112\n",
       "True     10225\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the class counts for target\n",
    "df_grouped['success'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Unnamed: 0</th>\n",
       "      <th>tmsp</th>\n",
       "      <th>country</th>\n",
       "      <th>amount</th>\n",
       "      <th>success_x</th>\n",
       "      <th>PSP</th>\n",
       "      <th>3D_secured</th>\n",
       "      <th>card</th>\n",
       "      <th>Failure (0)</th>\n",
       "      <th>Success (1)</th>\n",
       "      <th>Total</th>\n",
       "      <th>success%</th>\n",
       "      <th>Failure%</th>\n",
       "      <th>fee_success</th>\n",
       "      <th>fee_failure</th>\n",
       "      <th>average_fee</th>\n",
       "      <th>first_psp</th>\n",
       "      <th>last_psp</th>\n",
       "      <th>success_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9238</td>\n",
       "      <td>2019-01-10 03:49:12</td>\n",
       "      <td>Austria</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Moneycard</td>\n",
       "      <td>0</td>\n",
       "      <td>Diners</td>\n",
       "      <td>1286</td>\n",
       "      <td>368</td>\n",
       "      <td>1654</td>\n",
       "      <td>22.2</td>\n",
       "      <td>77.8</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.6660</td>\n",
       "      <td>Moneycard</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22742</td>\n",
       "      <td>2019-01-27 14:01:11</td>\n",
       "      <td>Austria</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>0</td>\n",
       "      <td>Master</td>\n",
       "      <td>2031</td>\n",
       "      <td>383</td>\n",
       "      <td>2414</td>\n",
       "      <td>15.9</td>\n",
       "      <td>84.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5795</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33737</td>\n",
       "      <td>2019-02-08 05:02:33</td>\n",
       "      <td>Austria</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>0</td>\n",
       "      <td>Diners</td>\n",
       "      <td>4174</td>\n",
       "      <td>1014</td>\n",
       "      <td>5188</td>\n",
       "      <td>19.5</td>\n",
       "      <td>80.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3900</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40539</td>\n",
       "      <td>2019-02-16 08:24:40</td>\n",
       "      <td>Austria</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>1</td>\n",
       "      <td>Master</td>\n",
       "      <td>4174</td>\n",
       "      <td>1014</td>\n",
       "      <td>5188</td>\n",
       "      <td>19.5</td>\n",
       "      <td>80.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3900</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21179</td>\n",
       "      <td>2019-01-25 04:18:26</td>\n",
       "      <td>Austria</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Moneycard</td>\n",
       "      <td>0</td>\n",
       "      <td>Master</td>\n",
       "      <td>1286</td>\n",
       "      <td>368</td>\n",
       "      <td>1654</td>\n",
       "      <td>22.2</td>\n",
       "      <td>77.8</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.6660</td>\n",
       "      <td>Moneycard</td>\n",
       "      <td>Moneycard</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23798</td>\n",
       "      <td>2019-01-28 23:48:19</td>\n",
       "      <td>Austria</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>0</td>\n",
       "      <td>Master</td>\n",
       "      <td>4174</td>\n",
       "      <td>1014</td>\n",
       "      <td>5188</td>\n",
       "      <td>19.5</td>\n",
       "      <td>80.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3900</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31712</td>\n",
       "      <td>2019-02-06 04:09:05</td>\n",
       "      <td>Austria</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>0</td>\n",
       "      <td>Diners</td>\n",
       "      <td>4174</td>\n",
       "      <td>1014</td>\n",
       "      <td>5188</td>\n",
       "      <td>19.5</td>\n",
       "      <td>80.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3900</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>36838</td>\n",
       "      <td>2019-02-12 00:25:52</td>\n",
       "      <td>Austria</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>1</td>\n",
       "      <td>Diners</td>\n",
       "      <td>2031</td>\n",
       "      <td>383</td>\n",
       "      <td>2414</td>\n",
       "      <td>15.9</td>\n",
       "      <td>84.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5795</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>49588</td>\n",
       "      <td>2019-02-27 23:46:42</td>\n",
       "      <td>Austria</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>0</td>\n",
       "      <td>Master</td>\n",
       "      <td>4174</td>\n",
       "      <td>1014</td>\n",
       "      <td>5188</td>\n",
       "      <td>19.5</td>\n",
       "      <td>80.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3900</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>Goldcard</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21448</td>\n",
       "      <td>2019-01-25 13:52:53</td>\n",
       "      <td>Austria</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>0</td>\n",
       "      <td>Diners</td>\n",
       "      <td>4174</td>\n",
       "      <td>1014</td>\n",
       "      <td>5188</td>\n",
       "      <td>19.5</td>\n",
       "      <td>80.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3900</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                tmsp  country  amount  success_x         PSP  \\\n",
       "0        9238 2019-01-10 03:49:12  Austria       6          0   Moneycard   \n",
       "1       22742 2019-01-27 14:01:11  Austria       6          1  Simplecard   \n",
       "2       33737 2019-02-08 05:02:33  Austria       6          0     UK_Card   \n",
       "3       40539 2019-02-16 08:24:40  Austria       6          1     UK_Card   \n",
       "4       21179 2019-01-25 04:18:26  Austria       7          0   Moneycard   \n",
       "5       23798 2019-01-28 23:48:19  Austria       7          0     UK_Card   \n",
       "6       31712 2019-02-06 04:09:05  Austria       7          0     UK_Card   \n",
       "7       36838 2019-02-12 00:25:52  Austria       7          1  Simplecard   \n",
       "8       49588 2019-02-27 23:46:42  Austria       7          0     UK_Card   \n",
       "9       21448 2019-01-25 13:52:53  Austria       8          1     UK_Card   \n",
       "\n",
       "   3D_secured    card  Failure (0)  Success (1)  Total  success%  Failure%  \\\n",
       "0           0  Diners         1286          368   1654      22.2      77.8   \n",
       "1           0  Master         2031          383   2414      15.9      84.1   \n",
       "2           0  Diners         4174         1014   5188      19.5      80.5   \n",
       "3           1  Master         4174         1014   5188      19.5      80.5   \n",
       "4           0  Master         1286          368   1654      22.2      77.8   \n",
       "5           0  Master         4174         1014   5188      19.5      80.5   \n",
       "6           0  Diners         4174         1014   5188      19.5      80.5   \n",
       "7           1  Diners         2031          383   2414      15.9      84.1   \n",
       "8           0  Master         4174         1014   5188      19.5      80.5   \n",
       "9           0  Diners         4174         1014   5188      19.5      80.5   \n",
       "\n",
       "   fee_success  fee_failure  average_fee   first_psp    last_psp  success_y  \n",
       "0            5          2.0       2.6660   Moneycard  Simplecard      False  \n",
       "1            1          0.5       0.5795  Simplecard  Simplecard       True  \n",
       "2            3          1.0       1.3900     UK_Card  Simplecard      False  \n",
       "3            3          1.0       1.3900     UK_Card     UK_Card       True  \n",
       "4            5          2.0       2.6660   Moneycard   Moneycard      False  \n",
       "5            3          1.0       1.3900     UK_Card     UK_Card      False  \n",
       "6            3          1.0       1.3900     UK_Card     UK_Card       True  \n",
       "7            1          0.5       0.5795  Simplecard  Simplecard       True  \n",
       "8            3          1.0       1.3900     UK_Card    Goldcard      False  \n",
       "9            3          1.0       1.3900     UK_Card     UK_Card       True  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data4 = pd.merge(data2, df_grouped, on=['tmsp', 'country', 'amount'], how='right')\n",
    "data4.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27498 entries, 0 to 27497\n",
      "Data columns (total 19 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   Unnamed: 0   27498 non-null  int64         \n",
      " 1   tmsp         27498 non-null  datetime64[ns]\n",
      " 2   country      27498 non-null  object        \n",
      " 3   amount       27498 non-null  int64         \n",
      " 4   success_x    27498 non-null  int64         \n",
      " 5   PSP          27498 non-null  object        \n",
      " 6   3D_secured   27498 non-null  int64         \n",
      " 7   card         27498 non-null  object        \n",
      " 8   Failure (0)  27498 non-null  int64         \n",
      " 9   Success (1)  27498 non-null  int64         \n",
      " 10  Total        27498 non-null  int64         \n",
      " 11  success%     27498 non-null  float64       \n",
      " 12  Failure%     27498 non-null  float64       \n",
      " 13  fee_success  27498 non-null  int64         \n",
      " 14  fee_failure  27498 non-null  float64       \n",
      " 15  average_fee  27498 non-null  float64       \n",
      " 16  first_psp    27498 non-null  object        \n",
      " 17  last_psp     27498 non-null  object        \n",
      " 18  success_y    27498 non-null  bool          \n",
      "dtypes: bool(1), datetime64[ns](1), float64(4), int64(8), object(5)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data4.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select few columns for model building\n",
    "data5 = data4[['tmsp','country','amount','success_x', 'PSP', '3D_secured',\n",
    "               'card', 'success%','Failure%','fee_success','fee_failure','average_fee', 'first_psp','last_psp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27498 entries, 0 to 27497\n",
      "Data columns (total 14 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   tmsp         27498 non-null  datetime64[ns]\n",
      " 1   country      27498 non-null  object        \n",
      " 2   amount       27498 non-null  int64         \n",
      " 3   success_x    27498 non-null  int64         \n",
      " 4   PSP          27498 non-null  object        \n",
      " 5   3D_secured   27498 non-null  int64         \n",
      " 6   card         27498 non-null  object        \n",
      " 7   success%     27498 non-null  float64       \n",
      " 8   Failure%     27498 non-null  float64       \n",
      " 9   fee_success  27498 non-null  int64         \n",
      " 10  fee_failure  27498 non-null  float64       \n",
      " 11  average_fee  27498 non-null  float64       \n",
      " 12  first_psp    27498 non-null  object        \n",
      " 13  last_psp     27498 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(4), int64(4), object(5)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data5.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract time of day as a new feature\n",
    "df.loc[:,'time_of_day'] = df['tmsp'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract day_of week as another feature\n",
    "df.loc[:, 'day_of_week_name'] = df['tmsp'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27498 entries, 0 to 27497\n",
      "Data columns (total 16 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   tmsp              27498 non-null  datetime64[ns]\n",
      " 1   country           27498 non-null  object        \n",
      " 2   amount            27498 non-null  int64         \n",
      " 3   success_x         27498 non-null  int64         \n",
      " 4   PSP               27498 non-null  object        \n",
      " 5   3D_secured        27498 non-null  int64         \n",
      " 6   card              27498 non-null  object        \n",
      " 7   success%          27498 non-null  float64       \n",
      " 8   Failure%          27498 non-null  float64       \n",
      " 9   fee_success       27498 non-null  int64         \n",
      " 10  fee_failure       27498 non-null  float64       \n",
      " 11  average_fee       27498 non-null  float64       \n",
      " 12  first_psp         27498 non-null  object        \n",
      " 13  last_psp          27498 non-null  object        \n",
      " 14  time_of_day       27498 non-null  int32         \n",
      " 15  day_of_week_name  27498 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(4), int32(1), int64(4), object(6)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further Visualizations with the new Features\n",
    "\n",
    "Additional analysis into success and fees is done to gain insights into the factors that influence both.\n",
    "1. Visualizing success rate for each hour of day for each PSP\n",
    "2. visualizing success rate on each day of week for different PSPs.\n",
    "3. visualize average fee associated with transacions via each PSP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         PSP  time_of_day  success_rate\n",
      "0   Goldcard            0     40.278161\n",
      "1   Goldcard            1     40.830208\n",
      "2   Goldcard            2     40.702198\n",
      "3   Goldcard            3     40.536250\n",
      "4   Goldcard            4     40.577228\n",
      "..       ...          ...           ...\n",
      "91   UK_Card           19     19.419373\n",
      "92   UK_Card           20     19.421585\n",
      "93   UK_Card           21     19.418957\n",
      "94   UK_Card           22     19.420748\n",
      "95   UK_Card           23     19.420918\n",
      "\n",
      "[96 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate success rate for each hour and each PSP\n",
    "agg_data = df.groupby(['PSP', 'time_of_day']).agg(success_rate=('success%', 'mean')).reset_index()\n",
    "print(agg_data)\n",
    "\n",
    "# Set the plot style\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Create the plot\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "# Loop through each PSP and plot its success rate over time of day\n",
    "for psp in agg_data['PSP'].unique():\n",
    "    psp_data = agg_data[agg_data['PSP'] == psp]\n",
    "    plt.plot(psp_data['time_of_day'], psp_data['success_rate'], marker='o', label=psp)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Hour of Day')\n",
    "plt.ylabel('Success Rate')\n",
    "plt.title('Success Rate by Time of Day for Each PSP')\n",
    "plt.xticks(range(0, 24))  # Ensure x-axis shows all hours\n",
    "plt.legend(title='PSP')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Calculate success rate for each day of the week and each PSP\n",
    "agg_data1 = df.groupby(['PSP', 'day_of_week_name']).agg(success_rate=('success%', 'mean')).reset_index()\n",
    "\n",
    "# Ensure day_of_week is in the correct order\n",
    "days_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "agg_data1['day_of_week_name'] = pd.Categorical(agg_data1['day_of_week_name'], categories=days_order, ordered=True)\n",
    "agg_data1.sort_values(by=['PSP', 'day_of_week_name'], inplace=True)\n",
    "\n",
    "\n",
    "# Set the plot style\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Create the plot\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# Create a bar plot\n",
    "sns.barplot(data=agg_data1, x='day_of_week_name', y='success_rate', hue='PSP',palette=\"husl\")\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Day of the Week')\n",
    "plt.ylabel('Success Rate')\n",
    "plt.title('Success Rate by Day of the Week for Each PSP')\n",
    "\n",
    "# Rotate x-axis labels for better readability\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Show the plot\n",
    "plt.legend(title='PSP')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate average transaction fee for each PSP\n",
    "avg_fees = df.groupby('PSP')['average_fee'].mean().reset_index()\n",
    "\n",
    "# Set the plot style\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Create the plot\n",
    "plt.figure(figsize=(9, 5))\n",
    "\n",
    "# Create a bar plot\n",
    "sns.barplot(data=avg_fees, x='PSP', y='average_fee',color='r')\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('PSP')\n",
    "plt.ylabel('Average Transaction Fee')\n",
    "plt.title('Average Transaction Fee by PSP')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "\n",
    "The data is prepared for building the model by;\n",
    "1. Scaling the numerical feartures such that they fall within the same range.\n",
    "2. Encoding categorical variables\n",
    "3. Imputing any mssing values by use of Median \n",
    "5. checking for correlated features\n",
    "\n",
    "The preprocessing follows the following steps.\n",
    "1. Define the processing pipeline: Transformers for numerical and categorical features.\n",
    "2. Fit and Transform the Data: Fit the preprocessor on the DataFrame and transform it.\n",
    "3. Retrieve the Actual Feature Names:concatenate the numerical feature names and the output feature names of the OneHotEncoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['tmsp', 'country', 'amount', 'success_x', 'PSP', '3D_secured', 'card',\n",
      "       'success%', 'Failure%', 'fee_success', 'fee_failure', 'average_fee',\n",
      "       'first_psp', 'last_psp', 'time_of_day', 'day_of_week_name'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>tmsp</th>\n",
       "      <th>country</th>\n",
       "      <th>amount</th>\n",
       "      <th>success_x</th>\n",
       "      <th>PSP</th>\n",
       "      <th>3D_secured</th>\n",
       "      <th>card</th>\n",
       "      <th>success%</th>\n",
       "      <th>Failure%</th>\n",
       "      <th>fee_success</th>\n",
       "      <th>fee_failure</th>\n",
       "      <th>average_fee</th>\n",
       "      <th>first_psp</th>\n",
       "      <th>last_psp</th>\n",
       "      <th>time_of_day</th>\n",
       "      <th>day_of_week_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-10 03:49:12</td>\n",
       "      <td>Austria</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Moneycard</td>\n",
       "      <td>0</td>\n",
       "      <td>Diners</td>\n",
       "      <td>22.2</td>\n",
       "      <td>77.8</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.6660</td>\n",
       "      <td>Moneycard</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-27 14:01:11</td>\n",
       "      <td>Austria</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>0</td>\n",
       "      <td>Master</td>\n",
       "      <td>15.9</td>\n",
       "      <td>84.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5795</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>14</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-02-08 05:02:33</td>\n",
       "      <td>Austria</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>0</td>\n",
       "      <td>Diners</td>\n",
       "      <td>19.5</td>\n",
       "      <td>80.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3900</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>Simplecard</td>\n",
       "      <td>5</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-02-16 08:24:40</td>\n",
       "      <td>Austria</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>1</td>\n",
       "      <td>Master</td>\n",
       "      <td>19.5</td>\n",
       "      <td>80.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3900</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>UK_Card</td>\n",
       "      <td>8</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-25 04:18:26</td>\n",
       "      <td>Austria</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Moneycard</td>\n",
       "      <td>0</td>\n",
       "      <td>Master</td>\n",
       "      <td>22.2</td>\n",
       "      <td>77.8</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.6660</td>\n",
       "      <td>Moneycard</td>\n",
       "      <td>Moneycard</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tmsp  country  amount  success_x         PSP  3D_secured  \\\n",
       "0 2019-01-10 03:49:12  Austria       6          0   Moneycard           0   \n",
       "1 2019-01-27 14:01:11  Austria       6          1  Simplecard           0   \n",
       "2 2019-02-08 05:02:33  Austria       6          0     UK_Card           0   \n",
       "3 2019-02-16 08:24:40  Austria       6          1     UK_Card           1   \n",
       "4 2019-01-25 04:18:26  Austria       7          0   Moneycard           0   \n",
       "\n",
       "     card  success%  Failure%  fee_success  fee_failure  average_fee  \\\n",
       "0  Diners      22.2      77.8            5          2.0       2.6660   \n",
       "1  Master      15.9      84.1            1          0.5       0.5795   \n",
       "2  Diners      19.5      80.5            3          1.0       1.3900   \n",
       "3  Master      19.5      80.5            3          1.0       1.3900   \n",
       "4  Master      22.2      77.8            5          2.0       2.6660   \n",
       "\n",
       "    first_psp    last_psp  time_of_day day_of_week_name  \n",
       "0   Moneycard  Simplecard            3         Thursday  \n",
       "1  Simplecard  Simplecard           14           Sunday  \n",
       "2     UK_Card  Simplecard            5           Friday  \n",
       "3     UK_Card     UK_Card            8         Saturday  \n",
       "4   Moneycard   Moneycard            4           Friday  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define numerical and categorical features\n",
    "numerical_features = ['amount', 'fee_success', 'fee_failure', 'average_fee', 'time_of_day', 'success%']\n",
    "categorical_features = ['country', 'PSP', '3D_secured', 'card', 'first_psp', 'last_psp', 'day_of_week_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define transformers for numerical and categorical features\n",
    "numerical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='median')),\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Combine transformers using ColumnTransformer\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numerical_transformer, numerical_features),\n",
    "        ('cat', categorical_transformer, categorical_features)\n",
    "    ])\n",
    "\n",
    "# Apply the transformations\n",
    "df_preprocessed = preprocessor.fit_transform(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.03700858,  0.70812909,  0.56220382, ...,  1.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-2.03700858, -1.02111328, -0.73513155, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-2.03700858, -0.15649209, -0.30268642, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       ...,\n",
       "       [ 3.04802497, -1.02111328, -0.73513155, ...,  0.        ,\n",
       "         1.        ,  0.        ],\n",
       "       [ 3.06882265, -0.15649209, -0.30268642, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 3.08962033, -0.15649209, -0.30268642, ...,  1.        ,\n",
       "         0.        ,  0.        ]])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27498, 33)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preprocessed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['amount', 'fee_success', 'fee_failure', 'average_fee', 'time_of_day', 'success%']\n",
      "['country_Austria' 'country_Germany' 'country_Switzerland' 'PSP_Goldcard'\n",
      " 'PSP_Moneycard' 'PSP_Simplecard' 'PSP_UK_Card' '3D_secured_0'\n",
      " '3D_secured_1' 'card_Diners' 'card_Master' 'card_Visa'\n",
      " 'first_psp_Goldcard' 'first_psp_Moneycard' 'first_psp_Simplecard'\n",
      " 'first_psp_UK_Card' 'last_psp_Goldcard' 'last_psp_Moneycard'\n",
      " 'last_psp_Simplecard' 'last_psp_UK_Card' 'day_of_week_name_Friday'\n",
      " 'day_of_week_name_Monday' 'day_of_week_name_Saturday'\n",
      " 'day_of_week_name_Sunday' 'day_of_week_name_Thursday'\n",
      " 'day_of_week_name_Tuesday' 'day_of_week_name_Wednesday']\n",
      "33\n"
     ]
    }
   ],
   "source": [
    "# Get feature names\n",
    "numerical_feature_names = numerical_features\n",
    "print(numerical_feature_names)\n",
    "categorical_feature_names = preprocessor.named_transformers_['cat'].get_feature_names_out(categorical_features)\n",
    "all_feature_names = list(numerical_feature_names) + list(categorical_feature_names)\n",
    "print(categorical_feature_names)\n",
    "print(len(all_feature_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         amount  fee_success  fee_failure  average_fee  time_of_day  success%  \\\n",
      "0     -2.037009     0.708129     0.562204     0.510632    -1.230336  0.274204   \n",
      "1     -2.037009    -1.021113    -0.735132    -0.763116     0.357136 -0.751001   \n",
      "2     -2.037009    -0.156492    -0.302686    -0.268329    -0.941705 -0.165170   \n",
      "3     -2.037009    -0.156492    -0.302686    -0.268329    -0.508758 -0.165170   \n",
      "4     -2.026610     0.708129     0.562204     0.510632    -1.086020  0.274204   \n",
      "...         ...          ...          ...          ...          ...       ...   \n",
      "27493  2.881644    -0.156492    -0.302686    -0.269550     0.790083 -0.181443   \n",
      "27494  2.892042    -0.156492    -0.302686    -0.269550     1.367346 -0.181443   \n",
      "27495  3.048025    -1.021113    -0.735132    -0.764642    -1.086020 -0.832367   \n",
      "27496  3.068823    -0.156492    -0.302686    -0.269550     1.367346 -0.181443   \n",
      "27497  3.089620    -0.156492    -0.302686    -0.269550    -1.086020 -0.181443   \n",
      "\n",
      "       country_Austria  country_Germany  country_Switzerland  PSP_Goldcard  \\\n",
      "0                  1.0              0.0                  0.0           0.0   \n",
      "1                  1.0              0.0                  0.0           0.0   \n",
      "2                  1.0              0.0                  0.0           0.0   \n",
      "3                  1.0              0.0                  0.0           0.0   \n",
      "4                  1.0              0.0                  0.0           0.0   \n",
      "...                ...              ...                  ...           ...   \n",
      "27493              0.0              0.0                  1.0           0.0   \n",
      "27494              0.0              0.0                  1.0           0.0   \n",
      "27495              0.0              0.0                  1.0           0.0   \n",
      "27496              0.0              0.0                  1.0           0.0   \n",
      "27497              0.0              0.0                  1.0           0.0   \n",
      "\n",
      "       ...  last_psp_Moneycard  last_psp_Simplecard  last_psp_UK_Card  \\\n",
      "0      ...                 0.0                  1.0               0.0   \n",
      "1      ...                 0.0                  1.0               0.0   \n",
      "2      ...                 0.0                  1.0               0.0   \n",
      "3      ...                 0.0                  0.0               1.0   \n",
      "4      ...                 1.0                  0.0               0.0   \n",
      "...    ...                 ...                  ...               ...   \n",
      "27493  ...                 0.0                  0.0               1.0   \n",
      "27494  ...                 0.0                  0.0               1.0   \n",
      "27495  ...                 0.0                  1.0               0.0   \n",
      "27496  ...                 1.0                  0.0               0.0   \n",
      "27497  ...                 0.0                  0.0               1.0   \n",
      "\n",
      "       day_of_week_name_Friday  day_of_week_name_Monday  \\\n",
      "0                          0.0                      0.0   \n",
      "1                          0.0                      0.0   \n",
      "2                          1.0                      0.0   \n",
      "3                          0.0                      0.0   \n",
      "4                          1.0                      0.0   \n",
      "...                        ...                      ...   \n",
      "27493                      0.0                      0.0   \n",
      "27494                      0.0                      0.0   \n",
      "27495                      0.0                      0.0   \n",
      "27496                      0.0                      0.0   \n",
      "27497                      0.0                      0.0   \n",
      "\n",
      "       day_of_week_name_Saturday  day_of_week_name_Sunday  \\\n",
      "0                            0.0                      0.0   \n",
      "1                            0.0                      1.0   \n",
      "2                            0.0                      0.0   \n",
      "3                            1.0                      0.0   \n",
      "4                            0.0                      0.0   \n",
      "...                          ...                      ...   \n",
      "27493                        0.0                      0.0   \n",
      "27494                        1.0                      0.0   \n",
      "27495                        0.0                      0.0   \n",
      "27496                        0.0                      1.0   \n",
      "27497                        0.0                      0.0   \n",
      "\n",
      "       day_of_week_name_Thursday  day_of_week_name_Tuesday  \\\n",
      "0                            1.0                       0.0   \n",
      "1                            0.0                       0.0   \n",
      "2                            0.0                       0.0   \n",
      "3                            0.0                       0.0   \n",
      "4                            0.0                       0.0   \n",
      "...                          ...                       ...   \n",
      "27493                        0.0                       1.0   \n",
      "27494                        0.0                       0.0   \n",
      "27495                        0.0                       1.0   \n",
      "27496                        0.0                       0.0   \n",
      "27497                        1.0                       0.0   \n",
      "\n",
      "       day_of_week_name_Wednesday  \n",
      "0                             0.0  \n",
      "1                             0.0  \n",
      "2                             0.0  \n",
      "3                             0.0  \n",
      "4                             0.0  \n",
      "...                           ...  \n",
      "27493                         0.0  \n",
      "27494                         0.0  \n",
      "27495                         0.0  \n",
      "27496                         0.0  \n",
      "27497                         0.0  \n",
      "\n",
      "[27498 rows x 33 columns]\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame with proper column names\n",
    "df_preprocessed = pd.DataFrame(df_preprocessed, columns=all_feature_names)\n",
    "\n",
    "print(df_preprocessed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for correlations: Compute the correlation matrix for correlations among numerical features\n",
    "corr_matrix = df1[numerical_features].corr()\n",
    "\n",
    "# Plot the correlation matrix\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Analysis\n",
    "\n",
    "1. There is a very strong positive correlation between success% and fee_success. This suggests that higher success rates might be associated with higher fees for successful transactions, possibly indicating a trade-off between transaction success and cost. Also the strong positive correlation between success% and average_fee indicates that higher transaction success rates might be associated with higher average fees.\n",
    "\n",
    "2. A very weak negative correlation exists between time_of_day and fee_success and fee_failure indicating almost no relationship between these two variables.\n",
    "\n",
    "3. Multi-Collinearity: High multi-collinearity is evident among fee_success, fee_failure, and average_fee, which may need to be addressed in modeling to avoid redundancy and instability.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance and Selection\n",
    "\n",
    "The most important features are computed by training a random forest classifier. \n",
    "This is based on mean decrease in impurity and the importance of a feature is the average of all the impurity decreases attributed to that feature across all trees.\n",
    "Feature importances are provided by the fitted attribute,feature_importances_ ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       feature  importance\n",
      "0                       amount    0.424165\n",
      "4                  time_of_day    0.261059\n",
      "25            last_psp_UK_Card    0.030579\n",
      "22           last_psp_Goldcard    0.023042\n",
      "24         last_psp_Simplecard    0.020302\n",
      "17                   card_Visa    0.015961\n",
      "23          last_psp_Moneycard    0.015921\n",
      "5                     success%    0.014713\n",
      "16                 card_Master    0.014306\n",
      "14                3D_secured_1    0.014202\n",
      "13                3D_secured_0    0.013755\n",
      "15                 card_Diners    0.011360\n",
      "3                  average_fee    0.010663\n",
      "1                  fee_success    0.010198\n",
      "31    day_of_week_name_Tuesday    0.010086\n",
      "32  day_of_week_name_Wednesday    0.010071\n",
      "30   day_of_week_name_Thursday    0.010007\n",
      "28   day_of_week_name_Saturday    0.009936\n",
      "27     day_of_week_name_Monday    0.009542\n",
      "26     day_of_week_name_Friday    0.009273\n",
      "29     day_of_week_name_Sunday    0.008899\n",
      "2                  fee_failure    0.007550\n",
      "7              country_Germany    0.006720\n",
      "9                 PSP_Goldcard    0.005591\n",
      "8          country_Switzerland    0.005192\n",
      "21           first_psp_UK_Card    0.005045\n",
      "18          first_psp_Goldcard    0.004719\n",
      "6              country_Austria    0.004545\n",
      "12                 PSP_UK_Card    0.003774\n",
      "10               PSP_Moneycard    0.002572\n",
      "20        first_psp_Simplecard    0.002301\n",
      "11              PSP_Simplecard    0.002016\n",
      "19         first_psp_Moneycard    0.001935\n"
     ]
    }
   ],
   "source": [
    "# compute feature importance using Random forests\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Assume target variable is 'success_x'\n",
    "X = df_preprocessed\n",
    "y = df1['success_x']\n",
    "\n",
    "# Fit a Random Forest model\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X, y)\n",
    "\n",
    "# Get feature importances\n",
    "importances = model.feature_importances_\n",
    "\n",
    "# Map importances to feature names\n",
    "feature_names = numerical_features + list(preprocessor.named_transformers_['cat']['onehot'].get_feature_names_out(categorical_features))\n",
    "feature_importances = pd.DataFrame({'feature': feature_names, 'importance': importances})\n",
    "\n",
    "# Sort by importance\n",
    "feature_importances = feature_importances.sort_values(by='importance', ascending=False)\n",
    "print(feature_importances)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27498, 33)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['amount', 'time_of_day', 'last_psp_UK_Card', 'last_psp_Goldcard', 'last_psp_Simplecard', 'card_Visa', 'last_psp_Moneycard', 'success%', 'card_Master', '3D_secured_1', '3D_secured_0', 'card_Diners', 'average_fee', 'fee_success', 'day_of_week_name_Tuesday']\n"
     ]
    }
   ],
   "source": [
    "# Select top-k features to use for building model(e.g., top 15)\n",
    "top_k = 15\n",
    "selected_features = feature_importances.head(top_k)['feature'].tolist()\n",
    "print(selected_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BUILDING THE MODEL\n",
    "\n",
    "A Baseline model and accurate model are built. For the choice of accurate model, the hyperparameters are tuned to improve the accuracy.\n",
    "1. Baseline logistic regression model using selected features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6243636363636363\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.65      0.73      4405\n",
      "           1       0.27      0.53      0.36      1095\n",
      "\n",
      "    accuracy                           0.62      5500\n",
      "   macro avg       0.56      0.59      0.55      5500\n",
      "weighted avg       0.73      0.62      0.66      5500\n",
      "\n",
      "[[2853 1552]\n",
      " [ 514  581]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report,confusion_matrix\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from collections import Counter\n",
    "\n",
    "\n",
    "# Subset the DataFrame to include only the selected features\n",
    "X_selected = X[selected_features]\n",
    "y = df1['success_x']\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_selected, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Apply SMOTE\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "# print(f\"Original dataset shape: {Counter(y_train)}\")\n",
    "# print(f\"Resampled dataset shape: {Counter(y_train_smote)}\")\n",
    "\n",
    "# Initialize and train the model\n",
    "model = LogisticRegression(class_weight='balanced', random_state=42)\n",
    "model.fit(X_train_smote, y_train_smote)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# # Evaluate the model\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting other classification models\n",
    "4 different algorithms are fitted in order to get a more accurate predictive model that fulfills business requirements.\n",
    "Algorithms include; SVM, Gradient_boost, XGboost and Random forest classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.svm import SVC\n",
    "import xgboost as xgb\n",
    "\n",
    "\n",
    "X = df1.drop(['success_x', 'tmsp'], axis=1)\n",
    "y = df1['success_x']\n",
    "\n",
    "# Encode categorical variables\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "label_encoder = LabelEncoder()\n",
    "y = label_encoder.fit_transform(y)\n",
    "\n",
    "# Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "# Standardize features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "smote = SMOTE(random_state=42)\n",
    "X1_train_smote, y1_train_smote = smote.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the 4 Models and Evaluating them by respective accuracy score and the Classification Reports. The associated probabilities are also \n",
    "printed(first 10) to provide an idea of likelihood of success/ failure of certain transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.81      0.84      4364\n",
      "           1       0.42      0.53      0.46      1136\n",
      "\n",
      "    accuracy                           0.75      5500\n",
      "   macro avg       0.64      0.67      0.65      5500\n",
      "weighted avg       0.77      0.75      0.76      5500\n",
      "\n",
      "Accuracy: 0.7494545454545455\n",
      "Predicted Probabilities (first 10): [0.31       0.25       0.42       0.56       0.         0.\n",
      " 0.18       0.73583333 0.29       0.        ]\n",
      "\n",
      "Gradient Boosting Classifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.87      0.86      4364\n",
      "           1       0.45      0.41      0.43      1136\n",
      "\n",
      "    accuracy                           0.78      5500\n",
      "   macro avg       0.65      0.64      0.64      5500\n",
      "weighted avg       0.77      0.78      0.77      5500\n",
      "\n",
      "Accuracy: 0.7756363636363637\n",
      "Predicted Probabilities (first 10): [0.55176956 0.34054424 0.37558592 0.39767002 0.31668721 0.32532084\n",
      " 0.23869227 0.5766564  0.49660748 0.23918132]\n",
      "\n",
      "XGBoost Classifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.90      0.87      4364\n",
      "           1       0.51      0.41      0.45      1136\n",
      "\n",
      "    accuracy                           0.80      5500\n",
      "   macro avg       0.68      0.65      0.66      5500\n",
      "weighted avg       0.78      0.80      0.79      5500\n",
      "\n",
      "Accuracy: 0.7954545454545454\n",
      "Predicted Probabilities (first 10): [0.36658353 0.23896094 0.36111054 0.5118007  0.04777666 0.00190764\n",
      " 0.00523909 0.6014254  0.4728816  0.0055269 ]\n",
      "\n",
      "Support Vector Machine Classifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.65      0.76      4364\n",
      "           1       0.37      0.80      0.51      1136\n",
      "\n",
      "    accuracy                           0.68      5500\n",
      "   macro avg       0.65      0.72      0.63      5500\n",
      "weighted avg       0.81      0.68      0.71      5500\n",
      "\n",
      "Accuracy: 0.6778181818181818\n"
     ]
    }
   ],
   "source": [
    "# 1. Random Forest\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "rf_model.fit(X1_train_smote, y1_train_smote)\n",
    "rf_preds = rf_model.predict(X_test)\n",
    "rf_probs = rf_model.predict_proba(X_test)[:, 1] \n",
    "print(\"Random Forest Classifier:\")\n",
    "print(classification_report(y_test, rf_preds))\n",
    "print(\"Accuracy:\", accuracy_score(y_test, rf_preds))\n",
    "print(\"Predicted Probabilities (first 10):\", rf_probs[:10])\n",
    "\n",
    "# 2. Gradient Boosting\n",
    "gb_model = GradientBoostingClassifier(random_state=42)\n",
    "gb_model.fit(X1_train_smote, y1_train_smote)\n",
    "gb_preds = gb_model.predict(X_test)\n",
    "gb_probs = gb_model.predict_proba(X_test)[:, 1] \n",
    "print(\"\\nGradient Boosting Classifier:\")\n",
    "print(classification_report(y_test, gb_preds))\n",
    "print(\"Accuracy:\", accuracy_score(y_test, gb_preds))\n",
    "print(\"Predicted Probabilities (first 10):\", gb_probs[:10])\n",
    "\n",
    "# 3. XGBoost\n",
    "xgb_model = xgb.XGBClassifier(random_state=42)\n",
    "xgb_model.fit(X1_train_smote, y1_train_smote)\n",
    "xgb_preds = xgb_model.predict(X_test)\n",
    "xgb_probs = xgb_model.predict_proba(X_test)[:, 1]  \n",
    "print(\"\\nXGBoost Classifier:\")\n",
    "print(classification_report(y_test, xgb_preds))\n",
    "print(\"Accuracy:\", accuracy_score(y_test, xgb_preds))\n",
    "print(\"Predicted Probabilities (first 10):\", xgb_probs[:10])\n",
    "\n",
    "# 4. Support Vector Machine\n",
    "svm_model = SVC(random_state=42)\n",
    "svm_model.fit(X1_train_smote, y1_train_smote)\n",
    "svm_preds = svm_model.predict(X_test)\n",
    "print(\"\\nSupport Vector Machine Classifier:\")\n",
    "print(classification_report(y_test, svm_preds))\n",
    "print(\"Accuracy:\", accuracy_score(y_test, svm_preds))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model results interpretation (XGBoost)\n",
    "\n",
    "The overall accuracy of 0.82 indicates that the model correctly predicts whether a transaction will be a success or failure 82% of the time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning for XGboost model\n",
    "\n",
    "I chose XGBoost as the best model, and performed hyperparameter tuning using randomized search to find the best parameters for model fitting. \n",
    "The algorithm has built-in regularization to prevent overfitting and it's highly flexible with a wide range of hyperparameters for tuning.\n",
    "There's a slight improvement in the accuracy Score for the model 82%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
      "Best Parameters: {'subsample': 0.8, 'n_estimators': 500, 'max_depth': 6, 'learning_rate': 0.01, 'gamma': 0.2, 'colsample_bytree': 0.7}\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.99      0.90      4364\n",
      "           1       0.87      0.16      0.27      1136\n",
      "\n",
      "    accuracy                           0.82      5500\n",
      "   macro avg       0.85      0.58      0.59      5500\n",
      "weighted avg       0.83      0.82      0.77      5500\n",
      "\n",
      "Accuracy: 0.8221818181818182\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300, 400, 500],\n",
    "    'learning_rate': [0.01, 0.05, 0.1, 0.2],\n",
    "    'max_depth': [3, 4, 5, 6, 7, 8],\n",
    "    'subsample': [0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "    'colsample_bytree': [0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "    'gamma': [0, 0.1, 0.2, 0.3, 0.4]\n",
    "}\n",
    "\n",
    "# Initialize the XGBoost model\n",
    "xgb_model = xgb.XGBClassifier(random_state=42)\n",
    "\n",
    "# Set up the random search with cross-validation\n",
    "random_search = RandomizedSearchCV(\n",
    "    estimator=xgb_model,\n",
    "    param_distributions=param_grid,\n",
    "    n_iter=100,  # Number of parameter settings that are sampled\n",
    "    scoring='accuracy',\n",
    "    cv=3,  # Number of cross-validation folds\n",
    "    verbose=2,\n",
    "    random_state=42,\n",
    "    n_jobs=-1  # Use all available cores\n",
    ")\n",
    "\n",
    "# Fit the random search model\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters and the best model\n",
    "best_params = random_search.best_params_\n",
    "best_model = random_search.best_estimator_\n",
    "\n",
    "print(\"Best Parameters:\", best_params)\n",
    "\n",
    "# Evaluate the best model on the test set\n",
    "y_pred = best_model.predict(X_test)\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the model and Deployment\n",
    " The model and pipeline are saved as a pickle file then read when trying to make predictions on unseen data \n",
    " as demonstrated below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "import xgboost as xgb\n",
    "\n",
    "# Define preprocessing pipeline\n",
    "numerical_features = ['amount', 'fee_success', 'average_fee', 'time_of_day']  \n",
    "categorical_features = ['3D_secured', 'card','last_psp']  \n",
    "\n",
    "# select few columns\n",
    "selected_cols = ['amount', 'fee_success', 'average_fee', 'time_of_day', 'card','3D_secured','last_psp']\n",
    "\n",
    "#define features and target\n",
    "X_1 = df1[selected_cols]\n",
    "y_1 = df1['success_x']\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', StandardScaler(), numerical_features),\n",
    "        ('cat', OneHotEncoder(drop='first',  handle_unknown='ignore'), categorical_features)\n",
    "    ])\n",
    "\n",
    "# Define the full pipeline with preprocessing and model\n",
    "model_pipeline = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', xgb.XGBClassifier(random_state=42))\n",
    "])\n",
    "\n",
    "\n",
    "# Train the pipeline\n",
    "model_pipeline.fit(X_1, y_1)\n",
    "\n",
    "# Save the pipeline\n",
    "with open('model_pipeline_xgb.pkl', 'wb') as f:\n",
    "    pickle.dump(model_pipeline, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(X_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [0 1]\n",
      "Predicted Probabilities: [[0.99847573 0.00152429]\n",
      " [0.12512094 0.87487906]]\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "\n",
    "# Load the pipeline\n",
    "with open('model_pipeline_xgb.pkl', 'rb') as f:\n",
    "    model_pipeline = pickle.load(f)\n",
    "\n",
    "#  `new_data` is the new data for prediction\n",
    "new_data = pd.DataFrame({\n",
    "    'amount': [400, 32],  \n",
    "    'fee_success': [3.0, 23],\n",
    "    'average_fee': [5.6, 6.7],\n",
    "    'time_of_day': [14, 1],\n",
    "    'card': ['Visa', 'Master'],\n",
    "    '3D_secured': [0, 1],\n",
    "    'last_psp': ['Simplecard',  'Goldcard'],\n",
    "    \n",
    "})\n",
    "\n",
    "# Make predictions\n",
    "predictions = model_pipeline.predict(new_data)\n",
    "predicted_probabilities = model_pipeline.predict_proba(new_data)\n",
    "\n",
    "print(\"Predictions:\", predictions)\n",
    "print(\"Predicted Probabilities:\", predicted_probabilities)\n"
   ]
  }
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